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Deep learning model for solar and wind energy forecasting considering Northwest China as an example Journal article
Li, Pengyu, Yang, Huiyu, Wu, Han, Wang, Yujia, Su, Hao, Zheng, Tianlong, Zhu, Fang, Zhang, Guangtao, Han, Yu. Deep learning model for solar and wind energy forecasting considering Northwest China as an example[J]. Results in Engineering, 2024, 24, 102939.
Authors:  Li, Pengyu;  Yang, Huiyu;  Wu, Han;  Wang, Yujia;  Su, Hao; et al.
Favorite | TC[WOS]:4 TC[Scopus]:4  IF:6.0/5.6 | Submit date：2024/10/10
Attention-based Spatial-temporal Graph Neural Network  Deep Learning  Long Short-term Memory  Solar Energy  Wind Energy  
Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning Journal article
Li, Tao, Bian, Zilin, Lei, Haozhe, Zuo, Fan, Yang, Ya Ting, Zhu, Quanyan, Li, Zhenning, Ozbay, Kaan. Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2024, 167, 104804.
Authors:  Li, Tao;  Bian, Zilin;  Lei, Haozhe;  Zuo, Fan;  Yang, Ya Ting; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.6/9.6 | Submit date：2024/09/03
Real-time Traffic Surveillance  Online Learning Control  Spatial–temporal Forecasting  Traffic State Estimation  Dynamic Route Planning  
Weakly Supervised Monocular 3D Object Detection by Spatial-Temporal View Consistency Journal article
Han, Wencheng, Tao, Runzhou, Ling, Haibin, Shen, Jianbing. Weakly Supervised Monocular 3D Object Detection by Spatial-Temporal View Consistency[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Authors:  Han, Wencheng;  Tao, Runzhou;  Ling, Haibin;  Shen, Jianbing
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:20.8/22.2 | Submit date：2024/10/10
Monocular 3d Object Detection  Production Cars Data  Spatial-temporal Consistency  Weakly Supervised Learning  
FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction Conference paper
Yang, Linghua, Chen, Wantong, He, Xiaoxi, Wei, Shuyue, Xu, Yi, Zhou, Zimu, Tong, Yongxin. FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction[C], New York, NY, USA:Association for Computing Machinery, 2024, 6105–6116.
Authors:  Yang, Linghua;  Chen, Wantong;  He, Xiaoxi;  Wei, Shuyue;  Xu, Yi; et al.
Favorite | TC[Scopus]:1 | Submit date：2024/09/11
Federated Learning  Traffic Prediction  Spatial-temporal Graph Neural Network  
Vehicle Trajectory Completion for Automatic Number Plate Recognition Data: A Temporal Knowledge Graph-Based Method Journal article
Long, Zhe, Chen, Jinjin, Zhang, Zuping. Vehicle Trajectory Completion for Automatic Number Plate Recognition Data: A Temporal Knowledge Graph-Based Method[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2023, 37(13), 2350029.
Authors:  Long, Zhe;  Chen, Jinjin;  Zhang, Zuping
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:0.9/1.0 | Submit date：2024/01/10
Automatic Number Plate Recognition (Anpr) Data  Knowledge Representation Learning  Link Prediction  Temporal Knowledge Graph  Vehicle Trajectory Completion  
A novel model for tourism demand forecasting with spatial–temporal feature enhancement and image-driven method Journal article
Dong, Yunxuan, Zhou, Binggui, Yang, Guanghua, Hou, Fen, Hu, Zheng, Ma, Shaodan. A novel model for tourism demand forecasting with spatial–temporal feature enhancement and image-driven method[J]. NEUROCOMPUTING, 2023, 556, 126663.
Authors:  Dong, Yunxuan;  Zhou, Binggui;  Yang, Guanghua;  Hou, Fen;  Hu, Zheng; et al.
Favorite | TC[WOS]:4 TC[Scopus]:5  IF:5.5/5.5 | Submit date：2023/08/30
Deep Learning  Feature Enhancement  Spatial Series To Image Series  Spatial–temporal Learning  Tourism Demand Forecasting  
A graph-attention based spatial-temporal learning framework for tourism demand forecasting Journal article
Zhou, Binggui, Dong, Yunxuan, Yang, Guanghua, Hou, Fen, Hu, Zheng, Xu, Suxiu, Ma, Shaodan. A graph-attention based spatial-temporal learning framework for tourism demand forecasting[J]. Knowledge-Based Systems, 2023, 263, 110275.
Authors:  Zhou, Binggui;  Dong, Yunxuan;  Yang, Guanghua;  Hou, Fen;  Hu, Zheng; et al.
Favorite | TC[WOS]:14 TC[Scopus]:13  IF:7.2/7.4 | Submit date：2023/04/03
Tourism Demand Forecasting  Dynamic Spatial Connections  Spatial-temporal Learning  Graph Neural Network  Attention Mechanism  
A novel probabilistic framework with interpretability for generator coherency identification Journal article
Liu, Fengrui, Yin, Yikun, Li, Baitong. A novel probabilistic framework with interpretability for generator coherency identification[J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2022, 143, 108474.
Authors:  Liu, Fengrui;  Yin, Yikun;  Li, Baitong
Favorite | TC[WOS]:1 TC[Scopus]:7  IF:5.0/4.6 | Submit date：2022/08/02
Generator Coherency Identification  Interpretability  Multi-task Learning  Spatial–temporal Auto-encoder  Wide-area Measurement  
Improving Video Temporal Consistency via Broad Learning System Journal article
Sheng, Bin, Li, Ping, Ali, Riaz, Chen, C. L.P.. Improving Video Temporal Consistency via Broad Learning System[J]. IEEE Transactions on Cybernetics, 2022, 52(7), 6662-6675.
Authors:  Sheng, Bin;  Li, Ping;  Ali, Riaz;  Chen, C. L.P.
Favorite | TC[WOS]:79 TC[Scopus]:79  IF:9.4/10.3 | Submit date：2022/05/13
Incremental Learning  Temporally Broad Learning System (Tbls)  Video Temporal Consistency.  
Shortening passengers' travel time: A dynamic metro train scheduling approach using deep reinforcement learning Journal article
Wang, Zhaoyuan, Pan, Zheyi, Chen, Shun, Ji, Shenggong, Yi, Xiuwen, Zhang, Junbo, Wang, Jingyuan, Gong, Zhiguo, Li, Tianrui, Zheng, Yu. Shortening passengers' travel time: A dynamic metro train scheduling approach using deep reinforcement learning[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 35(5), 5282-5295.
Authors:  Wang, Zhaoyuan;  Pan, Zheyi;  Chen, Shun;  Ji, Shenggong;  Yi, Xiuwen; et al.
Favorite | TC[WOS]:2 TC[Scopus]:4  IF:8.9/8.8 | Submit date：2022/05/17
Metro Systems  Spatio-temporal Data  Neural Network  Deep Reinforcement Learning  Urban Computing