Article citation information:Krawczyk, T., Papis, M., Bielawski, R., Rządkowski, W. Possible applications of artificial intelligence algorithms in F-16 aircraft. Scientific Journal of Silesian University of Technology. Series Transport. 2024, 123, 101-131. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2024.123.5.Tomasz KRAWCZYK, Mateusz PAPIS, Radosław BIELAWSKI, Witold RZĄDKOWSKIPOSSIBLE APPLICATIONS OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN F-16 AIRCRAFTSummary. The F-16 aircraft, widely used by the Polish Army Air Force, requires modifications based on Artificial Intelligence (AI) algorithms to enhance its combat capabilities and performance. This study aims to develop comprehensive guidelines for this purpose by first describing F-16 systems and categorizing AI algorithms. Machine learning, deep learning, fuzzy logic, evolutionary algorithms, and swarm intelligence are reviewed for their potential applications in modern aircraft. Subsequently, specific algorithms applicable to F-16 systems are identified, with conclusions drawn on their suitability based on system features. The resultant analysis informs potential F-16 modifications and anticipates future AI applications in military aircraft, facilitating the guidance of new algorithmic developments and offering benefits to similar aircraft types. Moreover, directions for future research and development work are delineated.Keywords: F-16 aircraft, Artificial Intelligence (AI), machine learning, deep learning, AI algorithms, combat capabilities1. INTRODUCTIONThe F-16 Fighting Falcon is a 4th generation fighter that is in service with many air forces. Currently, approximately 4,600 aircraft have been produced, out of which, 2280 are in active service. This aircraft is used in fighter missions as well as in assault missions. It is very popular due to its versatility of use in various military missions, as well as a favourable balance of shoot-downs of enemy aircraft – currently over 70 shoot-downs of enemy aircraft. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"https://www.valiantaircommand.com/f-16-fighting-falcon","accessed":{"date-parts":[["2022","4","13"]]},"author":[{"dropping-particle":"","family":"Valiant Air Command","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"F-16 Fighting Falcon Serial Number - 78-0025 \"Affectionately known as ... the Viper\"","type":"webpage"},"uris":["http://www.mendeley.com/documents/?uuid=6258db69-5f2c-4990-baac-5aaa99844ac9"]},{"id":"ITEM-2","itemData":{"URL":"https://defence24.pl/przemysl/f-16-nadal-popularny-analiza","accessed":{"date-parts":[["2022","4","20"]]},"author":[{"dropping-particle":"","family":"Szopa","given":"Maciej","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2020"]]},"title":"F-16 nadal popularny","type":"webpage"},"uris":["http://www.mendeley.com/documents/?uuid=2da394a5-3b38-4468-b64e-95cdbe5f036b"]}],"mendeley":{"formattedCitation":"[109, 116]","plainTextFormattedCitation":"[109, 116]","previouslyFormattedCitation":"[109, 116]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[113, 120]. Despite the F-16 being replaced by the F-35, it is expected that the F-16 will continue to be in high demand, requiring modifications over time to meet battlefield tasks. One such modification is the possibility of using Artificial Intelligence (AI), mostly machine learning (ML) and deep learning (DL) algorithms in the F-16 aircraft. Work on such modifications has already been initiated by DARPA (Defense Advanced Research Projects Agency), which conducts research based on the F-16 aircraft in the field of self-piloting as well as air combat by artificial intelligence. The AI program of Heron Systems was used for this purpose. It should be noted that the current tests performed under the framework of research and development were conducted only in a virtual environment. As part of the test, a virtual 1-on-1, AI duel was organized against an experienced F-16 pilot, who was defeated in five duels by the AI. It is worth noting that the research and development program did not include the use of AI as a component supporting the pilot during air combat, which may be important for the users of F-16 aircraft. However, the DARPA program has opened a new direction for the implementation of AI algorithms in military aviation and in particular for the F-16 aircraft ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"https://breakingdefense.com/2020/08/ai-slays-top-f-16-pilot-in-darpa-dogfight-simulation/","accessed":{"date-parts":[["2022","4","20"]]},"author":[{"dropping-particle":"","family":"Hitchens","given":"Theresa","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2020"]]},"title":"AI Slays Top F-16 Pilot In DARPA Dogfight Simulation","type":"webpage"},"uris":["http://www.mendeley.com/documents/?uuid=7613b6bf-1fd8-476b-ae2b-f73753ba17e6"]},{"id":"ITEM-2","itemData":{"URL":"https://www.aerosociety.com/news/ai-and-the-future-of-air-combat/","author":[{"dropping-particle":"","family":"Hunt","given":"Edward","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2021"]]},"title":"AI and the future of air combat","type":"webpage"},"uris":["http://www.mendeley.com/documents/?uuid=7137a2c7-c997-4ba0-aed7-e1450437e1ce"]}],"mendeley":{"formattedCitation":"[37, 46]","plainTextFormattedCitation":"[37, 46]","previouslyFormattedCitation":"[37, 46]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[41,50].The considerable number of F-16 aircraft produced will provide a substantial amount of data to feed the prepared artificial intelligence algorithms. Ongoing research will enable these aircraft to adapt to meet the demands of modern air combat. Consequently, the F-16 aircraft will become an adaptive platform capable of utilizing AI technologies to optimize performance, decision-making, and mission execution.The capability of AI to pilot and participate in air combats autonomously or for the AI component to assist the pilot of a military aircraft in the execution of a combat mission opens up new opportunities in military aviation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Artificial Intelligence (AI) today is one of the hottest buzzwords in business and industry, and it has military applications, too. In fact, industry has developed AI as a technology over the last 60 years, and periodically it becomes mainstream news. Progress has not always been smooth, particularly in the 20th century when funding for AI research was principally public sector, primarily aimed at academic research and the military. Enthusiasm waxed and waned, as AI did not consistently deliver the desired level of capability. However, in the 21st century we have seen industries outside the defence sector, such as large Internet providers and companies in the automotive field, make significant contributions to AI development. These developments are of significant interest as they may provide answers how to further improve unmanned systems’ autonomy, and improve our ability to process vast amounts of information and data better within military command and control (C2) systems. The purpose of this article is to provide a basic understanding of AI, its current development, and the realistic progress that can be expected for military applications, with examples related to air power, cyber, C2, training, and human-machine teaming. While legal and ethical concerns about the use of AI cannot be ignored, their details cannot be discussed here because this essay is focused on technological achievements and future concepts. However, the authors will address the question of reliability and trust that responsible commanders need to ask about such AI applications.","author":[{"dropping-particle":"","family":"Fawkes","given":"Andy J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Menzel","given":"Lieutenant Colonel Martin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"JAPCC","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"page":"70-77","title":"The Future Role of Artificial Intelligence Military Opportunities and Challenges","type":"article-journal","volume":"27"},"uris":["http://www.mendeley.com/documents/?uuid=0811d759-5fbc-4888-bfe2-bfb80b7d5729"]}],"mendeley":{"formattedCitation":"[25]","plainTextFormattedCitation":"[25]","previouslyFormattedCitation":"[25]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[29]. Current AI algorithms can contribute to solving many problems in military aviation such as full “utilization” of aerodynamics and aircraft mechanics in the flight control system, optimization of armament use during air combat, operation of radar systems and Electronic Attack Jammer Pods (EAJP), aircraft control and decision-making at critical moments of flight or air combat, optimization of power unit control, optimization of fuel consumption, real-time generation and analysis of data from on-board sensors, predictive maintenance, etc. The area of application of AI algorithms can be considered modularly depending on the specific purpose of the aircraft, e.g.: fighter, assault, reconnaissance, or electronic warfare mission. The F-16 aircraft is a universal combat platform that allows for the modular implementation of AI algorithms in this respect. The wide application of Artificial Intelligence algorithms in military and civil aviation and the characteristics of individual systems of the F-16 aircraft will allow detailing the possibilities of implementation of particular algorithms in the case of F-16 aircraft systems.In this research, the focus was on exploring the possibilities of applying specific artificial intelligence algorithms to the functionalities of F-16 aircraft systems. Consequently, successive stages of the research were developed, which are described in the subsequent sections of this paper:characteristics of the F-16 aircraft systems in which AI algorithms can be applied;overview of artificial intelligence algorithms;overview of research on the application of AI algorithms in aviation;assessment of potential applications of artificial intelligence algorithms in F-16 to improve aircraft performance based on matrix analysis;discussion and conclusions.2. CHARACTERISTICS OF THE F-16 AIRCRAFTThe capabilities of AI algorithms can be applied to the following systems of the F-16 aircraft:general characteristics of the airframe structure;aircraft engine;flight control system;fuel system;aircraft weapon;radar system.In addition, airworthiness and maintenance management should be considered as a separate additional system.Tab. 1 provides basic data on F-16 aircraft systems based on ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Matusiak","given":"W.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Przegląd Konstrukcji Lotniczych","id":"ITEM-1","issue":"2","issued":{"date-parts":[["1996"]]},"publisher":"Agencja Lotnicza Altair","publisher-place":"Warsaw","title":"F-16 C","type":"article-magazine","volume":"28"},"uris":["http://www.mendeley.com/documents/?uuid=4e0f9590-7970-4498-8f2c-df2743a40926"]},{"id":"ITEM-2","itemData":{"author":[{"dropping-particle":"","family":"Wasilewski","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Typy Broni i Uzbrojenia","id":"ITEM-2","issue":"210","issued":{"date-parts":[["2004"]]},"publisher":"Bellona & Agencja Wydawnicza CB","title":"Samolot myśliwski F-16 C/D Block 52+.","type":"article-magazine"},"uris":["http://www.mendeley.com/documents/?uuid=4d4b446e-00fd-49c7-96ce-7ae2b74f3edb"]},{"id":"ITEM-3","itemData":{"author":[{"dropping-particle":"","family":"Adamski","given":"M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Problemy Techniki Uzbrojenia","id":"ITEM-3","issue":"38","issued":{"date-parts":[["2009"]]},"page":"41-46","title":"Analiza możliwości bojowych samolotów F-16 i MiG-29","type":"article-journal","volume":"110"},"uris":["http://www.mendeley.com/documents/?uuid=048cbf5d-9a4a-422b-9d91-bf3363a94e44"]},{"id":"ITEM-4","itemData":{"author":[{"dropping-particle":"","family":"Davis","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-4","issued":{"date-parts":[["2014"]]},"publisher":"Hayness Publishing","publisher-place":"Sparford","title":"General Dynamics F-16 Fighting Falcon Manual","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=241f117b-e50e-480e-b0b4-bdb7ade6bc08"]},{"id":"ITEM-5","itemData":{"author":[{"dropping-particle":"","family":"Tomaszek","given":"Henryk","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wróblewski","given":"Mirosław","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-5","issued":{"date-parts":[["2001"]]},"publisher":"Bellona","publisher-place":"Warsaw","title":"Podstawy oceny efektywności eksploatacji systemów uzbrojenia lotniczego","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=06552abf-22f6-4c6c-9af7-b4f8f2258f20"]}],"mendeley":{"formattedCitation":"[1, 20, 75, 112, 126]","plainTextFormattedCitation":"[1, 20, 75, 112, 126]","previouslyFormattedCitation":"[1, 20, 75, 112, 126]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[1, 24, 79, 116, 130]. More detailed information can be found in ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3846/aviation.2022.17592","ISSN":"1648-7788","abstract":"Because the Polish Air Force currently uses F-16 and MiG-29 aircraft, the aim of the study was to conduct a comparison of the susceptibility to destruction in combat of these two aircraft. The first part of the work concerned the analysis of individual critical components, such as: general characteristics of the airframe structure, aircraft engine, flight control system, fuel system, aircraft weapons, radar system. The index of susceptibility to destruction in combat was defined considering the listed critical components and the following types of enemy weapon: aircraft gun, air-to-air missile, anti-aircraft gun, surface-to-air missile. The analysis proved that the aircraft have similar susceptibility to damage in combat, a slight advantage of the F-16 aircraft in this respect was determined. The presented scheme can be used to analyze other aircraft. Proposals of aircraft modifications, directions of further actions, possibilities of using the described method were presented. The method can be used to making decisions by governments regarding the purchase aircraft for their fleets and identify aircraft critical components with high susceptibility to destruction in order to introduce appropriate modifications by military aircraft manufacturers.","author":[{"dropping-particle":"","family":"Papis","given":"Mateusz","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Krawczyk","given":"Tomasz","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Aviation","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2022","10","12"]]},"page":"131-137","title":"Comparison of MiG-29 and F-16 aircraft in the field of susceptibility to destruction in combat","type":"article-journal","volume":"26"},"uris":["http://www.mendeley.com/documents/?uuid=64b5937c-2071-4abd-a922-9caa854dd976"]}],"mendeley":{"formattedCitation":"[84]","plainTextFormattedCitation":"[84]","previouslyFormattedCitation":"[84]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[88]. Tab. 1F-16 systemsGeneral characteristics of the airframe structureA single-engine, light fighter aircraft. It was built in a classic mid-wing configuration. Its basic dimensions are: wingspan of 9.8 m, aircraft length of 14.8 m, wing area: 27.87 m2. The fuselage has a semi-monocoque construction, covering densely supported by frames and half frames.Aircraft engineThe power unit (single-engine) of the F-16 aircraft consists of a Pratt & Whitney F100-PW-229 engine with 79.13 kN and 128.91 kN thrust with afterburning. It is a two-flow engine with a hydraulically regulated nozzle. Flight control systemA fly-by-wire control system based on the Lear Siegler flight parameters computer, which uses data, among others from yoke (control column), control surface position transmitters, accelerometers, gyroscopes, angle of attack, and slide transmitters, aerodynamic data computer. Moreover, the system includes hydraulic actuators of control surfaces.Fuel systemThe F-16 engine is supplied with fuel from five fuselage tanks and two wing tanks, with a total capacity of 3,986 l. The fuel tanks have a self-sealing design. It is possible to mount additional fuel tanks.Aircraft weaponThe primary weaponry is the General Electric M61 A1 six-barrel cannon (20 mm calibre). Suspended armament: medium-range AIM-120 AMRAAM air-to-air missiles, LAU-114 launchers for firing short-range Sidewinder, and medium-range AMRAAM air-to-air missiles.Guided air-to-ground armament consists of AGM-65A/B/D/G Maverick and AS30L missiles, AGM-88 HARM and AGM-45 Shrike anti-radiation guided missiles, AGM-84 Harpoon or AGM-119 Penguin Mk 3 air-to-air guided missiles. Unguided missiles of 70 mm calibre can be fired from LAU-68 and LAU-88 multi-barrel launchers. The aircraft's bombarding armament consists of Paveway II series guided bombs. The aircraft is also adapted to carry B43 nuclear bombs.Radar systemA common radar used in F-16 aircraft is the Westinghouse AN/APG-68(V)5 (AN.APG-68 in older versions of the F-16C), operating in the I/J waveband. The (V)5 variant added an SA (Situation Awareness) module to warn the pilot of a threat. Starting with Block 50/52, a DTS digital map projector was added. Under ideal conditions, the maximum detection range for large targets (bombers) at high altitudes is 270 km. For small targets, it decreases to about 170 km. As regards targets visible on the ground, the analogous values are 230/130 km respectively. The radar can start tracking a target at a distance equal to about 60% of the detection distance. It is possible to track up to 10 targets simultaneously. The situation as seen by the radar is presented on multifunctional Honeywell indicators. The targets tracked by the station are also presented on the GEC-Arconi wide-angle head-up display (HUD). The AN/APG-68 radar prepares data necessary for air-to-air and air-to-ground missiles. The latest versions of the radar dedicated to F-16 are the AN/APG-80 and AN/APG-83, which can track more targets simultaneously.3. ARTIFICIAL INTELLIGENCE ALGORITHMS AND THEIR APPLICATIONS IN AVIATIONThe first major step in the ongoing research is a review of Artificial Intelligence algorithms. The next stages of the ongoing research are shown in the diagram (Fig. 1). AI algorithms that may be used in aviation were identified. For this purpose, a study of the literature's current state was conducted, which, combined with a review of AI algorithms, enabled the development of a summary presented in the form of a table, illustrating the applications of AI algorithms in specific areas of aircraft systems. The next step involved analysing the potential applications of AI algorithms in relation to the functionality of the F-16 aircraft's systems. The last step was to develop proposals for modifying the aircraft systems using selected algorithms.62230174625Overview of Artificial Intelligence (AI) algorithms: Machine Learning (ML), Deep Learning (DL) and others: fuzzy logic, evolutionary and swarm intelligence algorithmsOverview of research on the application of AI algorithms in aviationOverview of the functions of F-16 aircraft systems in terms of their application of AI algorithmsAssignment of algorithms to functions of F-16 aircraft systems 0Overview of Artificial Intelligence (AI) algorithms: Machine Learning (ML), Deep Learning (DL) and others: fuzzy logic, evolutionary and swarm intelligence algorithmsOverview of research on the application of AI algorithms in aviationOverview of the functions of F-16 aircraft systems in terms of their application of AI algorithmsAssignment of algorithms to functions of F-16 aircraft systems Fig. 1. Steps of the conducted analysis Overview of artificial intelligence algorithmsThe term Artificial Intelligence (AI) was pioneered by John McCarthy and presented at the Dartmouth Conference in 1956. In its shortened form, the term means “the intelligence exhibited by artificial devices” ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"0738-4602","abstract":"The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page. Copies of the typescript are housed in the archives at Dartmouth College and Stanford University. The first 5 papers state the proposal, and the remaining pages give qualifications and interests of the four who proposed the study. In the interest of brevity, this article reproduces only the proposal itself, along with the short autobiographical statements of the proposers.","author":[{"dropping-particle":"","family":"McCarthy","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Minsky","given":"M L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shannon","given":"C E","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Ai Magazine","id":"ITEM-1","issued":{"date-parts":[["1955"]]},"title":"A proposal for the Dartmouth summer research project on artificial intelligence - August 31, 1955","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=8844429a-75c8-4130-aab8-5ec3ef7004ce"]}],"mendeley":{"formattedCitation":"[76]","plainTextFormattedCitation":"[76]","previouslyFormattedCitation":"[76]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[80]. Since then, many definitions of the term AI have emerged. One of them was proposed by Andreas Kaplan and Michael Haenlein in 2019 ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.bushor.2018.08.004","ISSN":"00076813","abstract":"Artificial intelligence (AI)—defined as a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control.","author":[{"dropping-particle":"","family":"Kaplan","given":"Andreas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haenlein","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Business Horizons","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2019","1"]]},"page":"15-25","title":"Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence","type":"article-journal","volume":"62"},"uris":["http://www.mendeley.com/documents/?uuid=a11ddfe4-1073-44d4-bd42-162490f35acb"]}],"mendeley":{"formattedCitation":"[52]","plainTextFormattedCitation":"[52]","previouslyFormattedCitation":"[52]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[56]. They defined AI as “the ability of a system to correctly interpret data from external sources, learn from that data, and use that knowledge to perform specific tasks and achieve goals through flexible adaptation.” From the perspective of defining algorithms relating to the term artificial intelligence, the following can be identified: machine learning algorithms, deep learning algorithms, fuzzy logic, evolutionary algorithms, and swarm intelligence, among others. The overview of selected algorithms is presented below.Machine learning (ML) algorithms analyse data, learn from it, and decide based on that data. Three techniques of machine learning algorithms can be identified — supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the user provides the algorithm with a pair of input data and desired output data, and the algorithm itself finds a way to produce the desired output data given the indicated input data. In unsupervised learning, only the input data is known, and no known output data is provided to the algorithm ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"1449369413","ISSN":"1098-6596","abstract":"Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein−protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM- GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.","author":[{"dropping-particle":"","family":"Müller","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guido","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"publisher":"O'Reilly Media","title":"Introduction to Machine Learning with Python: A Guide for Data Scientists","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=697ffb3f-e1e6-4f4e-8743-9452d620729d"]}],"mendeley":{"formattedCitation":"[79]","plainTextFormattedCitation":"[79]","previouslyFormattedCitation":"[79]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[83]. Reinforcement learning is the third branch of machine learning, in which the agent determines its optimal behaviour (action) in the environment based on the feedback (reward) it receives. This feedback is known as a reinforcement signal. The agent's goal is to maximize its cumulative reward over time ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9781492035640","abstract":"Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple production-ready Python frameworks: scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of...","author":[{"dropping-particle":"","family":"Patel","given":"Ankur A","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2019"]]},"publisher":"O'Reilly Media","title":"Hands-On Unsupervised Learning Using Python","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=4d278dde-38a5-4d03-a684-48d136288e61"]}],"mendeley":{"formattedCitation":"[85]","plainTextFormattedCitation":"[85]","previouslyFormattedCitation":"[85]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[89]. The overview of selected ML algorithms is presented in Table 2. Tab. 2Overview of selected ML algorithmsGroupExamples of algorithmssupervised learning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9781492032649","abstract":"Second edition. \"2nd edition updated for TensorFlow 2\"--Page 1 of cover. Includes index. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. Part I, The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- Part II, Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Representation learning and generative learning using autoencoders and GANs ; Reinforcemen…","author":[{"dropping-particle":"","family":"Géron","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"O’Reilly Media","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"publisher":"O’Reilly Media","title":"Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=701dc206-8ae1-4234-b4c3-f28618cdedf8"]},{"id":"ITEM-2","itemData":{"ISBN":"1449369413","ISSN":"1098-6596","abstract":"Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein−protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM- GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.","author":[{"dropping-particle":"","family":"Müller","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guido","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2018"]]},"publisher":"O'Reilly Media","title":"Introduction to Machine Learning with Python: A Guide for Data Scientists","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=697ffb3f-e1e6-4f4e-8743-9452d620729d"]}],"mendeley":{"formattedCitation":"[30, 79]","plainTextFormattedCitation":"[30, 79]","previouslyFormattedCitation":"[30, 79]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[34, 83]linear regression (LinR); logistic regression (LogR); Support-Vector Machines (SVM); decision trees and random forests (DT&RF); naive Bayes classifier (NBC); k-means algorithm (k-m S); neural networks (NN); ensemble learning (EL)unsupervised learning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9781492032649","abstract":"Second edition. \"2nd edition updated for TensorFlow 2\"--Page 1 of cover. Includes index. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. Part I, The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- Part II, Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Representation learning and generative learning using autoencoders and GANs ; Reinforcemen…","author":[{"dropping-particle":"","family":"Géron","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"O’Reilly Media","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"publisher":"O’Reilly Media","title":"Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=701dc206-8ae1-4234-b4c3-f28618cdedf8"]},{"id":"ITEM-2","itemData":{"ISBN":"9781492035640","abstract":"Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple production-ready Python frameworks: scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of...","author":[{"dropping-particle":"","family":"Patel","given":"Ankur A","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issued":{"date-parts":[["2019"]]},"publisher":"O'Reilly Media","title":"Hands-On Unsupervised Learning Using Python","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=4d278dde-38a5-4d03-a684-48d136288e61"]}],"mendeley":{"formattedCitation":"[30, 85]","plainTextFormattedCitation":"[30, 85]","previouslyFormattedCitation":"[30, 85]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[34, 89]Hierarchical Cluster Analysis (HCS); Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Hierarchical DBSCAN (HDBSCAN); novelty and outlier detection (NOD) — one-sided support vector machine, isolation forest algorithms; Dimensionality-reduction and visualization algorithms (D-R&V) - Principal Component Analysis, Locally Linear Embedding, t-Distributed Stochastic Neighbor Embedding; Independent Components Analysis (ICA) algorithm; k-means algorithm (k-m U); apriori algorithm (apriori); Singular Value Decomposition (SVD)reinforcement learning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1146/annurev-control-053018-023825","ISSN":"25735144","abstract":"This article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications. It reviews the general formulation, terminology, and typical experimental implementations of reinforcement learning as well as competing solution paradigms. In order to compare the relative merits of various techniques, it presents a case study of the linear quadratic regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. It also describes how merging techniques from learning theory and control can provide nonasymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. The article concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.","author":[{"dropping-particle":"","family":"Recht","given":"Benjamin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Annual Review of Control, Robotics, and Autonomous Systems","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"A Tour of Reinforcement Learning: The View from Continuous Control","type":"article"},"uris":["http://www.mendeley.com/documents/?uuid=735dfc2d-0c3d-482c-ad85-baf995dc87db"]},{"id":"ITEM-2","itemData":{"ISBN":"0262193981","ISSN":"00976156","abstract":"The Reinforcement Learning Problem 1 1.1 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . 2 1.2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Elements of Reinforcement Learning . . . . . . . . . . . . . . 7 1.4 Limitations and Scope . . . . . . . . . . . . . . . . . . . . . . 9 1.5 An Extended Example: Tic-Tac-Toe . . . . . . . . . . . . . . 10 1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.7 History of Reinforcement Learning . . . . . . . . . . . . . . . 16 1.8 Bibliographical Remarks . . . . . . . . . . . . . . . . . . . . . 25 I Tabular Solution Methods 27 2 Multi-arm Bandits 31 2.1 An n-Armed Bandit Problem . . . . . . . . . . . . . . . . . . 32 2.2 Action-Value Methods . . . . . . . . . . . . . . . . . . . . . . 33 2.3 Incremental Implementation . . . . . . . . . . . . . . . . . . . 36 2.4 Tracking a Nonstationary Problem . . . . . . . . . . . . . . . 38 2.5 Optimistic Initial Values . . . . . . . . . . . . . . . . . . . . . 39 2.6 Upper-Confidence-Bound Action Selection . . . . . . . . . . . 41 2.7 Gradient Bandits . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.8 Associative Search (Contextual Bandits) . . . . . . . . . . . . 46 2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3 Finite Markov Decision Processes 53 3.1 The Agent–Environment Interface . . . . . . . . . . . . . . . . 53 3.2 Goals and Rewards . . . . . . . . . . . . . . . . . . . . . . . . 57 3.3 Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.4 Unified Notation for Episodic and Continuing Tasks . . . . . . 61 ∗3.5 The Markov Property . . . . . . . . . . . . . . . . . . . . . . . 62 3.6 Markov Decision Processes . . . . . . . . . . . . . . . . . . . . 67 3.7 Value Functions . . . . . . . . . . . . . . . . . . . . . . . . . . 70 3.8 Optimal Value Functions . . . . . . . . . . . . . . . . . . . . . 75 3.9 Optimality and Approximation . . . . . . . . . . . . . . . . . 79 3.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4 Dynamic Programming 89 4.1 Policy Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2 Policy Improvement . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3 Policy Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4.4 Value Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.5 Asynchronous Dynamic Programming . . . . . . . . . . . . . . 101 4.6 Generalized Policy Iteration…","author":[{"dropping-particle":"","family":"Sutton","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barto","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"MIT Press","id":"ITEM-2","issued":{"date-parts":[["2018"]]},"publisher":"MIT Press Cambridge","title":"Reinforcment Learning: An Introduction","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=b8a4362a-8802-43cf-9e7b-6dfae391c4d5"]},{"id":"ITEM-3","itemData":{"ISBN":"9781492032649","abstract":"Second edition. \"2nd edition updated for TensorFlow 2\"--Page 1 of cover. Includes index. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. Part I, The fundamentals of machine learning. The machine learning landscape ; End-to-end machine learning project ; Classification ; Training models ; Support vector machines ; Decision trees ; Ensemble learning and random forests ; Dimensionality reduction ; Unsupervised learning techniques -- Part II, Neural networks and deep learning. Introduction to artificial neural networks with Keras ; Training deep neural networks ; Custom models and training with TensorFlow ; Loading and preprocessing data with TensorFlow ; Deep computer vision using convolutional neural networks ; Processing sequences using RNNs and CNNs ; Natural language processing with RNNs and attention ; Representation learning and generative learning using autoencoders and GANs ; Reinforcemen…","author":[{"dropping-particle":"","family":"Géron","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"O’Reilly Media","id":"ITEM-3","issued":{"date-parts":[["2019"]]},"publisher":"O’Reilly Media","title":"Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=701dc206-8ae1-4234-b4c3-f28618cdedf8"]}],"mendeley":{"formattedCitation":"[30, 90, 108]","plainTextFormattedCitation":"[30, 90, 108]","previouslyFormattedCitation":"[30, 90, 108]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[34, 94, 112]Agent policy (AP); state value function (SVF); Q-learning and deep Q-learning (QL&DQL); value function (VF); Monte Carlo methods (MC); Temporal Difference learning (TD); REINFORCE algorithm (REINF); combined algorithms (CA)The term Deep Learning (DL) refers to algorithms whose origins date back to attempts at modelling using neural networks. While in the case of Machine Learning, single-layer neural networks are used, in the case of Deep Learning, we are dealing with organized circuits consisting of many layers — at least two layers. Therefore, the term 'Deep learning' refers primarily to the extensive structure of a network consisting of many layers. The more layers, the deeper the network. A deeper network allows for better solutions to complex real-world problems, e.g., those that are characterized by significant non-linearity. When considering Deep Learning, it is necessary to indicate possible learning techniques that constitute the basis for building models of this class. Deep Learning is divided into three basic learning techniques:Supervised or discriminative learning — in which discriminative functions are used for supervision or classification. Supervised learning requires labelling the values of the target function. In supervised learning, labelled data must be provided by humans.Unsupervised or generative deep learning — which uses data without labelling it. The unsupervised learning process is carried out by sorting and classifying data. These processes involve correlation analysis or analysis of statistical distributions. In general, it can be said that in unsupervised learning, the system independently recognizes patterns through sorting and classification.Hybrid learning — a technique that is based on the construction of models based on combinations of the above-mentioned techniques. The overview of selected DL algorithms based on ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s42979-021-00815-1","ISSN":"2662-995X","abstract":"Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.","author":[{"dropping-particle":"","family":"Sarker","given":"Iqbal H.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"SN Computer Science","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2021","11","18"]]},"page":"420","title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","type":"article-journal","volume":"2"},"uris":["http://www.mendeley.com/documents/?uuid=733253ec-a19c-42af-9db0-df571b5ddc3e"]}],"mendeley":{"formattedCitation":"[95]","plainTextFormattedCitation":"[95]","previouslyFormattedCitation":"[95]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[99] is presented in Table 3. Tab. 3Overview of selected DL algorithms, based on ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s42979-021-00815-1","ISSN":"2662-995X","abstract":"Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.","author":[{"dropping-particle":"","family":"Sarker","given":"Iqbal H.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"SN Computer Science","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2021","11","18"]]},"page":"420","title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","type":"article-journal","volume":"2"},"uris":["http://www.mendeley.com/documents/?uuid=733253ec-a19c-42af-9db0-df571b5ddc3e"]}],"mendeley":{"formattedCitation":"[95]","plainTextFormattedCitation":"[95]","previouslyFormattedCitation":"[95]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[99]GroupExamples of algorithmssupervised or discriminative deep learningmultilayer perceptron (MLP); convolutional neural network (CVN), recurrent neural network (RNN);unsupervised or generative deep learninggenerative adversarial network (GAN); autoencoder (autoE); self-organizing /Kohonen map (SOM); restricted Boltzmann machine (RBM); deep belief network (DBN)hybrid learninghybrid deep neural networks (HDNN), deep transfer learning (DTL), deep reinforcement learning (DRL)Other artificial intelligence algorithms include fuzzy logic, evolutionary algorithms, and swarm intelligence algorithms. Fuzzy logic is a form of multivalued logic in which the truth value of variables can be any real number between 0 and 1. It is used to handle the concept of partial truth, in which the truth value can range from completely true to completely false ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-1-4615-5217-8","ISBN":"978-1-4613-7377-3","abstract":"Mathematical Principles of Fuzzy Logic provides a systematic study of the formal theory of fuzzy logic. The book is based on logical formalism demonstrating that fuzzy logic is a well-developed logical theory. It includes the theory of functional systems in fuzzy logic, providing an explanation of what can be represented, and how, by formulas of fuzzy logic calculi. It also presents a more general interpretation of fuzzy logic within the environment of other proper categories of fuzzy sets stemming either from the topos theory, or even generalizing the latter. This book presents fuzzy logic as the mathematical theory of vagueness as well as the theory of commonsense human reasoning, based on the use of natural language, the distinguishing feature of which is the vagueness of its semantics.","author":[{"dropping-particle":"","family":"Novák","given":"Vilém","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perfilieva","given":"Irina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Močkoř","given":"Jiří","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Mathematical Principles of Fuzzy Logic","id":"ITEM-1","issued":{"date-parts":[["1999"]]},"publisher":"Springer US","publisher-place":"Boston, MA","title":"Mathematical Principles of Fuzzy Logic","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=54debf83-59a6-4908-8926-11bb190eff1a"]}],"mendeley":{"formattedCitation":"[83]","plainTextFormattedCitation":"[83]","previouslyFormattedCitation":"[83]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[87]. Among the Artificial Intelligence algorithms that can be used, there is an evolutionary algorithm that refers to the mechanisms of species development known from biology. This algorithm uses the principles of evolution to solve complex optimization problems. The evolutionary algorithm consists in iteratively determining the best solution in terms of the adopted quality criterion, e.g. reliability. Currently, the most famous evolutionary algorithm is the genetic algorithm, which uses mutations and recombination of chromosomes of individuals, their selection and generational replacement ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICGTSPICC.2016.7955308","ISBN":"978-1-5090-0467-6","abstract":"Evolutionary algorithm (EA) emerges as an important optimization and search technique in the last decade. EA is a subset of Evolutionary Computations (EC) and belongs to set of modern heuristics based search method. Due to flexible nature and robust behavior inherited from Evolutionary Computation, it becomes efficient means of problem solving method for widely used global optimization problems. It can be used successfully in many applications of high complexity. This paper presents a critical overview of Evolutionary algorithms and its generic procedure for implementation. It further discusses the various practical advantages using evolutionary algorithms over classical methods of optimization. It also includes unusual study of various invariants of EA like Genetic Programming (GP), Genetic Algorithm (GA), Evolutionary Programming (EP) and Evolution Strategies (ES). Extensions of EAs in the form of Memetic algorithms (MA) and distributed EA are also discussed. Further the paper focuses on various refinements done in area of EA to solve real life problems.","author":[{"dropping-particle":"","family":"Vikhar","given":"Pradnya A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)","id":"ITEM-1","issued":{"date-parts":[["2016","12"]]},"page":"261-265","publisher":"IEEE","title":"Evolutionary algorithms: A critical review and its future prospects","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=3991db02-5a46-46a0-bde0-8ce9b5c410db"]}],"mendeley":{"formattedCitation":"[119]","plainTextFormattedCitation":"[119]","previouslyFormattedCitation":"[119]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[123]. Swarm intelligence is found in biological systems such as ant colonies, bees, flocks of birds, animal husbandry, and bacterial growth. The operation of swarm intelligence is based on so-called agents that act according to specific rules, often in a decentralized structure. The action of a single agent does not manifest intelligence, but a group of agents acting according to the rules can be described as swarm intelligence. This community, operating on the basis of the above-mentioned principles, is capable of self-organization. This action is based on interactions between agents; for example, a swarm of ants creates specific paths in an organized manner. Swarm intelligence can be implemented in a similar way, such as for the operation of drones ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/TRO.2021.3071615","ISSN":"1552-3098","abstract":"Cooperative control of multirobot systems (MRSs) has earned significant research interest over the past two decades due to its potential applications in multidisciplinary engineering problems. In contrast to a single specialized robot, the MRS can be designed to offer flexibility, reconfigurability, robustness to faults, and cost-effectiveness in solving complex and challenging tasks. In this article, we aim to develop a unified cluster formation containment coordination framework for networked robots that can be decomposed into two layers containing the leaders and the followers. According to the proposed methodology, the leader robots are first distributed into a set of distinct and nonoverlapping clusters depending on the positions and priorities of the targets exploiting a game-theoretic rule. Then, they are steered to attain the desired formations around the corresponding targets. Subsequently, the follower robots are made to converge into the convex hull spanned by the leaders of the individual clusters. A prototype search and rescue operation is considered to highlight the usefulness of the proposed coordination framework. Furthermore, real-time hardware experiments were conducted on miniature mobile robots to validate the feasibility of the theoretical results.","author":[{"dropping-particle":"","family":"Hu","given":"Junyan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhowmick","given":"Parijat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jang","given":"Inmo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Arvin","given":"Farshad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lanzon","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Transactions on Robotics","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2021","12"]]},"page":"1936-1955","title":"A Decentralized Cluster Formation Containment Framework for Multirobot Systems","type":"article-journal","volume":"37"},"uris":["http://www.mendeley.com/documents/?uuid=49544210-f418-4ec7-ab33-094d242124af"]},{"id":"ITEM-2","itemData":{"DOI":"10.1109/TCDS.2020.3018549","ISSN":"2379-8920","abstract":"The robotic shepherding problem has earned significant research interest over the last few decades due to its potential application in precision agriculture. In this article, we first modeled the sheep flocking behavior using adaptive protocols and artificial potential field methods. Then, we designed a coordination algorithm for robotic dogs. An occlusion-based motion control strategy was proposed to herd the sheep to the desired location. Compared to formation-based techniques, the proposed control strategy provides more flexibility and efficiency when herding a large number of sheep. Simulation and lab-based experiments, using real robots and a global vision-based tracking system, were carried out to validate the effectiveness of the proposed approach.","author":[{"dropping-particle":"","family":"Hu","given":"Junyan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Turgut","given":"Ali Emre","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Krajnik","given":"Tomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lennox","given":"Barry","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Arvin","given":"Farshad","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Transactions on Cognitive and Developmental Systems","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2022","3"]]},"page":"126-135","title":"Occlusion-Based Coordination Protocol Design for Autonomous Robotic Shepherding Tasks","type":"article-journal","volume":"14"},"uris":["http://www.mendeley.com/documents/?uuid=e5039db0-003b-4e52-88f5-aefbe60385e0"]},{"id":"ITEM-3","itemData":{"DOI":"10.1109/TCSII.2021.3074705","ISSN":"1549-7747","abstract":"Coordination of robot swarms has received significant research interest over the last decade due to its wide real-world applications including precision agriculture, target surveillance, planetary exploration, etc. Many of these practical activities can be formulated as a formation tracking problem. This brief aims to design a robust control strategy for networked robot swarms subjected to nonlinear dynamics and unknown disturbances. Firstly, a robust adaptive formation coordination protocol is proposed for robot swarms, which utilizes only local information for tracking a dynamic target with uncertain maneuvers. A rigorous theoretical proof utilizing the Lyapunov stability approach is then provided to guarantee the control performance. Towards the end, real-time hardware experiments with wheeled mobile robots are conducted to validate the robustness and feasibility of the proposed formation coordination approach.","author":[{"dropping-particle":"","family":"Hu","given":"Junyan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Turgut","given":"Ali Emre","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lennox","given":"Barry","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Arvin","given":"Farshad","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Transactions on Circuits and Systems II: Express Briefs","id":"ITEM-3","issue":"1","issued":{"date-parts":[["2022","1"]]},"page":"114-118","title":"Robust Formation Coordination of Robot Swarms With Nonlinear Dynamics and Unknown Disturbances: Design and Experiments","type":"article-journal","volume":"69"},"uris":["http://www.mendeley.com/documents/?uuid=95029226-93af-4ae5-b99b-2f2b0267a449"]}],"mendeley":{"formattedCitation":"[40–42]","plainTextFormattedCitation":"[40–42]","previouslyFormattedCitation":"[40–42]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[44-46].Overview of research on the application of AI algorithms in aviationInterest in Artificial Intelligence techniques has contributed to many studies at the level of basic and development research. Below are presented selected studies related to possible applications of Artificial Intelligence algorithms in aviation divided into: machine learning, deep learning, and other Artificial Intelligence algorithms such as fuzzy logic, evolutionary algorithms, and swarm intelligence.Tab. 4Selected applications of ML algorithms – supervised learningSUPERVISED LEARNINGAlgorithmSelected applicationslinear regression Maintenance and aircraft equipment failure analysis ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1155/2020/8616039","ISSN":"1058-9244","abstract":"There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K -means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment.","author":[{"dropping-particle":"","family":"Celikmih","given":"Kadir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Inan","given":"Onur","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Uguz","given":"Harun","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Programming","id":"ITEM-1","issued":{"date-parts":[["2020","8","28"]]},"page":"1-10","title":"Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method","type":"article-journal","volume":"2020"},"uris":["http://www.mendeley.com/documents/?uuid=26f1432d-d6ee-46d4-8b4a-6fcdf290db64"]}],"mendeley":{"formattedCitation":"[11]","plainTextFormattedCitation":"[11]","previouslyFormattedCitation":"[11]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[15]; Wind prediction – fuel consumption ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2021-0519","ISBN":"978-1-62410-609-5","abstract":"Many research groups have been committed to developing numerical models for weather forecasts. The models are currently used to predict weather patterns and trends in the aviation industry. In particular, pilots receive wind information predicted by the models and use the forecast to not only calculate how much fuel is needed for a flight but also optimize flight routes by seeking favorable winds. One potential issue is that the models provide relatively coarse wind information in both space and time, which potentially leads to inaccurate calculation of fuel consumption. This research aims to yield a continuous wind prediction model by combining a supervised learning algorithm with the Inverse Distance Weighting technique. Specifically, this research compares three different supervised learning algorithms that include Gaussian Process, Multi-Layer Perceptron, and Support Vector Machine to identify the most appropriate algorithm. The selected algorithm is then compared to a linear interpolation method that is widely used in current flight planning systems for obtaining continuous wind information. A case study is performed with the real Delta Airlines flight 1944 to evaluate the proposed methodology. The results show that 1) the Support Vector Machine provides a better wind prediction compared to the other models, 2) the supervised learning-based regression method performs better than the linear interpolation method in wind predictions, and 3) there are 16 seconds of difference between the real flight (12,117 seconds) and the simulated flight (12,101 seconds) for the cruise portion, indicating that the proposed methodology generates valid results as long as input wind data is provided accurately.","author":[{"dropping-particle":"","family":"Kim","given":"Junghyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Chao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Briceno","given":"Simon I.