Flight maneuver intelligent recognition based on deep variational autoencoder network
The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is the particularly urgent and important requirement for the current talent training strategy of national and military to increase efficiency an...
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| Veröffentlicht in: | EURASIP journal on advances in signal processing Jg. 2022; H. 1; S. 1 - 23 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Springer International Publishing
12.03.2022
Springer Springer Nature B.V SpringerOpen |
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| ISSN: | 1687-6180, 1687-6172, 1687-6180 |
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| Abstract | The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is the particularly urgent and important requirement for the current talent training strategy of national and military to increase efficiency and speed up all aspects of pilot training, reduce the training cycle and reduce the elimination rate. To this end, this paper uses deep variational auto-encoder network and adaptive dynamic time warping algorithms as support to explore the establishment of an integrated evaluation system for flight maneuver recognition and quality evaluation, solve the industry difficulty faced by current flight training data mining applications, and achieve accurate recognition and reliable quality evaluation of flight regimes under the background of high mobility. It will fully explore the benefits of existing airborne flight data for military trainee pilots, support the personalized and accurate training of flight talents, and reduce the rate of talent elimination. |
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| AbstractList | The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is the particularly urgent and important requirement for the current talent training strategy of national and military to increase efficiency and speed up all aspects of pilot training, reduce the training cycle and reduce the elimination rate. To this end, this paper uses deep variational auto-encoder network and adaptive dynamic time warping algorithms as support to explore the establishment of an integrated evaluation system for flight maneuver recognition and quality evaluation, solve the industry difficulty faced by current flight training data mining applications, and achieve accurate recognition and reliable quality evaluation of flight regimes under the background of high mobility. It will fully explore the benefits of existing airborne flight data for military trainee pilots, support the personalized and accurate training of flight talents, and reduce the rate of talent elimination. Abstract The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is the particularly urgent and important requirement for the current talent training strategy of national and military to increase efficiency and speed up all aspects of pilot training, reduce the training cycle and reduce the elimination rate. To this end, this paper uses deep variational auto-encoder network and adaptive dynamic time warping algorithms as support to explore the establishment of an integrated evaluation system for flight maneuver recognition and quality evaluation, solve the industry difficulty faced by current flight training data mining applications, and achieve accurate recognition and reliable quality evaluation of flight regimes under the background of high mobility. It will fully explore the benefits of existing airborne flight data for military trainee pilots, support the personalized and accurate training of flight talents, and reduce the rate of talent elimination. |
| ArticleNumber | 21 |
| Audience | Academic |
| Author | Tian, Wei Zhang, Hong Li, Hui Xiong, Yuan |
| Author_xml | – sequence: 1 givenname: Wei orcidid: 0000-0002-2214-5417 surname: Tian fullname: Tian, Wei email: twhztwhz@126.com organization: Jiangxi Hongdu Aviation Industry Group Co. Ltd, Naval Aviation University – sequence: 2 givenname: Hong surname: Zhang fullname: Zhang, Hong organization: Jiangxi Hongdu Aviation Industry Group Co. Ltd – sequence: 3 givenname: Hui surname: Li fullname: Li, Hui organization: Naval Aviation University – sequence: 4 givenname: Yuan surname: Xiong fullname: Xiong, Yuan organization: Jiangxi Hongdu Aviation Industry Group Co. Ltd |
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| Keywords | Variational autoencoder Flight quality evaluation Deep neural network Flight maneuver recognition Dynamic time warping |
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| Snippet | The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is... Abstract The selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination... |
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| SubjectTerms | Aircraft maneuvers Aircraft pilots Algorithms Artificial Intelligence and Cognitive Computing for Smart Internet of Vehicle Coders Consumption (Economics) Data mining Deep neural network Dynamic time warping Engineering Flight maneuver recognition Flight quality evaluation Flight training Pilot training Pilots Pilots and pilotage Quality assessment Quantum Information Technology Recognition Signal,Image and Speech Processing Spintronics Variational autoencoder |
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| Title | Flight maneuver intelligent recognition based on deep variational autoencoder network |
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| Volume | 2022 |
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