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Computational Experiments for Complex Social Systems: Integrated Design of Experiment System 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 5, 页码: 1175-1189
作者:  Xiao Xue;  Xiangning Yu;  Deyu Zhou;  Xiao Wang;  Chongke Bi;  Shufang Wang;  Fei-Yue Wang
Adobe PDF(11890Kb)  |  收藏  |  浏览/下载：34/4  |  提交时间：2024/04/10
Artificial society  computational experiments  model integration  operation engine  technology integration  
Parallel Transportation in TransVerse: From Foundation Models to DeCAST 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 卷号: 24, 期号: 12, 页码: 15310-15327
作者:  Zhao, Chen;  Wang, Xiao;  Lv, Yisheng;  Tian, Yonglin;  Lin, Yilun;  Wang, Fei-Yue
Adobe PDF(4139Kb)  |  收藏  |  浏览/下载：143/0  |  提交时间：2023/11/16
Intelligent Transportation Systems (ITS)  Cyber-Physical-Social Systems (CPSS)  Artificial Systems, Computational Experiments, Parallel Execution (ACP)  Decentralized/Distributed Autonomous Operations and Organizations (DAO)  
Steps Toward Industry 5.0: Building “6S” Parallel Industries With Cyber-Physical-Social Intelligence 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 8, 页码: 1692-1703
作者:  Xingxia Wang;  Jing Yang;  Yutong Wang;  Qinghai Miao;  Fei-Yue Wang;  Aijun Zhao;  Jian-Ling Deng;  Lingxi Li;  Xiaoxiang Na;  Ljubo Vlacic
Adobe PDF(15811Kb)  |  收藏  |  浏览/下载：119/24  |  提交时间：2023/07/20
ACP  artificial intelligence  CPS  CPSS  Industry 4.0  Industry 5.0  parallel industries  parallel intelligence  
Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 3, 页码: 603-631
作者:  Qinghai Miao;  Yisheng Lv;  Min Huang;  Xiao Wang;  Fei-Yue Wang
Adobe PDF(11937Kb)  |  收藏  |  浏览/下载：874/145  |  提交时间：2023/03/02
Machine learning  parallel learning  parallel systems  sim-to-real  syn-to-real  virtual-to-real  
STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 1, 页码: 226-238
作者:  Zebing Wei;  Hongxia Zhao;  Zhishuai Li;  Xiaojie Bu;  Yuanyuan Chen;  Xiqiao Zhang;  Yisheng Lv;  Fei-Yue Wang
Adobe PDF(7068Kb)  |  收藏  |  浏览/下载：267/66  |  提交时间：2023/01/03
Deep learning  graph neural network (GNN)  multi-stream  spatial-temporal feature extraction  temporal graph  traffic prediction  
Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 10, 页码: 1752-1762
作者:  Xianglei Zhu;  Wen Hu;  Zejian Deng;  Jinwei Zhang;  Fengqing Hu;  Rui Zhou;  Keqiu Li;  Fei-Yue Wang
Adobe PDF(4329Kb)  |  收藏  |  浏览/下载：160/58  |  提交时间：2022/09/08
Cut-in behavior  interaction-aware  mixed traffic  risk assessment  trajectory prediction  
基于踪片Tracklet关联的视觉目标跟踪: 现状与展望 期刊论文
自动化学报, 2017, 卷号: 43, 期号: 11, 页码: 1869-1885
作者:  刘雅婷;  王坤峰;  王飞跃
Adobe PDF(5908Kb)  |  收藏  |  浏览/下载：188/43  |  提交时间：2022/06/16
Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 9, 期号: 11, 页码: 1-15
作者:  Yang, Linyao;  Lv, Chen;  Wang, Xiao;  Qiao, Ji;  Ding, Weiping;  Zhang, Jun;  Wang, Fei-Yue
Adobe PDF(1600Kb)  |  收藏  |  浏览/下载：185/24  |  提交时间：2022/06/15
entity alignment  integer programming  knowledge fusion  knowledge graph embedding  power dispatch  
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2021, 卷号: 0, 期号: 0, 页码: 0
作者:  Chizhan Zhang;  Fenghua Zhu;  Yisheng Lv;  Peijun Ye;  Feiyue Wang
Adobe PDF(4431Kb)  |  收藏  |  浏览/下载：229/53  |  提交时间：2021/06/16
Taxi demand prediction  taxi zone clustering  heterogeneity analysis  deep learning  
A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction 期刊论文
Journal of Automatica Sinica, 2020, 期号: *, 页码: *
作者:  Li Xuesong;  Liu Yating;  Wang Kunfeng;  Wang Fei-Yue
浏览  |  Adobe PDF(5533Kb)  |  收藏  |  浏览/下载：169/47  |  提交时间：2020/06/08
Trajectory prediction  recurrent attention and interaction model  Long Short-Term Memory  deep learning