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mavris","given":"Dimitri N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA Scitech 2021 Forum","id":"ITEM-1","issued":{"date-parts":[["2021","1","11"]]},"publisher":"American Institute of Aeronautics and Astronautics","publisher-place":"Reston, Virginia","title":"Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=3553aaec-92ea-47fc-a3e0-b3e5a008c05c"]}],"mendeley":{"formattedCitation":"[53]","plainTextFormattedCitation":"[53]","previouslyFormattedCitation":"[53]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[57];Identification of complex dynamic data-driven failure models for more accurate flight planning and control under emergency conditions ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-319-95504-9_5","ISBN":"9783319955049","abstract":"Spatio-temporal data streams are often related in complex ways, for example, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors. Some of these factors are controllable such as engine inputs or the airframe's angle of attack, while others contextual, such as air density, or turbulence. It is therefore critical to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity, a malfunctioning pitot-static system, or other abnormal flight conditions. In this paper, we extend our PILOTS programming language [1] to support machine learning techniques that will help data scientists: (1) create parameterized failure models from data and (2) continuously train a statistical model as new evidence (data) arrives. The linear regression approach learns parameters of a linear model to minimize least squares error for given training data. The Bayesian approach classifies operating modes according to supervised offline training and can discover new statistically significant modes online. As shown in Tuninter 1153 simulation result, dynamic Bayes classifier finds discrete error states on the fly while the error signatures approach requires every error state predefined. Using synthetic data, we compare the accuracy, response time, and adaptability of these machine learning techniques. Future dynamic data driven applications systems (DDDAS) using machine learning can identify complex dynamic data-driven failure models, which will in turn enable more accurate flight planning and control for emergency conditions.","author":[{"dropping-particle":"","family":"Chen","given":"Sida","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Imai","given":"Shigeru","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhu","given":"Wennan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Varela","given":"Carlos A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Handbook of Dynamic Data Driven Applications Systems","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"page":"97-121","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=c5ffabf2-5ca7-44af-8439-5268f0260670"]}],"mendeley":{"formattedCitation":"[12]","plainTextFormattedCitation":"[12]","previouslyFormattedCitation":"[12]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[12];Modelling nonlinear and unstable aerodynamics during the design of future high-performance fighters and improving the angle of attack dynamics ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICNC.2012.6234640","ISBN":"978-1-4577-2133-5","ISSN":"21579555","abstract":"Accurately modeling nonlinear and unsteady aerodynamics at high attitude flight plays an important role in design of future high performance fighters. In the meanwhile, it also can improve the prediction of high angle of attack dynamics of normal aircraft configurations. Support vector machines (SVMs), known as a novel type of learning machines based on statistical learning theory and structural risk minimization (SRM) principle, can be used for handle regression problems. By denoting a set of nonlinear transformations from the complex input space to a high-dimensional feature space, SVMs can approximate the regression function by a linear regression in the feature space. Such implementation is so simple that it can be analyzed mathematically. By employing SVMs, the present work models the unsteady pitching oscillation aerodynamic data of a 1/10 scaled aircraft model. Here, the input data are established from the wind tunnel experiments at different frequencies and amplitudes. To make comparison, the artificial neural networks (ANNs) technique is also used. It turned out that SVMs can overcome the ANNs's inherent drawback of slow training convergence speed. Consequently, SVMs demonstrate high potentials for dealing with the chosen modeling of unsteady aerodynamics. © 2012 IEEE.","author":[{"dropping-particle":"","family":"Chen","given":"Yongliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2012 8th International Conference on Natural Computation","id":"ITEM-1","issued":{"date-parts":[["2012","5"]]},"page":"431-435","publisher":"IEEE","title":"Modeling of longitudinal unsteady aerodynamics at high angle-of-attack based on support vector machines","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=ef900a69-2373-42ac-a1e6-3a23aaa190f0"]}],"mendeley":{"formattedCitation":"[14]","plainTextFormattedCitation":"[14]","previouslyFormattedCitation":"[14]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[14];logistic regressionA digital twin of avionics systems – system performance and fault location ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-030-58868-7_44","ISBN":"9783030588670","ISSN":"18674941","abstract":"Machine learning methods find wide application to build digital twins of avionics systems under the integrated system health management. This allows representing the system performance and managing all stages of the system life cycle that significantly saves resources of manufacturers. In this work, a possibility of the machine learning methods application was researched for avionics objects health analysis and faults localization challenges to solve classification problems under building digital twins in ISHM. Decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors methods were considered. The research results show that some methods are effective for the health analysis challenge, and some methods are effective for the faults localization challenge. The application of the results will allow getting a base to build digital twins of avionics systems.","author":[{"dropping-particle":"V.","family":"Trusova","given":"Kseniya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Springer Proceedings in Physics","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"383-397","title":"Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=2f6f0b50-81bb-44f1-8dca-cafa19c69ddb"]}],"mendeley":{"formattedCitation":"[113]","plainTextFormattedCitation":"[113]","previouslyFormattedCitation":"[113]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[117]; Predictive maintenance, aircraft repair, and overhaul ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/RAMS48030.2020.9153694","ISBN":"978-1-7281-3690-5","ISSN":"0149144X","abstract":"As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.","author":[{"dropping-particle":"","family":"Apostolidis","given":"Asteris","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pelt","given":"Maurice","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stamoulis","given":"Konstantinos P.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2020 Annual Reliability and Maintainability Symposium (RAMS)","id":"ITEM-1","issued":{"date-parts":[["2020","1"]]},"page":"1-7","publisher":"IEEE","title":"Aviation Data Analytics in MRO Operations: Prospects and Pitfalls","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=54eca12e-8ef9-493f-a769-60f76a3f0545"]}],"mendeley":{"formattedCitation":"[5]","plainTextFormattedCitation":"[5]","previouslyFormattedCitation":"[5]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[5];Support-Vector MachinesMaintenance and aircraft equipment failure analysis ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1155/2020/8616039","ISSN":"1058-9244","abstract":"There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K -means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment.","author":[{"dropping-particle":"","family":"Celikmih","given":"Kadir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Inan","given":"Onur","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Uguz","given":"Harun","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Programming","id":"ITEM-1","issued":{"date-parts":[["2020","8","28"]]},"page":"1-10","title":"Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method","type":"article-journal","volume":"2020"},"uris":["http://www.mendeley.com/documents/?uuid=26f1432d-d6ee-46d4-8b4a-6fcdf290db64"]}],"mendeley":{"formattedCitation":"[11]","plainTextFormattedCitation":"[11]","previouslyFormattedCitation":"[11]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[11]; Wind prediction – fuel consumption ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2021-0519","ISBN":"978-1-62410-609-5","abstract":"Many research groups have been committed to developing numerical models for weather forecasts. The models are currently used to predict weather patterns and trends in the aviation industry. In particular, pilots receive wind information predicted by the models and use the forecast to not only calculate how much fuel is needed for a flight but also optimize flight routes by seeking favorable winds. One potential issue is that the models provide relatively coarse wind information in both space and time, which potentially leads to inaccurate calculation of fuel consumption. This research aims to yield a continuous wind prediction model by combining a supervised learning algorithm with the Inverse Distance Weighting technique. Specifically, this research compares three different supervised learning algorithms that include Gaussian Process, Multi-Layer Perceptron, and Support Vector Machine to identify the most appropriate algorithm. The selected algorithm is then compared to a linear interpolation method that is widely used in current flight planning systems for obtaining continuous wind information. A case study is performed with the real Delta Airlines flight 1944 to evaluate the proposed methodology. The results show that 1) the Support Vector Machine provides a better wind prediction compared to the other models, 2) the supervised learning-based regression method performs better than the linear interpolation method in wind predictions, and 3) there are 16 seconds of difference between the real flight (12,117 seconds) and the simulated flight (12,101 seconds) for the cruise portion, indicating that the proposed methodology generates valid results as long as input wind data is provided accurately.","author":[{"dropping-particle":"","family":"Kim","given":"Junghyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Chao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Briceno","given":"Simon I.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mavris","given":"Dimitri N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA Scitech 2021 Forum","id":"ITEM-1","issued":{"date-parts":[["2021","1","11"]]},"publisher":"American Institute of Aeronautics and Astronautics","publisher-place":"Reston, Virginia","title":"Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=3553aaec-92ea-47fc-a3e0-b3e5a008c05c"]}],"mendeley":{"formattedCitation":"[53]","plainTextFormattedCitation":"[53]","previouslyFormattedCitation":"[53]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[57];Modelling nonlinear and unstable aerodynamics during the design of future high-performance fighters and improving the angle of attack dynamics ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICNC.2012.6234640","ISBN":"978-1-4577-2133-5","ISSN":"21579555","abstract":"Accurately modeling nonlinear and unsteady aerodynamics at high attitude flight plays an important role in design of future high performance fighters. In the meanwhile, it also can improve the prediction of high angle of attack dynamics of normal aircraft configurations. Support vector machines (SVMs), known as a novel type of learning machines based on statistical learning theory and structural risk minimization (SRM) principle, can be used for handle regression problems. By denoting a set of nonlinear transformations from the complex input space to a high-dimensional feature space, SVMs can approximate the regression function by a linear regression in the feature space. Such implementation is so simple that it can be analyzed mathematically. By employing SVMs, the present work models the unsteady pitching oscillation aerodynamic data of a 1/10 scaled aircraft model. Here, the input data are established from the wind tunnel experiments at different frequencies and amplitudes. To make comparison, the artificial neural networks (ANNs) technique is also used. It turned out that SVMs can overcome the ANNs's inherent drawback of slow training convergence speed. Consequently, SVMs demonstrate high potentials for dealing with the chosen modeling of unsteady aerodynamics. © 2012 IEEE.","author":[{"dropping-particle":"","family":"Chen","given":"Yongliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2012 8th International Conference on Natural Computation","id":"ITEM-1","issued":{"date-parts":[["2012","5"]]},"page":"431-435","publisher":"IEEE","title":"Modeling of longitudinal unsteady aerodynamics at high angle-of-attack based on support vector machines","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=ef900a69-2373-42ac-a1e6-3a23aaa190f0"]}],"mendeley":{"formattedCitation":"[14]","plainTextFormattedCitation":"[14]","previouslyFormattedCitation":"[14]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[14];A digital twin of avionics systems – system performance and fault location ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-030-58868-7_44","ISBN":"9783030588670","ISSN":"18674941","abstract":"Machine learning methods find wide application to build digital twins of avionics systems under the integrated system health management. This allows representing the system performance and managing all stages of the system life cycle that significantly saves resources of manufacturers. In this work, a possibility of the machine learning methods application was researched for avionics objects health analysis and faults localization challenges to solve classification problems under building digital twins in ISHM. Decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors methods were considered. The research results show that some methods are effective for the health analysis challenge, and some methods are effective for the faults localization challenge. The application of the results will allow getting a base to build digital twins of avionics systems.","author":[{"dropping-particle":"V.","family":"Trusova","given":"Kseniya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Springer Proceedings in Physics","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"383-397","title":"Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=2f6f0b50-81bb-44f1-8dca-cafa19c69ddb"]}],"mendeley":{"formattedCitation":"[113]","plainTextFormattedCitation":"[113]","previouslyFormattedCitation":"[113]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[117]; Low-altitude obstacle detection and classification ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9781713830016","abstract":"Aviation statistics identify collision with terrain and obstacles as a leading cause of helicopter accidents. Assisting helicopter pilots in detecting the presence of obstacles can partly mitigate the risk of collisions. However, only a limited number of helicopters in operation have an installed helicopter terrain awareness and warning system (HTAWS), while the cost of active obstacle warning systems remains prohibitive for many civil operators. In this work, we apply machine learning to automate obstacle detection and classification in combination with any commercially-available airborne optical sensor. While numerous techniques for learning-based object detection have appeared in the literature, many of them are data- and computation-intensive. Our approach seeks to balance the performance in regards to the detection and classification accuracy on the one hand, and the amount of training data and runtime performance on the other hand. Specifically, our approach combines the invariant feature extraction ability of pre-trained deep Convolutional Neural Networks (CNNs) and the high-speed training and classification ability of a novel, proprietary frequency-domain Support Vector Machine (SVM) method. In this paper, we present the CNN+SVM method for efficient obstacle detection and classification. We describe the experimental setup comprising datasets of pre-defined classes of obstacles - pylons, chimneys, antennas, towers, wind turbines, flying aircraft - from airborne video sequences of low-altitude helicopter flight. We analyze the performance results using average precision, average recall, and runtime performance metrics on representative test data. Finally, we present a simple architecture for a real-time, on-board evaluation of automatic vision-based obstacle detection.","author":[{"dropping-particle":"","family":"Ammalladene-Venkata","given":"Manogna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Halbe","given":"Omkar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seidel","given":"Christian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Groitl","given":"Christine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stahl","given":"Christoph","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"77th Annual Vertical Flight Society Forum and Technology Display, FORUM 2021: The Future of Vertical Flight","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"title":"Deep Learning based obstacle awareness from airborne optical sensors","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=8b9c33a3-804a-4342-a114-01cbc86375c6"]}],"mendeley":{"formattedCitation":"[3]","plainTextFormattedCitation":"[3]","previouslyFormattedCitation":"[3]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[3];Classification of different types of signals in radar systems ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s10470-017-0944-0","ISSN":"0925-1030","abstract":"In modern radar systems, low probability of intercept (LPI) waveforms are used to make detection by a potential adversary difficult. This is accomplished using wideband waveforms, frequency hopping, and continuous waveforms (FMCW) to reduce the signal profile. The low signal profile of the LPI signal enables the radar to perform detection and or target tracking while the target remains unaware. Several modulation techniques such as polytime codes, polyphase codes, FSK, and FMCW are used to produce LPI signals for transmission. This paper looks at the ability of spectral correlation along with a support vector machine in order to automatically classify the different LPI signal types in a non-cooperative environment.","author":[{"dropping-particle":"","family":"Vanhoy","given":"Garrett","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schucker","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bose","given":"Tamal","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Analog Integrated Circuits and Signal Processing","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2017","5","20"]]},"page":"305-313","title":"Classification of LPI radar signals using spectral correlation and support vector machines","type":"article-journal","volume":"91"},"uris":["http://www.mendeley.com/documents/?uuid=210778cb-68b8-4b25-8ad2-76a83934f292"]}],"mendeley":{"formattedCitation":"[117]","plainTextFormattedCitation":"[117]","previouslyFormattedCitation":"[117]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[121];Identification of tactical manoeuvre of target based on air combat manoeuvre element ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.15918/j.tbit1001-0645.2018.08.009","ISSN":"10010645","abstract":"The online identification for tactical maneuver of target can provide important information for air combat situation sensing, mission planning and aid decision making. Since the radar data cannot provide the posture information, it is difficult to describe the tactical maneuver of the enemy vehicle. In this paper, a two-stage recognition system for tactical maneuver of target based on air combat maneuver element (ACME) was proposed. First, the information from airborne radar was analyzed. Then, tactical maneuver was decomposed into eleven ACMEs according to the trajectory analyzing, thus simplifying the maneuver representation. And then, a recognition method utilizing random forest and support vector machine was designed, in the first stage, recognizing ACMEs with trajectory parameters, in the second stage, identifying tactical maneuver based on ACMEs sequence and motion parameters. Finally, the simulation results show that typical maneuver can be represented effectively by ACMEs and identified correctly by the recognition method.","author":[{"dropping-particle":"","family":"Jia","given":"Zhen Ze","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fan","given":"Xiao Guang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xue","given":"Ming Hao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Song","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2018"]]},"page":"820-827","title":"Online Identification Method for Tactical Maneuver of Target Based on Air Combat Maneuver Element","type":"article-journal","volume":"38"},"uris":["http://www.mendeley.com/documents/?uuid=52c48de3-2b1e-4057-b01a-f37c44ce07c0"]}],"mendeley":{"formattedCitation":"[49]","plainTextFormattedCitation":"[49]","previouslyFormattedCitation":"[49]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[53]; Recognition of tactical intent of multi-aircraft cooperative air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1155/2021/9558838","ISSN":"1687-5974","abstract":"Accurately identifying the tactical intention of the target can facilitate the prediction of the opponent’s behavior and improve the efficiency of collaborative decision. We have observed that traditional methods could achieve high recognition rate on conventional tactical intent. Nevertheless, their performance would deteriorate seriously when recognizing cooperative tactical intention in multiaircraft air combat environment. The main reason resides on key features that are difficult to extract for traditional methods. To this end, this paper proposes a novel approach to recognizing tactical intention of multiaircraft cooperative air combat. Specifically, we employ support vector machine (SVM) to forecast the attack intention based on 19 low correlation features. The purpose of the employment of SVM is to avoid local optimization and reduce data dimension. Moreover, we use three models, i.e., dynamic Bayesian network (DBN), radar model, and threat assessment model to extract crucial information regarding maneuver occupancy, silent penetration, and attack tendency. The extracted information would make great contribution to the recognition accuracy of six types of cooperative tactics. Finally, we learn a decision tree model on train samples processed by above two phases to classify different tactical intention. In order to verify the effectiveness of the proposed method, we use data sets from a loop simulation platform. The experimental results have approved the superiority of our method via the comparison to several baseline methods with respect to recognition rate and efficiency. In addition, we underline that our method also performs well on incomplete and uncertain information.","author":[{"dropping-particle":"","family":"Guanglei","given":"Meng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Runnan","given":"Zhao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Biao","given":"Wang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mingzhe","given":"Zhou","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yu","given":"Wang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiao","given":"Liang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Aerospace Engineering","editor":[{"dropping-particle":"","family":"He","given":"Shaoming","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2021","11","9"]]},"page":"1-18","title":"Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat","type":"article-journal","volume":"2021"},"uris":["http://www.mendeley.com/documents/?uuid=a84b804e-d480-4c55-b6ea-f4504abfbea3"]}],"mendeley":{"formattedCitation":"[33]","plainTextFormattedCitation":"[33]","previouslyFormattedCitation":"[33]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[37];Target threat assessment model in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"10015965","abstract":"Target threat assessment method for air combat was a key problem of cooperative multiple target attack. Aiming at the deficiencies of traditional target threat assessment method for air combat in calculating the weights, a new assessment method based on support vector machines for regression was proposed. An improved distance threat model was proposed based on analyzing the existing target threat assessment methods for air combat which had disadvantages in distance threat model. The model of target threat assessment for air combat was established based on support vector machines for regression, then using this model the threat of some supposed air combat target was evaluated. The simulation results show that the method has good ability of forecast, which can accomplish target threat assessment for air combat fleetly and exactly.","author":[{"dropping-particle":"","family":"Guo","given":"Hui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Haojun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Ling","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2010"]]},"page":"123-126","title":"Target threat assessment of air combat based on support vector machines for regression","type":"article-journal","volume":"36"},"uris":["http://www.mendeley.com/documents/?uuid=d43241e7-8bdb-45d6-8063-feae8bb7ca3a"]}],"mendeley":{"formattedCitation":"[34]","plainTextFormattedCitation":"[34]","previouslyFormattedCitation":"[34]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[38].Decision trees and random forestsA digital twin of avionics systems – system performance and fault location ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-030-58868-7_44","ISBN":"9783030588670","ISSN":"18674941","abstract":"Machine learning methods find wide application to build digital twins of avionics systems under the integrated system health management. This allows representing the system performance and managing all stages of the system life cycle that significantly saves resources of manufacturers. In this work, a possibility of the machine learning methods application was researched for avionics objects health analysis and faults localization challenges to solve classification problems under building digital twins in ISHM. Decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors methods were considered. The research results show that some methods are effective for the health analysis challenge, and some methods are effective for the faults localization challenge. The application of the results will allow getting a base to build digital twins of avionics systems.","author":[{"dropping-particle":"V.","family":"Trusova","given":"Kseniya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Springer Proceedings in Physics","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"383-397","title":"Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=2f6f0b50-81bb-44f1-8dca-cafa19c69ddb"]}],"mendeley":{"formattedCitation":"[113]","plainTextFormattedCitation":"[113]","previouslyFormattedCitation":"[113]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[117]; Fuel consumption analysis ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s13272-019-00422-0","ISSN":"1869-5582","abstract":"With the aid of recording systems such as the flight data recorder, information from aircraft sensors can be transmitted in-situ to airlines or maintenance providers during the flight in form of reports, or stored for subsequent analysis in the postprocessing. This allows the determination of current aircraft performance with regard to fuel consumption or emissions. At present, data in a highly aggregated form (predominantly averages) are used for statistical estimations or physical models. The metrics are calculated on a rolling basis as performance indicators of the aircraft and are then compared with book values from the manuals or with data from the performance monitoring system. However, this procedure represents only a situational, aggregated point evaluation of stable flight conditions. The data aggregation is based on strict validity limits for parameter variations. Furthermore, trigger conditions of the recording logic determine the number and quality of the transmitted reports, such, that only a few data points are available for performance analyses. To improve realistic performance analyses, the time series of all aircraft sensors over the entire flight mission (full-flight data) can be used. In contrast to physical models, this study presents data-based approaches using machine-learning tools from the field of artificial intelligence, to develop fuel flow models based on full-flight data. The proposed methods result in detailed statements for the diagnosis of aircraft fuel consumption. The paper deals with the model development and results of different analyses based on a variety of an airline’s operational flight data records. This study describes the learning methods and shows results for two different data-based models, including neural networks and decision trees. Finally, future applications for the models and an outlook on the authors’ activities will be provided.","author":[{"dropping-particle":"","family":"Baumann","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klingauf","given":"U.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"CEAS Aeronautical Journal","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2020","1","30"]]},"page":"277-287","title":"Modeling of aircraft fuel consumption using machine learning algorithms","type":"article-journal","volume":"11"},"uris":["http://www.mendeley.com/documents/?uuid=49fac4fa-fe63-401a-b71c-30917ea9f07b"]}],"mendeley":{"formattedCitation":"[6]","plainTextFormattedCitation":"[6]","previouslyFormattedCitation":"[6]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[6]Aerodynamics modelling based on decision tree and random forest using flight data ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/1.C035034","ISSN":"0021-8669","author":[{"dropping-particle":"","family":"Kumar","given":"Ajit","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghosh","given":"Ajoy Kanti","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Aircraft","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2019","1"]]},"page":"403-409","title":"Decision Tree– and Random Forest–Based Novel Unsteady Aerodynamics Modeling Using Flight Data","type":"article-journal","volume":"56"},"uris":["http://www.mendeley.com/documents/?uuid=4dc09681-af4f-4caf-bbf3-df94d49606ed"]}],"mendeley":{"formattedCitation":"[60]","plainTextFormattedCitation":"[60]","previouslyFormattedCitation":"[60]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[64]Modelling of aircraft nonlinear unsteady aerodynamics at high-angle attackIdentification of tactical manoeuvre of target based on air combat manoeuvre element ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.15918/j.tbit1001-0645.2018.08.009","ISSN":"10010645","abstract":"The online identification for tactical maneuver of target can provide important information for air combat situation sensing, mission planning and aid decision making. Since the radar data cannot provide the posture information, it is difficult to describe the tactical maneuver of the enemy vehicle. In this paper, a two-stage recognition system for tactical maneuver of target based on air combat maneuver element (ACME) was proposed. First, the information from airborne radar was analyzed. Then, tactical maneuver was decomposed into eleven ACMEs according to the trajectory analyzing, thus simplifying the maneuver representation. And then, a recognition method utilizing random forest and support vector machine was designed, in the first stage, recognizing ACMEs with trajectory parameters, in the second stage, identifying tactical maneuver based on ACMEs sequence and motion parameters. Finally, the simulation results show that typical maneuver can be represented effectively by ACMEs and identified correctly by the recognition method.","author":[{"dropping-particle":"","family":"Jia","given":"Zhen Ze","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fan","given":"Xiao Guang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xue","given":"Ming Hao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Song","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2018"]]},"page":"820-827","title":"Online Identification Method for Tactical Maneuver of Target Based on Air Combat Maneuver Element","type":"article-journal","volume":"38"},"uris":["http://www.mendeley.com/documents/?uuid=52c48de3-2b1e-4057-b01a-f37c44ce07c0"]}],"mendeley":{"formattedCitation":"[49]","plainTextFormattedCitation":"[49]","previouslyFormattedCitation":"[49]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[53]; Improving the anti-jamming effectiveness of infrared air-to-air missiles ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.13700/j.bh.1001-5965.2020.033","ISSN":"10015965","abstract":"In order to estimate the performance of infrared air-to-air missiles and improve its combat effectiveness, an overall valid evaluation of missiles' anti-jamming capability is required. However, due to the infinite number of countermeasure situations, most scholars currently study and analyze them based on typical countermeasure scenarios, which is inadequate. For this reason, the improved Latin hypercube sampling method was used to design sampling points in the whole range. Firstly, the infrared countermeasure principle and simulation system were explained and constructed, and the range and the type of input parameters were determined. Secondly, the Latin hypercube sampling method was improved and optimized, and the generated sampling results were discretized as needed to meet the needs of the decoy discrete parameter setting. Finally, the initial parameter combinations generated above were used to run the simulation system, and the obtained data were given to the random forest model as learning sample sets. After tuning the parameters and adjusting the loss matrix, the anti-jamming effectiveness evaluation model of the infrared air-to-air missiles was obtained and the prediction accuracy was 90. 