CASIA OpenIR
(本次检索基于用户作品认领结果)

浏览/检索结果: 共21条,第1-10条 帮助

限定条件            
已选(0)清除 条数/页:   排序方式:
ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 卷号: 23, 期号: 11, 页码: 20217-20229
作者:  Zhang, Wenwen;  Wang, Jiangong;  Wang, Yutong;  Wang, Fei-Yue
收藏  |  浏览/下载:75/0  |  提交时间:2023/03/20
Adaptation models  Representation learning  Feature extraction  Task analysis  Semantics  Generative adversarial networks  Object detection  Object detection  unsupervised domain adaptation  distribution alignment  domain-invariant representation  
HMDRL: Hierarchical Mixed Deep Reinforcement Learning to Balance Vehicle Supply and Demand 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 页码: 12
作者:  Xi, Jinhao;  Zhu, Fenghua;  Ye, Peijun;  Lv, Yisheng;  Tang, Haina;  Wang, Fei-Yue
Adobe PDF(3316Kb)  |  收藏  |  浏览/下载:231/29  |  提交时间:2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 23, 期号: 6, 页码: 5128-5137
作者:  Li, Xiaoshuang;  Ye, Peijun;  Jin, Junchen;  Zhu, Fenghua;  Wang, Fei-Yue
Adobe PDF(2319Kb)  |  收藏  |  浏览/下载:267/51  |  提交时间:2022/01/27
Generative adversarial networks  Data models  Gallium nitride  Task analysis  Complex systems  Intelligent traffic signal operations  deep behavioral cloning  
Acting As A Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2020, 期号: Accepted, 页码: Accepted
作者:  Yuanyuan Chen;  Hongyu Chen;  Peijun Ye;  Yisheng Lv;  Fei-Yue Wang
Adobe PDF(2382Kb)  |  收藏  |  浏览/下载:254/55  |  提交时间:2020/10/16
Traffic Flow Prediction  Ensemble Learning  Deep Learning  
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 8, 页码: 3049-3058
作者:  Chen, Yuanyuan;  Lv, Yisheng;  Wang, Xiao;  Li, Lingxi;  Wang, Fei-Yue
浏览  |  Adobe PDF(2273Kb)  |  收藏  |  浏览/下载:395/102  |  提交时间:2019/08/28
Deep learning  social transportation  traffic information detection  social media  text mining  
Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 6, 页码: 2395-2400
作者:  Lin, Yilun;  Dai, Xingyuan;  Li, Li;  Wang, Fei-Yue
Adobe PDF(623Kb)  |  收藏  |  浏览/下载:345/151  |  提交时间:2019/05/07
Traflic flow prediction  deep learning  generative adversarial network  
Capturing Car-Following Behaviors by Deep Learning 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 卷号: 19, 期号: 3, 页码: 910-920
作者:  Wang, Xiao;  Jiang, Rui;  Li, Li;  Lin, Yilun;  Zheng, Xinhu;  Wang, Fei-Yue
Adobe PDF(3543Kb)  |  收藏  |  浏览/下载:424/165  |  提交时间:2018/10/10
Microscopic Car-following Model  Deep Learning  Recurrent Neural Network (Rnn)  Gated Recurrent Unit (Gru) Neural Networks  
A Survey of Traffic Data Visualization 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 卷号: 16, 期号: 6, 页码: 2970-2984
作者:  Chen, Wei;  Guo, Fangzhou;  Wang, Fei-Yue
浏览  |  Adobe PDF(3269Kb)  |  收藏  |  浏览/下载:830/439  |  提交时间:2016/01/18
Traffic  Traffic Data Visualization  Visual Analysis  Data-driven Intelligent Transportation System  
Scanning the Issue and Beyond: Merton's Laws and Mertionian Systems for ITS 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 卷号: 16, 期号: 6
作者:  Wang, Fei-Yue
Adobe PDF(247Kb)  |  收藏  |  浏览/下载:185/6  |  提交时间:2016/01/18
A Security and Privacy Review of VANETs 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 卷号: 16, 期号: 6, 页码: 2985-2996
作者:  Qu, Fengzhong;  Wu, Zhihui;  Wang, Fei-Yue;  Cho, Woong
Adobe PDF(1031Kb)  |  收藏  |  浏览/下载:532/230  |  提交时间:2016/01/18
Vanets  Security  Privacy  Survey