• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1135-1143
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• 2007

The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.

• Communications for Statistical Applications and Methods
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• 제17권6호
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• pp.803-810
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• 2010

The singular value decomposition of a rectangular matrix is a basic tool to understand the structure of the data and particularly the relationship between row and column factors. However, conventional singular value decomposition used the least squares method and is not robust to outliers. We propose a simple robust singular value decomposition algorithm based on the weighted least absolute deviation which is not sensitive to leverage points. Its implementation is easy and the computation time is reasonably low. Numerical results give the data structure and the outlying information.

• Journal of the Korean Data and Information Science Society
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• 제9권1호
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• pp.63-70
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• 1998

The singular value decomposition is a tool which is used to find a linear structure of reduced dimension and to give interpretation of the lower dimensional structure about multivariate data. In this paper the singular value decomposition is reviewed from both algebraic and geometric point of view and, is illustrated the way which the tool is used in the multivariate techniques finding a simpler geometric structure for the data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.49-50
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• 2011

본 논문에서는 WT(Wavelet Transform)와 SVD(Singular Value Decomposition)를 함께 사용한 WSVD(Wavelet Singular Value Decomposition)를 이용하여 발전기 탈락 시의 전압 변동 특성을 분석하였다. WSVD 특성 분석을 위해 부산 지역의 345kV급 송전계통을 EMTP-RV로 모델링하였으며, 이 계통모델에서 발전기 탈락을 모의하였다. MATLAB을 통해 이 때 측정된 전압의 WSVD를 계산하여 발전기 탈락에 따른 특성을 분석하였다.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.47-48
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• 2005

Multidimensional scaling is a multivariate technique for constructing a configuration of n points in Euclidean space using information about the distances between the objects. This can be done by the singular value decomposition of the data matrix. But it is known that the singular value decomposition is not resistant. In this study, we provide a resistant version of the multidimensional scaling.

• Journal of the Korean Statistical Society
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• 제25권1호
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• pp.49-66
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• 1996

The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.

• 전자공학회논문지
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• 제50권3호
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• pp.212-218
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• 2013

본 논문에서는 SAR (Synthetic Aperture Radar) 영상에 SVD (Singular Value Decomposition) - Pseudo Spectrum 알고리즘을 적용하고 그 성능을 기존 알고리즘과 비교한다. 이 논문의 목적은 SAR 영상의 해상도 및 목표물 분해능을 높이고자 하는 것이다. 본 논문에서는 신호 성분으로 이루어진 Hankel Matrix와 SVD (Singular Value Decomposition) 방법을 사용하여 잡음에 강인하고 sidelobe이 적으며 스펙트럼 추정에서 해상도를 높인 SVD-Pseudo Spectrum 방법을 제안하였다. 또한 분해될 목표물을 모델링하여 알고리즘의 성능을 분석하고 SVD-Pseudo Spectrum 방법이 기존의 퓨리에 변환 기반 방법과 고해상도 기술 기반의 MUSIC 방법보다 더 좋은 성능을 가짐을 보인다.

• 대한방사선기술학회지:방사선기술과학
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• 제42권2호
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• pp.119-130
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• 2019

Positron emission tomography (PET) is widely used imaging modality for effective and accurate functional testing and medical diagnosis using radioactive isotopes. However, PET has difficulties in acquiring images with high image quality due to constraints such as the amount of radioactive isotopes injected into the patient, the detection time, the characteristics of the detector, and the patient's motion. In order to overcome this problem, we have succeeded to improve the image quality by using the dynamic image reconstruction method based on singular value decomposition. However, there is still some question about the characteristics of the proposed technique. In this study, the characteristics of reconstruction method based on singular value decomposition was estimated over computational simulation. As a result, we confirmed that the singular value decomposition based reconstruction technique distinguishes the images well when the signal - to - noise ratio of the input image is more than 20 decibels and the feature vector angle is more than 60 degrees. In addition, the proposed methode to estimate the characteristics of reconstruction technique can be applied to other spatio-temporal feature based dynamic image reconstruction techniques. The deduced conclusion of this study can be useful guideline to apply medical image into SVD based dynamic image reconstruction technique to improve the accuracy of medical diagnosis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.205-206
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• 2011

In this paper, algorithm for fault classification and detection of generator dropping using wavelet singular value decomposition (WSVD) is proposed. Busan area upper 345kV is modeled and generator dropping is simulated in EMTP-RV. Characteristic of generator dropping is analyzed and this algorithm is deducted by calculating WSVD in MATLAB.

• 제어로봇시스템학회논문지
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• 제10권4호
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• pp.364-370
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• 2004

In this paper, we propose a FDI method, which comes from singular value decomposition of measurement matrix fur redundant sensors. We analyze the performance of the proposed FDI method by comparing with the GLT method in two ways such as FDI performance and GN＆C performance. Also, we propose a GN＆C performance index by combining FDI and GN＆C performance.