4%. Through simulation, the effectiveness of the model was verified under different IR countermeasures and different measurement errors..","author":[{"dropping-particle":"","family":"Niu","given":"Deqing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Youli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Yang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Xin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Danxu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Pengfei","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics","id":"ITEM-1","issue":"9","issued":{"date-parts":[["2021"]]},"page":"1874-1883","title":"Modeling of anti-jamming effectiveness evaluation of infrared air-to-air missile","type":"article-journal","volume":"47"},"uris":["http://www.mendeley.com/documents/?uuid=3e840fc8-6c84-45aa-a34d-d3091c8aeb2a"]}],"mendeley":{"formattedCitation":"[82]","plainTextFormattedCitation":"[82]","previouslyFormattedCitation":"[82]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[86]; Recognition of tactical intent of multi-aircraft cooperative air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1155/2021/9558838","ISSN":"1687-5974","abstract":"Accurately identifying the tactical intention of the target can facilitate the prediction of the opponent’s behavior and improve the efficiency of collaborative decision. We have observed that traditional methods could achieve high recognition rate on conventional tactical intent. Nevertheless, their performance would deteriorate seriously when recognizing cooperative tactical intention in multiaircraft air combat environment. The main reason resides on key features that are difficult to extract for traditional methods. To this end, this paper proposes a novel approach to recognizing tactical intention of multiaircraft cooperative air combat. Specifically, we employ support vector machine (SVM) to forecast the attack intention based on 19 low correlation features. The purpose of the employment of SVM is to avoid local optimization and reduce data dimension. Moreover, we use three models, i.e., dynamic Bayesian network (DBN), radar model, and threat assessment model to extract crucial information regarding maneuver occupancy, silent penetration, and attack tendency. The extracted information would make great contribution to the recognition accuracy of six types of cooperative tactics. Finally, we learn a decision tree model on train samples processed by above two phases to classify different tactical intention. In order to verify the effectiveness of the proposed method, we use data sets from a loop simulation platform. The experimental results have approved the superiority of our method via the comparison to several baseline methods with respect to recognition rate and efficiency. In addition, we underline that our method also performs well on incomplete and uncertain information.","author":[{"dropping-particle":"","family":"Guanglei","given":"Meng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Runnan","given":"Zhao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Biao","given":"Wang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mingzhe","given":"Zhou","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yu","given":"Wang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiao","given":"Liang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Aerospace Engineering","editor":[{"dropping-particle":"","family":"He","given":"Shaoming","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2021","11","9"]]},"page":"1-18","title":"Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat","type":"article-journal","volume":"2021"},"uris":["http://www.mendeley.com/documents/?uuid=a84b804e-d480-4c55-b6ea-f4504abfbea3"]}],"mendeley":{"formattedCitation":"[33]","plainTextFormattedCitation":"[33]","previouslyFormattedCitation":"[33]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[37];Engagement decision support tool for air combat engagement ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/LARS/SBR/WRE54079.2021.9605380","ISBN":"978-1-6654-0761-8","abstract":"This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simula\\c{c}\\~ao Aeroespacial - ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.","author":[{"dropping-particle":"","family":"Dantas","given":"Joao P. A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Costa","given":"Andre N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Geraldo","given":"Diego","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Maximo","given":"Marcos R. O. A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yoneyama","given":"Takashi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE)","id":"ITEM-1","issued":{"date-parts":[["2021","10","11"]]},"page":"96-101","publisher":"IEEE","title":"Engagement Decision Support for Beyond Visual Range Air Combat","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=ac7572d5-516d-47af-bacc-9d22cf86e09f"]}],"mendeley":{"formattedCitation":"[17]","plainTextFormattedCitation":"[17]","previouslyFormattedCitation":"[17]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[21].Naive Bayes classifierIdentification of complex dynamic data-driven failure models for more accurate flight planning and control under emergency conditions ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-319-95504-9_5","ISBN":"9783319955049","abstract":"Spatio-temporal data streams are often related in complex ways, for example, while the airspeed that an aircraft attains in cruise phase depends on the weight it carries, it also depends on many other factors. Some of these factors are controllable such as engine inputs or the airframe's angle of attack, while others contextual, such as air density, or turbulence. It is therefore critical to develop failure models that can help recognize errors in the data, such as an incorrect fuel quantity, a malfunctioning pitot-static system, or other abnormal flight conditions. In this paper, we extend our PILOTS programming language [1] to support machine learning techniques that will help data scientists: (1) create parameterized failure models from data and (2) continuously train a statistical model as new evidence (data) arrives. The linear regression approach learns parameters of a linear model to minimize least squares error for given training data. The Bayesian approach classifies operating modes according to supervised offline training and can discover new statistically significant modes online. As shown in Tuninter 1153 simulation result, dynamic Bayes classifier finds discrete error states on the fly while the error signatures approach requires every error state predefined. Using synthetic data, we compare the accuracy, response time, and adaptability of these machine learning techniques. Future dynamic data driven applications systems (DDDAS) using machine learning can identify complex dynamic data-driven failure models, which will in turn enable more accurate flight planning and control for emergency conditions.","author":[{"dropping-particle":"","family":"Chen","given":"Sida","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Imai","given":"Shigeru","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhu","given":"Wennan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Varela","given":"Carlos A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Handbook of Dynamic Data Driven Applications Systems","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"page":"97-121","publisher":"Springer International Publishing","publisher-place":"Cham","title":"Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=c5ffabf2-5ca7-44af-8439-5268f0260670"]}],"mendeley":{"formattedCitation":"[12]","plainTextFormattedCitation":"[12]","previouslyFormattedCitation":"[12]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[12];A digital twin of avionics systems — system performance and fault location ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-030-58868-7_44","ISBN":"9783030588670","ISSN":"18674941","abstract":"Machine learning methods find wide application to build digital twins of avionics systems under the integrated system health management. This allows representing the system performance and managing all stages of the system life cycle that significantly saves resources of manufacturers. In this work, a possibility of the machine learning methods application was researched for avionics objects health analysis and faults localization challenges to solve classification problems under building digital twins in ISHM. Decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors methods were considered. The research results show that some methods are effective for the health analysis challenge, and some methods are effective for the faults localization challenge. The application of the results will allow getting a base to build digital twins of avionics systems.","author":[{"dropping-particle":"V.","family":"Trusova","given":"Kseniya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Springer Proceedings in Physics","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"383-397","title":"Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=2f6f0b50-81bb-44f1-8dca-cafa19c69ddb"]}],"mendeley":{"formattedCitation":"[113]","plainTextFormattedCitation":"[113]","previouslyFormattedCitation":"[113]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[117]; Recognition of tactical intent of multi-aircraft cooperative air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1155/2021/9558838","ISSN":"1687-5974","abstract":"Accurately identifying the tactical intention of the target can facilitate the prediction of the opponent’s behavior and improve the efficiency of collaborative decision. We have observed that traditional methods could achieve high recognition rate on conventional tactical intent. Nevertheless, their performance would deteriorate seriously when recognizing cooperative tactical intention in multiaircraft air combat environment. The main reason resides on key features that are difficult to extract for traditional methods. To this end, this paper proposes a novel approach to recognizing tactical intention of multiaircraft cooperative air combat. Specifically, we employ support vector machine (SVM) to forecast the attack intention based on 19 low correlation features. The purpose of the employment of SVM is to avoid local optimization and reduce data dimension. Moreover, we use three models, i.e., dynamic Bayesian network (DBN), radar model, and threat assessment model to extract crucial information regarding maneuver occupancy, silent penetration, and attack tendency. The extracted information would make great contribution to the recognition accuracy of six types of cooperative tactics. Finally, we learn a decision tree model on train samples processed by above two phases to classify different tactical intention. In order to verify the effectiveness of the proposed method, we use data sets from a loop simulation platform. The experimental results have approved the superiority of our method via the comparison to several baseline methods with respect to recognition rate and efficiency. In addition, we underline that our method also performs well on incomplete and uncertain information.","author":[{"dropping-particle":"","family":"Guanglei","given":"Meng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Runnan","given":"Zhao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Biao","given":"Wang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mingzhe","given":"Zhou","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yu","given":"Wang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiao","given":"Liang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Aerospace Engineering","editor":[{"dropping-particle":"","family":"He","given":"Shaoming","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2021","11","9"]]},"page":"1-18","title":"Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat","type":"article-journal","volume":"2021"},"uris":["http://www.mendeley.com/documents/?uuid=a84b804e-d480-4c55-b6ea-f4504abfbea3"]}],"mendeley":{"formattedCitation":"[33]","plainTextFormattedCitation":"[33]","previouslyFormattedCitation":"[33]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[37];Anti-interference recognition of aerial infrared targets ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s12596-021-00701-2","ISSN":"0972-8821","abstract":"The development of infrared decoy countermeasure technologies has resulted in the complexity of the air combat environment. Therefore, higher requirements are put forward considering the infrared imaging-guided air-to-air missile anti-interference target recognition technology. The release of infrared decoys destroys the completeness, saliency and stability of target features. It is difficult for statistical pattern recognition methods based on feature fusion and matching to cover all of the confrontation conditions. In the present study, a Bayesian probabilistic recognition model is proposed that deals with uncertain information about targets and decoys. The proposed model deals with the interference of the air battlefield with the confrontation environment. Moreover, it simulates some functions of the human visual cognition and improves the target recognition ability when interference exists. Furthermore, numerous simulation data samples are utilized to solve the network structure as well as parameters of the Bayesian recognition model and the probabilistic definitions of the target and interference are achieved. Finally, a new aerial infrared target recognition algorithm is constructed based on prior information and the probability recognition model. Results of simulation experiments indicate that the recognition rate of the anti-interference recognition algorithm based on the Bayesian network reaches 90.73%, which is 6.905% higher than that of the anti-interference recognition algorithm based on Naive Bayes classifier in the tested air combat anti-interference simulation image dataset. Moreover, it can solve the problem of anti-interference target recognition to a certain extent such as false targets and target occlusion.","author":[{"dropping-particle":"","family":"Li","given":"Shaoyi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Kai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ma","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tian","given":"Xiaoqian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Xi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Optics","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2021","6","22"]]},"page":"264-277","title":"Anti-interference recognition method of aerial infrared targets based on the Bayesian network","type":"article-journal","volume":"50"},"uris":["http://www.mendeley.com/documents/?uuid=9e4f2538-8d57-4c28-9ee7-df2e32cc2033"]}],"mendeley":{"formattedCitation":"[67]","plainTextFormattedCitation":"[67]","previouslyFormattedCitation":"[67]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[71].K-means algorithmA digital twin of avionics systems – system performance and fault location ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-030-58868-7_44","ISBN":"9783030588670","ISSN":"18674941","abstract":"Machine learning methods find wide application to build digital twins of avionics systems under the integrated system health management. This allows representing the system performance and managing all stages of the system life cycle that significantly saves resources of manufacturers. In this work, a possibility of the machine learning methods application was researched for avionics objects health analysis and faults localization challenges to solve classification problems under building digital twins in ISHM. Decision tree classification, random forest classification, support vector machine, kernel support vector machine, naive Bayes, logistic regression, k-nearest neighbors methods were considered. The research results show that some methods are effective for the health analysis challenge, and some methods are effective for the faults localization challenge. The application of the results will allow getting a base to build digital twins of avionics systems.","author":[{"dropping-particle":"V.","family":"Trusova","given":"Kseniya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Springer Proceedings in Physics","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"383-397","title":"Machine Learning Methods Application for the Avionics Systems Health Analysis and Faults Localization Challenges","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=2f6f0b50-81bb-44f1-8dca-cafa19c69ddb"]}],"mendeley":{"formattedCitation":"[113]","plainTextFormattedCitation":"[113]","previouslyFormattedCitation":"[113]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[117]; Decision-making rules in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.12305/j.issn.1001-506X.2021.07.18","ISSN":"1001506X","abstract":"Aiming at the problem of lacking of knowledge about air combat decision-making behind the abundant combat training data, a method for constructing air combat decision-making knowledge is proposed. Firstly, this method analyzes the process of decision-making knowledge based on the influencing factors of air combat decision-making, and chooses to use production rules to represent air combat decision-making knowledge. Secondly, because it is difficult to meet the discrete measurement requirements of data mining algorithms due to the presence of noisy data and attribute data in combat training. The k-means clustering algorithm is applied to detect outliers and the minimum description length principle algorithm discretizes the continuous attributes. Finally, based on the preprocessed data, rough set is used to extract the minimum knowledge of air combat decision rules, and the reasoning and application of air combat decision-making knowledge are realized by building an fuzzy logical inference system. Simulation shows that this method can effectively extract the knowledge of air combat decision rules and the reasoning and application of knowledge.","author":[{"dropping-particle":"","family":"Lyu","given":"Yue","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Aiwu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Zhanwu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xi","given":"Zhifei","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2021"]]},"page":"1866-1874","title":"Research on the construction method of air combat decision knowledge","type":"article-journal","volume":"43"},"uris":["http://www.mendeley.com/documents/?uuid=4a2cdff8-c879-4c32-bb87-18aa2eb7a2ac"]}],"mendeley":{"formattedCitation":"[72]","plainTextFormattedCitation":"[72]","previouslyFormattedCitation":"[72]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[76];Radar scanning, signal acquisition, and processing, one-dimensional range image, SAR radar, ISAR image recognition, radar tracking and guidance ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s00521-019-04494-1","ISSN":"0941-0643","abstract":"With the advancement of machine learning and radar technology, machine learning is becoming more and more widely used in the field of radar. Radar scanning, signal acquisition and processing, one-dimensional range image, radar SAR, ISAR image recognition, radar tracking and guidance are all integrated into machine learning technology, but machine learning technology relies heavily on human machine learning experts for radar signal recognition. In order to realize the automation of radar signal recognition by machine learning, this paper proposes an automatic machine learning AUTO-SKLEARN system and applies it to radar radiation source signals. Identification: Firstly, this paper briefly introduces the classification of traditional machine learning algorithms and the types of algorithms specifically included in each type of algorithm. On this basis, the machine learning Bayesian algorithm is introduced. Secondly, the automatic machine learning AUTO based on Bayesian algorithm is proposed. -SKLEARN system, elaborates the process of AUTO-SKLEARN system in solving automatic selection algorithm and hyperparameter optimization, including meta-learning and its program implementation and automatic model integration construction. Finally, this paper introduces the process of automatic machine learning applied to radar emitter signal recognition. Through data simulation and experiment, the effect of traditional machine learning k-means algorithm and automatic machine learning AUTO-SKLEARN system in radar signal recognition is compared, which shows that automatic machine learning is feasible for radar signal recognition. The automatic machine learning AUTO-SKLEARN system can significantly improve the accuracy of the radar emitter signal recognition process, and the scheme is more reliable in signal recognition stability.","author":[{"dropping-particle":"","family":"Li","given":"Peng","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neural Computing and Applications","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2020","4","11"]]},"page":"1959-1969","title":"Research on radar signal recognition based on automatic machine learning","type":"article-journal","volume":"32"},"uris":["http://www.mendeley.com/documents/?uuid=51ad6eaa-4ace-4753-b6b6-9c5ac7d59fac"]}],"mendeley":{"formattedCitation":"[66]","plainTextFormattedCitation":"[66]","previouslyFormattedCitation":"[66]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[74].Neural networksModelling nonlinear and unstable aerodynamics during the design of future high-performance fighters and improving the angle of attack dynamics ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICNC.2012.6234640","ISBN":"978-1-4577-2133-5","ISSN":"21579555","abstract":"Accurately modeling nonlinear and unsteady aerodynamics at high attitude flight plays an important role in design of future high performance fighters. In the meanwhile, it also can improve the prediction of high angle of attack dynamics of normal aircraft configurations. Support vector machines (SVMs), known as a novel type of learning machines based on statistical learning theory and structural risk minimization (SRM) principle, can be used for handle regression problems. By denoting a set of nonlinear transformations from the complex input space to a high-dimensional feature space, SVMs can approximate the regression function by a linear regression in the feature space. Such implementation is so simple that it can be analyzed mathematically. By employing SVMs, the present work models the unsteady pitching oscillation aerodynamic data of a 1/10 scaled aircraft model. Here, the input data are established from the wind tunnel experiments at different frequencies and amplitudes. To make comparison, the artificial neural networks (ANNs) technique is also used. It turned out that SVMs can overcome the ANNs's inherent drawback of slow training convergence speed. Consequently, SVMs demonstrate high potentials for dealing with the chosen modeling of unsteady aerodynamics. © 2012 IEEE.","author":[{"dropping-particle":"","family":"Chen","given":"Yongliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2012 8th International Conference on Natural Computation","id":"ITEM-1","issued":{"date-parts":[["2012","5"]]},"page":"431-435","publisher":"IEEE","title":"Modeling of longitudinal unsteady aerodynamics at high angle-of-attack based on support vector machines","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=ef900a69-2373-42ac-a1e6-3a23aaa190f0"]}],"mendeley":{"formattedCitation":"[14]","plainTextFormattedCitation":"[14]","previouslyFormattedCitation":"[14]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[14];Flight aerodynamic parameters' estimation of longitudinal and transverse directional motion ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1017/aer.2019.123","ISSN":"0001-9240","abstract":"The research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.","author":[{"dropping-particle":"","family":"Verma","given":"H. O.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Peyada","given":"N. K.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Aeronautical Journal","id":"ITEM-1","issue":"1272","issued":{"date-parts":[["2020","2","1"]]},"page":"271-295","title":"Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm","type":"article-journal","volume":"124"},"uris":["http://www.mendeley.com/documents/?uuid=0d6d2427-10e9-4b8a-98e1-816cbca92cdb"]}],"mendeley":{"formattedCitation":"[118]","plainTextFormattedCitation":"[118]","previouslyFormattedCitation":"[118]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[122];Cooperative attack for beyond-visual-range air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.7688/j.issn.1000-1646.2013.03.18","ISSN":"10001646","abstract":"In order to solve the problem of beyond visual range (BVR) formation cooperative attack, a muti-objective distributed neural network decision model was established. Based on the situation assessment function, the advantage and disadvantage regions in BVR air combat were divided. Moreover, four air combat patterns for the specific regions, which could be taken as the training samples for the GA-BP situation assessment neural network, were proposed. Therefore, the situation advantage matrix of multi-aircraft cooperative air combat was obtained. According to the situation assessment matrix, the effective attack order could be obtained with the Hopfield neural network. A concept of second round attack was introduced, which could maximize the superiority of attack situation to the enemy attack and minimize the threat level of enemy attack. The simulated results of BVR multi-aircraft cooperative attack show that after analyzing each index for air combat, the proposed model can provide the situation assessment indexes for both sides and effective attack order.","author":[{"dropping-particle":"","family":"Xiao","given":"Liang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Zhong Shu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Shenyang Gongye Daxue Xuebao/Journal of Shenyang University of Technology","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2013"]]},"page":"338-344","title":"Cooperative attack decision for BVR air combat based on neural network","type":"article-journal","volume":"35"},"uris":["http://www.mendeley.com/documents/?uuid=8e607430-4db6-42a1-9ae0-fd4b793ff8cb"]}],"mendeley":{"formattedCitation":"[130]","plainTextFormattedCitation":"[130]","previouslyFormattedCitation":"[130]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[134];Reconfigurable flight control systems in case of aerodynamic coefficients changes or control surfaces failure ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/TCST.2003.821957","ISSN":"1063-6536","abstract":"An adaptive controller design method based on neural network is proposed for reconfigurable flight control systems in the presence of variations in aerodynamic coefficients or control effectiveness deficiencies caused by control surface damage. The neural network based adaptive nonlinear controller is developed by using the backstepping technique for command following of the angle of attack, sideslip angle, and bank angle. online learning neural networks are implemented to compensate the control effectiveness decrease and guarantee the robustness to the uncertainties due to aerodynamic coefficients variations. The main feature of the proposed controller is that the adaptive controller is designed by assuming that all of the nonlinear functions of the system have uncertainties, whereas most of the previous works assume that only some of the nonlinear functions are unknown. Neural networks learn through the weight update rules that are derived from the Lyapunov control theory. The closed-loop stability of the error states is also investigated. A nonlinear dynamic model of a high performance aircraft is used to demonstrate the effectiveness of the proposed control law. © 2004, The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.","author":[{"dropping-particle":"","family":"Shin","given":"D.-H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kim","given":"Youdan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Transactions on Control Systems Technology","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2004","1"]]},"page":"87-100","title":"Reconfigurable Flight Control System Design Using Adaptive Neural Networks","type":"article-journal","volume":"12"},"uris":["http://www.mendeley.com/documents/?uuid=131042fc-ee8c-41b0-8774-8ab579bc9dd9"]}],"mendeley":{"formattedCitation":"[103]","plainTextFormattedCitation":"[103]","previouslyFormattedCitation":"[103]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[107]Detection, identification, and accommodation of sensor failures in a flight control system that assumes no physical redundancy in sensory capabilities ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/87.709495","ISSN":"10636536","abstract":"Detection, identification, and accommodation of sensor failures can be a challenging task for complex dynamic systems. This paper presents the comparison of two different approaches for the task of sensor failure detection, identification, and accommodation in a flight control system assumed to be without physical redundancy in the sensory capabilities. The first approach is based on the use of a set of on-line learning neural networks; the second approach is based on the use of a bank of Kalman filters. The objective is to evaluate the robustness of both schemes; the comparison is performed through testing of the schemes for several types of failures presenting different level of complexity in terms of detectability. The required computational effort for both schemes is also evaluated. For each of these failure types this comparison is performed at nominal conditions, that is with the system model and its noise perfectly modeled (with the Kalman filter scheme performing at nominal conditions), and at conditions where discrepancies occur for the modeling of the system as well as the system and measurement noises. While the Kalman-filter-based scheme takes advantage of its robustness capabilities, the neural-network-based scheme, starting from a random numerical architecture, relies on the learning accumulated either on-line or from off-line simulations. The study reveals that on-line learning neural architectures have potential for on-line estimation purposes in a sensor validation scheme, particularly in the case of poorly modeled dynamics. © 1998 IEEE.","author":[{"dropping-particle":"","family":"Napolitano","given":"M.R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Windon","given":"D.A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Casanova","given":"J.L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Innocenti","given":"Mario","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silvestri","given":"Giovanni","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Transactions on Control Systems Technology","id":"ITEM-1","issue":"5","issued":{"date-parts":[["1998"]]},"page":"596-611","title":"Kalman filters and neural-network schemes for sensor validation in flight control systems","type":"article-journal","volume":"6"},"uris":["http://www.mendeley.com/documents/?uuid=0cc71558-6164-48a2-b6c8-4717c8133bca"]}],"mendeley":{"formattedCitation":"[80]","plainTextFormattedCitation":"[80]","previouslyFormattedCitation":"[80]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[84]Ensemble learningCapability to break through air defence ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.16182/j.issn1004731x.joss.19-0238","ISSN":"1004731X","abstract":"Aiming at the two difficulties in characteristic index digging of combat system of systems (CSoS), namely operation data generation and digging method selection, this paper proposes a new digging method, that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched: (1) the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis (PCA); this method is applied to dig the characteristic indexes of air defense ability. (2)the method based on ensemble learning, generates test data by bagging, trains model by CART decision trees, and obtains the characteristic indexes by PCA; this method is applied to dig the characteristic indexes of air defense breakthrough ability.","author":[{"dropping-particle":"","family":"Yang","given":"Yongli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Xiaofeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rong","given":"Ming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yin","given":"Xiaojing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Wenxiang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xitong Fangzhen Xuebao / Journal of System Simulation","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2019"]]},"page":"1048-1054","title":"Characteristic Index Digging of Combat SoS Capability Based on Machine Learning","type":"article-journal","volume":"31"},"uris":["http://www.mendeley.com/documents/?uuid=d1527083-1277-4099-a313-f31a11a71512"]}],"mendeley":{"formattedCitation":"[137]","plainTextFormattedCitation":"[137]","previouslyFormattedCitation":"[137]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[141];Aircraft reliability prediction based on selected parameters of its operation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.37313/1990-5378-2021-23-1-111-114","ISSN":"19905378","abstract":"To ensure the reliable functioning of a technical object, it is necessary to predict its state for the upcoming time interval. Let the technical state of the object be characterized at a certain point in time by a set of parameters established by the technical documentation for the object. It is assumed that for certain values of these parameters, the object may be in a good or faulty state. It is required by the values of these parameters to estimate the state of the object in the upcoming time interval. Supervised machine learning methods can be applied to solve this problem. However, to obtain good results in predicting the state of an object, it is necessary to choose the correct training model. One of the disadvantages of machine learning models is high bias and too much scatter. In this paper, to reduce the scatter of the model, it is proposed to use ensemble machine learning methods, namely, the bagging procedure. The main idea of the ensemble of methods is that with the right combination of weak models, more accurate and robust models can be obtained. The purpose of bagging is to create an ensemble model that is more reliable than the individual models that make up it. One of the big advantages of bagging is its concurrency, since different ensemble models are trained independently of each other. The effectiveness of the proposed approach is shown by the example of predicting the technical state of an object by eight parameters of its functioning. To assess the effectiveness of the application of ensemble machine learning methods for predicting the technical state of an object, the quality criteria of binary classification are used: accuracy, completeness, and F-measure. It is shown that the use of ensemble machine learning methods can improve the accuracy of predicting the state of a technical object by 4% –9% in comparison with basic machine learning methods. This approach can be used by specialists to predict the technical condition of objects in many technical applications, in particular, in aviation.","author":[{"dropping-particle":"","family":"Kuvayskova","given":"Yu.E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Izvestiya of Samara Scientific Center of the Russian Academy of Sciences","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021"]]},"page":"111-114","title":"Application of Ensemble Machine Leearnings Methods for Predicting the Technical State of an Object","type":"article-journal","volume":"23"},"uris":["http://www.mendeley.com/documents/?uuid=50fc0494-d1c1-4601-a72a-1eb985549e53"]}],"mendeley":{"formattedCitation":"[61]","plainTextFormattedCitation":"[61]","previouslyFormattedCitation":"[61]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[65];Targets (aircraft) classification using kinematic data only – ADS-B system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.23919/IRS.2019.8768094","ISBN":"978-3-7369-9860-5","ISSN":"21555753","abstract":"We propose a simple yet efficient method to classify multivariate time series with an arbitrary number of timesteps, and we apply it to the classification of targets (either aircrafts or vessels) using kinematic data only. We use data obtained from the Automatic Identification System (AIS) and the Automatic Dependent Surveillance-Broadcast (ADS-B) to get labelled trajectories for supervised learning, as a proof of concept for later use on radar tracks. The method consists in extracting statistical features from each temporal variable (speed, acceleration, etc.), and then feeding them to a Gradient Boosting classifier. We show that the performance of this method is on par with the state of the art, as the classification accuracy is close to 86AIS data, and thus that it could be used in radars for the classification of targets based on their trajectory.","author":[{"dropping-particle":"","family":"Ginoulhac","given":"Raphael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barbaresco","given":"Frederic","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schneider","given":"Jean-Yves","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pannier","given":"Jean-Marie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Savary","given":"Sebastien","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2019 20th International Radar Symposium (IRS)","id":"ITEM-1","issued":{"date-parts":[["2019","6"]]},"page":"1-10","publisher":"IEEE","title":"Target Classification Based On Kinematic Data From AIS/ADS-B, Using Statistical Features Extraction and Boosting","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=64410133-a03c-4b69-8b97-06e63061d7c5"]}],"mendeley":{"formattedCitation":"[31]","plainTextFormattedCitation":"[31]","previouslyFormattedCitation":"[31]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[35].Tab. 5Selected applications of ML algorithms – unsupervised learningUNSUPERVISED LEARNINGAlgorithmSelected applicationsHierarchical Cluster AnalysisAnomaly detection from numerical and text data to enhance flight safety ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/aerospace7100143","ISSN":"2226-4310","abstract":"The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for the analysis of aviation safety narratives based on text-based accounts of in-flight events and categorical metadata parameters which accompany them. An extensive pre-processing routine is presented, including a comparison between numeric models of textual representation for the purposes of document classification. A framework for categorizing and visualizing narratives is presented through a combination of k-means clustering and 2-D mapping with t-Distributed Stochastic Neighbor Embedding (t-SNE). A cluster post-processing routine is developed for identifying driving factors in each cluster and building a hierarchical structure of cluster and sub-cluster labels. The Aviation Safety Reporting System (ASRS), which includes over a million de-identified voluntarily submitted reports describing aviation safety incidents for commercial flights, is analyzed as a case study for the methodology. The method results in the identification of 10 major clusters and a total of 31 sub-clusters. The identified groupings are post-processed through metadata-based statistical analysis of the learned clusters. The developed method shows promise in uncovering trends from clusters that are not evident in existing anomaly labels in the data and offers a new tool for obtaining insights from text-based safety data that complement existing approaches.","author":[{"dropping-particle":"","family":"Rose","given":"Rodrigo L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Puranik","given":"Tejas G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mavris","given":"Dimitri N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Aerospace","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2020","9","28"]]},"page":"143","title":"Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives","type":"article-journal","volume":"7"},"uris":["http://www.mendeley.com/documents/?uuid=8244e917-66bf-4ac5-a64c-4d4f15039360"]}],"mendeley":{"formattedCitation":"[92]","plainTextFormattedCitation":"[92]","previouslyFormattedCitation":"[92]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[96];Classification of aviation material consumption data ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3969/j.issn.1001-506X.2019.12.19","ISSN":"1001506X","abstract":"To solve the problem that it is difficult to classify aviation material consumption data, a classification model based on consumption fluctuation clustering analysis is established. Based on the fluctuation of the consumption sequence, the aviation materials are converted into two-dimensional graphs. Due to its short service time and small quantity of aviation material samples, the unsupervised classification algorithm is used to classify the aviation material. Aiming at the limitation of traditional clustering algorithms, a hierarchical partition clustering algorithm is proposed, and the \"tolerance to distance ratio\" parameter is used to select a good environment for the initial center. The simulation results show that the hierarchical partition clustering algorithm is more stable and efficient.","author":[{"dropping-particle":"","family":"Xue","given":"Yongliang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Zhenlin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics","id":"ITEM-1","issue":"12","issued":{"date-parts":[["2019"]]},"page":"2802-2806","title":"Aviation material classification research based on consumption volatility clustering analysis","type":"article-journal","volume":"41"},"uris":["http://www.mendeley.com/documents/?uuid=c7c55fc7-12db-4f6b-ab2f-ffda6526cab3"]}],"mendeley":{"formattedCitation":"[134]","plainTextFormattedCitation":"[134]","previouslyFormattedCitation":"[134]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[138];Data-driven prediction method to estimate turbofan engine's remaining life ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.17531/ein.2016.4.18","ISSN":"15072711","abstract":"Turbofan engines will gradually degrade until failure occurs or life ends if without maintenance. Reliable degradation assessment and remaining useful life (RUL) estimation make sense on both aviation safety and rational maintenance decisions. This paper proposes a data-driven prognostic method on the premise of run-to-failure (RtF) data which are multivariate sensory data collected from the engines operating from normal to failure. After necessary pre-processing to the data, clustering analysis is executed to generate the clusters which represent the multi-states of the degradation process. The failure state cluster is extracted, and then the distance between the pre-processed data and the cluster is calculated. Therefore, one-dimensional time series are generated and defined as the health indices. Afterwards the degradation models are built based on the health indices. Finally, the RUL of a testing unit can be estimated by similarity analysis with the models. Hierarchical clustering (HC) and relevance vector machine (RVM) are the main algorithms employed in this paper. To validate the proposition, a case study is performed on turbofan engines data from Prognostics Center of Excellence (PCoE) at NASA Ames Research Center, and sufficient comparisons were given.","author":[{"dropping-particle":"","family":"Shi","given":"Jianming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Yongxiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Gong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Xuzhi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Eksploatacja i Niezawodnosc - Maintenance and Reliability","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2016","9","17"]]},"page":"621-631","title":"Health index synthetization and remaining useful life estimation for turbofan engines based on run-to-failure datasets","type":"article-journal","volume":"18"},"uris":["http://www.mendeley.com/documents/?uuid=de9cdfc9-5d0a-4594-845f-da51142b5ae6"]}],"mendeley":{"formattedCitation":"[101]","plainTextFormattedCitation":"[101]","previouslyFormattedCitation":"[101]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[105];Flight anomaly detection during the approach phase ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2020-1851","ISBN":"978-1-62410-595-1","abstract":"Safety is of paramount importance in aviation due to the catastrophic consequences of accidents. Consequently, efforts have been made over the years to research and improve flight safety analysis techniques. Recent improvements in data storage and processing capabilities enable new techniques in flight safety analysis. The predominant safety analysis technique used by airlines is event exceedance analysis, which combines Flight Operations Quality Assurance (FOQA) data and predefined parameter safety thresholds to identify risks to safety. However, there is a need to continuously enhance risk identification and improve flight safety analysis. There are several areas where event exceedance analysis is limited. Event exceedance analysis is constrained by a reliance on pre-determined safety thresholds, and the analysis of parameters at a single point in time with no consideration for the behavior of the aircraft prior to that point. Furthermore, the pre-defined safety thresholds often are not consistent across airports and airframes, and analysis is thus limited within airport or airframe type. This research addresses these gaps by developing and implementing a robust methodology for identifying anomalous flights, and providing further insight into risk and safety trends in the approach phase of flight. This was achieved by 1) leveraging a Hierarchical clustering algorithm to categorize airports into clusters, 2) detecting anomalous flights by leveraging the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and varying the length of the approach phase across the National Airspace System (NAS) as well as on an airport cluster level, 3) combining clustering results with an anomaly scoring algorithm to compare the detection of anomalous flights with current exceedance analysis event definitions and finally 4) investigating the impact of the length of the approach phase on outlier identification. Flight cluster labels were obtained through DBSCAN and flight anomaly scores were obtained through an anomaly scoring algorithm. Results from these studies showed that event flights (designated by event exceedance analysis) on average had higher anomaly scores than non-event flights. Airport clustering was shown to have a non-negligible impact on flight anomaly score, and should be investigated further. Finally, identifying anomalies by varying the length of the approach phase from 10 to 600 seconds prior to touchdown showed significant var…","author":[{"dropping-particle":"","family":"Sheridan","given":"Kevin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Puranik","given":"Tejas G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mangortey","given":"Eugene","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pinon-Fischer","given":"Olivia J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kirby","given":"Michelle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mavris","given":"Dimitri N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA Scitech 2020 Forum","id":"ITEM-1","issued":{"date-parts":[["2020","1","6"]]},"publisher":"American Institute of Aeronautics and Astronautics","publisher-place":"Reston, Virginia","title":"An Application of DBSCAN Clustering for Flight Anomaly Detection During the Approach Phase","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=55679902-6744-4f34-b384-691f764e79f4"]}],"mendeley":{"formattedCitation":"[100]","plainTextFormattedCitation":"[100]","previouslyFormattedCitation":"[100]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[104];Classification of the environment during combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1088/1742-6596/1883/1/012091","ISSN":"1742-6588","abstract":"In order to realize the classification prediction of environment in the process of combat, we set up the classification prediction model. First, we divided data set into blocks because of the big scale, wide range of influencing factors and strong nonlinear relationship among them. The missile hit rate is reflected in the miss distance, clustering each block data to analysis the correlation factors that affect the miss distance. Second, according to the mutual information theory, the strong correlation factors affecting the miss distance are identified from three aspects which are throwing strategies, maneuver mode and the relative situation by calculating the mutual information matrix. Third, the strong correlation factors as the sample attributes of node splitting in the decision tree, a hierarchical prediction model of infrared interference environment is constructed. This method can better identify the strong correlation factors that affect the miss distance, and avoid the adverse impact on the classification prediction due to the difference of correlation factors. The results show that the method is suitable for the processing of miss distance data. The prediction accuracy of infrared interference environment classification is about 98%, and the generalization ability of the model is strong.","author":[{"dropping-particle":"","family":"Wu","given":"Xin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Youli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Bian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gan","given":"Yuepeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cai","given":"Yuxuan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Physics: Conference Series","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","4","1"]]},"page":"012091","title":"Hierarchical Prediction of Infrared interference Environment based on Mutual Information and Random Forest","type":"article-journal","volume":"1883"},"uris":["http://www.mendeley.com/documents/?uuid=f9579f4c-fbac-481f-8b61-50df8442c155"]}],"mendeley":{"formattedCitation":"[127]","plainTextFormattedCitation":"[127]","previouslyFormattedCitation":"[127]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[131].DBSCAN, HDBSCANFlight anomaly detection during the approach phase ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2020-1851","ISBN":"978-1-62410-595-1","abstract":"Safety is of paramount importance in aviation due to the catastrophic consequences of accidents. Consequently, efforts have been made over the years to research and improve flight safety analysis techniques. Recent improvements in data storage and processing capabilities enable new techniques in flight safety analysis. The predominant safety analysis technique used by airlines is event exceedance analysis, which combines Flight Operations Quality Assurance (FOQA) data and predefined parameter safety thresholds to identify risks to safety. However, there is a need to continuously enhance risk identification and improve flight safety analysis. There are several areas where event exceedance analysis is limited. Event exceedance analysis is constrained by a reliance on pre-determined safety thresholds, and the analysis of parameters at a single point in time with no consideration for the behavior of the aircraft prior to that point. Furthermore, the pre-defined safety thresholds often are not consistent across airports and airframes, and analysis is thus limited within airport or airframe type. This research addresses these gaps by developing and implementing a robust methodology for identifying anomalous flights, and providing further insight into risk and safety trends in the approach phase of flight. This was achieved by 1) leveraging a Hierarchical clustering algorithm to categorize airports into clusters, 2) detecting anomalous flights by leveraging the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and varying the length of the approach phase across the National Airspace System (NAS) as well as on an airport cluster level, 3) combining clustering results with an anomaly scoring algorithm to compare the detection of anomalous flights with current exceedance analysis event definitions and finally 4) investigating the impact of the length of the approach phase on outlier identification. Flight cluster labels were obtained through DBSCAN and flight anomaly scores were obtained through an anomaly scoring algorithm. Results from these studies showed that event flights (designated by event exceedance analysis) on average had higher anomaly scores than non-event flights. Airport clustering was shown to have a non-negligible impact on flight anomaly score, and should be investigated further. Finally, identifying anomalies by varying the length of the approach phase from 10 to 600 seconds prior to touchdown showed significant var…","author":[{"dropping-particle":"","family":"Sheridan","given":"Kevin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Puranik","given":"Tejas G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mangortey","given":"Eugene","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pinon-Fischer","given":"Olivia J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kirby","given":"Michelle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mavris","given":"Dimitri N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA Scitech 2020 Forum","id":"ITEM-1","issued":{"date-parts":[["2020","1","6"]]},"publisher":"American Institute