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CASIA OpenIR
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Parallel Emergency Management of Incidents by Integrating OODA and PREA Loops: The C2 Mechanism and Modes 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 卷号: 53, 期号: 4, 页码: 2160-2172
作者:  Yang, Dongsheng;  Li, Qiang;  Zhu, Fenghua;  Cui, Huachao;  Yi, Wen;  Qin, Jirong
收藏  |  浏览/下载：88/0  |  提交时间：2023/11/17
Edge command and control (edge C2)  incident C2  observe-orient-decide-act (OODA) loop  parallel command and control (parallel C2)  planning-readiness-execution-assessment (PREA) loop  
基于多核自适应网络的高速公路交通流预测方法 期刊论文
交通运输研究, 2021, 页码: 1-9
作者:  习金浩;  孟峰;  朱凤华;  贺正冰;  李润梅;  吕宜生
Adobe PDF(1804Kb)  |  收藏  |  浏览/下载：104/44  |  提交时间：2023/06/26
Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 页码: 14
作者:  Liang, Maohan;  Liu, Ryan Wen;  Zhan, Yang;  Li, Huanhuan;  Zhu, Fenghua;  Wang, Fei-Yue
收藏  |  浏览/下载：152/0  |  提交时间：2022/11/14
Traffic flow prediction  maritime traffic network  multi-graph convolutional network  automatic identification system (AIS)  
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：260/30  |  提交时间：2022/09/19
deep reinforcement learning  online ride-hailing system  hierarchical repositioning framework  parallel coordination mechanism  mixed state  
基于稀疏正则化的卷积神经网络模型剪枝方法 期刊论文
计算机工程, 2021, 卷号: 47, 期号: 10, 页码: 61-66
作者:  韦越;  陈世超;  朱凤华;  熊刚
Adobe PDF(1647Kb)  |  收藏  |  浏览/下载：232/63  |  提交时间：2022/04/08
深度学习, 模型剪枝 , 卷积神经网络 , 稀疏约束, 模型压缩  
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：366/123  |  提交时间：2022/04/08
Adaptive graph learning, Traffic prediction, Graph convolutional network, Expectation maximization, Deep learning  
PRECOM: A Parallel Recommendation Engine for Control, Operations, and Management on Congested Urban Traffic Networks 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 23, 期号: 7, 页码: 11
作者:  Jin, Junchen;  Rong, Dingding;  Pang, Yuqi;  Zhu, Fenghua;  Guo, Haifeng;  Ma, Xiaoliang;  Wang, Fei-Yue
收藏  |  浏览/下载：186/0  |  提交时间：2022/01/27
Engines  Control systems  Urban areas  Process control  Roads  Generators  Spatiotemporal phenomena  Spatial-temporal recommender system  urban traffic control  parallel traffic management  human-in-the-loop system  
Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 卷号: 99, 期号: 99, 页码: 10
作者:  Ye, Peijun;  Chen, Yuanyuan;  Zhu, Fenghua;  Lv, Yisheng;  Lu, Wanze;  Wang, Fei-Yue
收藏  |  浏览/下载：235/0  |  提交时间：2022/01/27
Calibration  Computational modeling  Mathematical model  Machine learning  Aggregates  Optimization  Bayes methods  Agent-based model (ABM)  calibration  Markovian process  
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：297/58  |  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  
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：226/52  |  提交时间：2021/06/16
Taxi demand prediction  taxi zone clustering  heterogeneity analysis  deep learning