F
Fenghua Zhu
Researcher at Chinese Academy of Sciences
Publications - 149
Citations - 2176
Fenghua Zhu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Intelligent transportation system. The author has an hindex of 20, co-authored 128 publications receiving 1384 citations.
Papers
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Journal ArticleDOI
Cyber-physical-social system in intelligent transportation
TL;DR: An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system and finally realizes the stepwise control and management of CPSS.
Journal ArticleDOI
Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management
TL;DR: This paper presents visions and works on integrating the artificial intelligent transportation systems and the real intelligent Transportation systems to create and enhance “intelligence” of IoT-enabled ITS, and presents some case studies to demonstrate the effectiveness of parallel transportation systems.
Journal ArticleDOI
Cyber-Physical-Social Systems: The State of the Art and Perspectives
Jun Jason Zhang,Fei-Yue Wang,Xiao Wang,Gang Xiong,Fenghua Zhu,Yisheng Lv,Jiachen Hou,Shuangshuang Han,Yong Yuan,Qingchun Lu,Yishi Lee +10 more
TL;DR: The blockchainized IoM technology and the concepts of parallel society are described to contribute to the transition from the current social construct to a futuristic intelligent society in China.
Journal ArticleDOI
DynaCAS: Computational Experiments and Decision Support for ITS
TL;DR: A real-time traffic estimation and prediction system (TrEPS) as an ITS support platform that resides at traffic management centers (TMCs) for dynamic route assignment (DRA) and other transportation operations.
Proceedings ArticleDOI
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
TL;DR: Wang et al. as mentioned in this paper proposed a learnable module that learns spatial contextual features from large-scale point clouds, called SCF, which mainly consists of three blocks, including the local polar representation block, the dual-distance attentive pooling block, and the global contextual feature block.