A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

2012 ◽  
Vol 42 (1) ◽  
pp. 593-604 ◽  
Author(s):  
Marcelo A.A. Lima ◽  
Augusto S. Cerqueira ◽  
Denis V. Coury ◽  
Carlos A. Duque
Keyword(s):  
Independent Component Analysis ◽  
Power Quality ◽  
Component Analysis ◽  
Independent Component ◽  
Novel Method ◽  
Multiple Disturbance

Statistical Independence and Independent Component Analysis

2014 ◽  
Vol 553 ◽  
pp. 564-569
Author(s):  
Yaseen Unnisa ◽  
Danh Tran ◽  
Fu Chun Huang
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Statistical Testing ◽  
P Value ◽  
Statistical Independence ◽  
Mixing Matrix ◽  
Recent Method ◽  
Novel Method ◽  
Source Signals

Independent Component Analysis (ICA) is a recent method of blind source separation, it has been employed in medical image processing and structural damge detection. It can extract source signals and the unmixing matrix of the system using mixture signals only. This novel method relies on the assumption that source signals are statistically independent. This paper looks at various measures of statistical independence (SI) employed in ICA, the measures proposed by Bakirov and his associates, and the effects of levels of SI of source signals on the output of ICA. Firstly, two statistical independent signals in the form of uniform random signals and a mixing matrix were used to simulate mixture signals to be anlysed byfastICApackage, secondly noise was added onto the signals to investigate effects of levels of SI on the output of ICA in the form of soure signals, the mixing and unmixing matrix. It was found that for p-value given by Bakirov’s SI statistical testing of the null hypothesis H0is a good indication of the SI between two variables and that for p-value larger than 0.05, fastICA performs satisfactorily.

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