Yanwen Chong - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Yanwen Chong

Publications

20

Citations

281

Publications by Year

20082025

Co-Authors:

Xiaoshu ChenYun DingJinpeng FengXueqing GeYuanyuan Guo

Show All Co-Authors (15)

Yanwen Chong

Affiliation

State Key Laboratory of Information Engineering in Surveying

Mapping

and Remote Sensing, Wuhan University, Wuhan, China

Publication Topics

Convolutional Neural Network,
Graph Convolution,
Graph Convolutional Network,
Graph Structure,
Hyperspectral Image Classification,
Hyperspectral Image Datasets,
Image Classification,
Indian Pines,
Deep Learning-based Methods,
Spatial Information,
Bitrate,
Classification Methods

Biography

Yanwen Chong received the B.S. degree from Qufu Normal University, China, in 1995, and the M.S. and Ph.D. degrees from Wuhan University, Wuhan, China. He is currently a Professor with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His research interests include video processing, computer vision, and pattern recognition.(Based on document published on 1 October 2020).

Publications

20

Citations

281

Publications by Year

20082025

Co-Authors:

Xiaoshu Chen
Yun Ding
Jinpeng Feng
Xueqing Ge
Yuanyuan Guo

Show All Co-Authors (15)

Author's Published Works

Search History

Showing 1-7 of 7 results

Conferences (5)

Journals (2)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Michael Lepech(5)
Yirong Chen(5)
Maonan Wang(2)
Man-On Pun(2)
Jie Wang(2)
Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA(3)
Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China(1)
Shanghai AI Laboratory, China(1)
Xiangdao Chuxing, Shanghai, China(1)
Stanford Center at the Incheon Global Campus, Incheon, South Korea(1)
2019 Winter Simulation Conference (WSC)(1)
2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)(1)
2022 International Joint Conference on Neural Networks (IJCNN)(1)
2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)(1)
IEEE Open Journal of Vehicular Technology(1)
IEEE(7)
Auckland, New Zealand(1)
Chicago, IL, USA(1)
National Harbor, MD, USA(1)
Padua, Italy(1)
Vienna, Austria(1)
Traffic Conditions(3)
Traffic Data(3)
Traffic Flow(3)
Arrival Rate(2)
Life Cycle Assessment(2)

Select All on Page

Sort By

Results

Traffic Signal Cycle Control With Centralized Critic and Decentralized Actors Under Varying Intervention Frequencies

Maonan Wang;Yirong Chen;Yuheng Kan;Chengcheng Xu;Michael Lepech;Man-On Pun;Xi Xiong

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

HTML

Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effect...Show More

Traffic Signal Cycle Control With Centralized Critic and Decentralized Actors Under Varying Intervention Frequencies

Maonan Wang;Yirong Chen;Yuheng Kan;Chengcheng Xu;Michael Lepech;Man-On Pun;Xi Xiong

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 12 | Journal Article |

Scalable Reinforcement Learning Framework for Traffic Signal Control Under Communication Delays

Aoyu Pang;Maonan Wang;Yirong Chen;Man-On Pun;Michael Lepech

IEEE Open Journal of Vehicular Technology

Year: 2024 | Volume: 5 | Journal Article |

Cited by: Papers (3)

HTML

Vehicle-to-everything (V2X) technology is pivotal for enhancing road safety, traffic efficiency, and energy conservation through the communication of vehicles with their surrounding entities such as other vehicles, pedestrians, roadside infrastructure, and networks. Among these, traffic signal control (TSC) plays a significant role in roadside infrastructure for V2X. However, most existing works o...Show More

Scalable Reinforcement Learning Framework for Traffic Signal Control Under Communication Delays

Aoyu Pang;Maonan Wang;Yirong Chen;Man-On Pun;Michael Lepech

IEEE Open Journal of Vehicular Technology

Year: 2024 | Volume: 5 | Journal Article |

Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting

Yirong Chen;Ziyue Li;Wanli Ouyang;Michael Lepech

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

Year: 2023 | Conference Paper |

HTML

Accurate traffic forecasting is vital to intelligent transportation systems, which are widely adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling spatial-temporal dynamics in traffic data, among which the graph convolution network (GCN) is at the center for exploiting the spatial dependency embedded in the road network graphs. However, these GCN-based meth...Show More

Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting

Yirong Chen;Ziyue Li;Wanli Ouyang;Michael Lepech

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

Year: 2023 | Conference Paper |

@ME: A Fine-grained Route Recommendation System to Grab Impatient Passengers

Hanwen Hu;Yirong Chen;Jingchao Ouyang;Shiyou Qian;Jian Cao;Han Han;Jie Wang;Michael Lepech

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

HTML

Data analysis reveals that passengers can only endure a few minutes before taking a taxi. However, most existing route recommendation systems are not adequate to satisfy impatient customers due to two shortcomings: inaccurate demand forecast and the lack of an efficient supply-demand balance mechanism. In this paper, we propose a recommendation system called @ Me, which aims to dispatch vacant tax...Show More

@ME: A Fine-grained Route Recommendation System to Grab Impatient Passengers

Hanwen Hu;Yirong Chen;Jingchao Ouyang;Shiyou Qian;Jian Cao;Han Han;Jie Wang;Michael Lepech

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

Multi-Agent Context-Aware Dynamic-Scheduling for Large-scale Processing Networks

Shuhui Qu;Yirong Chen;Juergen Jasperneite;Michael D. Lepech;Jie Wang

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Year: 2020 | Conference Paper |

HTML

Optimally scheduling jobs in processing networks to meet multiple objectives for economic considerations and operational efficiencies has been a hot topic. However, most data-driven methods are rarely applied. One of the critical reasons is that the scheduling policy generated by these methods tends to bias toward the specific environment.In order to better deal with the discrepancy, this work in ...Show More

Multi-Agent Context-Aware Dynamic-Scheduling for Large-scale Processing Networks

Shuhui Qu;Yirong Chen;Juergen Jasperneite;Michael D. Lepech;Jie Wang

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Year: 2020 | Volume: 1 | Conference Paper |

Probabilistic Design of Sustainable Reinforced Concrete Infrastructure Repairs Using SIPmath

Melissa Zirps;Michael Lepech;Sam Savage

2019 Winter Simulation Conference (WSC)

Year: 2019 | Conference Paper |

HTML

The design, construction, and operation of civil infrastructure that is more environmentally, socially, and economically responsible over its life cycle from extraction of raw materials to end of life is increasingly desirable worldwide. This paper presents a probabilistic framework for the design of civil infrastructure that achieves targeted improvements in quantitative sustainability indicators...Show More

Probabilistic Design of Sustainable Reinforced Concrete Infrastructure Repairs Using SIPmath

Melissa Zirps;Michael Lepech;Sam Savage

2019 Winter Simulation Conference (WSC)

Year: 2019 | Conference Paper |

A framework for multiphysics modeling of natural environments for valuation of privately owned ecosystem services

Stephen D. Comello;Michael D. Lepech

Proceedings of the 2011 IEEE International Symposium on Sustainable Systems and Technology

Year: 2011 | Conference Paper |

HTML

Modeling of ecosystem services using a multiphysics approach shows promise in detailing the contribution and interaction of constituent underlying processes. Such detail is required to enable ecosystem service valuation based on functional substitutability, whereby the market value of an equivalent engineered system provides a basis of value amenable to international financial accounting norms. On...Show More

A framework for multiphysics modeling of natural environments for valuation of privately owned ecosystem services

Stephen D. Comello;Michael D. Lepech

Proceedings of the 2011 IEEE International Symposium on Sustainable Systems and Technology

Year: 2011 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.