Quantitative identification of near-fault pulse-like ground motions based on variational mode decomposition technique

2021 ◽  
Vol 151 ◽  
pp. 107009
Author(s):  
Jun Feng ◽  
Boming Zhao ◽  
Tianci Zhao
Keyword(s):  
Ground Motions ◽  
Decomposition Technique ◽  
Variational Mode Decomposition ◽  
Near Fault ◽  
Mode Decomposition ◽  
Quantitative Identification

Quantitative Identification of Near-Fault Pulse-Like Ground Motions Based on Energy

2013 ◽  
Vol 103 (5) ◽  
pp. 2591-2603 ◽  
Author(s):  
C. Zhai ◽  
Z. Chang ◽  
S. Li ◽  
Z. Chen ◽  
L. Xie
Keyword(s):  
Ground Motions ◽  
Near Fault ◽  
Quantitative Identification

scholarly journals An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Yuanxin Wang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Decomposition Technique ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Application Analysis ◽  
Evolution Algorithm ◽  
Decomposition Number ◽  
Selection Of

Variational mode decomposition is an adaptive nonrecursive signal decomposition and time-frequency distribution estimation method. The improper selection of the decomposition number will cause under decomposition or over decomposition, and the improper selection of the penalty factor will affect the bandwidth of modal components, so it is very necessary to look for the optimal parameter combination of the decomposition number and the penalty factor of variational mode decomposition. Hence, differential evolution algorithm is used to look for the optimization combination of the decomposition number and the penalty factor of variational mode decomposition because differential evolution algorithm has a good ability at global searching. The method is called adaptive variational mode decomposition technique with differential evolution algorithm. Application analysis and discussion of the adaptive variational mode decomposition technique with differential evolution algorithm are employed by combining with the experiment. The conclusions of the experiment are that the decomposition performance of the adaptive variational mode decomposition technique with differential evolution algorithm is better than that of variational mode decomposition.

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