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Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation 期刊论文
INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING, 2020, 卷号: 48, 期号: 1, 页码: 61-79
Authors:  Yang, Wensi;  Yao QF(么庆丰);  Ye KJ(叶可江);  Xu CZ(须成忠)
Adobe PDF(2861Kb)  |  Favorite  |  View/Download:129/25  |  Submit date:2019/11/30
Convolutional neural networks  Empirical mode decomposition  Remaining useful life  Reliability  
Path Planning Method With Improved Artificial Potential Field-A Reinforcement Learning Perspective 期刊论文
IEEE ACCESS, 2020, 卷号: 8, 页码: 135513-135523
Authors:  Yao QF(么庆丰);  Zheng ZY(郑泽宇);  Qi, Liang;  Yuan, Haitao;  Guo, Xiwang;  Zhao M(赵明);  Liu Z(刘智);  Yang TJ(杨天吉)
Adobe PDF(1998Kb)  |  Favorite  |  View/Download:100/13  |  Submit date:2020/08/22
Path planning  Learning (artificial intelligence)  Gravity  Potential energy  Mobile agents  Real-time systems  Reinforcement learning  neural network  potential field  path planning  
基于改进人工势场法的移动机器人路径规划方法 期刊论文
计算机应用研究, 2020, 卷号: 37, 期号: S2, 页码: 66-68, 72
Authors:  赵明;  郑泽宇;  么庆丰;  潘怡君;  刘智
Adobe PDF(1901Kb)  |  Favorite  |  View/Download:26/0  |  Submit date:2021/01/17
路径规划  人工势场  人工智能  自适应  
Face Attribute Recognition with Multimodal Fusion 会议论文
2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2020, Shenyang, China, November 20-22, 2020
Authors:  Zhao M(赵明);  Liu C(刘畅);  Zheng ZY(郑泽宇);  Yao QF(么庆丰);  Yang N(杨楠);  Xu YY(许原野)
Adobe PDF(194Kb)  |  Favorite  |  View/Download:9/0  |  Submit date:2021/02/27
Face attribute recognition  Multimodal fusion  Graph neural network  Multi-task learning  
Remaining useful life estimation by empirical mode decomposition and ensemble deep convolution neural networks 会议论文
2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019, San Francisco, CA, United states, June 17-20, 2019
Authors:  Yao QF(么庆丰);  Yang TJ(杨天吉);  Liu Z(刘智);  Zheng ZY(郑泽宇)
Adobe PDF(1920Kb)  |  Favorite  |  View/Download:95/21  |  Submit date:2019/10/13
Neural Networks  Ensemble Learning  Empirical Mode Decomposition  Remaining Useful Life