of Aeronautics and Astronautics","publisher-place":"Reston, Virginia","title":"An Application of DBSCAN Clustering for Flight Anomaly Detection During the Approach Phase","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=55679902-6744-4f34-b384-691f764e79f4"]}],"mendeley":{"formattedCitation":"[100]","plainTextFormattedCitation":"[100]","previouslyFormattedCitation":"[100]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[104];Diagnostics of aircraft engine faults ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.26467/2079-0619-2020-23-6-20-27","ISSN":"2542-0119","abstract":"The 21st century aviation and aerospace technologies have evolved and become more complex and technical. Turbofan jet engines as well as their cousins, the rocket engines (liquid/solid) have gone through several design upgrades and enhancements during the course of their design and exploitation. These technological upgrades have made engines very complex and expensive machines which need constant monitoring during their working phase. As the demand and use of such engines is growing steadily, both in the civilian and military sectors, it becomes necessary to monitor and predict the behavior of parametric data generated by these complex engines during their working phases. In this paper flight parameters such as Exhaust Gas Temperature (EGT), Engine Fan Speeds (N1 and N2), Fuel Flow (FF), Oil Temperature (OT), Oil Pressure (OP), Vibration and others where used to determine engine fault. All turbo fan engines go through several distinctly different working phases: Take-off phase, Cruise phase and Landing phase. Recording generated parametric data during these different phases leads to a massive amount of in-flight data and maintenance reports, which makes the task of designing and developing a fault diagnostic system highly challenging. It becomes imperative to use modern techniques in data analysis that can handle large volumes of generated data and provide clear visual results for determining the technical status of the engine under investigation/monitoring. These modern techniques should be able to give clear and objective assessment of the object under investigation. Cluster analysis methods based on Neural Networks such as c-means, k-means, self-organizing maps and DBSCAN algorithm have been used to build clusters. Differences in cluster groupings/patterns between healthy engine and engine with degraded performance are compared and used as the bases for defining faults. Fault diagnosis plays a crucial role in aircraft engine management. Timely and accurate detection of faults is the foundation on which maintenance turnaround times, operational costs and flight safety are based. The data used in this paper for analysis was obtained from flight data recorder during one flight cycle. The final decision on a fault is taken by an engineer.","author":[{"dropping-particle":"","family":"Buraimah","given":"I. J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Civil Aviation High Technologies","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2020","12","31"]]},"page":"20-27","title":"Using modern clustering techniques for parametric fault diagnostics of turbofan engines","type":"article-journal","volume":"23"},"uris":["http://www.mendeley.com/documents/?uuid=5d84d456-1766-41b6-81a9-d06b70cc9768"]}],"mendeley":{"formattedCitation":"[9]","plainTextFormattedCitation":"[9]","previouslyFormattedCitation":"[9]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[9];Detection of field data anomalies in automatic flight trajectories ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.19818/j.cnki.1671-1637.2020.04.018","ISSN":"16711637","abstract":"To effectively solve the various field data anomalies in the automatic dependent surveillance-broadcast (ADS-B) historical flight trajectories affected by the ground station distribution breadth, terrain blocking, electromagnetic interference and so on, an ADS-B data cleaning method was established, and implemented by four steps, such as determining the cleaning object, deleting the duplication of field, cleaning the abnormal point and correcting the time stamp. According to the existing sample ADS-B historical data, the track model was established and the validity was analyzed. The fields such as the time stamp, longitude, latitude, pressure altitude and ground speed were defined as the characteristic fields and cleaning objects. The time stamp, longitude and latitude of ADS-B track point sequence were deduplicated to delete the adjacent track points with repeated data. The outliers of characteristic fields were located through the method based on the density-based spatial clustering of applications with noise (DBSCAN) to improve the cleaning efficiency, detect and correct the abnormality. To make the change of track point state conform to the particle kinematic law, the time stamp was corrected by the field data of longitude, latitude, pressure altitude and ground speed of ADS-B track points, and the extended modified time stamp field was saved. Research result shows that 97.58% of the abnormal track points in the 516 sample flights are effectively identified and cleaned. The cleaned track point state changes more smoothly. The total flight duration before and after correction varies between 10-600 s. The correction effect of time stamp mainly depends on the accuracy of ground speed. The corrected time stamp should be selectively used according to the data characteristics of sample track in practical engineering applications. The established cleaning method of ADS-B data can provide a preliminary data processing platform for the trajectory analysis, evaluation and computing in civil aviation engineering projects.","author":[{"dropping-particle":"","family":"Wang","given":"Bing","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Jiaotong Yunshu Gongcheng Xuebao/Journal of Traffic and Transportation Engineering","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2020"]]},"page":"217-226","title":"Data cleaning method of ADS-B historical flight trajectories","type":"article-journal","volume":"20"},"uris":["http://www.mendeley.com/documents/?uuid=8730d3af-ee27-4b43-bd93-0966800a3f46"]}],"mendeley":{"formattedCitation":"[120]","plainTextFormattedCitation":"[120]","previouslyFormattedCitation":"[120]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[124];Identification of flight manoeuvres considering flight data recorder data ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/NTAD.2018.8551680","ISBN":"978-1-5386-7918-0","abstract":"Ever-changing situation in the aviation demands a change in flight training programs that would reflect present needs and threats in contrary to the traditional training that didn't changed much for decades. Therefore, new alternative training concepts have been developed that cover these needs. However, these concepts do not apply to initial training which seem to be a crucial phase of a pilot training. Thus, the aim was to create a software solution that would identify individual flight maneuvers and evaluate them so that the overall evaluation would be done by considering objective evaluation and flight instructors' subjective expertise. A study was done with strictly given flight schedules. For the purpose of automatic maneuver detection, density-based spatial clustering of applications with noise - DBSCAN clustering algorithm was used, which could determine maneuvers and thus exclude the noise from clusters of maneuvers. The results indicate that the proposed solution was able to identify the prescribed maneuvers with high sensitivity. The solution could be extended in the future to identify all flight maneuvers considering as many parameters from flight data recorder as possible and thus carry out complete objectively based pilot performance evaluation.","author":[{"dropping-particle":"","family":"Socha","given":"V.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hanakova","given":"L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Socha","given":"L.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bergh","given":"S.","non-dropping-particle":"Van den","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lalis","given":"A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kraus","given":"J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2018 XIII International Scientific Conference - New Trends in Aviation Development (NTAD)","id":"ITEM-1","issued":{"date-parts":[["2018","8"]]},"page":"132-136","publisher":"IEEE","title":"Automatic Detection of Flight Maneuvers with the Use of Density-based Clustering Algorithm","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=e120aae2-218e-476a-a93d-98f9ac8485f1"]}],"mendeley":{"formattedCitation":"[104]","plainTextFormattedCitation":"[104]","previouslyFormattedCitation":"[104]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[108].Novelty and outlier detection Predictive maintenance, aircraft repair, and overhaul ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/RAMS48030.2020.9153694","ISBN":"978-1-7281-3690-5","ISSN":"0149144X","abstract":"As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. 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The current CID processes include processing and analysing data from a vast network of sensors, platforms, and decision makers. CID plays an important role in generating the Common Tactical Air Picture (CTAP) which provides situational awareness to air warfare decision-makers. The Big \"CID\" Data and complexity of the problem pose challenges as well as opportunities. In this paper, we discuss CTAP and CID challenges and some Big Data and Deep Analytics solutions to address these challenges. We present a use case using a unique deep learning method, Lexical Link Analysis (LLA), which is able to associate heterogeneous data sources for object recognition and anomaly detection, both of which are critical for CTAP and CID applications.","author":[{"dropping-particle":"","family":"Zhao","given":"Ying","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kendall","given":"Tony","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"Bonnie","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","id":"ITEM-1","issued":{"date-parts":[["2016"]]},"page":"443-449","publisher":"SCITEPRESS - Science and Technology Publications","title":"Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=d3710f3f-fa88-486a-a64f-0ac5050550b2"]}],"mendeley":{"formattedCitation":"[142]","plainTextFormattedCitation":"[142]","previouslyFormattedCitation":"[142]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[146];Track anomaly detection ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2021-2388","abstract":"View Video Presentation: https://doi.org/10.2514/6.2021-2388.vidThe current practice of manually processing features for high-dimensional and heterogeneous aviation data is labor-intensive, does not scale well to new problems, and is prone to information loss, affecting the effectiveness and maintainability of machine learning (ML) procedures. This research explored an unsupervised learning method, autoencoder, to extract effective features for aviation machine learning problems. The study explored variants of autoencoders with the aim of forcing the learned representations of the input to assume useful properties. A flight track anomaly detection autoencoder was developed to demonstrate the versatility of the technique. The research results show that the autoencoder can not only automatically extract effective features for the flight track data, but also efficiently deep clean data, thereby reducing the workload of data scientists. Moreover, the research leveraged transfer learning to efficiently train models for multiple airports. Transfer learning can reduce model training times from days to hours, as well as improving model performance. The developed applications and techniques are shared with the whole aviation community to improve effectiveness of ongoing and future machine learning studies.","author":[{"dropping-particle":"","family":"Wang","given":"Liya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lucic","given":"Panta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Campbell","given":"Keith","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wanke","given":"Craig","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA AVIATION 2021 FORUM","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"title":"Autoencoding Features for Aviation Machine Learning Problems","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=d63e005b-4767-4cd5-be57-9f399f0840fe"]}],"mendeley":{"formattedCitation":"[122]","plainTextFormattedCitation":"[122]","previouslyFormattedCitation":"[122]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[126];Anomalies detection in the approach and take-off phases ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.trc.2019.11.011","ISSN":"0968090X","abstract":"This paper presents a machine learning approach to evaluate the performance of aircrafts using on-board sensor information on commercially scheduled flights with the aim to further improve system health monitoring strategies in air transportation. Logarithmic multivariate Gaussian models are trained to evaluate the performance of aircrafts at different flight phases (takeoff, ascent, cruise, etc.) separately. By including a forward synchronization, feature selection, and mini-batch training process, this model overcomes challenges introduced by the large size and high dimensionality of flight datasets. This framework also addresses the re-sampling issue in existing literature causing difficulties in handling time-series signals with different lengths. For demonstration and validation, the developed model is applied to analyze performance anomalies associated with the mechanical system and pilot operation in a historical flight dataset. Compared with existing literature focusing on similar datasets, this evaluation methodology shows promise in detecting performance anomalies especially at approach and takeoff phases. Therefore, the developed model is expected to be an effective addition to the current anomaly analysis and monitoring technologies for scheduled commercial flights. Applications include assisting transportation management systems by handling large amounts of historical flight datasets to analyze mechanical and operational anomalies, which may potentially improve future aeronautical system design and pilot training.","author":[{"dropping-particle":"","family":"Li","given":"Guoyi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lee","given":"Hyunseong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rai","given":"Ashwin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chattopadhyay","given":"Aditi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Transportation Research Part C: Emerging Technologies","id":"ITEM-1","issued":{"date-parts":[["2020","1"]]},"page":"20-39","title":"Analysis of operational and mechanical anomalies in scheduled commercial flights using a logarithmic multivariate Gaussian model","type":"article-journal","volume":"110"},"uris":["http://www.mendeley.com/documents/?uuid=90643581-2572-4929-92da-565425469d99"]}],"mendeley":{"formattedCitation":"[65]","plainTextFormattedCitation":"[65]","previouslyFormattedCitation":"[65]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[69].Dimensionality reduction and visualization algorithms Target threat assessment in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.7527/S1000-6893.2020.23895","ISSN":"10006893","abstract":"Target threat assessment is a key link in air combat. Due to the complex and diverse factors affecting the threat assessment of air combat targets and the correlation between the indicators, traditional assessment algorithm cannot obtain accurate and objective assessment results. This paper proposes a target threat assessment algorithm based on a Principal Component Analysis method and an Modified Particle Swarm Algorithm Optimized for Extreme Learning Machines (PCA-MPSO-ELM). Indicators affecting the degree of target threat values were comprehensively analyzed first, followed by linear changes in the original evaluation indicators using the principal component analysis method to obtain comprehensive variables, eliminating the correlation between the evaluation indicators and achieving dimensionality reduction of the evaluation data. On the basis of data pretreatment, the ELM neural network was established and the improved particle swarm algorithm was applied to the optimization of the input weights and threshold values of ELM to improve the accuracy of the target threat assessment model. Finally, air combat data was selected from the air combat maneuvering instrument, and sample data for target threat assessment was constructed using the threat index method. The accuracy analysis and real-time analysis of the assessment were carried out in simulation experiments, and the results showed that the proposed algorithm can achieve accurate and rapid target threat assessment in air combat.","author":[{"dropping-particle":"","family":"Xi","given":"Zhifei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"An","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kou","given":"Yingxin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Zhanwu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Aiwu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica","id":"ITEM-1","issue":"9","issued":{"date-parts":[["2020"]]},"page":"323895-323895","title":"Target threat assessment in air combat based on PCA-MPSO-ELM algorithm","type":"article-journal","volume":"41"},"uris":["http://www.mendeley.com/documents/?uuid=4a2b672e-2e43-4a68-8658-dc574f2715c5"]},{"id":"ITEM-2","itemData":{"DOI":"10.3969/j.issn.1001-506X.2021.01.18","ISSN":"1001506X","abstract":"Due to the complexity and diversity of the factors affecting the assessment of air combat target threats, which is characterized by contradiction, correlation and coupling. Therefore, the accurate and objective assessment results cannot be obtained using the traditional assessment methods. A target threat assessment method based on gray principal component is proposed. Firstly, the principal component analysis (PCA) method is used to analyze the correlation between indicators, and the grey correlation coefficient matrix between indicators is generated to replace the traditional correlation matrix or covariance matrix. The interrelated indexes are converted into independent components to reconstruct the target threat assessment system. Secondly, a grey correlation depth maximum entropy model is established to determine the original evaluation index weight. Then, the problem that the accumulative contribution rate is used as the objective weight and not taken into account the index importance difference is analyzed, the factor load is used as the important basis for the weight synthesis of main components. Finally, the effectiveness and rationality of grey principal component are illustrated by comparing and analyzing the results of different threat assessment methods through the simulation cases.","author":[{"dropping-particle":"","family":"Xi","given":"Zhifei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"An","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kou","given":"Yingxin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Zhanwu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Aiwu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2021"]]},"page":"147-155","title":"Air combat target threat assessment based on gray principal component","type":"article-journal","volume":"43"},"uris":["http://www.mendeley.com/documents/?uuid=5b1fe984-77c6-456d-a17d-10c8e7dea108"]}],"mendeley":{"formattedCitation":"[128, 129]","plainTextFormattedCitation":"[128, 129]","previouslyFormattedCitation":"[128, 129]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[132, 133];Assessment of air defence capabilities ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.16182/j.issn1004731x.joss.19-0238","ISSN":"1004731X","abstract":"Aiming at the two difficulties in characteristic index digging of combat system of systems (CSoS), namely operation data generation and digging method selection, this paper proposes a new digging method, that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched: (1) the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis (PCA); this method is applied to dig the characteristic indexes of air defense ability. (2)the method based on ensemble learning, generates test data by bagging, trains model by CART decision trees, and obtains the characteristic indexes by PCA; this method is applied to dig the characteristic indexes of air defense breakthrough ability.","author":[{"dropping-particle":"","family":"Yang","given":"Yongli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Xiaofeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rong","given":"Ming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yin","given":"Xiaojing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Wenxiang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xitong Fangzhen Xuebao / Journal of System Simulation","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2019"]]},"page":"1048-1054","title":"Characteristic Index Digging of Combat SoS Capability Based on Machine Learning","type":"article-journal","volume":"31"},"uris":["http://www.mendeley.com/documents/?uuid=d1527083-1277-4099-a313-f31a11a71512"]}],"mendeley":{"formattedCitation":"[137]","plainTextFormattedCitation":"[137]","previouslyFormattedCitation":"[137]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[141];Air combat out of sight ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"10015965","abstract":"The beyond visual range (BVR) air combat has become one of the most important air modes of modern air combat. The whole airspace division was made base on advantages and disadvantages of regional area. Four specific airspace situations were put forward. A new model was set up combined situation assessment model and formation combat capacity model. Using principal component analysis (PCA) to select input variables of formation combat capacity model, which can reduce the complexity of collecting data. Combined neural network was used for effectiveness evaluation of BVR. Combine genetic algorithms (GA) with back propagation (BP) neural network, using GA's global to search optimized BP network structure parameters, overcome the local convergence and other issues of BP algorithm effectively. The result shows that the model can limit the artificial factors, making the solution more objective and creditable.","author":[{"dropping-particle":"","family":"Xiao","given":"Liang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Zhongshu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2013"]]},"page":"1309-1313","title":"Modeling air combat situation assessment based on combat area division","type":"article-journal","volume":"39"},"uris":["http://www.mendeley.com/documents/?uuid=d9fc946a-50db-4494-9f78-20bf6408e7f6"]}],"mendeley":{"formattedCitation":"[131]","plainTextFormattedCitation":"[131]","previouslyFormattedCitation":"[131]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[135];Situation assessment model and formation combat capability model in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9787561228999","abstract":"The Beyond Visual Range (BVR) air combat has become the most important mode of modern air combat. In this paper, a new model is set up combining situation assessment model and the formation combat capacity model. Genetic BP Neural Network is used for the effectiveness evaluation of BVR. Firstly, the main factors of the situation assessment in BVR air combat are proposed and analyzed. Secondly, Analytic Hierarchy Process (AHP) model of combat capacity assessment in BVR is established. The main factors are obtained by using Principal Component Analysis(PCA)to select input variables. A new model is presented as an AHP model integrated from the two above models, then, combine Genetic Algorithms(GA)with BP neural network, using GA's global to search the optimized BP network structure parameters, overcome the local convergence and solve other issues of BP algorithm effectively. Using the new model to get the input variables, GA-BP hybrid modeling is applied to effectiveness evaluation of BVR. Finally, a typical 2-VS-4 air combat example is presented to verify the model's availability. The results show the order of the attack of the reds that make the effectiveness evaluation maximum. The results of the numerical example show that the model can limit the artificial factors, making the solution more objective and creditable. Data link plays an important role in BVR air combat.","author":[{"dropping-particle":"","family":"Liang","given":"Xiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Yaoyao","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of 2010 Asia-Pacific International Symposium on Aerospace Technology, APISAT 2010","id":"ITEM-1","issued":{"date-parts":[["2010"]]},"title":"Study on the effectiveness evaluation method of formation Beyond Visual Range air combat based on genetic BP Neural Network","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=ef8415e3-7890-45c6-847a-d8cc8e950577"]}],"mendeley":{"formattedCitation":"[68]","plainTextFormattedCitation":"[68]","previouslyFormattedCitation":"[68]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[72];Aircraft movement and position recognitionADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICMTMA.2009.637","ISBN":"978-0-7695-3583-8","abstract":"Locally linear embedding (LLE) is a prevalent manifold learning method in pattern recognition and machine learning. 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Computer simulation testifies the efficiency and accuracy of LLE in aircraft pose recognition. © 2009 IEEE.","author":[{"dropping-particle":"","family":"Yuan","given":"Wenting","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jia","given":"Peng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Luping","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shao","given":"Lin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2009 International Conference on Measuring Technology and Mechatronics Automation","id":"ITEM-1","issued":{"date-parts":[["2009"]]},"page":"454-457","publisher":"IEEE","title":"Aircraft Pose Recognition Using Locally Linear Embedding","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=64e79feb-b556-489b-8aae-8cef487589fd"]}],"mendeley":{"formattedCitation":"[140]","plainTextFormattedCitation":"[140]","previouslyFormattedCitation":"[140]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[144].Independent Components Analysis Air pressure measurement ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.probengmech.2011.08.010","ISSN":"02668920","abstract":"Aerodynamic pressure measurements are usually interpreted through statistical tools involving modal representations. The most popular approach is based on the Principal Component Analysis (PCA), which provides the theoretical basis for the well-known concept of coherent structure. In order to overcome some theoretical and practical limitations, alternative approaches can be formulated adapting techniques developed in the feature-recognition field. In this sense, the Independent Component Analysis (ICA) can be conceived as an evolution of PCA in which high-order statistics are used to identify a non-orthogonal modal representation; ICA has been used to solve the Blind Source Separation (BSS) problem as well as to extract features from random data. Both PCA and ICA show severe limitations when applied to represent (and interpret) propagating phenomena such as the pressure field generated by a vortex advected by the mean flow. To overcome these problems, an explicit description of the time evolution is introduced in the modal representation through two techniques, Dynamic-PCA and Dynamic-ICA; besides, the novel concept of dynamic coherent structure is presented and used as an interpretative tool. The application of the above-mentioned methods is demonstrated referring to the aerodynamic pressure field measured on a bluff body immersed in a turbulent boundary layer. © 2011 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Carassale","given":"Luigi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Probabilistic Engineering Mechanics","id":"ITEM-1","issued":{"date-parts":[["2012","4"]]},"page":"66-74","title":"Analysis of aerodynamic pressure measurements by dynamic coherent structures","type":"article-journal","volume":"28"},"uris":["http://www.mendeley.com/documents/?uuid=acb27be1-765a-4672-810a-374de99035ca"]}],"mendeley":{"formattedCitation":"[10]","plainTextFormattedCitation":"[10]","previouslyFormattedCitation":"[10]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[10];Flight dynamics and control effectiveness and missile guidance systems ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"1001506X","abstract":"To get the attitude of an experimental missile, a scheme to estimating the flight attitude of a rolling missile based on the independent component analysis (ICA) algorithm is proposed, thus offering a basis for further analysis on the flight dynamics performances and effectiveness of control and guide systems. A telemetry scheme based on two-axis liquid-droved rate gyroscope is studied and the telemetric data are processed by ICA algorithm to reconstruct the attitude of missiles. By constructing a special virtual measure variable, the ICA algorithm is introduced to transform the measure signals from body frame to quasi-body frame and the modulated effect caused by the rolling of missile body is removed. Thus the attitude information of rolling missiles would be obtained in the end. The simulation example shows the effectiveness of the proposed algorithm.","author":[{"dropping-particle":"","family":"Zou","given":"Yi Min","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Bo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2008"]]},"page":"1301-1303","title":"Estimation of the attitude of a rolling missile based on independent component analysis","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=8858b3c3-919a-4aba-b868-e8dc18c22fe4"]}],"mendeley":{"formattedCitation":"[144]","plainTextFormattedCitation":"[144]","previouslyFormattedCitation":"[144]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[148];Analysis of data collected from sensors during flight to assess aircraft condition ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/AERO.2008.4526621","ISBN":"1424414881","ISSN":"1095323X","abstract":"One goal of Integrated Vehicle Health Management (IVHM) for commercial airplane customers is to monitor sensor data to anticipate problems before Flight Deck Effects (FDEs) ground the airplane for unplanned maintenance. Airplane subsystems - such as flight and environmental control systems, electrical and hydraulic power - can have a high number of associated parameters. Monitoring sensor data streams individually can be inefficient, and fail to detect problems. A research effort at The Boeing Company is investigating anomaly detection algorithms for multivariate time series of parametric data. The multivariate process monitoring techniques account for correlation between parameters, and therefore alert when relationships between parameters change, as well as when mean levels of individual parameters change. Since many traditional multivariate process monitoring techniques are not suited for the high number of parameters in airplane subsystems, this paper discusses using dimension reduction techniques. One example is Principal Component Analysis (PCA). If the assumptions behind PCA are not met, then monitoring charts based on conventional PCA alone can show false alarms and bad detectability. Independent Component Analysis (ICA) is a recently developed method in which the goal is to decompose observed data into linear combinations of statistically independent components. ICA can be considered an extension of PCA since it uses PCA as an initial pre-whitening stage. Like Projection Pursuit Density Estimation, ICA searches for projections of the data that are most non-Gaussian. This paper discusses applying these techniques to snapshots of parameters, over many flights. The techniques anticipate FDEs before they occur, and contribution plots are used to suggest possible causes that make sense to subject matter experts. Early evidence indicates that charts based on ICA alert earlier and more consistently than those based on PCA. This paper discusses a number of technical and research challenges including how to: obtain training data that represents normal operating conditions, use density estimation to set limits, determine the correct number of independent components, use contribution plots to identify the parameters that are the most likely causes of the alert, and evaluate how well the techniques work for snapshots of flight data. ©2008 IEEE.","author":[{"dropping-particle":"","family":"Schimert","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Aerospace Conference Proceedings","id":"ITEM-1","issued":{"date-parts":[["2008"]]},"title":"Data-driven fault detection based on process monitoring using dimension reduction techniques","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=b99c06ea-04ef-414e-8382-d7b893c9651b"]}],"mendeley":{"formattedCitation":"[98]","plainTextFormattedCitation":"[98]","previouslyFormattedCitation":"[98]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[102];Radar target detection – objects background recognition in the airspace ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICECTECH.2011.5941740","ISBN":"978-1-4244-8678-6","abstract":"A sequence of radar images containing the target is collected using an avian radar system. Radar image is composed of complex background and the target (flying birds, aircrafts, missiles etc). The main objective is to detect the target using Fast Independent Component Analysis (Fast ICA). Fast ICA separates the background and target from the radar image. Constant False Alarm Rate (CFAR) determines the power threshold above which the return echoes have been originated from the target. Clutter suppression is done by Conventional CFAR segmentation method and Two parameter CFAR methods. A new scheme based on Fusion of Conventional CFAR and Two Parameter CFAR is adopted and an average detection of 85% is obtained. After fusion, the target is detected from the radar images. © 2011 IEEE.","author":[{"dropping-particle":"","family":"Greet","given":"L. Donisha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harris","given":"S.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2011 3rd International Conference on Electronics Computer Technology","id":"ITEM-1","issued":{"date-parts":[["2011","4"]]},"page":"216-219","publisher":"IEEE","title":"Detection and clutter suppression using fusion of conventional CFAR and two parameter CFAR","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=2d64ec7e-254e-48b4-b314-3c3d01b75f9c"]}],"mendeley":{"formattedCitation":"[32]","plainTextFormattedCitation":"[32]","previouslyFormattedCitation":"[32]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[36]k-means algorithmClassification of flights by manoeuvring conditions – analysis of human factors in aviation in the context of failure detection and identification ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.ifacol.2019.01.063","ISSN":"24058963","abstract":"This paper presents a human factors analysis in aviation within the context of failure detection and identification (FDI) using statistical data analysis and clustering. We used data from experiments in a motion-based flight simulator (SIVOR) with 4 experienced pilots performing a take-off maneuvre under three conditions: normal, under engine failure and under flap failure. We propose two metrics based on statistical data analysis to evaluate and compare human behavior during flight. We also use k-means clustering in order to classify flights according to maneuvre conditions and misclassified flights are further analyzed according to which pilot has performed it. Results show that for the statistical data analysis the behavior of one specific pilot has higher dissimilarity with all other pilots. Moreover, for the k-means clustering, most of the misclassified flights were performed by this same pilot.","author":[{"dropping-particle":"","family":"Kraemer","given":"Aline D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Villani","given":"Emília","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Arjoni","given":"Diego H.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IFAC-PapersOnLine","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"Aircraft FDI and human factors analysis of a take-off maneuvre using SIVOR flight simulator","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=24315888-a233-4ee3-b2e2-1fad97afb141"]}],"mendeley":{"formattedCitation":"[59]","plainTextFormattedCitation":"[59]","previouslyFormattedCitation":"[59]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[63].Apriori algorithmDiagnostics of overload events resulting from such phenomena as strong turbulence, crosswind, overspeed ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.13700/j.bh.1001-5965.2013.0656","ISSN":"10015965","abstract":"Most of current civil aircraft's exceedance event intelligent diagnosis models are \"black box\" model, which donot contribute to analyze the occurrence of civil aircraft's exceedance event. In order to overcome these shortcomings, a civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier (FAC) was proposed. First, quick access recorder's (QAR's) parameters value when exceedance event occured was extracted. Fuzzy C-means (FCM) cluster algorithm was adopted to preprocess extracted QAR's parameters value. Then, the library of fuzzy associative classification rule (FACR) was generated by Apriori algorithm. Genetic algorithm was used to prune the library of FACR. Finally, fuzzy classification reasoning method was integrated to build FAC. The FAC was verified with sample data generated by B737-800. Experiment results show that the FAC can diagnose exceedance event effectively, and its classification error rate is equivalent to least squares support vector machine (LS-SVM), but its interpretability is superior to LS-SVM.","author":[{"dropping-particle":"","family":"Gao","given":"Xiaoxia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huo","given":"Weigang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Feng","given":"Xingjie","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2014"]]},"page":"1366 -1371","title":"Civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=3145e94a-66c6-4312-b84d-cf2cb161e212"]}],"mendeley":{"formattedCitation":"[26]","plainTextFormattedCitation":"[26]","previouslyFormattedCitation":"[26]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[30];Aircraft control system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/GNCC42960.2018.9019047","ISBN":"978-1-5386-1171-5","abstract":"Data mining is one of the hot issues in the field of database research in recent years. Association rules analysis is one of the key technologies in data mining. Apriori algorithm is one of the main algorithms of association analysis, which is mainly used to find all the frequent itemsets in the database, and then generates association rules by frequent itemsets. This article intelligently handles a large number of aircraft control system experimental data, using apriori algorithm as the experimental data analysis technique, mining association rules from a large amount of experimental data, and extracting design experience that facilitates judgment and comparison, to improvie the efficiency and speed of control system design. Through simulation analysis, the results show that data mining of aircraft control system based on apriori algorithm is effective.","author":[{"dropping-particle":"","family":"Qian","given":"Xu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kai","given":"Yang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ya","given":"Ban","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kunfeng","given":"Lu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yao","given":"Zhao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qinghai","given":"Gong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","id":"ITEM-1","issued":{"date-parts":[["2018","8"]]},"page":"1-6","publisher":"IEEE","title":"Data Mining and Simulation Analysis of Aircraft Control System Based on Apriori Algorithm","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=7e1df1ad-83f3-4eb9-b42a-e11e58c7cc46"]}],"mendeley":{"formattedCitation":"[87]","plainTextFormattedCitation":"[87]","previouslyFormattedCitation":"[87]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[91].Singular Value DecompositionControl actuator failures ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2009-5759","ISBN":"978-1-60086-978-5","abstract":"In this paper we describe a new algorithm for accommodation of failures in morphing flight control actuators. The algorithm is well suited for the case of continuous actuators that can assume arbitrary positions between the saturation limits. The algorithm is based on introducing a high-order space of actual control inputs (ACI) and a low-order space of virtual control inputs (VCI), and singular value decomposition (SVD) of the problem into the control-law design in the VCI space, and control allocation which depends on the mapping between the VCI and ACI. Based on this decomposition, a new parameterization of failures is derived in such a way that the failures in the ACI space are reflected as pure parametric uncertainty in the VCI. On the basis of this parameterization, an adaptive observer and stable adaptive laws for estimation of uncertain parameters are designed, and a proof of stability is given. Performance of the overall system is evaluated on a simulation of F/A-18 aircraft where four standard flight control surfaces each are replaced by 25 morphing surfaces. Copyright © 2009 by Scientific Systems Company, Inc.","author":[{"dropping-particle":"","family":"Boskovic","given":"Jovan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Redding","given":"Joshua","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA Guidance, Navigation, and Control Conference","id":"ITEM-1","issued":{"date-parts":[["2009","8","10"]]},"publisher":"American Institute of Aeronautics and Astronautics","publisher-place":"Reston, Virigina","title":"Accommodation of Control Actuator Failures in Morphing Aircraft","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=99281711-164a-48ba-a1ca-7af2b8475a39"]}],"mendeley":{"formattedCitation":"[8]","plainTextFormattedCitation":"[8]","previouslyFormattedCitation":"[8]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[8];Fail-tolerant flight control system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/CCA.2015.7320736","ISBN":"978-1-4799-7787-1","abstract":"This paper addresses the development of an active fault tolerant control scheme. The methodology is based on a fault detection and diagnosis procedure relying on an unknown input observer designed via Singular Value Decomposition (SVD). Furthermore, the control reconfiguration mechanism is removed in favor of an additional control law directly injected into the nominal one to compensate for the effect of the fault. Therefore, the architecture proposed in this paper circumvents the decision-making problem, which usually requires both significant attention and time. Another advantage of the SVD strategy is that it does not require any tuning parameters, hence greatly simplifying its design. The control scheme is then applied to a linear model of a modern combat aircraft subject to actuators faults. Simulation results finally show the fault estimation and accommodation, leading to the enhancement of the flying quality.","author":[{"dropping-particle":"","family":"Drouot","given":"Adrien","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Noura","given":"Hassan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goerig","given":"Laurent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Piot","given":"Pascal","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2015 IEEE Conference on Control Applications (CCA)","id":"ITEM-1","issued":{"date-parts":[["2015","9"]]},"page":"960-965","publisher":"IEEE","title":"Actuators additive SVD-based fault-tolerant control for a combat aircraft","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=6d2a7530-7076-4383-8a01-b4a1558eb281"]}],"mendeley":{"formattedCitation":"[22]","plainTextFormattedCitation":"[22]","previouslyFormattedCitation":"[22]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[26];Aircraft engine health diagnostics ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.7964/jdxbgxb201303034","ISSN":"16715497","abstract":"Singular Value Decomposition (SVD) is a widely used nonlinear signal denoising approach. Its denoising effect is depending on the selection of singular values for the signal reconstitution. The singular value difference spectrum of singular value can be used to select the expected singular values adaptively. However, sometimes when dealing with signals with strong trend, such as aircraft engine health signals, it may hardly choose the right singular values and fail to restore the desired signals To solve such problem, a signal denoising approach based on Empirical Mode Decomposition (EMD) and SVD is proced. First, the original signal is decomposed by EMD and the trend component is extracted from the original signal. Then, SVD is used to denoise the detrended signal, and the difference spectrum of singular value is used to select the singular values adaptively. After denoising, the detrended part is added to the trend part to get the final signal. Results of Both simulation and real aircraft engine health signal experiment demonstrate that the proposed approach can precisely select the singular values for signal reconstitution and effectively reduce the noise.","author":[{"dropping-particle":"","family":"Lei","given":"Da","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhong","given":"Shi Sheng","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2013"]]},"page":"764-770","title":"Aircraft engine health signal denoising based on singular value decomposition and empirical mode decomposition methods","type":"article-journal","volume":"43"},"uris":["http://www.mendeley.com/documents/?uuid=bec0a17c-b7b4-4f76-877d-7df844f137f6"]}],"mendeley":{"formattedCitation":"[63]","plainTextFormattedCitation":"[63]","previouslyFormattedCitation":"[63]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[67].Tab. 6Selected applications of ML algorithms – reinforcement learningREINFORCEMENT LEARNINGAlgorithmSelected applicationsAgent policyHighly intelligent air combat strategies for autonomous air combat missions – potential-based reward shaping methods to improve the effectiveness of the air combat strategy generation algorithm ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/app10155198","ISSN":"2076-3417","abstract":"With the development of unmanned combat air vehicles (UCAVs) and artificial intelligence (AI), within visual range (WVR) air combat confrontations utilizing intelligent UCAVs are expected to be widely used in future air combats. As controlling highly dynamic and uncertain WVR air combats from the ground stations of the UCAV is not feasible, it is necessary to develop an algorithm that can generate highly intelligent air combat strategies in order to enable UCAV to independently complete air combat missions. In this paper, a 1-vs.