Generative Adversarial Network-based Fault Diagnosis Model for Railway Point Machine in Sustainable Railway Transportation

IF 1.1 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Sensor Networks Pub Date : 2023-01-01 DOI:10.1504/ijsnet.2023.10059163
Ning Zhao, Xiao Han
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基于生成对抗网络的可持续轨道交通点机故障诊断模型
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来源期刊
International Journal of Sensor Networks
International Journal of Sensor Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
2.40
自引率
27.30%
发文量
86
期刊介绍: IJSNet proposes and fosters discussion on and dissemination of issues related to research and applications of distributed and wireless/wired sensor and actuator networks. Sensor networks is an interdisciplinary field including many fields such as wireless networks and communications, protocols, distributed algorithms, signal processing, embedded systems, and information management. Topics covered include: -Energy efficiency, energy efficient protocols- Applications- Location techniques, routing, medium access control- Coverage, connectivity, longevity, scheduling, synchronisation- Network resource management, network protocols, lightweight protocols- Fault tolerance/diagnostics- Foundations- Data storage, query processing, system architectures, operating systems- In-network processing and aggregation- Learning of models from data- Mobility- Performance analysis- Sensor tasking and control- Security, privacy, data integrity- Modelling of systems/physical environments, simulation tools/environments.
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