Why the contrast function slope is important

2001 ◽  
Author(s):  
Marta Maneiro ◽  
Maria L. F. de Mattiello
Keyword(s):  
Contrast Function

A source contribution quantitative calculation method for mechanical systems based on the simplified independent component analysis with reference

2016 ◽  
Vol 230 (18) ◽  
pp. 3222-3240
Author(s):  
Jie Zhang ◽  
Zhousuo Zhang ◽  
Wei Cheng ◽  
Guanwen Zhu ◽  
Zhengjia He
Keyword(s):  
Independent Component Analysis ◽  
Prior Knowledge ◽  
Complex Structure ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Measurement ◽  
Contrast Function ◽  
Source Contribution ◽  
Quantitative Calculation ◽  
Reference Signals

The quantitative calculation of the source contribution is very important and critical for the identification of the main vibration sources and the reduction of vibration and noise in submarine. It is difficult to calculate the source contribution because of the submarine’s complex structure and the large amount of vibration sources. As a typical blind source separation method, independent component analysis (ICA) has recently been proved to be an effective method to solve the source identification problem in which the source signals and mixing models are unknown. However, the outcomes of the ICA algorithm are affected by random sampling and random initialization of variables. In our study, the prior knowledge of the vibration sources can be obtained through the vibration measurement of submarine. Obviously, information in addition to mixed signals from sensors can lead to a more accurate separation. Therefore the contrast function of ICA can be enhanced by the reference signals obtained by the prior knowledge. In this paper, a closeness measurement between the independent components and the reference signals obtained by the prior knowledge is introduced, and the closeness measurement is constructed to have the same optimization direction with the traditional contrast function: negentropy. The closeness measurement is used to enhance the contrast function and then the enhanced contrast function is optimized by means of the Newton iteration and the deflation approach. Thus the simplified independent component analysis with reference (ICA-R) algorithm is obtained. After that a method to quantitatively calculate the source contribution is proposed based on the outcomes of the simplified ICA-R. Finally, the effectiveness of the proposed method is verified by the numerical simulation studies. The performance offered by the proposed method is also investigated by the experiment: it appear as a very appealing tool for the quantitative calculation of the source contribution.

Dyadic Contrast Function for the Forward Model of Diffraction Tomography of Thin Cylindrical Objects

2017 ◽  
Vol 16 ◽  
pp. 991-994 ◽  
Author(s):  
Tadahiro Negishi ◽  
Vittorio Picco ◽  
Lorenzo Lo Monte ◽  
Danilo Erricolo
Keyword(s):  
Forward Model ◽  
Diffraction Tomography ◽  
Contrast Function

scholarly journals Subwavelength Hyperlens Resolution With Perfect Contrast Function

2018 ◽  
Vol 530 (3) ◽  
pp. 1700300 ◽  
Author(s):  
Andrey Novitsky ◽  
Taavi Repän ◽  
Sergei V. Zhukovsky ◽  
Andrei V. Lavrinenko
Keyword(s):  
Contrast Function

scholarly journals A new proof of a contrast function for bounded component analysis and further analysis

2018 ◽  
Vol 51 ◽  
pp. 93-109
Author(s):  
Wei Gao ◽  
Shen Fan ◽  
Roberto Togneri ◽  
Victor Sreeram
Keyword(s):  
Component Analysis ◽  
Contrast Function ◽  
Bounded Component
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