-1 WVR air combat strategy generation algorithm is proposed using the multi-agent deep deterministic policy gradient (MADDPG). A 1-vs.-1 WVR air combat is modeled as a two-player zero-sum Markov game (ZSMG). A method for predicting the position of the target is introduced into the model in order to enable the UCAV to predict the target’s actions and position. Moreover, to ensure that the UCAV is not limited by the constraints of the basic fighter maneuver (BFM) library, the action space is considered to be a continuous one. At the same time, a potential-based reward shaping method is proposed in order to improve the efficiency of the air combat strategy generation algorithm. Finally, the efficiency of the air combat strategy generation algorithm and the intelligence level of the resulting strategy is verified through simulation experiments. The results show that an air combat strategy using target position prediction is superior to the one that does not use target position prediction.","author":[{"dropping-particle":"","family":"Kong","given":"Weiren","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Deyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Zhen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Kai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zeng","given":"Lina","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Sciences","id":"ITEM-1","issue":"15","issued":{"date-parts":[["2020","7","28"]]},"page":"5198","title":"Maneuver Strategy Generation of UCAV for within Visual Range Air Combat Based on Multi-Agent Reinforcement Learning and Target Position Prediction","type":"article-journal","volume":"10"},"uris":["http://www.mendeley.com/documents/?uuid=ab8d0ffd-090a-4578-8af9-bc790c60422d"]}],"mendeley":{"formattedCitation":"[55]","plainTextFormattedCitation":"[55]","previouslyFormattedCitation":"[55]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[59];Avoiding enemy threats and gaining an advantage over them in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ACCESS.2021.3060426","ISSN":"2169-3536","abstract":"Beyond-visual-range (BVR) engagement becomes more and more popular in the modern air battlefield. The key and difficulty for pilots in the fight is maneuver planning, which reflects the tactical decision-making capacity of the both sides and determinates success or failure. In this paper, we propose an intelligent maneuver planning method for BVR combat with using an improved deep Q network (DQN). First, a basic combat environment builds, which mainly includes flight motion model, relative motion model and missile attack model. Then, we create a maneuver decision framework for agent interaction with the environment. Basic perceptive variables are constructed for agents to form continuous state space. Also, considering the threat of each side missile and the constraint of airfield, the reward function is designed for agents to training. Later, we introduce a training algorithm and propose perceptional situation layers and value fitting layers to replace policy network in DQN. Based on long short-term memory (LSTM) cell, the perceptional situation layer can convert basic state to high-dimensional perception situation. The fitting layer does well in mapping action. Finally, three combat scenarios are designed for agent training and testing. Simulation result shows the agent can avoid the threat of enemy and gather own advantages to threat the target. It also proves the models and methods of agents are valid and intelligent air combat can be realized.","author":[{"dropping-particle":"","family":"Hu","given":"Dongyuan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Rennong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zuo","given":"Jialiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Ze","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Ying","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Access","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"32282-32297","title":"Application of Deep Reinforcement Learning in Maneuver Planning of Beyond-Visual-Range Air Combat","type":"article-journal","volume":"9"},"uris":["http://www.mendeley.com/documents/?uuid=477cf80c-5763-4755-901b-b21e053c5f8b"]}],"mendeley":{"formattedCitation":"[38]","plainTextFormattedCitation":"[38]","previouslyFormattedCitation":"[38]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[42];UCAV (Unmanned Combat Aaerial Vehicle) air combat autonomous manoeuvre decision for one-on-one within visual range ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICCA51439.2020.9264567","ISBN":"978-1-7281-9093-8","ISSN":"19483457","abstract":"Based on a robust multi-agent reinforcement learning (MARL) algorithm framework, an autonomous maneuver decision-making algorithm for UCAV air combat in one-on-one combat in the visible range is designed and implemented. This algorithm can solve the problem that the single agent reinforcement learning algorithm cannot converge during the training process due to the unstable environment. At the same time, considering the shortcomings of the MADDPG algorithm in a strong competitive environment, it is easy to obtain a very fragile strategy, which is only targeted at a specific equilibrium strategy. In this paper, a minimax module is introduced to obtain the expected perturbation, which can locally approach the worst-case perturbation through the gradient. Through simulation tests of algorithm convergence and policy quality, the algorithm is found to be effective.","author":[{"dropping-particle":"","family":"KONG","given":"Weiren","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"ZHOU","given":"Deyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"ZHANG","given":"Kai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"YANG","given":"Zhen","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2020 IEEE 16th International Conference on Control & Automation (ICCA)","id":"ITEM-1","issued":{"date-parts":[["2020","10","9"]]},"page":"506-512","publisher":"IEEE","title":"Air combat autonomous maneuver decision for one-on-one within visual range engagement base on robust multi-agent reinforcement learning","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=508c8112-14b6-4771-951a-7070be2c0651"]}],"mendeley":{"formattedCitation":"[58]","plainTextFormattedCitation":"[58]","previouslyFormattedCitation":"[58]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[62].State value functionClose air combat manoeuvre decision and taking a dominant position according to the opponent's strategy ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.12305/j.issn.1001-506X.2021.02.19","ISSN":"1001506X","abstract":"In order to solve the problem of complex combat information and difficult to quickly and accurately perceive situation and make decision in air combat, an algorithm combining game theory and deep reinforcement learning is proposed. Firstly, according to the typical one-to-one air combat process and the standard of random game, a two machine multi-state game model under the condition of red and blue confrontation in close air combat is constructed. Secondly, deep Q network (DQN) is used to deal with the continuous infinite state space of fighter. Then, the Minimax algorithm is used to construct a linear programming to solve the optimal value function of the stage game in each specific state, and the network approximation value function is trained. Finally, the optimal maneuver strategy is obtained according to the output of the network after training. The simulation results show that the algorithm has good adaptability and intelligence for the air combat. It can effectively select the favorable maneuver action and occupy the dominant position according to the air combat opponent's action strategy.","author":[{"dropping-particle":"","family":"Ma","given":"Wen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Hui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Zhuang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Zhiyong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Zhaoxin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Xiliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"14","title":"Close air combat maneuver decision based on deep stochastic game","type":"article-journal","volume":"9"},"uris":["http://www.mendeley.com/documents/?uuid=ad55241f-196e-4c06-8eb7-5361a2fe839b"]}],"mendeley":{"formattedCitation":"[74]","plainTextFormattedCitation":"[74]","previouslyFormattedCitation":"[74]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[78];Value function matching in a continuous state space using agent autoantagonism in human-machine confrontation — tactical decision-making to build a virtual AI pilot ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1088/1742-6596/1746/1/012028","ISSN":"1742-6588","abstract":"Aiming at the problem of the lack of intelligence of virtual machine opponents in the human-machine confrontation semi-physical simulation environment, it is proposed to apply the deep reinforcement learning method into tactical making-decision for building an AI virtual pilot with self-confrontation and self-learning ability. First, flight dynamics and kinematics are used to build basic flight models in the simulation environment, and a missile attack area is established for weapon model; Second, inspired by the framework of interaction between the agent and the environment in reinforcement learning, a tactical decision architecture for flight agent based on the one-to-one tactical confrontation process is organized. Finally, the improved DQN method is used to fit the value function in the continuous state space, and the network training is completed by means of agent self-antagonism and human-machine confrontation. the well-trained AI model can undertake the role of virtual opponents in human-machine confrontation environment, and shows a certain degree of intelligence in the confrontation process with pilots.","author":[{"dropping-particle":"","family":"Hu","given":"Dongyuan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zuo","given":"Jialiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zheng","given":"Wanze","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Ze","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Yu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Qiang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Physics: Conference Series","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","1","1"]]},"page":"012028","title":"Research on Application of LSTM-QDN in Intelligent Air Combat Simulation","type":"article-journal","volume":"1746"},"uris":["http://www.mendeley.com/documents/?uuid=eb7c57db-d8a1-4250-9958-dc9cc4818a2c"]}],"mendeley":{"formattedCitation":"[39]","plainTextFormattedCitation":"[39]","previouslyFormattedCitation":"[39]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[43].Q-learning and deep Q-learningIndependent decisions in air combat and effective decision-making policy in defeating the enemy ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ACCESS.2019.2961426","ISSN":"21693536","abstract":"With the development of artificial intelligence and integrated sensor technologies, unmanned aerial vehicles (UAVs) are more and more applied in the air combats. A bottleneck that constrains the capability of UAVs against manned vehicles is the autonomous maneuver decision, which is a very challenging problem in the short-range air combat undergoing highly dynamic and uncertain maneuvers of enemies. In this paper, an autonomous maneuver decision model is proposed for the UAV short-range air combat based on reinforcement learning, which mainly includes the aircraft motion model, one-to-one short-range air combat evaluation model and the maneuver decision model based on deep Q network (DQN). However, such model includes a high dimensional state and action space which requires huge computation load for DQN training using traditional methods. Then, a phased training method, called 'basic-confrontation', which is based on the idea that human beings gradually learn from simple to complex is proposed to help reduce the training time while getting suboptimal but efficient results. Finally, one-to-one short-range air combats are simulated under different target maneuver policies. Simulation results show that the proposed maneuver decision model and training method can help the UAV achieve autonomous decision in the air combats and obtain an effective decision policy to defeat the opponent.","author":[{"dropping-particle":"","family":"Yang","given":"Qiming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Jiandong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shi","given":"Guoqing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Jinwen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Yong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Access","id":"ITEM-1","issued":{"date-parts":[["2020"]]},"title":"Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=b60c2f14-ef9b-4ac6-8838-49d1c6abd7e6"]}],"mendeley":{"formattedCitation":"[135]","plainTextFormattedCitation":"[135]","previouslyFormattedCitation":"[135]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[139];UCAV decision-making in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-981-10-6463-0_24","ISBN":"9789811064623","ISSN":"18650929","abstract":"Based on deep reinforcement learning, an intelligent tactical decision method is proposed to solve the problem of Unmanned Combat Aerial Vehicle (UCAV) air combat decision-making. The increasing complexity of the air combat environment leads to a curse of dimensionality when using reinforcement learning to solve the air combat problem. In this paper, we employed the deep neural network as the function approximator, and combined it with Q-learning to achieve the accurate fitting of action-value function, which is a good way to reduce the curse of dimensionality brought by traditional reinforcement learning. In order to verify the validity of the algorithm, simulation of our deep Q-learning network (DQN) is carried out on the air combat platform. The simulation results show that the DQN algorithm has a good performance in both the reward and action-value utility. The proposed algorithm provides a new idea for the research of UCAV intelligent decision.","author":[{"dropping-particle":"","family":"Liu","given":"Pin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ma","given":"Yaofei","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Communications in Computer and Information Science","id":"ITEM-1","issued":{"date-parts":[["2017"]]},"page":"274-286","title":"A Deep Reinforcement Learning Based Intelligent Decision Method for UCAV Air Combat","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=442bccfd-99a4-450e-98fb-579c4e397b9c"]}],"mendeley":{"formattedCitation":"[69]","plainTextFormattedCitation":"[69]","previouslyFormattedCitation":"[69]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[73];Autonomous man-machine air combat system built from 3 subsystems: simulation of the air combat environment, simulation of manned aircraft operations, and a self-learning subsystem ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICARCV50220.2020.9305467","ISBN":"978-1-7281-7709-0","abstract":"With the development of artificial intelligence technology and the demand of air combat, autonomous maneuver decision of UAV has become a popular research direction. Scholars at home and abroad have researched on autonomous air combat maneuver decision of UAV in depth based on various technologies and have achieved some results, among which the maneuver decisions based on the reinforcement learning are more efficient. But at present, the simulation targets used to test the effectiveness of the UAV autonomous maneuver decision method are relatively fixed, which cannot reflect the complexity of the enemy's maneuver strategy under real air combat conditions. This paper designs and develops a set of UAV autonomous maneuver decision man-machine air combat system based on deep Q-learning network, which is built from three subsystem: air combat environment simulation subsystem, the manned aircraft operation simulation subsystem and UAV self-learning subsystem. It has been verified that the man-machine air combat system proposed in this paper can effectively simulate real air combat and verify the effectiveness of the UAV autonomous maneuver decision.","author":[{"dropping-particle":"","family":"Chen","given":"Yuyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Jiandong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Qiming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Yu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shi","given":"Guoqing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Yong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)","id":"ITEM-1","issued":{"date-parts":[["2020","12","13"]]},"page":"817-823","publisher":"IEEE","title":"Design and Verification of UAV Maneuver Decision Simulation System Based on Deep Q-learning Network","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=24d608ef-a557-4074-aee9-6e233c376a55"]}],"mendeley":{"formattedCitation":"[15]","plainTextFormattedCitation":"[15]","previouslyFormattedCitation":"[15]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[15];Air combat target assignment ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/SMC.2016.7844336","ISBN":"978-1-5090-1897-0","abstract":"Target assignment is an important yet difficult problem in air combat. Previous methods, e.g., neural network, genetic algorithm, particle swarm optimization and ant colony algorithm for target assignment have been proved to be either too slow or not stable as far as converging to the global optimum is concerned. In this paper, Q-learning is verified to be an appropriate reinforcement learning algorithm for air combat target assignment. Firstly, the air combat Agents are modeled in term of their attributes, structure and actions; secondly, the criteria of state-action pairs are defined and the Q-learning based algorithm for target assignment is provided; in addition, the compromise between the exploration and exploitation of the algorithm is also discussed. Case analysis shows that the presented algorithm avoids relying on prior knowledge and performs well in getting out of local optimum.","author":[{"dropping-particle":"","family":"Luo","given":"Peng-cheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Jun-jie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Che","given":"Wan-fang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","id":"ITEM-1","issued":{"date-parts":[["2016","10"]]},"page":"000779-000783","publisher":"IEEE","title":"Q-learning based air combat target assignment algorithm","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=38dcea9b-216c-4ac4-9372-9e5b1dcfdee4"]}],"mendeley":{"formattedCitation":"[71]","plainTextFormattedCitation":"[71]","previouslyFormattedCitation":"[71]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[75];Stealthy engagement manoeuvring strategy ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3969/j.issn.1001-506X.2012.01.18","ISSN":"1001506X","abstract":"Based on the Markov decision process theory, a reinforcement learning method for stealthy engagement strategy for air vehicles in 3D space is proposed. The advantage region and the exposure region for the environment modeling are established. In order to overcome the dimensional disaster problem, a Q-learning algorithm based on the radial basis function neural network (RBFNN) is put forward and a ranked sampling method is explained. Then simulations for two different situations are carried out, and the results show that the proposed algorithm is effective for the stealthy engagement strategy through reasonable ranked sampling methods.","author":[{"dropping-particle":"","family":"Xu","given":"An","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kou","given":"Ying Xin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yu","given":"Lei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Zhan Wu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2012"]]},"page":"97-101","title":"Stealthy engagement maneuvering strategy with Q-learning based on RBFNN for air vehicles","type":"article-journal","volume":"34"},"uris":["http://www.mendeley.com/documents/?uuid=e4e2351f-d380-4264-b933-b794aab4a58a"]}],"mendeley":{"formattedCitation":"[133]","plainTextFormattedCitation":"[133]","previouslyFormattedCitation":"[133]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[137].Value functionExplicit risk mitigation in adversarial environments (aircraft and enemy missiles) using control barrier functions ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICRA48506.2021.9561853","ISBN":"978-1-7281-9077-8","ISSN":"10504729","abstract":"Air Combat is a high-risk activity carried out by trained professionals operating sophisticated equipment. During this activity, a number of trade-offs have to be made, such as the balance between risk and efficiency. A policy that minimizes risk could have very low efficiency, and one that maximizes efficiency may involve very high risk. In this study, we use Reinforcement Learning (RL) to create Control Barrier Functions (CBF) that captures the current risk, in terms of worst-case future separation between the aircraft and an enemy missile. CBFs are usually designed manually as closed-form expressions, but for a complex system such as a guided missile, this is not possible. Instead, we solve an RL problem using high fidelity simulation models to find value functions with CBF properties, that can then be used to guarantee safety in real air combat situations. We also provide a theoretical analysis of what family of RL problems result in value functions that can be used as CBFs in this way. The proposed approach allows the pilot in an air combat scenario to set the exposure level deemed acceptable and continuously monitor the risk related to his/her own safety. Given input regarding acceptable risk, the system limits the choices of the pilot to those that guarantee future satisfaction of the provided bound.","author":[{"dropping-particle":"","family":"Scukins","given":"Edvards","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ogren","given":"Petter","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2021 IEEE International Conference on Robotics and Automation (ICRA)","id":"ITEM-1","issued":{"date-parts":[["2021","5","30"]]},"page":"10734-10740","publisher":"IEEE","title":"Using Reinforcement Learning to Create Control Barrier Functions for Explicit Risk Mitigation in Adversarial Environments","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=bbd1c949-ed78-4058-b33f-d486c4bb9975"]}],"mendeley":{"formattedCitation":"[99]","plainTextFormattedCitation":"[99]","previouslyFormattedCitation":"[99]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[103]; Collision avoidance by unnamed ships in unknown environments ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s12530-018-9253-9","ISSN":"1868-6478","abstract":"To solve the problem of intelligent collision avoidance by unmanned ships in unknown environments, a deep reinforcement learning obstacle avoidance decision-making (DRLOAD) algorithm is proposed. The problems encountered in unmanned ships’ intelligent avoidance decisions are analyzed, and the design criteria for a proposed decision algorithm are put forward. Based on the Markov decision process, an intelligent collision avoidance model is established for unmanned ships. The optimal strategy for an intelligent decision system is determined through the value function which maximizes the return for the mapping of the in unmanned ship’s state to behavior. A reward function is specifically designed for obstacle avoidance, approaching a target and safety. Finally, simulation experiments are carried out in multi-state obstacle environments, demonstrate the effectiveness of the proposed DRLOAD algorithm.","author":[{"dropping-particle":"","family":"Wang","given":"Chengbo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Xinyu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cong","given":"Longze","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Junjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Jiawei","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Evolving Systems","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2019","12","20"]]},"page":"649-658","title":"Research on intelligent collision avoidance decision-making of unmanned ship in unknown environments","type":"article-journal","volume":"10"},"uris":["http://www.mendeley.com/documents/?uuid=4bdac494-37f1-4a33-b9eb-7a09369bf790"]}],"mendeley":{"formattedCitation":"[121]","plainTextFormattedCitation":"[121]","previouslyFormattedCitation":"[121]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[125].Monte Carlo methodsManoeuvring decisions in short-range air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/CCDC.2018.8407416","ISBN":"978-1-5386-1244-6","abstract":"In order to the requirement of flexibility and real-time for maneuvering decision in short range air combat, a maneuvering decision model in short range air combat based on reinforcement learning is presented. The model has two characteristics, one is select the situational evaluation function to build the state space, what make the discrete state space get more representative. The other is use the thinking of Monte-Carlo reinforcement learning, the result of air combat was used as the basis for returning the reward, what ensure the continuity of maneuver movement. The model is use the control valve as the decision result and the decision time is less than 0.001 seconds. At last, the feasibility of the decision model was verified by two different experiments.","author":[{"dropping-particle":"","family":"Minglang","given":"Cui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haiwen","given":"Du","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhenglei","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"QingPeng","given":"Sun","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2018 Chinese Control And Decision Conference (CCDC)","id":"ITEM-1","issued":{"date-parts":[["2018","6"]]},"page":"1783-1788","publisher":"IEEE","title":"Maneuvering decision in short range air combat for unmanned combat aerial vehicles","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=102e6294-3525-442f-934d-32ea34f1c68c"]}],"mendeley":{"formattedCitation":"[77]","plainTextFormattedCitation":"[77]","previouslyFormattedCitation":"[77]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[81];UCAV fleet flight path planning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2021-0175","ISBN":"978-1-62410-609-5","abstract":"In this study, we develop a machine learning based fleet autonomy for Unmanned Combat Aerial Vehicles (UCAVs) utilizing a synthetic simulation-based wargame environment. Aircraft survivability is modeled as Markov processes. Mission success metrics are developed to introduce collision avoidance and survival probability of the fleet. Flight path planning is performed utilizing the proximal policy optimization (PPO) based reinforcement learning method to obtain attack patterns with a multi-objective mission success criteria corresponding to the mission success metrics. Performance of the proposed system is evaluated by utilizing the Monte Carlo analysis in which a wider initial position interval is used when compared to the defined interval in the training phase. This provides a preliminary insight about the generalization ability of the RL agent.","author":[{"dropping-particle":"","family":"Yuksek","given":"Burak","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Demirezen","given":"Umut M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Inalhan","given":"Gokhan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA Scitech 2021 Forum","id":"ITEM-1","issued":{"date-parts":[["2021","1","11"]]},"publisher":"American Institute of Aeronautics and Astronautics","publisher-place":"Reston, Virginia","title":"Development of UCAV Fleet Autonomy by Reinforcement Learning in a Wargame Simulation Environment","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=b94d49d1-5183-4385-a97b-a833aa723e0d"]}],"mendeley":{"formattedCitation":"[141]","plainTextFormattedCitation":"[141]","previouslyFormattedCitation":"[141]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[145];Influence of UCAV agility on short-range air combat effectiveness ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.ast.2019.105534","ISSN":"12709638","abstract":"The flight agility of an unmanned combat aerial vehicle (UCAV) determines its ability to rapidly transition from one state to another. In this paper, the influence of flight agility on short-range aerial combat effectiveness is quantitatively investigated. This research is based on one-on-one three-dimensional aerial combat engagements. First, a 6-DOF mathematical model of the UCAV is established, and a nonlinear dynamic inverse controller is designed. The flight agility is calculated based on the 180∘ flight heading reverse maneuver, and the influence of the control law parameters is studied. To implement autonomous intelligent aerial combat engagements, a three-dimensional approximate dynamic programming method is proposed. The performance of the designed algorithm is validated through both Monte Carlo simulations with various initial conditions and engagements with another algorithm for comparison. After training, the aerial combat simulation framework is constructed by combining the point-mass-based guidance law and a nonlinear UCAV model. Combat engagements are conducted between UCAVs with different configurations. The quantitative results are evaluated through Monte Carlo simulations and correlation analysis.","author":[{"dropping-particle":"","family":"Wang","given":"Maolin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Lixin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yue","given":"Ting","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Hailiang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Aerospace Science and Technology","id":"ITEM-1","issued":{"date-parts":[["2020","1"]]},"page":"105534","title":"Influence of unmanned combat aerial vehicle agility on short-range aerial combat effectiveness","type":"article-journal","volume":"96"},"uris":["http://www.mendeley.com/documents/?uuid=37ede8a3-5d88-4bcb-aa6b-dd7c5d580157"]}],"mendeley":{"formattedCitation":"[124]","plainTextFormattedCitation":"[124]","previouslyFormattedCitation":"[124]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[128];Temporal Difference learningReal-time generation of intended flight paths for UAV in a complex air combat environment ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1088/1742-6596/2010/1/012129","ISSN":"1742-6588","abstract":"Intention prediction plays an indispensable role in future informationized air combat, which will help the command and control center to make a more correct decision. In this paper, an intelligent intention prediction approach based on an improved double deep Q network (DDQN) is developed to generate real-time intention flight paths for unmanned aerial vehicles (UAVs) in complex air combat environment. Initially, by introducing an actual topographic map, the different threats of UAVs in different terrains on the map are analyzed, based on which, a terrain environment reward function is constructed. Secondly, by splitting a complete maneuver action into six basic maneuver units and determining the probability value of each unit, a maneuver reward function is obtained. Further, to improve the real-time performance and accuracy of the standard DDQN algorithm, an improved DDQN algorithm is proposed by using temporal-difference (TD) method and binary tree data structure. Finally, the simulation results verify show that the proposed algorithm has achieved better results under complex terrain conditions.","author":[{"dropping-particle":"","family":"Chen","given":"Tianpei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Haotian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Yuhui","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Physics: Conference Series","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","9","1"]]},"page":"012129","title":"Intention prediction of UAVs based on improved DDQN","type":"article-journal","volume":"2010"},"uris":["http://www.mendeley.com/documents/?uuid=f63557e4-e2df-45aa-949f-028a975aa469"]}],"mendeley":{"formattedCitation":"[13]","plainTextFormattedCitation":"[13]","previouslyFormattedCitation":"[13]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[13];Autonomous behaviour – the use of intelligent agents that enable the aircraft to adapt to unexpected situations and analyse past experiences to increase future mission performance ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9780983762447","abstract":"For Unmanned Aircraft Systems, more advanced autonomous capabilities are being pushed to use in the near future, in order to relieve human fatigue and workload. Autonomous behavior requires intelligent agents to adapt their behavior to unexpected situations and examine their past experiences to increase their future mission performance. To achieve adaptive behavior and decision making capabilities this study investigated the application of different Reinforcement Learning Models (Monte Carlo (MC) and Temporal Difference (TD)) and the effects of sensor sensitivity, as modeled through Signal Detection Theory (SDT), on the ability of RL algorithms to accomplish a target localization task. Three levels of sensor sensitivity are simulated and compared to the results of the same system using a perfect sensor. An evaluation of the system is performed using multiple metrics, including episodic reward curves and the time taken to locate all targets. Statistical analyses are employed to detect significant differences in the comparison of steady-state behavior of different systems. The study found that in general, TD models perform better than MC models, and sensor sensitivity has a significant effect on the agent performance as well.","author":[{"dropping-particle":"","family":"Quirion","given":"Nate","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Dahai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ludu","given":"Andrei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vincenzi","given":"Dennis","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IIE Annual Conference and Expo 2015","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"title":"Sensor sensitivity and reinforcement learning models on target acquisition task for autonomous vehicle","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=82c5e4df-2f9d-49c1-9536-fa3db25f64ce"]}],"mendeley":{"formattedCitation":"[89]","plainTextFormattedCitation":"[89]","previouslyFormattedCitation":"[89]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[93];Intelligent systems that support system learning, control, and decision-making ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1142/S2301385021500023","ISSN":"2301-3850","abstract":"In recent years, Unmanned Aerial Systems (UASs) development and application have achieved remarkable growth. With the advancement of technology, UASs nowadays feature more advanced autonomous capabilities than ever before. In order to achieve autonomous behavior, intelligent systems are required to be incorporated to support system learning, control and decision-making. With these capabilities, UASs can learn from their past experiences, through interacting with the task environment to adapt their behavior to enhance their future performance. Machine learning is one of the most commonly used techniques for UASs to acquire knowledge from their experience, and research in this area is still developing. In this study, Reinforcement Learning (RL) algorithms were used on autonomous aerial systems to achieve adaptive behavior and decision-making capabilities. The effects of UAS sensor sensitivity, as modeled through Signal Detection Theory (SDT), on the ability of RL algorithms to accomplish a target localization task were investigated. Three levels of sensor sensitivity were simulated and compared to the results of the same system using a perfect sensor, with the consideration of two RL algorithms, namely, Temporal Difference (TD) and Monte Carlo (MC) methods. Target localization and identification task were used as the test bed, and a hierarchical architecture was developed with two distinct agents. Mission performance was analyzed using multiple metrics, including episodic reward and the time taken to locate all targets. Statistical analyses were carried out to detect significant differences in the comparison of steady-state behavior of different factors. Results were discussed, and future research direction was given at the end of the paper.","author":[{"dropping-particle":"","family":"Quirion","given":"Nate","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Dahai","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Unmanned Systems","id":"ITEM-1","issue":"01","issued":{"date-parts":[["2021","1","2"]]},"page":"11-21","title":"Effect of Sensor Sensitivity on Autonomous Aerial Vehicle Target Localization","type":"article-journal","volume":"09"},"uris":["http://www.mendeley.com/documents/?uuid=1040e2e1-4abf-4ef7-9c36-df28c6cf630f"]}],"mendeley":{"formattedCitation":"[88]","plainTextFormattedCitation":"[88]","previouslyFormattedCitation":"[88]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[92].REINFORCE algorithmMulti-agent hierarchical policy gradient (MAHPG) algorithm capable of learning different strategies and moving beyond expert cognition through adversarial learning – air combat method for both defensive and offensive capabilities ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.engappai.2020.104112","ISSN":"09521976","abstract":"Air-to-air confrontation has attracted wide attention from artificial intelligence scholars. However, in the complex air combat process, operational strategy selection depends heavily on aviation expert knowledge, which is usually expensive and difficult to obtain. Moreover, it is challenging to select optimal action sequences efficiently and accurately with existing methods, due to the high complexity of action selection when involving hybrid actions, e.g., discrete/continuous actions. In view of this, we propose a novel Multi-Agent Hierarchical Policy Gradient algorithm (MAHPG), which is capable of learning various strategies and transcending expert cognition by adversarial self-play learning. Besides, a hierarchical decision network is adopted to deal with the complicated and hybrid actions. It has a hierarchical decision-making ability similar to humankind, and thus, reduces the action ambiguity efficiently. Extensive experimental results demonstrate that the MAHPG outperforms the state-of-the-art air combat methods in terms of both defense and offense ability. Notably, it is discovered that the MAHPG has the ability of Air Combat Tactics Interplay Adaptation, and new operational strategies emerged that surpass the level of experts.","author":[{"dropping-particle":"","family":"Sun","given":"Zhixiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Piao","given":"Haiyin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Zhen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Yiyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhan","given":"Guang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Deyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meng","given":"Guanglei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Hechang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Xing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qu","given":"Bohao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Yuanjie","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Engineering Applications of Artificial Intelligence","id":"ITEM-1","issued":{"date-parts":[["2021","2"]]},"page":"104112","title":"Multi-agent hierarchical policy gradient for Air Combat Tactics emergence via self-play","type":"article-journal","volume":"98"},"uris":["http://www.mendeley.com/documents/?uuid=bc241a28-31de-47d6-903f-e5f73c3388d2"]}],"mendeley":{"formattedCitation":"[107]","plainTextFormattedCitation":"[107]","previouslyFormattedCitation":"[107]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[111];Autonomous air combat in sight ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/electronics9071121","ISSN":"20799292","abstract":"With the development of unmanned aerial vehicle (UAV) and artificial intelligence (AI) technology, Intelligent UAV will be widely used in future autonomous aerial combat. Previous researches on autonomous aerial combat within visual range (WVR) have limitations due to simplifying assumptions, limited robustness, and ignoring sensor errors. In this paper, in order to consider the error of the aircraft sensors, we model the aerial combat WVR as a state-adversarial Markov decision process (SA-MDP), which introduce the small adversarial perturbations on state observations and these perturbations do not alter the environment directly, but can mislead the agent into making suboptimal decisions. Meanwhile, we propose a novel autonomous aerial combat maneuver strategy generation algorithm with high-performance and high-robustness based on state-adversarial deep deterministic policy gradient algorithm (SA-DDPG), which add a robustness regularizers related to an upper bound on performance loss at the actor-network. At the same time, a reward shaping method based on maximum entropy (MaxEnt) inverse reinforcement learning algorithm (IRL) is proposed to improve the aerial combat strategy generation algorithm’s efficiency. Finally, the efficiency of the aerial combat strategy generation algorithm and the performance and robustness of the resulting aerial combat strategy is verified by simulation experiments. Our main contributions are three-fold. First, to introduce the observation errors of UAV, we are modeling air combat as SA-MDP. Second, to make the strategy network of air combat maneuver more robust in the presence of observation errors, we introduce regularizers into the policy gradient. Third, to solve the problem that air combat’s reward function is too sparse, we use MaxEnt IRL to design a shaping reward to accelerate the convergence of SA-DDPG.","author":[{"dropping-particle":"","family":"Kong","given":"Weiren","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Deyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Zhen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Yiyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Kai","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Electronics (Switzerland)","id":"ITEM-1","issued":{"date-parts":[["2020"]]},"title":"UAV autonomous aerial combat maneuver strategy generation with observation error based on state-adversarial deep deterministic policy gradient and inverse reinforcement learning","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=1cc85321-abe0-4b18-9470-a82ee036be49"]}],"mendeley":{"formattedCitation":"[56]","plainTextFormattedCitation":"[56]","previouslyFormattedCitation":"[56]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[60];Air combat strategies generation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/CVIDL51233.2020.000-7","ISBN":"9781728194813","abstract":"The intelligence of the computer-generated force (CGF) is one of the important problems in air combat simulation. The air combat of CGF is modeled as a two-player zero-sum Markov game. An air combat strategies generation method of CGF is proposed to use a multi-agent deep deterministic policy gradient (MADDPG) algorithm. This paper proposes a potential-based reward shaping method to improve the efficiency of the air combat policy generation algorithm. Finally, the efficiency of the air combat policy generation algorithm and the intelligence level of the resulting policy is verified through simulation experiments. The simulation results show that this method has good convergence and better air combat performance with the strategy obtained by the DDPG algorithm.","author":[{"dropping-particle":"","family":"Kong","given":"Weiren","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Deyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Zhen","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings - 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020","id":"ITEM-1","issued":{"date-parts":[["2020","7"]]},"page":"651-655","publisher":"IEEE","title":"Air Combat Strategies Generation of CGF Based on MADDPG and Reward Shaping","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=2c899e2d-5e6b-480c-aa6e-9c43cfa76a8a"]}],"mendeley":{"formattedCitation":"[57]","plainTextFormattedCitation":"[57]","previouslyFormattedCitation":"[57]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[61];Maintaining of high-reliability target tracking in high-altitude dynamic 3D scenarios – various real-time navigation tasks in a dynamic and random electronic warfare environment ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.asoc.2020.106490","ISSN":"15684946","abstract":"To address the challenge of maintaining high robustness of target tracking in a 3D dynamic high-altitude scenario, this paper presents a method to formulate continuous strategic maneuvers for unmanned combat air vehicles (UCAVs) based on deep deterministic policy gradient (DDPG). DDPG is an efficient reinforcement learning approach that helps UCAV perform a variety of navigation tasks in real-time in a dynamic and random electronic warfare environment, and therefore possesses clear advantages over other technologies. First, create a target tracking simulator, Tracker, in the cognitive electronic warfare framework, and conduct a theoretical analysis of maneuvering bias produced by environmental observational errors. Tracker can automatically correlate the maximum physical overload with UCAV's attitude angles and desired movement commands. Second, shape the agent's behavior rewards under the inspiration of vector-based navigation to ensure that the DDPG's output is reliable. Finally, a DRL-based navigation decision framework is employed to validate the simulation for target tracking tasks in different environments and bring excellent results. In terms of behavior assessment, the agile maneuvers mastered by the agent are dissected by time segmentation of high-quality