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ORCIDAutonomous Intelligent Systems, Volume 2
Volume 2, Number 1, December 2022
- Adalberto Polenghi
, Irene Roda, Marco Macchi, Alessandro Pozzetti:
An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development.
- Tianci Wen, Yongchun Fang, Biao Lu:
Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances. - Wei Zhou, Dong Chen, Jun Yan, Zhaojian Li, Huilin Yin, Wancheng Ge:
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic. - Zhuoping Yu, Xinchen Hou, Bo Leng, Yuyao Huang:
Mass estimation method for intelligent vehicles based on fusion of machine learning and vehicle dynamic model. - Gautier Vanson, Pascale Marangé, Eric Levrat:
End-of-Life Decision making in circular economy using generalized colored stochastic Petri nets. - Michael O. Macaulay, Mahmood Shafiee:
Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure. 1-25 - Yutao Tang, Peng Yi, Yanqiong Zhang, Dawei Liu:
Nash equilibrium seeking over directed graphs. 1-8 - Shu Liang, Peng Yi, Yiguang Hong, Kaixiang Peng:
Exponentially convergent distributed Nash equilibrium seeking for constrained aggregative games. 1-8 - Andrew E. Bondoc, Mohsen Tayefeh, Ahmad Barari:
Learning phase in a LIVE Digital Twin for predictive maintenance. 1-10 - Kaijie Lu, Chong Chen, Tao Wang, Lianglun Cheng, Jian Qin:
Fault diagnosis of industrial robot based on dual-module attention convolutional neural network. 1-12 - Yuxin Liu, Chong Chen, Tao Wang, Lianglun Cheng:
An attention enhanced dilated CNN approach for cross-axis industrial robotics fault diagnosis. 1-11 - Yuki Miyashita, Toshiharu Sugawara:
Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning. 1-18 - Yanqiong Zhang, Chaoqun Liu, Yu-Ping Tian:
Distributed constrained aggregative games of uncertain Euler-Lagrange systems under unbalanced digraphs. 1-14 - Qian Zhao, Wu Zhao, Xin Guo, Kai Zhang, Miao Yu:
A dynamic customer requirement mining method for continuous product improvement. 1-17 - Huibin Fu, Ying Liu:
A deep learning-based approach for electrical equipment remaining useful life prediction. 1-12 - Hua Zhang, Xue He, Wei Yan, Zhigang Jiang, Shuo Zhu:
A machine learning-based approach for product maintenance prediction with reliability information conversion. 1-11
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