• Journal of KIISE:Information Networking
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• 제30권1호
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• pp.24-29
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• 2003

In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing schemes, which represents the information from observations as a set of random variables in the from of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other. which can be utilized in linear projection.. However, it has been known that ICA does not establish good performance in source separation by itself. Thus, in order to overcome this limitation, there have been many techniques that are designed to reinforce the good properties of ICA, which improves the mixed image separation. Unfortunately, the innovation process still needs to be studied since it yields inconsistent innovation process that is attached to the ICA, the expectation and maximization process is added. The results presented in this paper show that the proposed improves the image separation as presented in experiments.

• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2001년도 봄 학술발표논문집 Vol.28 No.1 (A)
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• pp.505-507
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• 2001

본 논문에서는 그람-슈미트 방법 및 독립 성분 해석(Independent Component Analysis, ICA)기법을 이용한 영상분리방법을 제안한다. 이 제안된 방법은 전처리 없이 ICA나 주성분 해석(Principal Component Analysis, PCA)을 이용한 것에 비해 개선된 영상을 보여준다. 이는 원래의 ICA 모델에 대하여 동일한 조건으로 일반화하여 그람-슈미트의 독립된 성분들이 ICA 모델에 충분히 동일하다는 것을 보여준다.

• Proceedings of the KIEE Conference
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2810-2812
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• 2001

본 논문에서는 독립성분해석을 이용한 영상분리에 있어서의 잡음에 대한 강인성에 대한 주성분해석과 비교 연구를 함으로써, 독립성분해석(Independent Component Analysis, ICA)기법의 효율성을 고찰하고 분석하고자 한다. 원래의 인식 시스템 모델에 잡음을 주었을 때, ICA를 이용한 영상 분리의 잡음에 대한 강인성은 주성분 해석(Principal Component Analysis, PCA)기법에서 보다 더 잡음에 강인한 성질을 내포하고 있는데, 이는 PCA 보다 ICA가 분리하려는 영상정보의 상호관계를 더 약화시키는 작용을 하기 때문이다. 이러한 특성은 모의실험을 통해 확인되었다.

• The KIPS Transactions:PartB
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• 제8B권4호
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• pp.389-394
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• 2001

본 논문에서는 독립 성분 해석(Independent Component Analysis, ICA) 기법과 인근 평균 및 정규화를 이용한 영상 분류 방법을 제안하였다. ICA에 잡음을 주어 영상을 분류하였을 때, 잡음에 대한 강인성을 증가시키기 위하여, 제안된 인근 평균 및 정규화를 전처리로 적용하였다. 제안된 방법은 전처리 없이 ICA에 주성분 해석(Principal Component Analysis, PCA)을 이용한 것에 비해 잡음에 대한 강인성을 증가시키는 것을 모의 실험을 통하여 확인하였다.

• The Korean Journal of Applied Statistics
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• 제27권1호
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.

• Journal of KIISE:Software and Applications
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• 제28권9호
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• pp.629-633
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• 2001

In this paper, we improve noise tolerance in image classification by combining ICA(Independent Component Analysis) with Normalization. When we add noise to the raw image data the degree of noise tolerance becomes N(0, 0.4) for PCA and N(0, 0.53) for ICA. However, when we use the preprocessing approach the degree of noise tolerance after Normalization becomes N(0, 0.75), which shows the improvement of noise tolerance in classification.

• Journal of Korea Water Resources Association
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• 제38권12호
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• pp.985-994
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• 2005

We examined problems of the principal component analysis(PCA), which is able to analyze at the low dimensionality as a methodologv to assess hydrologic time series, and introduced the theory and characteristics of independent component analysis(ICA) that can supplement problems of principal component analysis. We also applied the global sea surface temperature(SST) of the Nino region and assessed the correlation between El $\tilde{n}ino$-Southern Oscillation(ENSO) and SST. The results of examining separation-ability of principal components using mixed signals indicate that the independent component analysis is statistically superior compared to that of the principal component analysis. Finally, we assessed correlation between ENSO and global anomaly SST. The independent component analysis was applied to the $5^{\circ}{\times}5^{\circ}$(latitude and longitude) global anomaly SST in the Nino+3.4 region that is the El $\tilde{n}ino$ observation section. We assessed the correlation with the ENSO years. These results of the analysis show that only one independent component($86\%$) was able to represent the entire behavior and was consistent with the main ENSO years. Finally, we carried out independent component analysis for summer seasonal rainfalls at nine stations and could extract ICs to reflect geographical characteristics. The increasing trend has been shown at IC-1 and IC-2 since 1970s.

• Proceedings of the KIEE Conference
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.1053-1054
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• 2006

독립형 연료전지 전력변환 시스템에서 인버터는 출력주파수의 2배 성분의 리플을 가지고 있기 때문에 직류버스 전압에도 이 주파수 성분이 존재하게 된다. 이 리플 전류는 연료전지의 수명과 효율을 저하시키므로 제어기를 통해 줄여야 한다. 최근 특성비 지정법을 이용하여 해석적으로 기어기를 설계하는 방법이 제안되었다. 해석적인 방법으로 연료전지 인버터의 제어기를 설계한다. 이 제어기는 부하 변동에 대해서 응답속도가 빠르며 120 Hz 리플에 강인함을 확인한다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• 제2권2호
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• pp.22-30
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• 2003

Cutting force signals are very useful to evaluate the cutting state, but many disturbing factors are occurring during cutting. For the reliability of the analysis, selecting pure cutting force signals from the original ones is needed. In the current study, using the ICA(Independent Component Analysis) effective cutting force components are seperated from the original signals. And using this, as input data of MLP(Multi-Layer Perception) cutting forces are predicted Experimental results are then compared with the predicted ones to verify the validation of the proposed model.

• Journal of Sensor Science and Technology
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• 제10권5호
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• pp.286-291
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• 2001

We propose a new saliency map model for selecting an attended location in an arbitrary visual scene, which is one of the most important characteristics of human vision system. In selecting an attended location, an edge information can be considered as a feature basis to construct the saliency map. Edge filters are obtained from the independent component analysis(ICA) that is the best way to find independent edges in natural gray scenes. In order to reflect the non-uniform density in our retina, we use a multi-scaled pyramid input image instead of using an original input image. Computer simulation results show that the proposed saliency map model with multi-scale property successfully generates the plausible attended locations.