trajectories.","author":[{"dropping-particle":"","family":"You","given":"Shixun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Diao","given":"Ming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gao","given":"Lipeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Fulong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Huan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Soft Computing","id":"ITEM-1","issued":{"date-parts":[["2020","10"]]},"page":"106490","title":"Target tracking strategy using deep deterministic policy gradient","type":"article-journal","volume":"95"},"uris":["http://www.mendeley.com/documents/?uuid=ecd6e2d6-314d-4c72-9698-2cf05d1ced71"]}],"mendeley":{"formattedCitation":"[139]","plainTextFormattedCitation":"[139]","previouslyFormattedCitation":"[139]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[143];Deriving continuous and smooth control values to improve autonomous control accuracy – manoeuvring in aerial combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICCA.2019.8899703","ISBN":"978-1-7281-1164-3","ISSN":"19483457","abstract":"Based on the reinforcement learning theory, this paper establishes the learning model of the autonomous air combat maneuver decision of the UAV. Aiming at the problem that traditional reinforcement learning and DQN algorithm cannot deal with continuous action space, a policy gradient based DDPG algorithm is adopted, which enables the model to output continuous and smooth control values, thus improving the accuracy of autonomous control of UAV. The DDPG algorithm explores the action space by adding noise to the action values. The randomly generated initial action value combination contains a large number of invalid or low-quality individuals, which leads to inefficient learning and localization. Aiming at this problem, this paper proposes to use the optimization algorithm to generate the air combat maneuver action value, and add the optimization action as the initial sample to the DDPG replay buffer. This method filters out a large number of invalid action values and guarantees the correctness of the action value. At the same time, the possibility of exploring diversity is preserved, and the learning efficiency of the DDPG algorithm is improved.","author":[{"dropping-particle":"","family":"Yang","given":"Qiming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhu","given":"Yan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Jiandong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qiao","given":"Shasha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Jieling","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2019 IEEE 15th International Conference on Control and Automation (ICCA)","id":"ITEM-1","issued":{"date-parts":[["2019","7"]]},"page":"37-42","publisher":"IEEE","title":"UAV Air Combat Autonomous Maneuver Decision Based on DDPG Algorithm","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=de8d69e6-9ee2-4d57-a061-984abcf38eda"]}],"mendeley":{"formattedCitation":"[136]","plainTextFormattedCitation":"[136]","previouslyFormattedCitation":"[136]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[140].Combined algorithmsEffectively selecting a favourable manoeuvre action and taking a dominant position according to the opponent's strategy of action in air combat – value function and Q-learning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.12305/j.issn.1001-506X.2021.02.19","ISSN":"1001506X","abstract":"In order to solve the problem of complex combat information and difficult to quickly and accurately perceive situation and make decision in air combat, an algorithm combining game theory and deep reinforcement learning is proposed. Firstly, according to the typical one-to-one air combat process and the standard of random game, a two machine multi-state game model under the condition of red and blue confrontation in close air combat is constructed. Secondly, deep Q network (DQN) is used to deal with the continuous infinite state space of fighter. Then, the Minimax algorithm is used to construct a linear programming to solve the optimal value function of the stage game in each specific state, and the network approximation value function is trained. Finally, the optimal maneuver strategy is obtained according to the output of the network after training. The simulation results show that the algorithm has good adaptability and intelligence for the air combat. It can effectively select the favorable maneuver action and occupy the dominant position according to the air combat opponent's action strategy.","author":[{"dropping-particle":"","family":"Ma","given":"Wen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Hui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Zhuang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Zhiyong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Zhaoxin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Xiliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"14","title":"Close air combat maneuver decision based on deep stochastic game","type":"article-journal","volume":"9"},"uris":["http://www.mendeley.com/documents/?uuid=ad55241f-196e-4c06-8eb7-5361a2fe839b"]}],"mendeley":{"formattedCitation":"[74]","plainTextFormattedCitation":"[74]","previouslyFormattedCitation":"[74]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[78];Intent prediction based on improved dual depth Q network (DDQN) for real-time generation (using temporal difference methods) of intended flight paths for UAVs in a complex air combat environment ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1088/1742-6596/2010/1/012129","ISSN":"1742-6588","abstract":"Intention prediction plays an indispensable role in future informationized air combat, which will help the command and control center to make a more correct decision. In this paper, an intelligent intention prediction approach based on an improved double deep Q network (DDQN) is developed to generate real-time intention flight paths for unmanned aerial vehicles (UAVs) in complex air combat environment. Initially, by introducing an actual topographic map, the different threats of UAVs in different terrains on the map are analyzed, based on which, a terrain environment reward function is constructed. Secondly, by splitting a complete maneuver action into six basic maneuver units and determining the probability value of each unit, a maneuver reward function is obtained. Further, to improve the real-time performance and accuracy of the standard DDQN algorithm, an improved DDQN algorithm is proposed by using temporal-difference (TD) method and binary tree data structure. Finally, the simulation results verify show that the proposed algorithm has achieved better results under complex terrain conditions.","author":[{"dropping-particle":"","family":"Chen","given":"Tianpei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Haotian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Yuhui","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Physics: Conference Series","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","9","1"]]},"page":"012129","title":"Intention prediction of UAVs based on improved DDQN","type":"article-journal","volume":"2010"},"uris":["http://www.mendeley.com/documents/?uuid=ced52683-9f76-47a6-912e-1d2ef4ae0502"]}],"mendeley":{"formattedCitation":"[13]","plainTextFormattedCitation":"[13]","previouslyFormattedCitation":"[13]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[13].Tab. 7Selected applications of DL algorithmsSUPERVISED OR DISCRIMINATIVE DEEP LEARNINGAlgorithmSelected applicationsMultilayer perceptronReal-time detection of the level of faults in a turbine engine disk ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.icte.2021.01.009","ISSN":"24059595","abstract":"Fault detection of rotating engine components in the aircraft engine is a challenging task that must constantly be monitored to provide aviation safety. In this paper, we propose a novel approach based on multi-layer perceptron (MLP) to detect in real time the degree of faults in a turbine engine disk due to a crack. To further improve detection accuracy while reducing computational complexity, the recursive feature elimination (RFE) is applied as a potent feature selection method. Satisfactorily, simulation results show that the proposed framework is robust to changes in operating conditions and outperforms comparative approaches.","author":[{"dropping-particle":"","family":"Garcia","given":"Carla E.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Camana","given":"Mario R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koo","given":"Insoo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"ICT Express","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","3"]]},"note":"06 MLP + aircraft\n\nOparty na uczeniu maszynowym schemat wykrywania uszkodzeń wieloklasowych w tarczach silników turbinowych\n\nWykrywanie uszkodzeń wirujących elementów silnika lotniczego jest trudnym zadaniem, które musi być stale monitorowane w celu zapewnienia bezpieczeństwa lotniczego. W niniejszym artykule zaproponowano nowatorskie podejście oparte na perceptronie wielowarstwowym (MLP) do wykrywania w czasie rzeczywistym stopnia uszkodzenia tarczy silnika turbinowego spowodowanego pęknięciem. Aby jeszcze bardziej zwiększyć dokładność wykrywania przy jednoczesnym zmniejszeniu złożoności obliczeniowej, zastosowano rekursywną eliminację cech (RFE) jako silną metodę selekcji cech. Wyniki symulacji pokazują, że proponowany system jest odporny na zmiany warunków pracy i przewyższa podejścia porównawcze.","page":"15-22","title":"Machine learning-based scheme for multi-class fault detection in turbine engine disks","type":"article-journal","volume":"7"},"uris":["http://www.mendeley.com/documents/?uuid=d3ef824b-5aca-4d99-a2ec-e19b3ec82edf"]}],"mendeley":{"formattedCitation":"[29]","plainTextFormattedCitation":"[29]","previouslyFormattedCitation":"[29]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[33];Aircraft engine thrust control ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1108/AEAT-05-2020-0089","ISSN":"1748-8842","abstract":"Purpose: The purpose of this study is to make artificial neural network (ANN)-based prediction about thrust using the flight control parameters of aircrafts. Design/methodology/approach: In today’s transportation, airplanes have an important place because of their safety, quality and speed. One of the most important parameters affecting the secure flying of aircrafts is the thrust value of aircraft engines. Determining the optimum thrust value should be investigated. If thrust value is less than optimum level, the flight safety runs a risk. Otherwise, fuel consumption goes high and some unwanted vibrations occur that cause uncomfortable flight. In this study, multi-layer perceptron ANNs, which are one of the intelligent optimization methods and frequently used in the literature, are preferred to predict the optimum thrust value during take-off, cruise and landing. The actual flight data, which is taken from the black box of an Airbus A319 aircraft, is used to train ANN models using back propagation algorithms. Velocity, altitude and ambient temperature values of the aircraft are selected as inputs and the thrust value is selected as output. During the training process of ANN, eight different training algorithms with different structures are used to figure out optimum ANN model with minimum error. Findings: Different ANN models were trained using eight different training algorithms. The ANN model with minimum error has multi-layer perceptron structure, which is trained using Levenberg–Marquardt (LM) algorithm. Research limitations/implications: To obtain the ANN structure with minimum error training, process takes more than a day depending on the capacity of a computer for LM training algorithm. But after training process, the trained ANN model produces sufficient output in a few milliseconds. Practical implications: Totally 15,670 input-output data sets are obtained from an Airbus A319 aircraft. 12,889 of them are used as training data and the rest of the data sets, selected randomly are used as test data. Test data sets are never used in training phase, and the obtained results show that the ANN model successfully predicts thrust value using unseen input data. Social implications: The ANN could be used as an alternative method to predict other flight control parameters of aircrafts. Originality/value: To the best of authors’ knowledge, this study is the first example in literature to predict the thrust value of the aircraft using ANN.","author":[{"dropping-particle":"","family":"Yildirim Dalkiran","given":"Fatma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Toraman","given":"Mustafa","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Aircraft Engineering and Aerospace Technology","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","2","16"]]},"note":"07 MLP + aircraft\nPrzewidywanie siły ciągu samolotu przy użyciu sztucznych sieci neuronowych\n\nStreszczenie\nCel - Celem pracy jest prognozowanie ciągu samolotu za pomocą sztucznych sieci neuronowych (ANN) z wykorzystaniem parametrów sterowania lotem\nsamolotów.\nProjekt/metodologia/podejście - We współczesnym transporcie samoloty zajmują ważne miejsce ze względu na ich bezpieczeństwo, jakość i szybkość. Jednym z\nz najważniejszych parametrów wpływających na bezpieczeństwo lotów samolotów jest wartość ciągu silników lotniczych. Wyznaczenie optymalnej wartości ciągu\nnależy zbadać. Jeżeli wartość ciągu jest mniejsza od optymalnej, bezpieczeństwo lotu jest zagrożone. W przeciwnym razie wzrasta zużycie paliwa i pojawiają się\nniepożądane drgania, które powodują dyskomfort lotu. W niniejszej pracy wykorzystano wielowarstwowe perceptrony ANN, które są jedną z inteligentnych metod optymalizacji i są często stosowane w literaturze.\nDo przewidywania optymalnej wartości ciągu podczas startu, przelotu i lądowania preferowane są wielowarstwowe sieci perceptronowe, które są jedną z inteligentnych metod optymalizacji i są często stosowane w literaturze. Rzeczywiste dane lotu\nDane z rzeczywistego lotu, pobrane z czarnej skrzynki samolotu Airbus A319, są wykorzystywane do trenowania modeli ANN przy użyciu algorytmów propagacji wstecznej. Prędkość,\nwartości prędkości, wysokości i temperatury otoczenia samolotu są wybierane jako dane wejściowe, a wartość ciągu jest wybierana jako dane wyjściowe. Podczas procesu trenowania\nANN stosuje się osiem różnych algorytmów szkoleniowych o różnej strukturze w celu wyznaczenia optymalnego modelu ANN o minimalnym błędzie.\nWnioski - wytrenowano różne modele ANN przy użyciu ośmiu różnych algorytmów szkoleniowych. Model ANN o minimalnym błędzie ma strukturę wielowarstwowego\nperceptron, który jest trenowany przy użyciu algorytmu Levenberga-Marquardta (LM).\nOgraniczenia/ implikacje badań - Uzyskanie struktury ANN z minimalnym błędem szkolenia zajmuje więcej niż jeden dzień, w zależności od wydajności komputera dla algorytmu szkolenia LM.\nwydajności komputera dla algorytmu LM. Jednak po zakończeniu procesu treningu wytrenowany model ANN wytwarza wystarczające dane wyjściowe w ciągu kilku milisekund.\nImplikacje praktyczne - Z samolotu Airbus A319 uzyskano łącznie 15 670 zestawów danych wejściowych i wyjściowych. 12 889 z nich jest wykorzystywanych jako dane treningowe\na pozostałe zestawy danych, wybrane losowo, są używane jako dane testowe. Zbiory danych testowych nigdy nie są wykorzystywane w fazie treningowej, a uzyskane wyniki pokazują, że\nże model ANN z powodzeniem przewiduje wartość ciągu przy użyciu niewidzianych danych wejściowych.\nImplikacje społeczne - ANN można wykorzystać jako alternatywną metodę do przewidywania innych parametrów sterowania lotem samolotów.\nOryginalność/wartość - Według najlepszej wiedzy autorów, niniejsze opracowanie jest pierwszym w literaturze przykładem przewidywania wartości ciągu samolotu przy użyciu ANN","page":"35-41","title":"Predicting thrust of aircraft using artificial neural networks","type":"article-journal","volume":"93"},"uris":["http://www.mendeley.com/documents/?uuid=fb0dd905-2cdc-4ccb-ac3c-e323b635f059"]}],"mendeley":{"formattedCitation":"[138]","plainTextFormattedCitation":"[138]","previouslyFormattedCitation":"[138]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[142];Predicting the time required to capture an enemy aircraft in a combat situation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.swevo.2012.03.003","ISSN":"22106502","abstract":"In this paper, a multi-layer perceptron based on 6 important features is proposed for predicting the time needed by our aircraft to approach enemy aircraft when they are in a combat situation. Then the prediction module is incorporated into a SOMO-based decision aid for solving multi-aircraft assignment problems. The problem of assigning our aircraft to meet head-on enemy aircraft in a combat situation is formulated as a constrained resource allocation problem. The objective function of the multi-aircraft assignment problem is a measure for computing the expected overall predominance value of aircraft over enemy aircraft based on four predominance factors. The SOMO algorithm which is an extension version of the SOM algorithm for optimization is executed to find an assignment such that the expected overall predominance value of aircraft can be maximized. Four hundred combat situations are simulated to test the effectiveness of the proposed SOMO-based decision aid. The simulation results demonstrate that the SOMO-based decision aid could provide appealing solutions within 0.5 s even for combat situations with a large number of aircraft. © 2012 Elsevier B.V. All rights reserved.","author":[{"dropping-particle":"","family":"Su","given":"Mu-Chun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lai","given":"Shih-Chang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Shih-Chieh","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"You","given":"Liang-Fu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Swarm and Evolutionary Computation","id":"ITEM-1","issued":{"date-parts":[["2012","10"]]},"page":"39-46","title":"A new approach to multi-aircraft air combat assignments","type":"article-journal","volume":"6"},"uris":["http://www.mendeley.com/documents/?uuid=48f8787e-2e6d-4ea5-8bcb-b7e9a445a46c"]}],"mendeley":{"formattedCitation":"[106]","plainTextFormattedCitation":"[106]","previouslyFormattedCitation":"[106]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[110].Convolutional neural networkAircraft target classification ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3788/LOP56.231006","ISSN":"1006-4125","abstract":"Aiming at the problems of low classification accuracy and less classification types in the classification for aircraft targets by using conventional methods and neural networks, the feasibility of deep convolutional neural network (DCNN) models is studied. To match model capacity, avoid overfitting, and improve classification performance, a nine-layer DCNN model is designed and optimized with stochastic gradient descent optimizer. Six representative types of aircrafts are selected in the dataset, and two regularization cascade methods are proposed to prevent overfitting and speed up the model convergence. Finally, an aircraft classification accuracy of 99. 1 % is achieved, which demonstrates the effectiveness of the DCNN model in aircraft target classification. By analyzing the classification results of the normalized confusion matrix, the accuracy of the self-classification of each type of aircraft is given. In addition, a group of comparative experiments are designed to test the same dataset with the classic AlexNet. The results show that the proposed DCNN model is superior to the AlexNet classification algorithm with an accuracy improvement of 95.5%. This model effectively solves the problem of low accuracy in aircraft target classification at present and proves that the DCNN model has certain reference values and application prospects in the classification research of military and civil aviation aircraft targets.","author":[{"dropping-particle":"","family":"Ma Juncheng","given":"马俊成","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao Hongdong","given":"赵红东","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang Dongxu","given":"杨东旭","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kang Qing","given":"康晴","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Laser & Optoelectronics Progress","id":"ITEM-1","issue":"23","issued":{"date-parts":[["2019"]]},"note":"CNN aircraft","page":"231006","title":"Design and Optimization of Deep Convolutional Neural Network for Aircraft Target Classification","type":"article-journal","volume":"56"},"uris":["http://www.mendeley.com/documents/?uuid=f8b1008a-0989-4d97-a692-bd16e46bc3b8"]}],"mendeley":{"formattedCitation":"[73]","plainTextFormattedCitation":"[73]","previouslyFormattedCitation":"[73]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[77];Adverse event precursor – Identification of factors relevant to an adverse event and their signatures that can be tracked during flight ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/1.I010971","ISSN":"2327-3097","abstract":"In recent years, there has been a rapid growth in applying machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen before the adverse event itself. Therefore, precursor mining provides many benefits, including the identification of factors relevant to the occurrence of an adverse event and their signatures, which can be tracked throughout a flight to alert the operators of the potential for an adverse event in the future. This work proposes using the multiple-instance learning framework, a weakly supervised learning task, combined with a carefully designed binary classifier leveraging a Multi-Head Convolutional Neural Network–Recurrent Neural Network (MHCNN-RNN) architecture. Multiclass classifiers are then created and compared, enabling the prediction of different adverse events for any given flight by combining binary classifiers, and by modifying the MHCNN-RNN to handle multiple outputs. Results obtained showed that the multiple binary classifiers perform better and are able to accurately forecast high-speed and high-path-angle events during the approach phase. Multiple binary classifiers are also capable of determining the aircraft parameters that are correlated to these events. The identified parameters can be considered precursors to the events and may be studied/tracked further to prevent these events in the future.","author":[{"dropping-particle":"","family":"Bleu Laine","given":"Marc-Henri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Puranik","given":"Tejas G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mavris","given":"Dimitri N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Matthews","given":"Bryan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Aerospace Information Systems","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2022","1"]]},"page":"22-36","title":"Multiclass Multiple-Instance Learning for Predicting Precursors to Aviation Safety Events","type":"article-journal","volume":"19"},"uris":["http://www.mendeley.com/documents/?uuid=b1b8735b-7e3c-4e6d-9f3b-22c3753fb0a6"]}],"mendeley":{"formattedCitation":"[7]","plainTextFormattedCitation":"[7]","previouslyFormattedCitation":"[7]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[7];Terrain reconnaissance and warning system – low altitude flight ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9781713830016","abstract":"Aviation statistics identify collision with terrain and obstacles as a leading cause of helicopter accidents. Assisting helicopter pilots in detecting the presence of obstacles can partly mitigate the risk of collisions. However, only a limited number of helicopters in operation have an installed helicopter terrain awareness and warning system (HTAWS), while the cost of active obstacle warning systems remains prohibitive for many civil operators. In this work, we apply machine learning to automate obstacle detection and classification in combination with any commercially-available airborne optical sensor. While numerous techniques for learning-based object detection have appeared in the literature, many of them are data- and computation-intensive. Our approach seeks to balance the performance in regards to the detection and classification accuracy on the one hand, and the amount of training data and runtime performance on the other hand. Specifically, our approach combines the invariant feature extraction ability of pre-trained deep Convolutional Neural Networks (CNNs) and the high-speed training and classification ability of a novel, proprietary frequency-domain Support Vector Machine (SVM) method. In this paper, we present the CNN+SVM method for efficient obstacle detection and classification. We describe the experimental setup comprising datasets of pre-defined classes of obstacles - pylons, chimneys, antennas, towers, wind turbines, flying aircraft - from airborne video sequences of low-altitude helicopter flight. We analyze the performance results using average precision, average recall, and runtime performance metrics on representative test data. Finally, we present a simple architecture for a real-time, on-board evaluation of automatic vision-based obstacle detection.","author":[{"dropping-particle":"","family":"Ammalladene-Venkata","given":"Manogna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Halbe","given":"Omkar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seidel","given":"Christian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Groitl","given":"Christine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stahl","given":"Christoph","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"77th Annual Vertical Flight Society Forum and Technology Display, FORUM 2021: The Future of Vertical Flight","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"title":"Deep Learning based obstacle awareness from airborne optical sensors","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=8b9c33a3-804a-4342-a114-01cbc86375c6"]}],"mendeley":{"formattedCitation":"[3]","plainTextFormattedCitation":"[3]","previouslyFormattedCitation":"[3]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[3];Flight approach phases – risk prediction and decision support ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1115/1.4049992","ISSN":"1530-9827","abstract":"The maintenance and improvement of safety are among the most critical concerns in civil aviation operations. Due to the increased availability of data and improvements in computing power, applying artificial intelligence technologies to reduce risk in aviation safety has gained momentum. In this paper, a framework is developed to build a predictive model of future aircraft trajectory that can be utilized online to assist air crews in their decision-making during approach. Flight data parameters from the approach phase between certain approach altitudes (also called gates) are utilized for training an offline model that predicts the aircraft’s ground speed at future points. This model is developed by combining convolutional neural networks (CNNs) and long short-term memory (LSTM) layers. Due to the myriad of model combinations possible, hyperband algorithm is used to automate the hyperparameter tuning process to choose the best possible model. The validated offline model can then be used to predict the aircraft’s future states and provide decision-support to air crews. The method is demonstrated using publicly available Flight Operations Quality Assurance (FOQA) data from the National Aeronautics and Space Administration (NASA). The developed model can predict the ground speed at an accuracy between 1.27% and 2.69% relative root-mean-square error. A safety score is also evaluated considering the upper and lower bounds of variation observed within the available data set. Thus, the developed model represents an improved performance over existing techniques in literature and shows significant promise for decision-support in aviation operations.","author":[{"dropping-particle":"","family":"Lee","given":"HyunKi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Puranik","given":"Tejas G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mavris","given":"Dimitri N.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Computing and Information Science in Engineering","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2021","8","1"]]},"title":"Deep Spatio-Temporal Neural Networks for Risk Prediction and Decision Support in Aviation Operations","type":"article-journal","volume":"21"},"uris":["http://www.mendeley.com/documents/?uuid=5efb169c-a372-40f3-bf06-bae040cfbdcc"]}],"mendeley":{"formattedCitation":"[62]","plainTextFormattedCitation":"[62]","previouslyFormattedCitation":"[62]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[66]Recurrent neural networkAircraft manoeuvres – determining aircraft position, heading, acceleration, and other information ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.18178/wcse.2019.06.050","ISBN":"9789811416842","abstract":"Aviation surveillance information fusion is aimed at merging the detection data from multiple sources of the same target aircraft to obtain more accurate monitoring information, including aircraft position, heading, acceleration and other information. The traditional Kalman filter-based fusion technology has shortcomings, such as poor integration in the maneuvering state, and it takes a lot of manpower and material resources to repeat the adjustment. Therefore, this paper uses the recurrent neural network to conduct the experiment of aviation surveillance information fusion. Firstly, the recurrent neural network is used to identify the maneuver state of the aircraft, and the weighted least squares method is used to predict the position of the aircraft according to the maneuvering state, so as to obtain the monitoring information of each radar at the same time. After that, the recurrent neural network model is used to fuse the monitoring information of multiple radars. The experimental results show that the maneuvering state discriminant model based on recurrent neural network can effectively identify the maneuvering state. The least square method based on maneuvering state can accurately predict the position of the aircraft. The aeronautical surveillance information fusion model based on recurrent neural network can also obtain more accurate fusion results. The whole process includes four parts: preprocessing, maneuver status discrimination, position prediction and information fusion. The total time is about 500ms.","author":[{"dropping-particle":"","family":"Gao","given":"Zhanchun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Song","given":"Anyu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"publisher":"WCSE","title":"Aviation Surveillance Information Fusion Technology Based on Recurrent Neural Network","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=d5976614-a7a2-460b-b7f2-fbfda3d7ffcb"]}],"mendeley":{"formattedCitation":"[28]","plainTextFormattedCitation":"[28]","previouslyFormattedCitation":"[28]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[32];Aircraft engine vibration prediction ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.asoc.2018.09.013","ISSN":"15684946","abstract":"This article expands on research that has been done to develop a recurrent neural network (RNN) capable of predicting aircraft engine vibrations using long short-term memory (LSTM) neurons. LSTM RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, making this approach ungeneralizable across multiple engines. In initial work, multiple LSTM RNN architectures were proposed, evaluated and compared. This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network. A parallelized version of the ACO neuroevolution algorithm has been developed and the evolved LSTM RNNs were compared to the previously used fixed topology. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, evolving 1000 different LSTM cell structures using 208 cores over 21 days. The new evolved LSTM cells showed an improvement of 1.34%, reducing the mean prediction error from 5.61% to 4.27% when predicting excessive engine vibrations 10 s in the future, while at the same time dramatically reducing the number of weights from 21,170 to 13,150. The optimized LSTM also performed significantly better than traditional Nonlinear Output Error (NOE), Nonlinear AutoRegression with eXogenous (NARX) inputs, and Nonlinear Box–Jenkins (NBJ) models, which only reached error rates of 15.73%, 12.06% and 15.05%, respectively. Furthermore, the LSTM regularization method was used to validate the ACO. The ACO LSTM out performed the regularized LSTM by 3.35%. The NOE, NARX, and NBJ models were also regularized for cross validation, and the mean prediction errors were 8.70%, 9.40%, and 9.43% respectively, which gives credit for the ant colony optimized LSTM RNN.","author":[{"dropping-particle":"","family":"ElSaid","given":"AbdElRahman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jamiy","given":"Fatima","non-dropping-particle":"El","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Higgins","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wild","given":"Brandon","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Desell","given":"Travis","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Soft Computing","id":"ITEM-1","issued":{"date-parts":[["2018","12","10"]]},"page":"969-991","title":"Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration","type":"article-journal","volume":"73"},"uris":["http://www.mendeley.com/documents/?uuid=7b285b49-085a-4dbe-8566-52cf55d8e960"]}],"mendeley":{"formattedCitation":"[23]","plainTextFormattedCitation":"[23]","previouslyFormattedCitation":"[23]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[27];Flight dynamics of a highly manoeuvrable aircraft ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.cja.2014.03.017","ISSN":"10009361","abstract":"In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks (ANNs). In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks (RNNs) and a proposed structure called hybrid combined recurrent neural network have been used and compared. In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.","author":[{"dropping-particle":"","family":"Roudbari","given":"Alireza","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saghafi","given":"Fariborz","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Chinese Journal of Aeronautics","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2014","8"]]},"page":"759-771","title":"Intelligent modeling and identification of aircraft nonlinear flight","type":"article-journal","volume":"27"},"uris":["http://www.mendeley.com/documents/?uuid=1a7cfa70-0492-4d32-8ae1-6e0a6fe5bfa0"]}],"mendeley":{"formattedCitation":"[93]","plainTextFormattedCitation":"[93]","previouslyFormattedCitation":"[93]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[97].UNSUPERVISED OR GENERATIVE DEEP LEARNINGGenerative adversarial networkTrajectory planning ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/6.2021-0775","ISBN":"978-1-62410-609-5","abstract":"Flight trajectories expectedly differ from each other, yet they follow a set of patterns that are flown previously and optimized for different conditions. These patterns may or may not be trivial and can be influenced by a number of effects, including airspace utilization, con-trollers’ cognitive complexity, weather, NOTAMs. Due to the high variance, it is a rather compelling task to accurately classify trajectories into a desired set of categories based purely on its statistical properties. Therefore, we propose a hybrid methodology to generalize over the flight trajectories and decide whether they are abnormal or not: a) with a more statistical approach considering the time-based features of the trajectories, and b) with a more pattern based approach. The statistical approach utilizing the LSTM autoencoders enables to train the model with the history and rapidly predict the class of the flight, inherently considering the time-based features of a flight trajectory. The pattern generalization is achieved via a Generative Adversarial Network (GAN), which generates samples that look like real trajectories. The abnormality detection in the patterns is done with an autoencoder part of GAN that learns how to reconstruct typical sequentially dependent trajectory data after applying dimension reduction. While LSTM autoencoders enable to capture the class of a flight with short time windows, GAN predicts pattern anomaly with larger window observations. Hence, this heterogeneous approach allows us to sweep the airspace with asynchronous frequencies. The obtained results show that the proposed architecture is quite capable of the abnormality classification of the trajectories as it accomplishes to accurately detect simulated fighter aircraft trajectories in commercial flight heavy airspaces, and the generative model provides good utility to capture the pattern-based distribution of flight trajectories.","author":[{"dropping-particle":"","family":"Aksoy","given":"Muhammet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ozdemir","given":"Orhan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guner","given":"Guney","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Baspinar","given":"Baris","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koyuncu","given":"Emre","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AIAA Scitech 2021 Forum","id":"ITEM-1","issued":{"date-parts":[["2021","1","11"]]},"publisher":"American Institute of Aeronautics and Astronautics","publisher-place":"Reston, Virginia","title":"Flight Trajectory Pattern Generalization and Abnormal Flight Detection with Generative Adversarial Network","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=646952fb-90a0-41f0-a3f5-79e03bc7937e"]}],"mendeley":{"formattedCitation":"[2]","plainTextFormattedCitation":"[2]","previouslyFormattedCitation":"[2]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[2];Detection of dynamic obstacles in the air and on the runway – applications in a HUD (Head-Up Display) system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.26583/sv.11.4.04","ISSN":"2079-3537","abstract":"Long range infrared cameras may provide increasing crew situational awareness in limited vision and night conditions. Similar cameras are installed in modern civil aircraft's as part of an improved vision system. Correct thermal image interpretation by the crew requires certain experience, due to the fact that view of the scene very different from the visible range and may change within time of day and season. This paper discusses the deep generative-adversary neural network to automatically convert thermal images to semantically similar color images of the visible range.","author":[{"dropping-particle":"","family":"Kniaz","given":"V.V.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kozyrev","given":"M.I.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bordodymov","given":"A.N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Papazian","given":"A.V.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yakhanov","given":"A.V.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Scientific Visualization","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2019"]]},"title":"Segmentation and visualization of obstacles for the enhanced vision system using generative adversarial networks","type":"article-journal","volume":"11"},"uris":["http://www.mendeley.com/documents/?uuid=460451ac-02c3-4a37-aa79-fc7474639dd8"]}],"mendeley":{"formattedCitation":"[54]","plainTextFormattedCitation":"[54]","previouslyFormattedCitation":"[54]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[58];Synthetic aperture radar for high-resolution images of stationary objects ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICPR48806.2021.9413023","ISBN":"978-1-7281-8808-9","ISSN":"10514651","abstract":"Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches. To address this problem, this paper proposes and evaluates a novel data augmentation approach that leverages the more readily available visible-band imagery via a generative domain transfer model. The model can synthesise large volumes of non-visible domain imagery by image-to-image (I2I) translation from the visible image domain. Furthermore, we show that the generation of interpolated mixed class (non-visible domain) image examples via our novel Conditional CycleGAN Mixup Augmentation (C2GMA) methodology can lead to a significant improvement in the quality of non-visible domain classification tasks that otherwise suffer due to limited data availability. Focusing on classification within the Synthetic Aperture Radar (SAR) domain, our approach is evaluated on a variation of the Statoil/C-CORE Iceberg Classifier Challenge dataset and achieves 75.4% accuracy, demonstrating a significant improvement when compared against traditional data augmentation strategies (Rotation, Mixup, and MixCycleGAN).","author":[{"dropping-particle":"","family":"Sasaki","given":"Hiroshi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Willcocks","given":"Chris G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Breckon","given":"Toby P.