no code implementations • 1 May 2022 • Wenbin Song, Di wu, Weiming Shen, Benoit Boulet
One of the key points of EFD is developing a generic model to extract robust and discriminative features from different equipment for early fault detection.
no code implementations • 27 Apr 2022 • Wenbin Song, Di wu, Weiming Shen, Benoit Boulet
To address this problem, many transfer learning based EFD methods utilize historical data to learn transferable domain knowledge and conduct early fault detection on new target bearings.
1 code implementation • 24 Apr 2022 • Wenbin Song, Mingrui Zhang, Joseph G. Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew D. Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen, Jun Wang
However, mesh movement methods, such as the Monge-Ampere method, require the solution of auxiliary equations, which can be extremely expensive especially when the mesh is adapted frequently.
1 code implementation • NeurIPS 2021 • Jianhong Wang, Wangkun Xu, Yunjie Gu, Wenbin Song, Tim C. Green
This paper presents a problem in power networks that creates an exciting and yet challenging real-world scenario for application of multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 16 Mar 2021 • David Mguni, Taher Jafferjee, Jianhong Wang, Nicolas Perez-Nieves, Tianpei Yang, Matthew Taylor, Wenbin Song, Feifei Tong, Hui Chen, Jiangcheng Zhu, Jun Wang, Yaodong Yang
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.