F
Fei-Yue Wang
Researcher at Chinese Academy of Sciences
Publications - 807
Citations - 29342
Fei-Yue Wang 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 65, co-authored 699 publications receiving 20606 citations. Previous affiliations of Fei-Yue Wang include University of Arizona & Rensselaer Polytechnic Institute.
Papers
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Traffic Flow Prediction With Big Data: A Deep Learning Approach
TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.
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Data-Driven Intelligent Transportation Systems: A Survey
TL;DR: A survey on the development of D2ITS is provided, discussing the functionality of its key components and some deployment issues associated with D2 ITS Future research directions for the developed system are presented.
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Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications
TL;DR: The developments and applications described here clearly indicate that PtMS is effective for use in networked complex traffic systems and is closely related to emerging technologies in cloud computing, social computing, and cyberphysical-social systems.
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Adaptive Dynamic Programming: An Introduction
TL;DR: Some recent research trends within the field of adaptive/approximate dynamic programming (ADP), including the variations on the structure of ADP schemes, the development of ADPs algorithms and applications, and many recent papers have provided convergence analysis associated with the algorithms developed.
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Reduction and axiomization of covering generalized rough sets
William Zhu,Fei-Yue Wang +1 more
TL;DR: It has been proved that the reduct of a covering is the minimal covering that generates theSame covering lower approximation or the same covering upper approximation, so this concept is also a technique to get rid of redundancy in data mining.