| 1 |
JIANG Z Y, ZHAO T D, WANG S H, et al. New model-based analysis method with multiple constraints for integrated modular avionics dynamic reconfiguration process[J]. Processes, 2020, 8 (5): 574.
doi: 10.3390/pr8050574
|
| 2 |
KIM T. A practical cache partitioning method for multi-core processor on a commercial safety-critical partitioned RTOS[J]. IEEE Access, 2025, 13, 25505- 25519.
doi: 10.1109/ACCESS.2025.3538540
|
| 3 |
STEWART D, LIU J J, COFER D, et al. AADL-based safety analysis using formal methods applied to aircraft digital systems[J]. Reliability Engineering & System Safety, 2021, 213, 107649.
|
| 4 |
YE Y G, LI H Q, WANG Q S, et al. Fault diagnosis of railway wheelsets: a review[J]. Measurement, 2025, 242, 116169.
doi: 10.1016/j.measurement.2024.116169
|
| 5 |
YE M Y, YAN X A, HUA X, et al. MRCFN: a multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios[J]. Expert Systems with Applications, 2025, 259, 125214.
doi: 10.1016/j.eswa.2024.125214
|
| 6 |
KOUGIATSOS N, REPPA V. A distributed cyber-physical framework for sensor fault diagnosis of marine internal combustion engines[J]. IEEE Trans. on Control Systems Technology, 2024, 32 (5): 1718- 1729.
doi: 10.1109/TCST.2024.3378992
|
| 7 |
SAMIR M, ELHATTAB M, ASSI C, et al. Optimizing age of information through aerial reconfigurable intelligent surfaces: a deep reinforcement learning approach[J]. IEEE Trans. on Vehicular Technology, 2021, 70 (4): 3978- 3983.
doi: 10.1109/TVT.2021.3063953
|
| 8 |
SAFARI S, ANSARI M, KHDR H, et al. A survey of fault-tolerance techniques for embedded systems from the perspective of power, energy, and thermal issues[J]. IEEE Access, 2022, 10, 12229- 12251.
doi: 10.1109/ACCESS.2022.3144217
|
| 9 |
WANG H C, NIU W S. A review on key technologies of the distributed integrated modular avionics system[J]. International Journal of Wireless Information Networks, 2018, 25 (3): 358- 369.
doi: 10.1007/s10776-018-0412-5
|
| 10 |
PAUL S, CRUZ E, DUTTA A, et al. Formal verification of safety-critical aerospace systems[J]. IEEE Aerospace and Electronic Systems Magazine, 2023, 38 (5): 72- 88.
doi: 10.1109/MAES.2023.3238378
|
| 11 |
ZHAO C, ZIO E, SHEN W M. Domain generalization for cross-domain fault diagnosis: an application-oriented perspective and a benchmark study[J]. Reliability Engineering & System Safety, 2024, 245, 109964.
|
| 12 |
LIU Y P, JIANG H K, YAO R H, et al. Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples[J]. Mechanical Systems and Signal Processing, 2024, 216, 111507.
doi: 10.1016/j.ymssp.2024.111507
|
| 13 |
HAN P J, HU W T, ZHAI Z J, et al. A model-based optimization method of ARINC 653 multicore partition scheduling[J]. Aerospace, 2024, 11 (11): 915.
doi: 10.3390/aerospace11110915
|
| 14 |
PEIERRA R, BECKER L B, LISBOA C A. Optimization of task and message scheduling in IMA systems using a genetic algorithm with adaptive operators[J]. IEEE Trans. on Industrial Informatics, 2022, 18 (8): 5304- 5314.
|
| 15 |
MATEI I, PIOTROWSKI W, PEREZ A, et al. System resilience through health monitoring and reconfiguration[J]. ACM Transactions on Cyber-Physical Systems, 2024, 8 (1): 7.
|
| 16 |
ZHAO C X, DONG L, LI H, et al. Safety assessment of the reconfigurable integrated modular avionics based on STPA[J]. International Journal of Aerospace Engineering, 2021, 2021 (1): 8875872.
|
| 17 |
QASIM L, HEIN A M, OLARU S, et al. An ontology for system reconfiguration: integrated modular avionics IMA case study[M]//MADNI A M, BOEHM B, ERWIN D, et al, ed. Recent Trends and Advances in Model Based Systems Engineering. Cham: Springer International Publishing, 2022: 189−198.
|
| 18 |
TRIVEDI A, SRINIVASAN D, SANYAL K, et al. A survey of multiobjective evolutionary algorithms based on decomposition[J]. IEEE Trans. on Evolutionary Computation, 2016, 21 (3): 440- 462.
|
| 19 |
HE Z M, LI J C, WU F, et al. DeRL: coupling decomposition in action space for reinforcement learning task[J]. IEEE Trans. on Emerging Topics in Computational Intelligence, 2023, 8 (1): 1030- 1043.
|
| 20 |
ZHANG Y, ZHU H H, TANG D B, et al. Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems[J]. Robotics and Computer-Integrated Manufacturing, 2022, 78, 102412.
doi: 10.1016/j.rcim.2022.102412
|
| 21 |
LI J C, SHI H B, HWANG K S. Using goal-conditioned reinforcement learning with deep imitation to control robot arm in flexible flat cable assembly task[J]. IEEE Trans. on Automation Science and Engineering, 2023, 21 (4): 6217- 6228.
|
| 22 |
SILVA F L D, COSTA A H R. A survey on transfer learning for multiagent reinforcement learning systems[J]. Journal of Artificial Intelligence Research, 2019, 64 (1): 645- 703.
|
| 23 |
LIANG J X, MIAO H T, LI K, et al. A review of multi-agent reinforcement learning algorithms[J]. Electronics, 2025, 14 (4): 820.
doi: 10.3390/electronics14040820
|
| 24 |
LI C, DONG S K, YANG S D, et al. Coordinating multi-agent reinforcement learning via dual collaborative constraints[J]. Neural Networks, 2025, 182, 106858.
doi: 10.1016/j.neunet.2024.106858
|
| 25 |
HU K, XU K, XIA Q F, et al. An overview: attention mechanisms in multi-agent reinforcement learning[J]. Neurocomputing, 2024, 598, 128015.
doi: 10.1016/j.neucom.2024.128015
|
| 26 |
ALBRECHT S V, CHRISTIANOS F, SCHAFER L. Multi-agent reinforcement learning: foundations and modern approaches[M]. Cambridge: MIT Press, 2024.
|
| 27 |
RASHID T, SAMVELYAN M, SCHROEDER D W C, et al. QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning[J]. IEEE Access, 2018, 7, 4295- 4304.
|
| 28 |
ZHAO T Y, CHEN T, ZHANG B. QMIX-GNN: a graph neural network-based heterogeneous multi-agent reinforcement learning model for improved collaboration and decision-making[J]. Applied Sciences, 2025, 15 (7): 3794.
doi: 10.3390/app15073794
|
| 29 |
YANG Y, LI J T, PAN L L. Multi-robot path planning based on a deep reinforcement learning DQN algorithm[J]. CAAI Transactions on Intelligence Technology, 2020, 5 (3): 177- 183.
doi: 10.1049/trit.2020.0024
|
| 30 |
ALMAHAMID F, GROLINGER K. Agile DQN: adaptive deep recurrent attention reinforcement learning for autonomous UAV obstacle avoidance[J]. Scientific Reports, 2025, 15 (1): 18043.
doi: 10.1038/s41598-025-03287-y
|