A5101941156- Transportation Planning and Optimization
- Advanced Machining and Optimization Techniques
- Machine Fault Diagnosis Techniques
- Aviation Industry Analysis and Trends
- Supply Chain Resilience and Risk Management
- Global Urban Networks and Dynamics
- Advanced Computational Techniques and Applications
- Insect-Plant Interactions and Control
- Advanced machining processes and optimization
- Advanced Algorithms and Applications
- Mineral Processing and Grinding
- Infrastructure Resilience and Vulnerability Analysis
- Risk and Safety Analysis
- Horticultural and Viticultural Research
- Complex Network Analysis Techniques
Beijing Jiaotong University
2019-2021
Ministry of Transport
2020-2021
Tianjin University
2011
Railway transportation service networks (RTSNs) are crucial components of the system, and temporal network theory can be used to reveal their features. In this research, features three RTSNs studied. Using theory, separated into a series continued slips according time order changing rules metrics analyzed. This research also discusses reliability railway system from practical perspective. The results show that possess periodic patterns respond organization modes, reflect these Furthermore,...
Both the high-speed railway and air transportation network are backbone of interregional transport cover important cities in a country. Taking as nodes, comprehensive consisting railways civil aviation can be constructed. This undertakes huge passenger task, so failure this will cause serious economic losses even casualties. In Air-High-Speed Railway Transportation Network (A-HSRTN), two modes operate independently alternatives. The analysis A-HSRTN helps planners to have more understanding...
The fault diagnosis of rolling bearing plays a significant role in rotating machinery. This paper makes comparison between the acoustic emission and vibration signal outer race pitting. are processed by wavelet transform, Hilbert envelope transform FFT transform. Finally, spectrum charts signals drew out. Based on analysis results, conclusion can be drawn that is superior to bearing.
Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method predict tool status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force indicate status wear. Meanwhile, support vector machine (SVM) model employed distinguish The result classification different proved that can be recognize-features high recognition precision.