New independent component analysis method using higher order statistics with application to remote sensing images

2002 ◽  
Vol 41 (7) ◽  
pp. 1717 ◽  
Author(s):  
Xiaohui Zhang
Keyword(s):  
Remote Sensing ◽  
Independent Component Analysis ◽  
Order Statistics ◽  
Component Analysis ◽  
Higher Order ◽  
Independent Component ◽  
Higher Order Statistics ◽  
Analysis Method ◽  
Remote Sensing Images ◽  
Independent Component Analysis Method

scholarly journals Evaluation of Harmonic Contributions for Multi Harmonic Sources System Based on Mixed Entropy Screening and an Improved Independent Component Analysis Method

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 323
Author(s):  
Jinshuai Zhao ◽  
Honggeng Yang ◽  
Xiaoyang Ma ◽  
Fangwei Xu
Keyword(s):  
Independent Component Analysis ◽  
Global Positioning System ◽  
Component Analysis ◽  
Potential Method ◽  
Independent Component ◽  
Analysis Method ◽  
Data Segment ◽  
Global Positioning ◽  
Calculation Process ◽  
Independent Component Analysis Method

Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.

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