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A multi-stage stochastic dispatching method for electricity‑hydrogen integrated energy systems driven by model and data Journal article
Yang, Zhixue, Ren, Zhouyang, Li, Hui, Sun, Zhiyuan, Feng, Jianbing, Xia, Weiyi. A multi-stage stochastic dispatching method for electricity‑hydrogen integrated energy systems driven by model and data[J]. Applied Energy, 2024, 371, 123668.
Authors:  Yang, Zhixue;  Ren, Zhouyang;  Li, Hui;  Sun, Zhiyuan;  Feng, Jianbing; et al.
Favorite | TC[WOS]:4 TC[Scopus]:9  IF:10.1/10.4 | Submit date：2024/07/04
Chance-constrained  Electricity‑hydrogen Integrated Energy Systems  Hydrogen Energy  Multi-agent Deep Reinforcement Learning  Uncertainty  
Learning-based Autonomous Channel Access in the Presence of Hidden Terminals Journal article
Shao,Yulin, Cai,Yucheng, Wang,Taotao, Guo,Ziyang, Liu,Peng, Luo,Jiajun, Gunduz,Deniz. Learning-based Autonomous Channel Access in the Presence of Hidden Terminals[J]. IEEE Transactions on Mobile Computing, 2024, 23(5), 3680 - 3695.
Authors:  Shao,Yulin;  Cai,Yucheng;  Wang,Taotao;  Guo,Ziyang;  Liu,Peng; et al.
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:7.7/6.5 | Submit date：2023/08/03
Hidden Terminal  Multi-agent Deep Reinforcement Learning  Multiple Channel Access  Proximal Policy Optimization  Wi-fi  
Emergency Control Method of Multi-Modal Passenger Flow in Urban Rail Transit Journal article
Zhu, Guangyu, Mu, Liang, Sun, Ranran, Zhang, Nuo, Wu, Bo, Zhang, Peng, Law, Rob. Emergency Control Method of Multi-Modal Passenger Flow in Urban Rail Transit[J]. IEEE Transactions on Automation Science and Engineering, 2023, 1 - 11.
Authors:  Zhu, Guangyu;  Mu, Liang;  Sun, Ranran;  Zhang, Nuo;  Wu, Bo; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:5.9/6.0 | Submit date：2024/02/22
Emergency Control  Multi-agent Deep Reinforcement Learning  Multi-modal Passenger Flow  Urban Rail Transit  
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning Journal article
Li,Zhenning, Yu,Hao, Zhang,Guohui, Dong,Shangjia, Xu,Cheng Zhong. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 125, 103059.
Authors:  Li,Zhenning;  Yu,Hao;  Zhang,Guohui;  Dong,Shangjia;  Xu,Cheng Zhong
Favorite | TC[WOS]:75 TC[Scopus]:96  IF:7.6/9.6 | Submit date：2021/05/31
Multi-agent Reinforcement Learning  Knowledge Sharing  Adaptive Traffic Signal Control  Deep Learning  Transportation Network