Yi Yang
6 Papers
Yi Yang is an academic researcher. The author has contributed to research in topics: Computer science & Markov decision process. The author has an hindex of 2, co-authored 4 publications.
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Papers
Data-driven prognostic method based on self-supervised learning approaches for fault detection
TL;DR: This paper proposes a data-driven method in a self-supervised manner, which is different from previous prognostic methods, and shows that the algorithm outperforms other fault detection methods.
81
Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control
TL;DR: The DT automated conveyor system (DT-ACS) is proposed that constructs the road map to implement the RIC-based conveyors under the background of a smart factory, and profit-sharing-based deep Q-networks (PDQNs) have been proposed to cope with the Ric optimization problem.
33
Optimal Look-ahead Control of CSPS System by Deep Q-Network and Profit Sharing
Jiaxiang Cheng,Tian Wang,Pengbo Dai,Yi Yang,Lei Ren,Hichem Snoussi +5 more
- 01 Nov 2018
TL;DR: The results show that the combination of DQN and PS can effectively optimize the performance of look-ahead control when the parameters are selected reasonably.
3
Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood
TL;DR: The experiment results show that the OEAN can reduce the complexity of the agents’ interaction description, meanwhile the top-coal caving performance can be improved significantly and the convergence property of the proposed methodology proved that the Q-value function can approach the global Nash equilibrium value using the iteration mechanism.
Event prediction via spatio-temporal sequence analysis
ZeXian Li,Tian Wang,Yi Yang,Yan Wang,Peng Shi,Hichem Snoussi +5 more
- 01 Nov 2019
TL;DR: The means of deep learning is proposed to conduct research on sequence images’ event prediction in time and space dimensions to solve the problem of missing semantic information and lack of external information in the current event prediction research.