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CASIA OpenIR
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The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0 期刊论文
Information Fusion, 2024, 卷号: 107, 页码: 1-16
作者:  Xiao Wang;  Yutong Wang;  Jing Yang;  Xiaofeng Jia;  Lijun Li;  Weiping Ding;  Fei-Yue Wang
Adobe PDF(4446Kb)  |  收藏  |  浏览/下载：46/7  |  提交时间：2024/06/06
Multi-source data fusion  CPSS  Industrial metaverses  Parallel manufacturing  Social manufacturing  
A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 卷号: 8, 期号: 3, 页码: 2077-2087
作者:  Sun, Yao;  Hu, Yunfeng;  Zhang, Hui;  Wang, Feiyue;  Chen, Hong
收藏  |  浏览/下载：87/0  |  提交时间：2023/11/17
Combined CO2 estimation model  deterioration factor  OBD-independent  parallel supervision system  vehicle CO2 emissions  
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：186/9  |  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：133/15  |  提交时间：2023/06/25
painting captioning  
ArtCap: A Dataset for Image Captioning of Fine Art Paintings 期刊论文
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2022, 页码: 12
作者:  Lu, Yue;  Guo, Chao;  Dai, Xingyuan;  Wang, Fei-Yue
Adobe PDF(5137Kb)  |  收藏  |  浏览/下载：285/54  |  提交时间：2023/02/22
Dataset construction  image captioning  painting captioning  
HMDRL: Hierarchical Mixed Deep Reinforcement Learning to Balance Vehicle Supply and Demand 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 卷号: 23, 期号: 11, 页码: 21861-21872
作者:  Xi, Jinhao;  Zhu, Fenghua;  Ye, Peijun;  Lv, Yisheng;  Tang, Haina;  Wang, Fei-Yue
Adobe PDF(3316Kb)  |  收藏  |  浏览/下载：319/42  |  提交时间：2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
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：213/28  |  提交时间：2022/06/15
entity alignment  integer programming  knowledge fusion  knowledge graph embedding  power dispatch  
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：340/49  |  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：238/72  |  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：420/137  |  提交时间：2022/04/08
Adaptive graph learning, Traffic prediction, Graph convolutional network, Expectation maximization, Deep learning