Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

2005 ◽  
Vol 18 (7) ◽  
pp. 857-866 ◽  
Author(s):  
Tienfuan Kerh ◽  
S.B. Ting
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Peak Acceleration ◽  
High Speed Rail ◽  
Rail System ◽  
Network Estimation

scholarly journals Risks of Climate Change with Respect to the Singapore-Malaysia High Speed Rail System

Climate ◽  
2016 ◽  
Vol 4 (4) ◽  
pp. 65 ◽  
Author(s):  
Sazrul Binti Sa’adin ◽  
Sakdirat Kaewunruen ◽  
David Jaroszweski
Keyword(s):  
Climate Change ◽  
High Speed ◽  
High Speed Rail ◽  
Rail System

Application of Benefit-Cost Analysis to the Proposed California High-Speed Rail System

2001 ◽  
Vol 1742 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Daniel Brand ◽  
Mark R. Kiefer ◽  
Thomas E. Parody ◽  
Shomik R. Mehndiratta
Keyword(s):  
Cost Analysis ◽  
High Speed ◽  
High Speed Rail ◽  
Benefit Cost Analysis ◽  
Rail System ◽  
Benefit Cost

Operating and Maintenance Costs of the TGV High‐Speed Rail System

1989 ◽  
Vol 115 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Nicholas M. Brand ◽  
Marc M. Lucas
Keyword(s):  
High Speed ◽  
High Speed Rail ◽  
Maintenance Costs ◽  
Rail System

Moving element analysis of discretely supported high-speed rail systems

2017 ◽  
Vol 232 (3) ◽  
pp. 783-797 ◽  
Author(s):  
Jian Dai ◽  
Kok Keng Ang ◽  
Minh Thi Tran ◽  
Van Hai Luong ◽  
Dongqi Jiang
Keyword(s):  
High Speed ◽  
Railway Track ◽  
High Speed Train ◽  
Element Analysis ◽  
High Speed Rail ◽  
Mass System ◽  
Rail System ◽  
Rail Systems ◽  
Track System ◽  
The Difference

In this paper, a computational scheme in conjunction with the moving element method has been proposed to investigate the dynamic response of a high-speed rail system in which the discrete sleepers on the subgrade support the railway track. The track foundation is modeled as a beam supported by uniformly spaced discrete spring-damper units. The high-speed train is modeled as a moving sprung-mass system that travels over the track. The effect of the stiffness of the discrete supports, train speed, and railhead roughness on the dynamic behavior of the train–track system has been investigated. As a comparison, the response of a continuously supported high-speed rail system that uses a foundation stiffness equivalent to that of a discretely supported track has been obtained. The difference in results between the “equivalent” continuously supported and the discretely supported high-speed rails has been compared and discussed. In general, the study found that a high-speed train that travels over a discretely supported track produces more severe vibrations than that travels over a continuously supported track of equivalent foundation stiffness.

Open big data from ticketing website as a useful tool for characterizing spatial features of the Chinese high-speed rail system

2018 ◽  
Vol 63 (2) ◽  
pp. 265-277 ◽  
Author(s):  
Sheng Wei ◽  
Jiangang Xu ◽  
Jingwei Sun ◽  
Xuejiao Yang ◽  
Ran Xin ◽  
...  
Keyword(s):  
Big Data ◽  
High Speed ◽  
High Speed Rail ◽  
Spatial Features ◽  
Rail System

Temporal and Spatial Distributions of Primary Delays in a High-Speed Rail System

2019 ◽  
Author(s):  
Chao Wen ◽  
Zhongcan Li ◽  
Javad Lessan ◽  
Hongguo Shi ◽  
Liping Fu ◽  
...  
Keyword(s):  
High Speed ◽  
Spatial Distributions ◽  
High Speed Rail ◽  
Rail System ◽  
Temporal And Spatial

Ridership studies for the proposed Florida high speed rail system

1990 ◽  
Author(s):  
A. Masood Forooqi
Keyword(s):  
High Speed ◽  
High Speed Rail ◽  
Rail System

scholarly journals An Improved Prediction Model of IGBT Junction Temperature Based on Backpropagation Neural Network and Kalman Filter

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Dou
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Rapid Development ◽  
Junction Temperature ◽  
Insulated Gate Bipolar Transistor ◽  
Network Estimation ◽  
The Stability

With the rapid development of emerging technologies such as electric vehicles and high-speed railways, the insulated gate bipolar transistor (IGBT) is becoming increasingly important as the core of the power electronic devices. Therefore, it is imperative to maintain the stability and reliability of IGBT under different circumstances. By predicting the junction temperature of IGBT, the operating condition and aging degree can be roughly evaluated. However, the current predicting approaches such as optical, physical, and electrical methods have various shortcomings. Hence, the backpropagation (BP) neural network can be applied to avoid the difficulties encountered by conventional approaches. In this article, an advanced prediction model is proposed to obtain accurate IGBT junction temperature. This method can be divided into three phases, BP neural network estimation, interpolation, and Kalman filter prediction. First, the validities of the BP neural network and Kalman filter are verified, respectively. Then, the performances of them are compared, and the superiority of the Kalman filter is proved. In the future, the application of neural networks or deep learning in power electronics will create more possibilities.

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