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2020 25th International Conference on Pattern Recognition (ICPR)","id":"ITEM-1","issued":{"date-parts":[["2021","1","10"]]},"page":"5083-5090","publisher":"IEEE","title":"Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=d312867d-4934-428f-95db-3112906b8859"]}],"mendeley":{"formattedCitation":"[96]","plainTextFormattedCitation":"[96]","previouslyFormattedCitation":"[96]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[100].AutoencoderAirspace tracking – detection and prediction of movement to indicate abnormal, dangerous situations in the airspace ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-030-88081-1_58","ISBN":"9783030880804","ISSN":"16113349","abstract":"With the rapid increase in the number of flights all over the world, the management and control of flight operations has become difficult in recent years. Moreover, the expectations for the aviation sector indicate that this increase will continue in the upcoming years. Therefore, safer and systematic monitoring systems by eliminating the requirement of human-dependent tracking during the air travel of an aircraft and automating the detection of abnormal situations has become a major problem in aviation sector. With the recent advances in artificial intelligence, a safer and systematic tracking system for controlling the airspace by eliminating the need for human-dependent tracking during the flight of aircraft in the air has become possible. In this study, we aimed to create a system that detects and predicts movements to indicate abnormal, dangerous situations in the airspace by monitoring radar flight data using machine learning and deep learning techniques. We applied two different methods, i.e., Proximity Based kNN and Auto Encoder We used real-life historical radar flight data set which consists of Flight Radar 24 data were converted from ADS-B messages for learning. We created simulation data and used this data for testing and validation for our trained model. Within the scope of this project, we also developed a system to monitor air traffic through radar tracks with our model and present the abnormal situations to the user through a visual interface for decision support. In this visualization, we present the abnormal situations if one of the algorithms labeled as anomaly. Results for both methods have shown that our findings were similar to the real-life predictions.","author":[{"dropping-particle":"","family":"Taşdelen","given":"Osman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Çarkacioglu","given":"Levent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Töreyin","given":"Behçet Uğur","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","id":"ITEM-1","issued":{"date-parts":[["2021"]]},"page":"771-783","title":"Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=d5f9f403-88f4-4812-9596-43e4e1b5d368"]}],"mendeley":{"formattedCitation":"[111]","plainTextFormattedCitation":"[111]","previouslyFormattedCitation":"[111]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[115];Aircraft complex system anomaly detection and classification ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1177/1748006X211001979","ISSN":"1748-006X","abstract":"Big data analytics with deep learning approach have attracted increasing attention in transportation engineering, involving operations, maintenance, and safety. In commercial aviation sectors, operational, and maintenance data produced on modern aircraft is increasing exponentially, and predictive analysis of these data is an exciting and promising field in aviation maintenance, which has a potential to revolutionize aerospace maintenance industry. This study illustrates the state-of-the-art applications of deep learning in big data analytics for predictive maintenance and a real-world case study for commercial aircraft. A Long Short-Term Memory network based Auto-Encoders (LSTM-AE) is proposed for complex aircraft system fault detection and classification, which makes use of the raw time-series data from heterogeneous sensors. The proposed method uses nominal time-series samples corresponding to healthy behavior of the system to learn a reconstruction model based on LSTM-AE framework. Then the system health index (HI) and fault feature vectors are derived from the reconstruction error matrix for fault detection and classification. The proposed method is demonstrated on a real-world data set from a commercial aircraft fleet. The typical PCV faults as well as the 390 F sensor and 450 F sensor faults due to sense line air leakage are successfully detected and distinguished based on the extracted features. The case study results show that the computed HI can effectively characterize the health state of the aircraft system and different fault types can be identified with high confidence, which is helpful for line fault troubleshooting.","author":[{"dropping-particle":"","family":"Ning","given":"Shungang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sun","given":"Jianzhong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Cui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yi","given":"Yang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2021","10","14"]]},"page":"923-940","title":"Applications of deep learning in big data analytics for aircraft complex system anomaly detection","type":"article-journal","volume":"235"},"uris":["http://www.mendeley.com/documents/?uuid=966e3c1e-1284-4f47-90da-9c1046f2a3ed"]}],"mendeley":{"formattedCitation":"[81]","plainTextFormattedCitation":"[81]","previouslyFormattedCitation":"[81]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[85];Failure analysis of flight control actuators ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.measurement.2021.109235","ISSN":"02632241","abstract":"The actuator of aircraft is the direct executor of flight control signal. When it fails, the aircraft will lose control and even crash. However, due to the low frequency of fault occurrence and the lack of real fault data, the amount of health data and fault data is seriously imbalanced. The research on fault diagnosis of aircraft actuator under imbalanced data has practical engineering significance. Inspired by the ensemble model for solving the problem of imbalanced data classification, this paper proposes an innovative ensemble model called ensemble deep auto-encoder based extreme learning machine (DELM-AE). DELM-AE is a deep network constructed by multi-layer extreme learning machine based auto-encoder, which has the advantages of strong feature mining ability, high accuracy and fast speed. Firstly, the fault simulation model of flight control actuator is established, and then residual analysis and feature extraction are carried out on the data. Finally, compared with other common shallow model (extreme learning machine, support vector machine, back propagation neural network), ensemble model (random forest) and deep networks, the advantages of the proposed method in accuracy, processing speed, robustness and ability to deal with imbalanced data are proved.","author":[{"dropping-particle":"","family":"Jia","given":"Zhen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Zhenbao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cai","given":"Yongyi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Measurement","id":"ITEM-1","issued":{"date-parts":[["2021","5"]]},"page":"109235","title":"A novel fault diagnosis method for aircraft actuator based on ensemble model","type":"article-journal","volume":"176"},"uris":["http://www.mendeley.com/documents/?uuid=e3f65914-703e-49fe-81a1-0743a0caad19"]}],"mendeley":{"formattedCitation":"[48]","plainTextFormattedCitation":"[48]","previouslyFormattedCitation":"[48]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[52];Aircraft design, dynamics, and control ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/TAES.2021.3056086","ISSN":"0018-9251","abstract":"In recent decades, deep learning (DL) has become a rapidly growing research direction, redefining the state-of-the-art performances in a wide range of techniques, such as object detection and speech recognition. In the aircraft design, dynamics, and control field, many works hinge on the information-rich data-driven approach, which includes the fusion-based prognostic and health management, the airliner's flight safety monitoring, intelligent sensing, and flight control systems development. While DL provides great potentials to solve these data-driven problems, a systematic review and discussion as to how the DL has been/can be used for these problems are still missing in relation to the rapidly developing and widely used DL techniques. In this article, we aim to address this urgent issue to provide a timely overview of the state-of-the-art for applying DL to the aircraft design, dynamics, and control field. In particular, we briefly introduce five representative DL methods, i.e., deep neural network, deep autoencoder, deep belief network, convolutional neural network, and recurrent neural network. Mathematical definitions for each method are presented, and illustrative applications are also discussed. We then review the existing DL-based works that have appeared in the aircraft design, dynamics, and control field. The review efforts are divided into two major groups, i.e., the own-ship aircraft modeling, wherein the works have been/can be implemented online for the aircraft design/dynamics/control, and other airplanes research works, wherein DL-based schemes provide offline monitoring of the aircraft operation. We then summarize the data sources and DL architectures. Referring to the experiences of DL research works/techniques development in other related fields, future opportunities, challenges, and potential solutions for implementing DL in the aircraft design, dynamics, and control field are also discussed.","author":[{"dropping-particle":"","family":"Dong","given":"Yiqun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tao","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Youmin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ai","given":"Jianliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Transactions on Aerospace and Electronic Systems","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2021","8"]]},"page":"2346-2368","title":"Deep Learning in Aircraft Design, Dynamics, and Control: Review and Prospects","type":"article-journal","volume":"57"},"uris":["http://www.mendeley.com/documents/?uuid=a661f1db-88a8-4045-9752-90faecc66800"]}],"mendeley":{"formattedCitation":"[21]","plainTextFormattedCitation":"[21]","previouslyFormattedCitation":"[21]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[25].Self-organizing (Kohonen) mapCondition assessment and diagnosis of a turbojet engine during operation using thermal imaging ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/app9112253","ISSN":"2076-3417","abstract":"There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.","author":[{"dropping-particle":"","family":"Andoga","given":"Rudolf","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Főző","given":"Ladislav","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schrötter","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Češkovič","given":"Marek","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Szabo","given":"Stanislav","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bréda","given":"Róbert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schreiner","given":"Michal","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Sciences","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2019","5","31"]]},"page":"2253","title":"Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines","type":"article-journal","volume":"9"},"uris":["http://www.mendeley.com/documents/?uuid=18d6f034-03da-4d2e-86af-9536c63e769b"]}],"mendeley":{"formattedCitation":"[4]","plainTextFormattedCitation":"[4]","previouslyFormattedCitation":"[4]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[4];Measuring signals from aircraft sensors during flight ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/WSOM.2017.8020013","ISBN":"978-1-5090-6638-4","abstract":"Multiple signals are measured by sensors during a flight or a test bench and their analysis represent a big interest for engineers. These signals are actually multivariate time series created by the sensors present on the aircraft engines. Each of them can be decomposed into series of stabilized phases, well known by the experts, and transient phases. Transient phases are merely explored but they reveal a lot of information when the engine is running. The aim of our project is converting these time series into a succession of labels, designing transient and stabilized phases. This transformation of the data will allow to derive several perspectives: on one hand, tracking similar behaviours or patterns seen during a flight; on the other, discovering hidden structures. Labelling signals coming from the engines of the aircraft also helps in the detection of frequent or rare sequences during a flight. Statistical analysis and scoring are more convenient with this new representation. This manuscript proposes a methodology for automatically indexing all engine transient phases. First, the algorithm computes the start and the end points of each phase and builds a new database of transient patterns. Second, the transient patterns are clustered into a small number of typologies, which will provide the labels. The clustering is implemented with Self-Organizing Maps [SOM]. All algorithms are applied on real flight measurements with a validation of the results from expert knowledge.","author":[{"dropping-particle":"","family":"Faure","given":"Cynthia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Olteanu","given":"Madalina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bardet","given":"Jean-Marc","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lacaille","given":"Jerome","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","id":"ITEM-1","issued":{"date-parts":[["2017","6"]]},"page":"1-8","publisher":"IEEE","title":"Using self-organizing maps for clustering anc labelling aircraft engine data phases","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=dea64790-9b7e-4a91-8d7e-a113fd9d28e2"]}],"mendeley":{"formattedCitation":"[24]","plainTextFormattedCitation":"[24]","previouslyFormattedCitation":"[24]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[28];Engine measurements based on variables such as core speed, oil pressure, and quantity, fan speed, etc., along with environmental variables such as external temperature, altitude, aircraft speed ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-3-642-02397-2_5","ISBN":"3642023967","ISSN":"03029743","abstract":"Aircraft engines are designed to be used during several tens of years. Their maintenance is a challenging and costly task, for obvious security reasons. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too. The maintenance can be improved if an efficient procedure for the prediction of failures is implemented. The primary source of information on the health of the engines comes from measurement during flights. Several variables such as the core speed, the oil pressure and quantity, the fan speed, etc. are measured, together with environmental variables such as the outside temperature, altitude, aircraft speed, etc. In this paper, we describe the design of a procedure aiming at visualizing successive data measured on aircraft engines. The data are multi-dimensional measurements on the engines, which are projected on a self-organizing map in order to allow us to follow the trajectories of these data over time. The trajectories consist in a succession of points on the map, each of them corresponding to the two-dimensional projection of the multi-dimensional vector of engine measurements. Analyzing the trajectories aims at visualizing any deviation from a normal behavior, making it possible to anticipate an operation failure. However rough engine measurements are inappropriate for such an analysis; they are indeed influenced by external conditions, and may in addition vary between engines. In this work, we first process the data by a General Linear Model (GLM), to eliminate the effect of engines and of measured environmental conditions. The residuals are then used as inputs to a Self-Organizing Map for the easy visualization of trajectories. © 2009 Springer-Verlag Berlin Heidelberg.","author":[{"dropping-particle":"","family":"Cottrell","given":"Marie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gaubert","given":"Patrice","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eloy","given":"Cédric","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"François","given":"Damien","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hallaux","given":"Geoffroy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lacaille","given":"Jérôme","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Verleysen","given":"Michel","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","id":"ITEM-1","issued":{"date-parts":[["2009"]]},"page":"37-44","title":"Fault Prediction in Aircraft Engines Using Self-Organizing Maps","type":"chapter"},"uris":["http://www.mendeley.com/documents/?uuid=1d7d1cd1-4bd7-4493-8b3b-6d28c44d7c62"]}],"mendeley":{"formattedCitation":"[16]","plainTextFormattedCitation":"[16]","previouslyFormattedCitation":"[16]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[16].Restricted Boltzmann machineInertial navigation system – error parameter estimation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/s19173682","ISSN":"14248220","PMID":"31450626","abstract":"To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method.","author":[{"dropping-particle":"","family":"Guo","given":"Weilin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xian","given":"Yong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Daqiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Bing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ren","given":"Leliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Sensors (Switzerland)","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"Hybrid IRBM-BPNN approach for error parameter estimation of SINS on aircraft","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=c77162e9-2950-40d4-a6dd-7421adb3ac08"]}],"mendeley":{"formattedCitation":"[35]","plainTextFormattedCitation":"[35]","previouslyFormattedCitation":"[35]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[39];Predictive maintenance of an aircraft component ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.asoc.2020.106278","ISSN":"15684946","abstract":"In this work, we endeavor to investigate and propose a novel unsupervised online learning algorithm, namely the Online Restricted Boltzmann Machine (O-RBM). The O-RBM is able to construct and adapt the architecture of a Restricted Boltzmann Machine (RBM) artificial neural network, according to the statistics of the streaming input data. Specifically, for a training data that is not fully available at the onset of training, the proposed O-RBM begins with a single neuron in the hidden layer of the RBM, progressively adds and suitably adapts the network to account for the variations in streaming data distributions. Such an unsupervised learning helps to effectively model the probability distribution of the entire data stream, and generates robust features. We will demonstrate that such unsupervised representations can be used for discriminative classifications on a set of multi-category and binary classification problems for unstructured image and structured signal data sets, having varying degrees of class-imbalance. We first demonstrate the O-RBM algorithm and characterize the network evolution using the simple and conventional multi-class MNIST image dataset, aimed at recognizing hand-written digit. We then benchmark O-RBM performance to other machine learning, neural network and Class RBM techniques using a number of public non-stationary datasets. Finally, we study the performance of the O-RBM on a real-world problem involving predictive maintenance of an aircraft component using time series data. In all these studies, it is observed that the O-RBM converges to a stable, concise network architecture, wherein individual neurons are inherently discriminative to the class labels despite unsupervised training. It can be observed from the performance results that on an average O-RBM improves accuracy by 2.5%–3% over conventional offline batch learning techniques while requiring at least 24%–70% fewer neurons.","author":[{"dropping-particle":"","family":"Savitha","given":"Ramasamy","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ambikapathi","given":"ArulMurugan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rajaraman","given":"Kanagasabai","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Applied Soft Computing","id":"ITEM-1","issued":{"date-parts":[["2020","7"]]},"page":"106278","title":"Online RBM: Growing Restricted Boltzmann Machine on the fly for unsupervised representation","type":"article-journal","volume":"92"},"uris":["http://www.mendeley.com/documents/?uuid=2d9da405-4b8a-4866-8d10-2cffc63b1cbc"]}],"mendeley":{"formattedCitation":"[97]","plainTextFormattedCitation":"[97]","previouslyFormattedCitation":"[97]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[101];Aircraft auxiliary power unit (APU) – performance sensing data prediction ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/s19183935","ISSN":"1424-8220","PMID":"31547292","abstract":"The aircraft auxiliary power unit (APU) is responsible for environmental control in the cabin and the main engines starting the aircraft. The prediction of its performance sensing data is significant for condition-based maintenance. As a complex system, its performance sensing data have a typically nonlinear feature. In order to monitor this process, a model with strong nonlinear fitting ability needs to be formulated. A neural network has advantages of solving a nonlinear problem. Compared with the traditional back propagation neural network algorithm, an extreme learning machine (ELM) has features of a faster learning speed and better generalization performance. To enhance the training of the neural network with a back propagation algorithm, an ELM is employed to predict the performance sensing data of the APU in this study. However, the randomly generated weights and thresholds of the ELM often may result in unstable prediction results. To address this problem, a restricted Boltzmann machine (RBM) is utilized to optimize the ELM. In this way, a stable performance parameter prediction model of the APU can be obtained and better performance parameter prediction results can be achieved. The proposed method is evaluated by the real APU sensing data of China Southern Airlines Company Limited Shenyang Maintenance Base. Experimental results show that the optimized ELM with an RBM is more stable and can obtain more accurate prediction results.","author":[{"dropping-particle":"","family":"Liu","given":"Xiaolei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Liansheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Lulu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guo","given":"Qing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Peng","given":"Xiyuan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Sensors","id":"ITEM-1","issue":"18","issued":{"date-parts":[["2019","9","12"]]},"page":"3935","title":"Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine","type":"article-journal","volume":"19"},"uris":["http://www.mendeley.com/documents/?uuid=9c30cbb0-0a20-40e8-929e-babfe62c4106"]}],"mendeley":{"formattedCitation":"[70]","plainTextFormattedCitation":"[70]","previouslyFormattedCitation":"[70]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[74].Deep belief networkFault detection in the aircraft fuel system ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.neucom.2017.01.032","ISSN":"18728286","abstract":"Fault diagnosis for aircraft fuel system can not only improve flight security, but also reduce the huge cost due to regular maintenance. It remains a problem because of the complicated system and the heterogeneous failure modes, especially the different failure modes that have similar impacts on the system. This paper uses the deep quantum inspired neural network (DQINN) which is an improved deep quantum network (DQN) to solve such problem. This method is the combination of classical deep belief network (DBN) and quantum inspired neural network (QINN). For the purpose of inheriting the advantages of DBN and QINN, the structure of DQINN is built in a new fashion. From a system perspective, the DQINN is constructed by the linear superposition of multiple DBNs with quantum intervals in the last hidden layer. Experiments conducted on standard datasets show that DQINN outperforms other three classical algorithms. Finally, a normal model of aircraft fuel system is built and four kinds of common failure modes of the core components are injected into this model, respectively. And the DQINN is applied to the aircraft fuel system fault diagnosis.","author":[{"dropping-particle":"","family":"Gao","given":"Zehai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ma","given":"Cunbao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Song","given":"Dong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Yang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurocomputing","id":"ITEM-1","issued":{"date-parts":[["2017"]]},"title":"Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis","type":"article-journal"},"uris":["http://www.mendeley.com/documents/?uuid=f84e2f85-28ad-4352-97e1-d12ea4804145"]}],"mendeley":{"formattedCitation":"[27]","plainTextFormattedCitation":"[27]","previouslyFormattedCitation":"[27]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[31];Fault diagnosis of essential aircraft parts ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3901/JME.2019.07.027","ISSN":"0577-6686","abstract":"The key mechanical parts of aircraft will inevitably generate multifarious faults due to the severe working conditions with high temperature, fast speed, heavy load, large disturbance and strong impact. The faults of aircraft key parts often show some characteristics such as weakness, randomness, coupling, diversity, uncertainty and so on. Therefore, using the traditional methods based on advanced signal processing techniques, feature extraction and feature selection, it is a great challenge to diagnose the various faults of aircraft key parts. As a very promising tool in the field of intelligent fault diagnosis, deep learning can largely get rid of the dependence on manual feature design and engineering diagnosis experience, which can directly establish accurate mapping relationships between the raw data and various operation conditions. The basic theory of four kinds of popular deep learning models are briefly introduced, including deep belief network, convolutional neural network, deep auto-encoder and recurrent neural network. The recent research work of deep learning on fault diagnosis is summarized. These four deep models are respectively used for intelligent fault diagnosis and prognosis of mechanical parts. The results confirm that deep learning models are able to automatically capture the representative information from the massive measured data through multiple feature transformations, and directly establish the accurate mapping relationships between the raw data and various operation conditions.","author":[{"dropping-particle":"","family":"Jiang","given":"Hongkai","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Mechanical Engineering","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2019"]]},"page":"27","title":"Deep Learning Theory with Application in Intelligent Fault Diagnosis of Aircraft","type":"article-journal","volume":"55"},"uris":["http://www.mendeley.com/documents/?uuid=ea7c174e-943f-44aa-a951-83735f29a61c"]}],"mendeley":{"formattedCitation":"[50]","plainTextFormattedCitation":"[50]","previouslyFormattedCitation":"[50]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[54];Identify hidden features responsible for system failure – particle filter in a turbofan engine ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.neucom.2019.07.075","ISSN":"09252312","abstract":"The prediction of remaining useful life plays a significant role in prognostics and health management, which can give helpful reference for maintenance. The construction of health indicator is an important part of prediction, and a suitable health indicator can reflect the degradation degree of the system and provide useful information for remaining useful life estimation. This paper presents an unsupervised health indicator construction method based on deep belief network and combines it with particle filter for remaining useful life prediction. Firstly, deep belief network is trained to extract the hidden characteristics corresponding to fault state of a system, and the distance between degraded state and failed state is used to construct health indicator. Afterwards, the remaining useful life before failure is predicted by particle filter which is improved by introducing a fuzzy inference system. At last, a case study using aircraft engine dataset is performed to verify the effectiveness of the proposed method, and the results show that the proposed method gives better prediction accuracy compared to traditional methods.","author":[{"dropping-particle":"","family":"Peng","given":"Kaixiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jiao","given":"Ruihua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dong","given":"Jie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pi","given":"Yanting","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Neurocomputing","id":"ITEM-1","issued":{"date-parts":[["2019","10"]]},"page":"19-28","title":"A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter","type":"article-journal","volume":"361"},"uris":["http://www.mendeley.com/documents/?uuid=544d5b90-7e1f-4eaa-a5d8-71b9a4f6d8f6"]}],"mendeley":{"formattedCitation":"[86]","plainTextFormattedCitation":"[86]","previouslyFormattedCitation":"[86]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[90];Aircraft design, dynamics, and control ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/TAES.2021.3056086","ISSN":"0018-9251","abstract":"In recent decades, deep learning (DL) has become a rapidly growing research direction, redefining the state-of-the-art performances in a wide range of techniques, such as object detection and speech recognition. In the aircraft design, dynamics, and control field, many works hinge on the information-rich data-driven approach, which includes the fusion-based prognostic and health management, the airliner's flight safety monitoring, intelligent sensing, and flight control systems development. While DL provides great potentials to solve these data-driven problems, a systematic review and discussion as to how the DL has been/can be used for these problems are still missing in relation to the rapidly developing and widely used DL techniques. In this article, we aim to address this urgent issue to provide a timely overview of the state-of-the-art for applying DL to the aircraft design, dynamics, and control field. In particular, we briefly introduce five representative DL methods, i.e., deep neural network, deep autoencoder, deep belief network, convolutional neural network, and recurrent neural network. Mathematical definitions for each method are presented, and illustrative applications are also discussed. We then review the existing DL-based works that have appeared in the aircraft design, dynamics, and control field. The review efforts are divided into two major groups, i.e., the own-ship aircraft modeling, wherein the works have been/can be implemented online for the aircraft design/dynamics/control, and other airplanes research works, wherein DL-based schemes provide offline monitoring of the aircraft operation. We then summarize the data sources and DL architectures. Referring to the experiences of DL research works/techniques development in other related fields, future opportunities, challenges, and potential solutions for implementing DL in the aircraft design, dynamics, and control field are also discussed.","author":[{"dropping-particle":"","family":"Dong","given":"Yiqun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tao","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Youmin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ai","given":"Jianliang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Transactions on Aerospace and Electronic Systems","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2021","8"]]},"page":"2346-2368","title":"Deep Learning in Aircraft Design, Dynamics, and Control: Review and Prospects","type":"article-journal","volume":"57"},"uris":["http://www.mendeley.com/documents/?uuid=a661f1db-88a8-4045-9752-90faecc66800"]}],"mendeley":{"formattedCitation":"[21]","plainTextFormattedCitation":"[21]","previouslyFormattedCitation":"[21]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[25].HYBRID LEARNINGHybrid deep neural networksImpending failures' detection by predicting the future behavioural state of turbofan engines ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.36001/ijphm.2021.v12i1.2378","ISSN":"2153-2648","abstract":"The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behavior health state of turbofan engines as well as its Remaining Useful Life. The use of deep learning techniques to estimate Remaining Useful Life has seen a growing interest over the last decade. However, hybrid deep learning methods have not been sufficiently explored yet by researchers.In this paper, we proposed two-hybrid methods combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bi-directional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN) to enhance the RUL estimation. The results indicate that the hybrid methods exhibit the most reliable RUL prediction accuracy and significantly outperform the most robust predictions in the literature.","author":[{"dropping-particle":"","family":"Remadna","given":"Ikram","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Terrissa","given":"Labib Sadek","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ayad","given":"Soheyb","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zerhouni","given":"Nourddine","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Prognostics and Health Management","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","5","3"]]},"title":"RUL Estimation Enhancement using Hybrid Deep Learning Methods","type":"article-journal","volume":"12"},"uris":["http://www.mendeley.com/documents/?uuid=6517577b-4e6d-4f97-bc41-1682b02cb1ff"]}],"mendeley":{"formattedCitation":"[91]","plainTextFormattedCitation":"[91]","previouslyFormattedCitation":"[91]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[95];Planning of fuel consumption ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/AERO.2019.8741729","ISBN":"978-1-5386-6854-2","ISSN":"1095323X","abstract":"This study implements deep learning techniques to estimate fuel burn of a jet aircraft. Current ground-based flight planning systems utilize aircraft type specific performance tables to determine fuel flows for given flight conditions and parameters such as altitude, mass and speed. These tables are corrected by a performance factor as the aircraft ages. Despite this update, planned fuel consumption may indeed not overlap with the actual one. In order to synchronize the base aircraft model with aircraft's actual performance, we propose using state-of-the-art deep learning algorithms for building data-driven models of fuel flows. Towards this goal, aircraft's on-board recorded trajectory and parameter data, namely Quick Access Recorder (QAR) data are utilized. The total dataset used within this study comprises of more than 1000 B777-300ER flights from a major European flag carrier airline. The deep neural network architecture is utilized for modeling the actual fuel flow specific to each aircraft and for each major flight mode (climb, cruise and descent namely). We have developed three neural network architectures (according to in-flight and ground based planning use cases) to present a tail-number specific correction factor to Base of Aircraft Data (BADA) models. First architecture involves a QAR data based black-box fuel flow model utilizing in-flight throttle data from all the engines. Comparison of this model with real flight data shows that precise estimation of fuel flow with mean errors lower than %0.1 can be achieved. The second architecture utilizes a physically consistent data regeneration of \\delta-{T} (delta thrust) using BADA formulation as to account for the ground planning phase where throttle information is not available. The third model involves a cascaded architecture which utilizes a neural network throttle estimator and the black-box QAR fuel flow model for again the ground planning phase. Comparison of the latter models with real flight data shows that precise estimation of fuel flow with mean absolute errors lower than %0.7 can be achieved at all the flight modes. Initial tests reflect the fact that even better accuracy can be achieved for all models as the data set size increases. Finally, ground based planning fuel flow models are applied to actual flight plans generated by ground based systems. Total trip fuel comparisons show discrepancies up to %3.5 total fuel loading weight, which may result in potential fuel savings by decrea…","author":[{"dropping-particle":"","family":"Uzun","given":"Mevlut","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Umut Demirezen","given":"M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koyuncu","given":"Emre","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Inalhan","given":"Gokhan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2019 IEEE Aerospace Conference","id":"ITEM-1","issued":{"date-parts":[["2019","3"]]},"note":"33_deep learning aircraft fuel system\n\nProjektowanie hybrydowego modelu osiągów lotu Digital-Twin poprzez uczenie maszynowe\n\nW niniejszym opracowaniu zastosowano techniki głębokiego uczenia do oszacowania zużycia paliwa przez samolot odrzutowy. Obecne naziemne systemy planowania lotu wykorzystują tabele osiągów specyficznych dla danego typu samolotu w celu określenia przepływu paliwa dla danych warunków lotu i parametrów, takich jak wysokość, masa i prędkość. Tabele te są korygowane o współczynnik osiągów w miarę starzenia się samolotu. Pomimo tej aktualizacji, planowane zużycie paliwa może nie pokrywać się z rzeczywistym. Aby zsynchronizować bazowy model samolotu z jego rzeczywistymi osiągami, proponujemy wykorzystanie najnowocześniejszych algorytmów głębokiego uczenia do budowy modeli przepływów paliwa opartych na danych. W tym celu wykorzystuje się zapisane na pokładzie samolotu dane dotyczące trajektorii i parametrów, czyli dane z rejestratora szybkiego dostępu (Quick Access Recorder - QAR). Całkowity zbiór danych wykorzystany w tym badaniu obejmuje ponad 1000 lotów samolotu B777-300ER jednej z głównych europejskich flagowych linii lotniczych. Architektura głębokich sieci neuronowych jest wykorzystywana do modelowania rzeczywistego przepływu paliwa dla każdego samolotu i dla każdego głównego trybu lotu (wznoszenie, przelot i opadanie). Opracowaliśmy trzy architektury sieci neuronowych (zgodnie z przypadkami zastosowania w planowaniu podczas lotu i na ziemi) w celu przedstawienia współczynnika korekcyjnego specyficznego dla numeru ogonowego w modelach bazy danych samolotu (BADA). Pierwsza architektura obejmuje model przepływu paliwa oparty na danych QAR, wykorzystujący dane o przepustnicach wszystkich silników podczas lotu. Porównanie tego modelu z rzeczywistymi danymi lotu pokazuje, że można uzyskać precyzyjne oszacowanie przepływu paliwa ze średnimi błędami mniejszymi niż %0,1. Druga architektura wykorzystuje fizycznie spójną regenerację danych \\delta-{T} (delta thrust) z wykorzystaniem formuły BADA, aby uwzględnić fazę planowania naziemnego, w której informacje o przepustnicy nie są dostępne. Trzeci model obejmuje architekturę kaskadową, która wykorzystuje estymator przepustnicy oparty na sieci neuronowej oraz model przepływu paliwa QAR z czarną skrzynką do ponownego uwzględnienia fazy planowania naziemnego. Porównanie tych modeli z rzeczywistymi danymi lotu pokazuje, że we wszystkich trybach lotu można uzyskać precyzyjne oszacowanie przepływu paliwa ze średnim błędem bezwzględnym poniżej %0,7. Wstępne testy wskazują, że wraz ze wzrostem wielkości zbioru danych dla wszystkich modeli można uzyskać jeszcze lepszą dokładność. Modele naziemnego planowania przepływu paliwa są stosowane do rzeczywistych planów lotu wygenerowanych przez systemy naziemne. Porównania całkowitego paliwa podróży wykazują rozbieżności do %3,5 całkowitej masy załadowanego paliwa, co może skutkować potencjalnymi oszczędnościami paliwa dzięki zmniejszeniu ładunku paliwa podczas startu. Porównanie planowanych i szacunkowych mas pokładowych paliwa (na podstawie faktycznie złożonych planów lotu) dla 91 lotów długodystansowych pokazuje, że proponowana metodyka na etapie planowania naziemnego pozwoliłaby uzyskać oszczędności w spalaniu paliwa rzędu 800 [k g] na lot. W przypadku typowej operacji obejmującej 100 lotów długodystansowych dziennie oznacza to roczne oszczędności rzędu 17 mln USD przy obecnych cenach paliwa lotniczego. Przewiduje się, że to podejście do modelowania osiągów "specyficznych dla numeru ogona" otworzy znaczne możliwości, w tym aktualizację modeli osiągów podczas lotu za pomocą metod uczenia maszynowego.","page":"1-14","publisher":"IEEE","title":"Design of a Hybrid Digital-Twin Flight Performance Model Through Machine Learning","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=a69e3e1f-d71d-4b65-979a-6e2b7267fbb0"]}],"mendeley":{"formattedCitation":"[115]","plainTextFormattedCitation":"[115]","previouslyFormattedCitation":"[115]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[119].Deep transfer learningTarget recognition using laser — real-time detection accuracy and speed ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/ICNLP52887.2021.00040","ISBN":"978-1-6654-1411-1","abstract":"In the process of high-speed missile-target intersection, target recognition technology is a very important part of the laser fuze system. All of existing target recognition methods have the problems of difficulty in obtaining target data, resulting in fewer sample and low target recognition accuracy. In this paper, we use the deep migration learning method on the target recognition task of laser imaging fuze, and the particle filter are used to optimize and improve it to increase the detection accuracy and speed, so as to meet the real-time requirements of target recognition of laser imaging fuze. The experiment uses the UG NX11.0 software to simulate the various flight attitudes of the fighter jets when the J- 20, clouds, and fog are scanned at different angles of the laser line. The obtained partial images are used to test the recognition method. The results show that, compared with the traditional Faster R-CNN, the improved Faster R-CNN reduces the detection time and achieve 97.3% recognition accuracy and 23 fps speed, even basically achieving real-time laser fuze target recognition.","author":[{"dropping-particle":"","family":"Tang","given":"Qian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"He","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2021 3rd International Conference on Natural Language Processing (ICNLP)","id":"ITEM-1","issued":{"date-parts":[["2021","3"]]},"page":"202-207","publisher":"IEEE","title":"Target Recognition Method of Laser Imaging Fuze Based on Deep Transfer Learning","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=e8594f8c-2d44-4251-902d-5a5700e2e1c4"]}],"mendeley":{"formattedCitation":"[110]","plainTextFormattedCitation":"[110]","previouslyFormattedCitation":"[110]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[114].Deep reinforcement learningThe problem of intelligent decision-making in multi-aircraft cooperative air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.16383/j.aas.c201059","ISSN":"02544156","abstract":"Multi-aircraft cooperation is the key part of air combat, and how to deal with the complex cooperation relationship between multi-entities is the essential problem to be solved urgently. In order to solve the problem of intelligent decision-making in multi-aircraft cooperative air combat, a deep-reinforcement-learning-based multi-aircraft cooperative air combat decision framework (DRL-MACACDF) is proposed in this paper. Based on proximal policy optimization (PPO), four algorithm enhancement mechanisms are designed to improve the synergistic degree of agents in multi-aircraft cooperative confrontation scenarios. The feasibility and practicability of the method are verified by the simulation on the wargame platform, and the interpretable review analysis of the antagonistic process data is carried out, and the cross research direction of the combination of reinforcement learning and traditional wargame deduction is discussed.","author":[{"dropping-particle":"","family":"Shi","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Feng","given":"Yang He","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cheng","given":"Guang Quan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Hong Lan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Jin Cai","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Zhong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"He","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Zidonghua Xuebao/Acta Automatica Sinica","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2021"]]},"page":"1610−1623","title":"Research on Multi-aircraft Cooperative Air Combat Method Based on Deep Reinforcement Learning","type":"article-journal","volume":"47"},"uris":["http://www.mendeley.com/documents/?uuid=561ce4e7-f5ca-4127-b24b-9ed89302cfd6"]}],"mendeley":{"formattedCitation":"[102]","plainTextFormattedCitation":"[102]","previouslyFormattedCitation":"[102]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[106];Control of the aircraft based on immediate observations of individual aircraft ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/1.G004106","ISSN":"1533-3884","abstract":"Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space is high dimensional, the fire propagates stochastically, the sensor information is imperfect, and the aircraft must coordinate with each other to accomplish their mission. This work presents two deep reinforcement learning approaches for training decentralized controllers that accommodate the high dimensionality and uncertainty inherent in the problem. The first approach controls the aircraft using immediate observations of the individual aircraft. The second approach allows aircraft to collaborate on a map of the wildfire's state and maintain a time history of locations visited, which are used as inputs to the controller. Simulation results show that both approaches allow the aircraft to accurately track wildfire expansions and outperform an online receding-horizon controller. Additional simulations demonstrate that the approach scales with different numbers of aircraft and generalizes to different wildfire shapes.","author":[{"dropping-particle":"","family":"Julian","given":"Kyle D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kochenderfer","given":"Mykel J.