scholarly journals Independent component analysis of natural time-varying images under the constraint of the minimum time delay

2010 ◽  
Vol 3 (9) ◽  
pp. 44-44
Author(s):  
M. Dastjerdi ◽  
D. W Dong
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yongshuo Zong ◽  
Jinling Chen ◽  
Siyi Tao ◽  
Cheng Wang ◽  
Jianbing Xiahou

In order to identify time-varying transient modal parameters only from nonstationary vibration response measurement signals for slow linear time-varying (SLTV) structures which are weakly damped, a moving window differential evolution (DE) independent component analysis- (ICA-) based operational modal analysis (OMA) method is proposed in this paper. Firstly, in order to overcome the problems in traditional ICA-based OMA, such as easy to go into local optima and difficult-to-identify high-order modal parameters, we combine DE with ICA and propose a differential evolution independent component analysis- (DEICA-) based OMA method for linear time invariant (LTI) structures. Secondly, we combine the moving widow technique with DEICA and propose a moving window differential evolution independent component analysis- (MWDEICA-) based OMA method for SLTV structures. The MWDEICA-based OMA method has high global searching ability, robustness, and complexity of time and space. The modal identification results in a three-degree-of-freedom structure with slow time-varying mass show that this MWDEICA-based OMA method can identify transient time-varying modal parameters effectively only from nonstationary vibration response measurement signals and has better performances than moving window traditional ICA-based OMA.


2006 ◽  
Vol 18 (10) ◽  
pp. 2495-2508 ◽  
Author(s):  
Tobias Blaschke ◽  
Pietro Berkes ◽  
Laurenz Wiskott

We present an analytical comparison between linear slow feature analysis and second-order independent component analysis, and show that in the case of one time delay, the two approaches are equivalent. We also consider the case of several time delays and discuss two possible extensions of slow feature analysis.


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