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Edge Computing-Aided Framework of Fault Detection for Traction Control Systems in High-Speed Trains

  • Published:2020-02 Issue:2 Volume:69 Page:1309-1318
  • ISSN:0018-9545
  • Container-title:IEEE Transactions on Vehicular Technology
  • language:
  • Short-container-title:IEEE Trans. Veh. Technol.

Author:

Chen HongtianORCID,Jiang BinORCID,Chen WenORCID,Li Ziheng

Funder

National Natural Science Foundation of China

Priority Academic Program Development of Jiangsu Higher Education Institutions

Graduate Research and Innovation Projects of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Cited by 34 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Throughput Maximization for Result Multicasting by Admitting Delay-Aware Tasks in MEC Networks for High-Speed Railways;IEEE Transactions on Vehicular Technology;2024-06

2. End-edge-cloud collaborative learning-aided prediction for high-speed train operation using LSTM;Transportation Research Part C: Emerging Technologies;2024-03

3. Computer aided translation technology based on edge computing intelligent algorithm;International Journal of Data Mining and Bioinformatics;2024

4. Overview of fault prognosis for traction systems in high-speed trains: A deep learning perspective;Engineering Applications of Artificial Intelligence;2023-11

5. High-Speed Train Traction System Reliability Analysis;Propulsion Systems - Recent Advances, New Perspectives and Applications;2023-10-11
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