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Guidance, Control, and Dynamics","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2019","8"]]},"page":"1768-1778","title":"Distributed Wildfire Surveillance with Autonomous Aircraft Using Deep Reinforcement Learning","type":"article-journal","volume":"42"},"uris":["http://www.mendeley.com/documents/?uuid=48ec1c38-d83c-4a9f-a147-2b5ece43adfb"]}],"mendeley":{"formattedCitation":"[51]","plainTextFormattedCitation":"[51]","previouslyFormattedCitation":"[51]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[55];Strategy generation for manoeuvring in pursuit in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1155/2020/7180639","ISSN":"1024-123X","abstract":"In a one-on-one air combat game, the opponent’s maneuver strategy is usually not deterministic, which leads us to consider a variety of opponent’s strategies when designing our maneuver strategy. In this paper, an alternate freeze game framework based on deep reinforcement learning is proposed to generate the maneuver strategy in an air combat pursuit. The maneuver strategy agents for aircraft guidance of both sides are designed in a flight level with fixed velocity and the one-on-one air combat scenario. Middleware which connects the agents and air combat simulation software is developed to provide a reinforcement learning environment for agent training. A reward shaping approach is used, by which the training speed is increased, and the performance of the generated trajectory is improved. Agents are trained by alternate freeze games with a deep reinforcement algorithm to deal with nonstationarity. A league system is adopted to avoid the red queen effect in the game where both sides implement adaptive strategies. Simulation results show that the proposed approach can be applied to maneuver guidance in air combat, and typical angle fight tactics can be learnt by the deep reinforcement learning agents. For the training of an opponent with the adaptive strategy, the winning rate can reach more than 50%, and the losing rate can be reduced to less than 15%. In a competition with all opponents, the winning rate of the strategic agent selected by the league system is more than 44%, and the probability of not losing is about 75%.","author":[{"dropping-particle":"","family":"Wang","given":"Zhuang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Hui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Haolin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Zhaoxin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Mathematical Problems in Engineering","id":"ITEM-1","issued":{"date-parts":[["2020","6","30"]]},"page":"1-17","title":"Improving Maneuver Strategy in Air Combat by Alternate Freeze Games with a Deep Reinforcement Learning Algorithm","type":"article-journal","volume":"2020"},"uris":["http://www.mendeley.com/documents/?uuid=df68b613-21bc-4e6a-b929-52759fcec070"]}],"mendeley":{"formattedCitation":"[125]","plainTextFormattedCitation":"[125]","previouslyFormattedCitation":"[125]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[129];System of autonomously locating and navigating to an emitter and optically recognizing its associated vehicle ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1109/AERO50100.2021.9438308","ISBN":"978-1-7281-7436-5","ISSN":"1095323X","abstract":"The USA Air Force Test Pilot School (USAF TPS), in partnership with Lockheed Martin Skunk Works, developed and tested a suite of algorithms and artificial intelligence (AI) agents flown on an F-16D. The system was designed to autonomously locate and navigate to an emitter and optically recognize its associated vehicle under the project Have SPI-DERs. This project was the first to explore the use of AI agents trained in simulation using deep reinforcement learning and tested on a high-performance aircraft in the real world performing routing and computer vision tasks. During flight test, the Have SPIDERS project set out to demonstrate the SUT's ability to find and fix the emitter through three phases. The first phase passively detected the emitter using a small antenna array and routed the aircraft using a neural network to reduce the uncertainty volume of the emitter's location. The second phase routed the aircraft using a second neural network to an image point where the emitter could be optically identified. The final phase used a third neural network to optically recognize a vehicle collocated with the emitter. The first two phases each used two feedforward neural networks functioning as actor-critic algorithms. The third phase used a RetinaNet architecture. This paper uses the Have SPIDERS project as a vehicle to discuss lessons learned that can be applied to future AI flight test. These lessons focus on six areas for consideration during system development, test planning, and execution: understanding system under test (SUT) development, instrumenting the AI to understand its behavior, planning for unpredictability, simulation-to-real-world generalization, hardware choices, and manned surrogate testing considerations.","author":[{"dropping-particle":"","family":"Rountree","given":"Joshua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hipelius","given":"Patrick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dienst","given":"Brian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aronoff","given":"Jonathan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neely","given":"Ryan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Steigerwald","given":"Robert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Griffis","given":"Skylar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schweinitz","given":"David","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lee","given":"Chiawei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hefron","given":"Ryan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"2021 IEEE Aerospace Conference (50100)","id":"ITEM-1","issued":{"date-parts":[["2021","3","6"]]},"page":"1-15","publisher":"IEEE","title":"Testing Artificial Intelligence in High-Performance, Tactical Aircraft","type":"paper-conference"},"uris":["http://www.mendeley.com/documents/?uuid=bf2624f3-5f00-4df1-b826-79d3710a6d2f"]}],"mendeley":{"formattedCitation":"[94]","plainTextFormattedCitation":"[94]","previouslyFormattedCitation":"[94]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[98].Tab. 8Selected applications of fuzzy logic, evolutionary, and swarm intelligence algorithmsAlgorithmsSelected applicationsFuzzy logic (FL)Air combat attack algorithm consisting of navigation steps and reference velocity calculations ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s40435-021-00803-6","ISSN":"2195-268X","abstract":"Unmanned combat air vehicles (UCAV) are finding increasing use in military applications due to their autonomy, processing capabilities and low cost with respect to manned flights. In order to use the UCAVs in the air to air combat, required algorithms must be carefully designed to enhance them with aerial combat competence. There are challanges for the design of this kind of an algorithm such as real-time operability and the case with inadequate number of sensors on the UAVs. In this paper a novel attack algorithm is introduced for UCAVs, with the assumption that in order to launch an effective attack the pursuer aircraft must be able to follow the target aircraft from behind with a predetermined distance. The attack algorithm consists of navigation and reference velocity calculation steps. In navigation part the reference values for bank, heading and flight path angles are computed. In velocity generation part, a PID based method and a fuzzy inference based method are employed to compute the reference velocity of the pursuer and the results obtained with these two approaches are compared. The performance of this algorithm is assessed with simulations performed in three different scenarios with different initial conditions for the pursuer and the target. Simulation results show that the effectiveness of the proposed fuzzy inference based air to air attack algorithm is better in action with respect to PID based algorithm since various effects that cause the change in velocity, can be incorporated into the fuzzy inference mechanism.","author":[{"dropping-particle":"","family":"İşci","given":"Hasan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Günel","given":"Gülay Öke","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Dynamics and Control","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2022","2"]]},"page":"230-242","title":"Fuzzy logic based air-to-air combat algorithm for unmanned air vehicles","type":"article-journal","volume":"10"},"uris":["http://www.mendeley.com/documents/?uuid=0d143ac4-cd36-428a-ada5-6427b0aa321d"]}],"mendeley":{"formattedCitation":"[47]","plainTextFormattedCitation":"[47]","previouslyFormattedCitation":"[47]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[51];Determining the optimal strategy for air combat at medium and long range. The parameters considered are: distance and azimuth position of the target, range, and projectile energy ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.47355/avia.v1i1.8","ISSN":"2715-6958","abstract":"Beyond Visual Range (BVR) air combat is a future trend of war tactic. In this situation, a fighter can attack the opponent before direct encounter. Its complexity arises due to the necessity to take into account the information of target’s maneuver, the specification of the missile, and the advantage of fighter position. In this paper, a simple BVR air combat system has been developed to give a fight strategy for pilot. Some important parameters are considered, such as the distance and the azimuth position of the target’s as well as the range and the energy of missile to reach the target. The information is processed to determine the fighter supremacy and the opponent’s threat factor. The result of the processing is used as an input of fuzzy logic algorithm to determine the optimal fighting strategy. The feasibility of the model and validity of the algorithm are verified by simulation under two typical situations","author":[{"dropping-particle":"","family":"Ummah","given":"Khairul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Setiadi","given":"Herlan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pasaribu","given":"Hisar Manongam","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anandito","given":"Dhani","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"AVIA","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2020","12","14"]]},"title":"A Simple Fight Decision Support System for BVR Air Combat Using Fuzzy Logic Algorithm","type":"article-journal","volume":"1"},"uris":["http://www.mendeley.com/documents/?uuid=c0a2bfc0-92f5-40e7-a2b3-2adcf362d511"]}],"mendeley":{"formattedCitation":"[114]","plainTextFormattedCitation":"[114]","previouslyFormattedCitation":"[114]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[118];Increasing manoeuvring effectiveness in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.21629/JSEE.2018.01.09","ISSN":"10044132","abstract":"To reach a higher level of autonomy for unmanned combat aerial vehicle (UCAV) in air combat games, this paper builds an autonomous maneuver decision system. In this system, the air combat game is regarded as a Markov process, so that the air combat situation can be effectively calculated via Bayesian inference theory. According to the situation assessment result, adaptively adjusts the weights of maneuver decision factors, which makes the objective function more reasonable and ensures the superiority situation for UCAV. As the air combat game is characterized by highly dynamic and a significant amount of uncertainty, to enhance the robustness and effectiveness of maneuver decision results, fuzzy logic is used to build the functions of four maneuver decision factors. Accuracy prediction of opponent aircraft is also essential to ensure making a good decision; therefore, a prediction model of opponent aircraft is designed based on the elementary maneuver method. Finally, the moving horizon optimization strategy is used to effectively model the whole air combat maneuver decision process. Various simulations are performed on typical scenario test and close-in dogfight, the results sufficiently demonstrate the superiority of the designed maneuver decision method.","author":[{"dropping-particle":"","family":"HUANG","given":"Changqiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"DONG","given":"Kangsheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"HUANG","given":"Hanqiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"TANG","given":"Shangqin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Systems Engineering and Electronics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2018","2"]]},"page":"86-97","title":"Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization","type":"article-journal","volume":"29"},"uris":["http://www.mendeley.com/documents/?uuid=5ae3139b-1b8a-43f9-9969-f387975e246d"]}],"mendeley":{"formattedCitation":"[44]","plainTextFormattedCitation":"[44]","previouslyFormattedCitation":"[44]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[48].Evolutionary (E)Making decisions regarding combat manoeuvres of unmanned aerial vehicles ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1051/jnwpu/20203861330","ISSN":"1000-2758","abstract":"With the continuous development of UAV technology, the trend of using UAV in the military battlefield is increasingly obvious, but the autonomous air combat capability of UAV needs to be further improved. The air combat maneuvering decision is the key link to realize the UAV autonomous air combat, and the genetic algorithm has good robustness and global searching ability which is suitable for solving large-scale optimization problems. This paper uses an improved genetic algorithm to model UAV air combat maneuvering decisions. Based on engineering application requirements, a typical simulation test scenario is established. The simulation results show that the air combat maneuvering decision model based on reinforcement genetic algorithm in this paper can obtain the correct maneuvering decision sequence and gain a position advantage in combat.","author":[{"dropping-particle":"","family":"Xie","given":"Jianfeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Qiming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dai","given":"Shuling","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Wanyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Jiandong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2020","12","2"]]},"page":"1330-1338","title":"Air Combat Maneuver Decision Based on Reinforcement Genetic Algorithm","type":"article-journal","volume":"38"},"uris":["http://www.mendeley.com/documents/?uuid=37a6a54f-3b97-45c4-b542-67abe6a045f9"]}],"mendeley":{"formattedCitation":"[132]","plainTextFormattedCitation":"[132]","previouslyFormattedCitation":"[132]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[136];Assign weapons targets in a dynamic, uncertain air combat environment ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.11990/jheu.201606094","ISSN":"10067043","abstract":"The corresponding evaluation and assignment model is established to solve the problem of weapons target assignment in dynamic uncertain environments. Firstly, the index system of air combat situation assessment is established and the interval analytic hierarchy process and fuzzy optimization are used to obtain the subjective and objective weight of these indexes. Secondly, corresponding effectiveness evaluation mode of multi-objective optimization is constructed with a dynamic environment, and the decision-making problem for cooperative multiple target attack is converted to the optimization problem for missile attack matching. Furthermore, the interval grey number-simulated annealing genetic algorithm is put forward to solve this problem. The proposed ISAGA designs a new selection operation and a special simulated annealing mechanism to enhance the global search ability. Finally, the reasonable attack scheme can be confirmed according to the optimal solution, and simulation results show that the proposed algorithm can be used to evaluate and optimize the weapons target assignment problem in uncertain environments and obtains a solution closer to the optimal solution in ten iterations, which is a consummation to some extent.","author":[{"dropping-particle":"","family":"Song","given":"Xiagan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jiang","given":"Ju","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Haiyan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2017"]]},"page":"1762-1768","title":"Application of improved simulated annealing genetic algorithm in cooperative air combat","type":"article-journal","volume":"38"},"uris":["http://www.mendeley.com/documents/?uuid=5d6a59cd-ce1b-4420-9bd7-795c2610b595"]},{"id":"ITEM-2","itemData":{"DOI":"10.1109/ACCESS.2021.3119363","ISSN":"2169-3536","abstract":"The Antagonistic Weapon-Target Assignment (AGWTA) problem is a crucial decision issue in Command Control (C2). Since this is a minimax problem, co-evolutionary algorithms can be used to solve it effectively. However, the co-evolutionary algorithm is originally designed for continuous minimax problems which loses its efficiency to discrete contexts. In this paper, a novel elitism co-evolutionary algorithm is proposed to solve the AGWTA. Firstly, an improved AGWTA model for air combat based on the attack and evasion strategies is proposed. Secondly, an elite cooperative genetic algorithm based on the framework of the co-evolutionary algorithm is put forward. In this proposed algorithm, a problem-specific coding method and evolution operator are designed. Meanwhile, an elite individual update mechanism is presented. Finally, based on the analysis of the relationship between the feasible solutions under the air combat environment, an evaluation index is proposed. Experiments show that the proposed algorithm has higher accuracy than traditional co-evolutionary algorithms for solving AGWTA problems.","author":[{"dropping-particle":"","family":"Huang","given":"Jichuan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Xiaoyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Zhen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kong","given":"Weiren","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Yiyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Deyun","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IEEE Access","id":"ITEM-2","issued":{"date-parts":[["2021"]]},"page":"139668-139684","title":"A Novel Elitism Co-Evolutionary Algorithm for Antagonistic Weapon-Target Assignment","type":"article-journal","volume":"9"},"uris":["http://www.mendeley.com/documents/?uuid=fccb303a-b301-4d9f-a995-059c2f74ff1a"]}],"mendeley":{"formattedCitation":"[45, 105]","plainTextFormattedCitation":"[45, 105]","previouslyFormattedCitation":"[45, 105]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[49, 109];Tactical combat against enemy formations — optimization of tactical attributes ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.2514/2.4689","ISSN":"0731-5090","abstract":"Air combat tactics become an ideal candidate for genetic algorithm (GA) optimization when the problem is formulated in a manner conducive to genetic coding. The large numbers of elements (aircraft and groups) in many-aircraft combat formations and their interrelated behavior are defined effectively by the application of a hierarchical coding scheme. By altering the structure of the performance function to vary the significance of several tactical attributes, the GA-optimized solutions have been tailored to make them sensible from an air combat point of view. The GA provides solutions with generalized characteristics when faced with similar enemy formations.","author":[{"dropping-particle":"","family":"Mulgund","given":"Sandeep","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harper","given":"Karen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zacharias","given":"Greg","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Krishnakumar","given":"Kalmanje","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Guidance, Control, and Dynamics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2001","1"]]},"page":"140-142","title":"Large-Scale Air Combat Tactics Optimization Using Genetic Algorithms","type":"article-journal","volume":"24"},"uris":["http://www.mendeley.com/documents/?uuid=e70e381b-a543-4df6-8053-17eecf8913b5"]}],"mendeley":{"formattedCitation":"[78]","plainTextFormattedCitation":"[78]","previouslyFormattedCitation":"[78]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[82];Searching for optimal partition resource parameters for minimum CPU utilization – time partitioning mechanism to reduce errors between avionics applications ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3390/electronics9081281","ISSN":"2079-9292","abstract":"The architecture of Integrated Modular Avionics (IMA) provides airborne software with a robust temporal partitioning mechanism, which achieves the reliable fault containment between avionics applications. However, the partition scheduling of an IMA system is a complex nonlinear non-convex optimization problem, making it difficult to solve the optimal temporal allocation for partitions using traditional analytical methods. This paper presents a model-based approach to optimizing the partition scheduling of IMA systems, whose temporal behavior is modeled as a network of timed automata. Given a system model, the optimizer employs a parallel genetic algorithm to search for the optimal partition resource parameters with respect to minimum processor occupancy. For each promising parameter combination, the schedulability constraints and processor occupancy of the system are precisely evaluated by Classical and Statistical Model Checking (i.e., CMC and SMC), respectively. We also apply SMC hypothesis testing to the fast falsification of non-schedulable solutions, thereby speeding up the schedulability verification based on CMC. Two case studies demonstrate that our proposed approach outperforms classical analytical methods on the processor occupancy of typical IMA systems.","author":[{"dropping-particle":"","family":"Han","given":"Pujie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhai","given":"Zhengjun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Lei","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Electronics","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2020","8","9"]]},"page":"1281","title":"A Model-Based Approach to Optimizing Partition Scheduling of Integrated Modular Avionics Systems","type":"article-journal","volume":"9"},"uris":["http://www.mendeley.com/documents/?uuid=82126e9b-9b77-4244-b38b-f1b64b2eb688"]}],"mendeley":{"formattedCitation":"[36]","plainTextFormattedCitation":"[36]","previouslyFormattedCitation":"[36]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[40].Swarm intelligence (SI)Weapon-Target Assignment (WTA) and minimizing threats from those targets ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1155/2018/6481635","ISSN":"1024-123X","abstract":"Weapon-target assignment (WTA) which is crucial in cooperative air combat explores assigning weapons to targets with the objective of minimizing the threats from those targets. Based on threat functions, there are four WTA models constrained by the payload and other tactical requirements established. The improvements of ant colony optimization are integrated with respect to the rules of path selection, pheromone update, and pheromone concentration interval, and algorithm AScomp is proposed based on the elite strategy of ant colony optimization (ASrank). We add garbage ants to ASrank; when the pheromone is updated, the elite ants are rewarded and the garbage ants are punished. A WTA algorithm is designed based on the improved ant colony optimization (WIACO). For the purpose of demonstration of WIACO in air combat, a real-time WTA simulation algorithm (RWSA) is proposed to provide the results of average damage, damage rate, and kill ratio. The following conclusions are drawn: (1) the third WTA model, considering the threats of both sides and hit probabilities, is the most effective among the four; (2) compared to the traditional ant colony algorithm, the WIACO requires fewer iterations and avoids local optima more effectively; and (3) WTA is better conducted when any fighter is shot down or any fighter’s missiles run out than along with the flight.","author":[{"dropping-particle":"","family":"Hu","given":"Xinwu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Luo","given":"Pengcheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Xiaonan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Mathematical Problems in Engineering","id":"ITEM-1","issued":{"date-parts":[["2018","10","11"]]},"page":"1-14","title":"Improved Ant Colony Optimization for Weapon-Target Assignment","type":"article-journal","volume":"2018"},"uris":["http://www.mendeley.com/documents/?uuid=01e3ee83-2910-4991-8ba5-38a28fa6ebf8"]}],"mendeley":{"formattedCitation":"[43]","plainTextFormattedCitation":"[43]","previouslyFormattedCitation":"[43]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[47];Multi-purpose air combat system – an autonomous control algorithm for multipurpose systems to improve the performance of UAVs in air combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1360/SSI-2019-0196","ISSN":"1674-7267","abstract":"Unmanned aerial vehicles (UAVs) are usually controlled by radio or by autonomous control algorithms. Compared with manned aerial vehicles, they have great advantages in performing dangerous tasks but, presently, no UAV system can cope with high-intensity air combat. In addition, the robustness of a single UAV cannot be guaranteed in air combat missions; on the other hand, a multi-UAV system not only ensures this robustness but also improves the mission success rate by using saturation attacks. Therefore, this paper presents a multi-UAV air combat system based on swarm intelligence. Considering the problem of multi-UAV cooperative arrival at the air battlefield and the accomplishment of combat tasks, the aerodynamic model of the aircrafts and the threat area on the path to the battlefield are modeled; the path planning is completed through an ant colony algorithm. Based on the control algorithm of single-UAV finite-state machine and the cooperation of multiple UAVs, an autonomous control algorithm for multi-UAV systems is proposed to improve the success rate of UAV clusters in air combat. The effectiveness of the proposed algorithm is tested with a simulation platform.","author":[{"dropping-particle":"","family":"Zhou","given":"Wenqing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kuang","given":"Minchi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhu","given":"Jihong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"SCIENTIA SINICA Informationis","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2020","3","1"]]},"page":"363-374","title":"An unmanned air combat system based on swarm intelligence","type":"article-journal","volume":"50"},"uris":["http://www.mendeley.com/documents/?uuid=647dd80c-9cda-47eb-8d5f-111e27485c90"]}],"mendeley":{"formattedCitation":"[143]","plainTextFormattedCitation":"[143]","previouslyFormattedCitation":"[143]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[147];Autonomous manoeuvre decision-making – autonomous manoeuvre strategy of UAV swarms in out-of-sight aerial combat ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s43684-021-00013-z","ISSN":"2730-616X","abstract":"Unmanned aerial vehicles (UAVs) have been found significantly important in the air combats, where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics. The key to empower the UAVs with such capability is the autonomous maneuver decision making. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. First, based on the process of air combat and the constraints of the swarm, the motion model of UAV and the multi-to-one air combat model are established. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. The results show that the algorithm is suitable for UAV swarms of different scales.","author":[{"dropping-particle":"","family":"Wang","given":"Luhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Jinwen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Zhao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Chunhui","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Autonomous Intelligent Systems","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2021","12","4"]]},"page":"15","title":"Autonomous maneuver strategy of swarm air combat based on DDPG","type":"article-journal","volume":"1"},"uris":["http://www.mendeley.com/documents/?uuid=851cc15d-0825-409e-826e-2e66fd357253"]}],"mendeley":{"formattedCitation":"[123]","plainTextFormattedCitation":"[123]","previouslyFormattedCitation":"[123]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[127].The conducted literature review of algorithms (Tables 4-8) shows that it is possible to develop objectives for a future aircraft equipped with advanced Artificial Intelligence. Depending on conditions, it may support the pilot during a combat mission or perform the mission independently.POSSIBLE APPLICATIONS OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN F-16In this subsection, assumptions are made for the F-16 aircraft as the baseline combat platform where a specific set of Artificial Intelligence algorithms can be applied. The reason for choosing the F-16 aircraft is a large number of in-service aircraft in the Air Force, which provides the necessary input to the algorithms. Moreover, important is the knowledge about the F-16 aircraft gathered during the operation, the manufacturer's knowledge about realized modifications, and laboratory tests conducted by DARPA. Table 9 presents a comparison of F-16 aircraft functionality with its systems equipped with artificial intelligence algorithms. Table 10 shows in detail which algorithms can be applied in a given case. During assigning algorithms, the abbreviations defined in Tables 2, 3, and 8 were used.Tab. 9A feature overview of F-16 aircraft systems regarding Artificial Intelligence algorithms,based on ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"83-900817-4-1","author":[{"dropping-particle":"","family":"Lewitowicz","given":"Jerzy","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2001"]]},"publisher":"Wydawnictwo Instytutu Technicznego Wojsk Lotniczych","publisher-place":"Warsaw","title":"Podstawy eksploatacji statków powietrznych. Tom 1. Statek powietrzny i elementy teorii","type":"book"},"uris":["http://www.mendeley.com/documents/?uuid=d73e615e-19df-411f-8ca7-7eebe9f0cdb4"]}],"mendeley":{"formattedCitation":"[64]","plainTextFormattedCitation":"[64]","previouslyFormattedCitation":"[64]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[68]FeaturesF-16 systemsI. Power unitII. Electrical and electronic installationIII. Hydraulic system/ servo drivesIV. Control systemV. Safety systemsVI. Fuel systemVII. Avionics and digital equipmentVIII. WeaponsIX. Airframe1. Engine thrustxxxxxx2. Aerodynamics and flight mechanicsxxxxxxx3. Communication (data transmission)xxx4. Systems activationxxxxxxxxx5. Systems monitoringxxxxxxxxx6. Providing intelligencexx7. Maintaining a safe distance: ground, objects on the ground, objects in the airxxxxxxx8. IFF (Identification friend or foe) / WE (electronic warfare) systems xx9. Navigationxx10. Target detection and identificationxx11. Use of weaponsxxxxxxxTab. 10Assignment of algorithms to F-16 systems and featuresFeaturesF-16 systemsI. Power unitII. Electrical and electronic installationIII. Hydraulic system / servo drivesIV. Control systemV. Safety systemsVI. Fuel systemVII. Avionics and digital equipmentVIII. WeaponsIX. Airframe1. Engine thrustHCS; DBSCAN, HDB-SCAN; NOD;ICA; SVD; MLP; SOMLinR; LogR; SVM; DT&RF; NBC; NN; HCS; DBSCAN, HDBSCAN; NOD; ICA; SVD; MLP; CVN; autoE; SOM; RBM; DBN; HDNNLinR; SVM; DT& RF; MLP; SOM; HDNN HCS; DBSCAN, HDB-SCAN; SVD; MLP; RNN; SOM; DBN; HDNNsame as for system VII2. Aerodyna-mics and flight mechanicsNN; DT&RF; SVM; DBSCAN, HDB-SCAN; NOD; apriori; VF; MC; TD; REINF; CA; CVN; RNN; DRL; FLLinR; SVM; DT&RF; NN; NOD; ICA; SVD; apriori; AP; SVF; VF; MC; TD; REINF; CA; RNN; DRL, FL; E; SIsame as for system ILinR; SVM; DT&RF; NN; DBSCAN, HDB-SCAN; ICA; apriori; AP; SVF; MC; TD; REINF; CA; CVN; RNN; GAN; autoE; RBN; DRL; FLLinR; SVM; DT&RF NN; NOD; apriori; VF; MC; TD; REINF; CA; RNN; DRL; FLsame as for system IILinR; SVM; DT&RF NN; NOD; apriori; SVF; REINF; MLP; CVN; RNN; autoE; SOM; RBM; HDNN3. Communi-cation (data trans-mission)LinR; LogR; SVM; DT&RF; NBC; k-m S; NN; D-R&V; SVD; MLP; CVN; GANsame as for system IIsame as for system II4. Systems activationNN; EL; AP; SVF; QL&DQL; VF; TD; REINF; CA; MLP; RNN; DRL; FL; E; SI5. Systems monitoringsupervised ML – all algorithms; unsupervised ML – all algorithms; supervised or discriminative DL – all algorithms; autoE; SOM; DBN, HDNNSVM; DT&RF; NBC; NN; EL; unsupervised ML– all algorithms;MLP; CVN; autoE; DTL; DRL; FL; E; SILinR; LogR; SVM; NBC; NN; EL; HCS; NOD; ICA; SVD; MLP; CVN; autoE; SOM; DBN; hybrid learning – all algorithms6. Providing intelligenceSVM; DT&RF; k-m S; EL; NOD; D-R&V; ICA; QL &DQL; TD; REINF; CA; CVN; GAN; autoE; DTL; DRLsame as for system II7. Maintaining a safe distance: ground, objects on the ground, objects in the airSVM; DT&RF; NBC; k-m S; NN; EL; HCS; DBSCAN, HDBSCAN; NOD; D-R&V; k-m U; apriori; reinforcement ML – all algorithms; CVN; RNN; GAN; autoE; RBM; DTL; DRL; FL; E; SIsame as for systems I-IVsame as for systems I-IV 8. IFF (Identifica-tion friend or foe) / WE (electronic warfare) systems SVM; DT&RF; NBC; k-m S; NOD; D-R&V; ICA; QL& DQL; GAN; autoE; DRL same as for system II9. NaviagationLinR; CVM; DT&RF; k-m S; NN; DBSCAN; HDB-SCAN; NOD; D-R&V; ICA; apriori; AP; QL&DQL; MC; TD; REINF; CA; MLP; CVN; RNN; GAN; autoE; RBM; HDNN; DRL; FL; E; SI same as for system II10. Target detection and identifica-tionSVM; DT&RF; NBC; NN; EL; HCS; D-R&V; ICA; AP; SVF; QL&DQL; REINF; CA; MLP; CVN; DTL; DRL; FL; E; SIsame as for system II11. Use of weaponsSVM; DT&RF; NBC; NN; EL; HCS; NOD; D-R&V; ICA; reinforcement ML – all algorithms; MLP; CVN; DTL; DRL; FL; E; SIsame as for systems II - VSVM; DT& RF; NBC; NN; EL; HCS; NOD; D-R&V; ICA; QL& DQL; REINF; CVN; DTL; FL; E; SIsame as for systems II - VBased on the compiled list of AI algorithms for the F-16 aircraft, a matrix analysis was performed – an analysis of the columns representing the systems of the F-16 aircraft and the rows representing the features. The main results of the analysis are as follows:Systems such as avionics and digital equipment, electrical and electronic installation are critical infrastructures. AI algorithms of these systems play an important role in every functionality of the F-16 aircraft. Both systems are interrelated and their reliability in individual functionalities is critical in the operation of the F-16 aircraft, for example: failure of any component of the electrical or electronic installation will prevent the correct operation of an algorithm responsible for specific functionality (feature). In turn, the malfunction of an algorithm from the area of avionics and digital equipment can also lead to the malfunction of a specific functionality leading to damage to a component of the electrical and electronic system, e.g.: the activation value set incorrectly by the algorithm for a given component may result in exceeding safe limits in the electrical or electronic installation.The systems activation and systems monitoring features, together with AI algorithms, are critical to the reliability and operation of all systems on the F-16 aircraft. For the systems' activation functionality, machine learning algorithms from the reinforcement learning group are particularly relevant, in turn, for the deep learning case, algorithms from the supervised deep Learning group. Fuzzy logic, evolutionary, and swarm intelligence are also applicable. In the case of the system's monitoring functionality, all supervised and unsupervised machine learning algorithms are distinguished, as well as all deep learning supervised or discriminative algorithms. They apply to systems such as power units, electrical and electronic installations, Hydraulic systems / servo drives, control systems, safety systems, fuel systems, avionics, and digital equipment. It should be noted that the two functionalities are interrelated. The algorithms responsible for monitoring enable the algorithms responsible for activating the systems to work properly.Functionalities such as aerodynamics and flight mechanics, maintaining a safe distance: the ground, objects on the ground, and objects in the air play a key role in the compilation. These features are associated with 7 of the 9 systems equipped with AI algorithms. In the case of the “aerodynamics and flight mechanics” functionality, such reinforcement learning algorithms in the respective systems can be distinguished: value function, Monte Carlo method, Time difference learning, REINFORCE, and combined algorithms. Supervised and unsupervised machine learning algorithms also play an important role. In turn, in the group of deep learning algorithms such as convolutional neural networks and recurrent neural networks. Fuzzy logic algorithms are also applicable. On the other hand, for the functionality of maintaining a safe distance, all the algorithms of reinforcement machine learning are applicable. For deep learning, all algorithms of supervised deep learning are applicable. Fuzzy logic, evolutionary, and swarm intelligence are also applicable. It should be noted that the two functionalities are interrelated. In particular, the correct simultaneous operation of algorithms in the systems for these two functionalities is important in situations such as flight in formation and also close or medium-range combat. In the use of weapons functionality, all reinforcement machine learning algorithms stand out but also selected supervised machine learning algorithms like: Support-Vector Machines, decision trees, and random forests, naive Bayes classifiers, neural networks. Among unsupervised machine learning algorithms, the following stand out: Hierarchical Cluster Analysis, novelty and outlier detection algorithms, visualization and dimensionality-reduction and visualization algorithms, and Independent Components Analysis algorithm. Fuzzy logic, evolutionary, and swarm intelligence algorithms are also applied. It is important to note that for a given functionality in comparison to a given system, there is a significant number of applications of different AI algorithms. This raises the question of which algorithms to choose. A suggestion could be a modular application, which involves the creation of sets and within them subsets of detailed algorithms for a given functionality within a given system. Another solution could be the wider use of combined algorithms within supervised machine learning, as well as unsupervised machine learning algorithms, combined algorithms within reinforcement machine learning, and hybrid deep learning networks. The area of the above applications requires further research in the near future. 5. DISCUSSIONThe developed compilation of functions with systems equipped with machine learning, deep learning, fuzzy logic, evolutionary, and swarm intelligence made it possible to obtain a comprehensive view of the possible directions of research and development of the F-16 aircraft. The results of the analysis based on the compilation indicated the outstanding features and systems that will be critical for the reliability and operation of the F-16 aircraft equipped with artificial intelligence algorithms. Table 9 also allows determining the progress of research work on aircraft equipped with artificial intelligence algorithms. An important role is played by sensitivity analysis of AI algorithms, which allows us to determine the relevance of the data feeding into the algorithms and also minimizes the probability of the algorithms making incorrect decisions. This is particularly crucial in the case of aircraft designed for specific missions, such as assault operation. The authors plan to develop these matters in future research.The matrix analysis prepared in this research enables an in-depth assessment of the application of artificial intelligence algorithms in specific cases. Presented below is one such example. So far, under DARPA's Air Combat Evolution (ACE) program, simulation tests of AlphaDogfigh in a laboratory environment (without real-world combat) were conducted. In this case, the AI agent algorithm which can quickly and effectively learn basic fighter manoeuvres and successfully employ them in a simulated dogfight was relevant here ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"https://www.darpa.mil/news-events/2020-08-26","accessed":{"date-parts":[["2022","8","17"]]},"author":[{"dropping-particle":"","family":"DARPA","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2020"]]},"title":"AlphaDogfight Trials Foreshadow Future of Human-Machine Symbiosis","type":"webpage"},"uris":["http://www.mendeley.com/documents/?uuid=fafcb929-ca4a-4099-8421-8c68b1090f1f"]}],"mendeley":{"formattedCitation":"[18]","plainTextFormattedCitation":"[18]","previouslyFormattedCitation":"[18]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[22]. In December 2022, DARPA ACE algorithm developers installed their AI software onto a modified F-16 test aircraft called the X-62A or VISTA at the Air Force Test Pilot School in California. Over several days, they conducted multiple flights, showcasing the AI's capability to control a full-scale fighter jet and provide invaluable live-flight data ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"https://www.darpa.mil/news-events/2023-02-13","author":[{"dropping-particle":"","family":"DARPA","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2023"]]},"title":"ACE Program’s AI Agents Transition from Simulation to Live Flight","type":"webpage"},"uris":["http://www.mendeley.com/documents/?uuid=16f487a5-e347-488f-8655-1c190fb819bb"]}],"mendeley":{"formattedCitation":"[19]","plainTextFormattedCitation":"[19]","previouslyFormattedCitation":"[19]"},"properties":{"noteIndex":0},"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}[23]. According to the matrix analysis in this research, it enables such actions as: highly intelligent air combat strategies enabling autonomous execution of combat missions in the air, avoiding threats from the enemy, and gaining an advantage over the enemy in air combat. It can also be concluded that this algorithm belongs to the functionality responsible for “systems activation”. Moreover, this algorithm has an overriding function in the reliability and operation of an AI-equipped aircraft. However, there is no information in the DARPA reports that would indicate what are the algorithms with which the other systems are equipped. Based on our research, it can be concluded that this algorithm is certainly supported by a group of “systems monitoring” functionality algorithms. In turn, individual systems could be equipped with selected algorithms that are indicated in the compilation.6. CONCLUSIONSThe presented compilation and analyses of the systems equipped with AI algorithms functions make it possible to develop objectives for the design structure to modify the F-16 aircraft. It is also possible to use this compilation for other military aircraft such as F-18, Rafael, Eurofighter Typhoon, and after completing the compilation with the features and systems of “STEALTH” technology also aircraft such as the F-22, and F-35. The overview can also be supplemented or detailed in the area of features and systems for aircraft versions whose tasks focus on assault missions, e.g.: tasks related to the neutralization of air defence systems, attack on moving columns of armoured warfare, attack on surface or underwater targets or electronic warfare missions. The compilation can also be a base for mapping future applications of AI algorithms in military aircraft, as well as for developing new AI algorithms. It should also not be forgotten that when looking for solutions using artificial intelligence methods for aviation-related tasks, you can be supported by solutions designed for other fields of science. For example, when looking for non-invasive methods of diagnosing the condition of an internal combustion engine, you can obtain knowledge from articles about the automotive industry [17-20].Based on this study, the following directions of research and development work can be indicated:development of dedicated ensemble learning methods based on machine learning algorithms or deep learning algorithms for the F-16 aircraft,development of sets of algorithms and their subsets for individual systems of the F-16 aircraft,development of hybrid learning algorithms in the area of deep learning for F-16 aircraft systems,development of AI algorithm configurations for dedicated variants of the F-16 aircraft (fighter, attack),advanced research on the application of reinforcement machine learning algorithms within the “systems activation” functionality in relation to the AI algorithms used in the systems for each functionality,advanced research in the area of “avionics and digital equipment” and “electrical and electronic installation” systems in relation to data sources that feed artificial intelligence algorithms,research and development studies regarding applications of artificial intelligence in relation to aerodynamics, flight mechanics, and maintaining a safe distance,development of applications of AI algorithms in weapon systems (short, medium, and long-range combat),research in the area of cybersecurity of aircraft equipped with AI algorithms,research and development studies in the area of pilot cooperation with artificial intelligence algorithms, e.g.: pilot surveillance of AI-controlled distributed UAVs or AI support of the pilot.The compilation presented in the article including analyses of functions and systems equipped with AI algorithms can also be used for modifications in the area of material and structure strength of the F-16 aircraft. 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Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, SCITEPRESS - Science and Technology Publications: 443-449. DOI: https://doi.org/10.5220/0006086904430449.Zhou W., M. Kuang, J. Zhu. 2020. „An unmanned air combat system based on swarm intelligence”. SCIENTIA SINICA Informationis 50(3): 363-374. DOI: https://doi.org/10.1360/SSI-2019-0196.Zou Y. M., B. Wang. 2008. „Estimation of the attitude of a rolling missile based on independent component analysis”. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 7: 1301-1303.Received 01.04.2024; accepted in revised form 30.05.2024Scientific Journal of Silesian University of Technology. Series Transport is licensed under a Creative Commons Attribution 4.0 International License PAGE 130T. Krawczyk, M. Papis, R. Bielawski, W. Rządkowski Faculty of Economic Sciences, University of Warsaw, Długa 44/50 Street, 00-241 Warsaw, Poland. Email: tj.krawczyk@uw.edu.pl. ORCID: https://orcid.org/0000-0001-5333-466X Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 24 Street, 00-665 Warsaw, Poland. Email: mateusz.papis@pw.edu.pl. ORCID: https://orcid.org/0000-0003-4565-5169 Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 24 Street, 00-665 Warsaw, Poland. Email: radek.bielawski@gmail.com. ORCID: https://orcid.org/0000-0002-5701-4476 Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 24 Street,00-665 Warsaw, Poland. Email: witold.rzadkowski@pw.edu.pl. ORCID: https://orcid.org/0000-0002-8586-2966Possible applications of artificial intelligence algorithms in F-16 aircraft PAGE 131.Scientific Journal of Silesian University of Technology. Series TransportZeszyty Naukowe Politechniki Śląskiej. 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DocumentLibraryFormDocumentLibraryFormDocumentLibraryFormJay14JournalArticle{78847417-C7D6-4DDE-8CF8-B976F232225D}JayantA.,Gupta, P., Garg, S.K. and Khan, M.TOPSIS-AHP based approach for selection of reverse logistics service provider: a case study of mobile phone industry.2014Procedia Engineering2147-2156971Pak17InternetSite{26F27E69-3E35-4B54-8919-09F53D07045E}PBSPakistan Bureau of Statistics, Population Census 2017, Government of Pakistan, Islamabad, Pakistan2017http://www.pbscensus.gov.pk/21December20172Sou18InternetSite{34BB1F91-464E-4B1A-80C2-2A7774E8084E}South Asia Terrorism Portal, Institute for Conflict Management, New Delhi, Delhi, India, Asia-Pacific (APAC)2018http://www.satp.org/2017December21SATP3Abb18Misc{9B8BFA9B-8126-458A-9B52-F50DC39751FA}2018http://morning.pk/2017/12/pakistan-railways-makes-record-loss-80b-rupees/AbbasShahidPakistan Railways Makes Record Loss of 80b RupeesDaily Morning Mail, Newspaper, December 18, 20172018March294PRC15Misc{4DDF6F05-C42A-417E-B7CE-45A0A903D253}PRCPeople Republic of China, Offical WebsiteChronology of China’s Belt and Road Initiative2015The State Council, the People'S Republic of China2018March29http://english.gov.cn/news/top_news/2015/04/20/content_281475092566326.htm5PC18InternetSite{DFC2AB07-2D4A-4FD8-9CB2-CC47F5040F19}Planning Commission, China Pakistan Economic Corridor (CPEC) offical Websitehttp://cpec.gov.pk/infrastructurePC2018Feburary222018Goverment of Pakistan, Islamabad, Pakistan6Dis10JournalArticle{B97640B9-8C45-4CB8-8DF3-89EE6500B970}DissanayakeDMorikawTakayukInvestigating Household Vehicle Ownership, Mode Choice, And Trip Sharing Decisions Using A Combined Revealed Preference/Stated Preference Nested Logit model: Case Study In Bangkok Metropolitan RegionJournal of Transport Geography2010402-4101837Lar13JournalArticle{44667924-069F-4366-8FE8-B5AA764A9DF6}LarsenK.BuliungR.FaulknerG.Safety and School Travel: How Does the Environment Along the Route Relate to Safety and Mode Choice?Transportation Research Record: Journal of the Transportation Research Board20139-18Transportation Research Board of the National Academies23278Joh06JournalArticle{AE03F1DD-ECED-4493-BE8A-0F699BC0FAF5}The Effects of Attitudes And Personality Traits On Mode Choice2006406JohanssonabM.v.HeldtT.JohanssonPer.Transportation Research Part A: Policy and Practice507-52539Bha06JournalArticle{6AFC1D23-37B6-43A4-BD4B-A8ADC7875942}BhatChandraR.SardesaiR.The Impact Of Stop-Making And Travel Time Reliability On Commute Mode ChoiceTransportation Research Part B: Methodological2006709-73040910Sch13JournalArticle{C520DC7E-4875-44F5-874B-D9C0266A3833}ScheinerJ.Holz-RauC.A comprehensive Study Of Life Course, Cohort, And Period Effects On Changes In Travel Mode UseTransportation Research Part A: Policy and Practice2013167-1814711Pra09JournalArticle{49193CDF-096F-49F5-99BD-C4E872107160}Route Choice Modeling: Past, Present and Future Research Directions2009PratoC.,GJournal of Choice Modelling65-1002112McF74BookSection{5595771F-7A70-48C5-AA13-14D94606361F}McFaddenD.ZarembkaP.Conditional Logit Analysis Of Qualitative Choice Behavior1974Academic Press, New York, USAFrontiers in Econometrics105-14213Ben85Book{05ED7905-E9A2-4E17-B75E-E36AEC5479D5}Ben-AkivaM.E.LermanS.R.Discrete Choice Analysis: Theory and Application to Travel Demand1985MIT Press14Hen07Book{36D919AC-C535-4A19-AF6F-094387C614A2}Applied Choice Analysis A Primer (Third Edition)2007Cambridge University PressHensherA.,D.RoseJ.M.GreeneW.H.15Tra09Book{ABF0AD82-089E-47AB-B927-72061DB8A17D}TrainE.K.Discrete Choice Methods With Simulation2009Cambridge University Press, UK16PR18InternetSite{5334F3A1-6E17-4E15-AFF0-0B95CBA002A6}Pakistan Railways, Government of Pakistan, Ministry of Railways, Islamabad, Pakistan.2018Pakistan Railwayshttps://www.pakrail.gov.pk/AboutUs.aspx2018Feburary1917MoF18Misc{0C75E6F7-8CCE-479C-A498-DF9D5C33A358}2018MoFEconomic Survey 2017-18Ministry of Finance, Government of Pakistan, Islamabad2018March3http://www.finance.gov.pk/survey/chapters_17/13-Transport_and_Communications.pdf18Kar17JournalArticle{C0FCA57D-3792-4553-A586-81F3E8F03B65}KarahaliosH.The application of the AHP-TOPSIS for evaluating ballast water treatment systems by ship operators2017Transportation Research Part D: Transport and Environment pp.172-1845219Mei08JournalArticle{FC593DA1-B80A-4760-BECA-7FC59F5595B8}A Review Of The Transportation Mode Choice And Carrier Selection LiteratureThe International Journal of Logistics Management2008183-211MeixellMaryJNorbisMario19220Bor09Report{87B8A83E-D024-4BBB-9DFB-D756AA639B41}BoranF.E.,Genç, S., Kurt, M. and Akay, D. 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