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CASIA OpenIR
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Foundation Models for Transportation Intelligence: ITS Convergence in TransVerse 期刊论文
IEEE Intelligent Systems, 2022, 卷号: 37, 期号: 6, 页码: 77-82
作者:  Zhao, Chen;  Dai, Xingyuan;  Yisheng Lv;  Tian, Yonglin;  Ren, Yuhai;  Wang, Fei-Yue
Adobe PDF(2790Kb)  |  收藏  |  浏览/下载：10/3  |  提交时间：2024/05/28
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:  Wang, Xingxia;  Yang, Jing;  Wang, Yutong;  Miao, Qinghai;  Wang, Fei-Yue;  Zhao, Aijun;  Deng, Jian-Ling;  Li, Lingxi;  Na, Xiaoxiang;  Vlacic, Ljubo
Adobe PDF(3058Kb)  |  收藏  |  浏览/下载：127/0  |  提交时间：2023/11/17
ACP  artificial intelligence  CPS  CPSS  Industry 4  0  Industry 5  parallel industries  parallel intelligence  
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：156/4  |  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)  
Generating Emotion Descriptions for Fine Art Paintings via Multiple Painting Representations 期刊论文
IEEE Intelligent Systems, 2023, 卷号: 38, 期号: 3, 页码: 31-40
作者:  Lu, Yue;  Guo, Chao;  Dai, Xingyuan;  Wang, Fei-Yue
Adobe PDF(1045Kb)  |  收藏  |  浏览/下载：114/15  |  提交时间：2023/06/25
painting captioning  
Learning Lightweight Dynamic Kernels With Attention Inside via Local-Global Context Fusion 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 页码: 15
作者:  Tian, Yonglin;  Shen, Yu;  Wang, Xiao;  Wang, Jiangong;  Wang, Kunfeng;  Ding, Weiping;  Wang, Zilei;  Wang, Fei-Yue
收藏  |  浏览/下载：295/0  |  提交时间：2023/03/20
Attention inside kernels  dynamic convolution  global context  local context  
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：195/27  |  提交时间：2022/06/15
entity alignment  integer programming  knowledge fusion  knowledge graph embedding  power dispatch  
Supervised assisted deep reinforcement learning for emergency voltage control of power systems 期刊论文
NEUROCOMPUTING, 2022, 卷号: 475, 页码: 69-79
作者:  Li, Xiaoshuang;  Wang, Xiao;  Zheng, Xinhu;  Dai, Yuxin;  Yu, Zhihong;  Zhang, Jun Jason;  Bu, Guangquan;  Wang, Fei-Yue
Adobe PDF(2551Kb)  |  收藏  |  浏览/下载：327/65  |  提交时间：2022/06/06
Deep reinforcement learning  Behavioral cloning  Dynamic demonstration  Emergency control  
SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers 期刊论文
NEUROCOMPUTING, 2022, 卷号: 481, 页码: 91-101
作者:  Liu, Yating;  Bai, Tianxiang;  Tian, Yonglin;  Wang, Yutong;  Wang, Jiangong;  Wang, Xiao;  Wang, Fei-Yue
Adobe PDF(2635Kb)  |  收藏  |  浏览/下载：311/44  |  提交时间：2022/06/06
Multi-object tracking  Transformer  Semantic task  Dynamic query  
A Semi-supervised End-to-end Framework for Transportation Mode Detection by Using GPS-enabled Sensing Devices 期刊论文
IEEE Internet of Things Journal, 2022, 卷号: 9, 期号: 10, 页码: 7842-7852
作者:  Zhishuai Li;  Gang Xiong;  Zebing Wei;  YIsheng Lv;  Noreen Anwar;  Fei-Yue Wang
Adobe PDF(2754Kb)  |  收藏  |  浏览/下载：223/67  |  提交时间：2022/04/08
Transportation mode detection , Semi-supervised learning, Human mobility , GPS trajectory.  
AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks 期刊论文
Transportation Research Part C, 2022, 期号: 99, 页码: 1-1
作者:  Wei Zhang;  Fenghua Zhu;  Yisheng Lv;  Chang Tan;  Wen Liu;  Xin Zhang;  Fei-Yue Wang
Adobe PDF(2619Kb)  |  收藏  |  浏览/下载：384/126  |  提交时间：2022/04/08
Adaptive graph learning, Traffic prediction, Graph convolutional network, Expectation maximization, Deep learning