Erratum for “Development of a New Regression Analysis Method Using Independent Component Analysis”

2013 ◽  
Vol 53 (11) ◽  
pp. 3113-3113
Author(s):  
H. Kaneko ◽  
M. Arakawa ◽  
K. Funatsu
Keyword(s):  
Regression Analysis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
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.

Using Independent Component Analysis to Process Magnetotelluric Data

2013 ◽  
Vol 295-298 ◽  
pp. 2795-2798 ◽  
Author(s):  
Jin Ling Cui ◽  
Ming Deng ◽  
Jian En Jing ◽  
En Ci Wang
Keyword(s):  
Independent Component Analysis ◽  
Noisy Data ◽  
Component Analysis ◽  
Independent Component ◽  
Impedance Tensor ◽  
Data Sets ◽  
Analysis Method ◽  
Magnetotelluric Data ◽  
Recorded Data ◽  
Robust Processing

It is much more difficult to estimate magnetotelluric(MT) impedance tensor in the sites which are contaminated by high noise. In order to estimate a precise impedance tensor, we examine a new method called independent component analysis (ICA) that is developed to remove the noise in the recorded data. ICA is a time series analysis method, in which complicated data sets can be separated into all underlying sources without knowing these sources or the way that they are mixed. In this paper, we use the ICA method to process real MT data. All results show that apparent resistivity and phases which are preprocessed by ICA and derived from impedance tensors are generally more stable than only robust processing. These results reveal that ICA has the potential to handle noisy data.

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