End-edge-cloud collaborative learning-aided prediction for high-speed train operation using LSTM-Reference-Cited by-同舟云学术

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End-edge-cloud collaborative learning-aided prediction for high-speed train operation using LSTM

Published:2024-03 Issue: Volume:160 Page:104527
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Yang Hui^ORCID,Wang Changyuan,Zhang Kunpeng,Dong Shuaiqiang

Publisher

Elsevier BV

Reference42 articles.

1. AI-enabled secure microservices in edge computing: Opportunities and challenges;Al-Doghman;IEEE Trans. Serv. Comput.,2022

2. Edge computing-aided framework of fault detection for traction control systems in high-speed trains;Chen;IEEE Trans. Veh. Technol.,2019

3. Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives;Chen;IEEE Trans. Intell. Transp. Syst.,2020

4. Toward distributed computing environments with serverless solutions in edge systems;Cicconetti;IEEE Commun. Mag.,2020

5. Data-driven perspectives for energy efficient operations in railway systems: Current practices and future opportunities;De Martinis;Transp. Res. C,2018

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