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Integrated avionics system safety optimization method based on deep reinforcement learning
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Changxiao ZHAO1, 2, Daojun LI1, Yixuan SUN1, Peng JING1, Yi TIAN1, 2, **
China Safety Science Journal | 2024, 34(7) : 123 - 131
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China Safety Science Journal | 2024, 34(7): 123-131
Safety engineering technology
Integrated avionics system safety optimization method based on deep reinforcement learning
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Changxiao ZHAO1, 2, Daojun LI1, Yixuan SUN1, Peng JING1, Yi TIAN1, 2, **
Affiliations
  • 1 School of Safety Engineering and Science,Civil Aviation University of China,Tianjin 300300,China
  • 2 Key Laboratory of Civil Aviation Airworthiness Certification Technology,Civil Aviation University of China,Tianjin 300300,China
Published: 2024-07-28 doi: 10.16265/j.cnki.issn1003-3033.2024.07.0228
Outline
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To solve the problem that traditional safety design methods based on manual inspection were difficult to cope with the explosion of optional residence solutions caused by the large-scale integration of avionics systems,an avionics system partition model,task model and safety criticality level quantification model were constructed,and the comprehensive design optimization considering safety was modeled as an MDP problem. An optimization method of Soft Action-Critic (SAC) algorithm based on Actor-Critic framework was proposed. In order to obtain the correlation between the parameter selection and training results of SAC algorithm,the sensitivity of the algorithm parameters was studied. At the same time,to verify the superiority of the optimization method based on the SAC algorithm in optimizing the comprehensive design considering safety,optimization comparison experiments were carried out with the Deep Deterministic Policy Gradient (DDPG) algorithm and the traditional allocation algorithm as the objects. The results show that under the optimal parameter combination,the maximum reward after using convergence of SAC algorithm increases by nearly 8% compared with other parameter combinations,and the convergence time is shortened by nearly 16.6%. Compared with the DDPG algorithm and the traditional allocation algorithm,the optimization method based on SAC algorithm has improved approximately 62%,7464%,8370%,2123% and 775% in terms of the maximum reward,cumulative constraint violation rate,partition balance risk effect,partition resource utilization and solution time

deep reinforcement learning  /  integrated modular avionics  /  safety  /  Markov decision process (MDP)  /  integrated design
Changxiao ZHAO, Daojun LI, Yixuan SUN, Peng JING, Yi TIAN. Integrated avionics system safety optimization method based on deep reinforcement learning[J]. China Safety Science Journal, 2024 , 34 (7) : 123 -131 . DOI: 10.16265/j.cnki.issn1003-3033.2024.07.0228
Year 2024 volume 34 Issue 7
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.07.0228
  • Receive Date:2024-01-18
  • Online Date:2025-07-09
  • Published:2024-07-28
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  • Received:2024-01-18
  • Revised:2024-04-21
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Affiliations
    1 School of Safety Engineering and Science,Civil Aviation University of China,Tianjin 300300,China
    2 Key Laboratory of Civil Aviation Airworthiness Certification Technology,Civil Aviation University of China,Tianjin 300300,China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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