• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.47-50
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• 2015

Speech Signals consist of signals of consonants and vowels, but the lasting time of vowels is much longer than that of consonants. It can be assumed that the correlations between signal blocks in speech signal is very high. Each speech signal is divided into blocks which have 128 speech data. FFT is applied to each block. Low frequency areas of the results of FFT is taken and Covariance matrix between blocks in a speech signal is extracted and finally eigenvalues of those matrix are obtained. It is studied that what the distribution of eigenvalues of various speech files is. The differences between speech signals and noise signals from cars are also studied.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.10
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• pp.1451-1456
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• 2013

This paper propose a method for fast convergence technique in frequency domain independent component analysis (FDICA) using eigenanalysis. It important, such as SONAR system, to eliminate the interference sources through fast algorithm. Through eigenanalysing a two-by-two delayed mixture case, information of delay can be used for initial weighting parameters. Simulations show the improved performances in convergence speed and noise rejection rate. The proposed method can present close weights for optimal convergence, noise can be diminished drastically about 3 times epoch, and get the better resultss with 1~3dB than the conventional method.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.963-977
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• 2011

PCA(Principal Component Analysis) is a well-studied statistical technique and an important tool for handling multivariate data. Although many algorithms exist for PCA, most of them are unsuitable for real time applications or high dimensional problems. Since it is desirable to avoid extensive matrix operations in such cases, alternative solutions are required to calculate the eigenvalues and eigenvectors of the sample covariance matrix. Erdogmus et al. (2004) proposed Recursive PCA(RPCA), which is a fast adaptive on-line solution for PCA, based on the first order perturbation theory. It facilitates the real-time implementation of PCA by recursively approximating updated eigenvalues and eigenvectors. However, the performance of the RPCA method becomes questionable as the size of newly-added data increases. In this paper, we modified the RPCA method by taking advantage of the mathematical relation of eigenvalues and eigenvectors of sample covariance matrix. We compared the performance of the proposed algorithm with that of RPCA, and found that the accuracy of the proposed method remarkably improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.293-296
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• 2016

Speech Signals consist of signals of consonants and vowels, but the lasting time of vowels is much longer than that of consonants. It can be assumed that the correlations between signal blocks in speech signal is very high. But the correlations between signal blocks in various frequency bands can be quite different. Each speech signal is divided into blocks which have 128 speech data. FFT is applied to each block. Various frequency areas of the results of FFT are taken and Covariance matrix between blocks in a speech signal is extracted and finally eigenvalues of those matrix are obtained. It is studied that in the eigenvalues of various frequency bands which band can be used to get more reliable result.

• Convergence Security Journal
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• v.12 no.3
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• pp.3-10
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• 2012

This study aimed at making a way of securing the access by using PCA. We got our result through using Box-Plot and PCA with the access data of the area of security level A~E at K(IPS)center. In order to perform PCA, We confirmed the extracted value of commonality has no problem in performing PCA because VIF is below 2.902. Based on this result, We classified people into Green-list, Blue-list, Red-list, and Black-list in a standard of security level with 1.453, as the eigen value of 1 main element, 1.283, as eigen value of 2 main elementm, 1.142, as the eigen value of 3 main element.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1479-1486
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• 2011

In this paper, a simple and robust algorithm is proposed for detecting straight line segments in a steel plate image. Line detection from a steel plate image is a fundamental task for analyzing and understanding of the image. The proposed algorithm is based on small eigenvalue analysis. The proposed approach scans an input edge image from the top left comer to the bottom right comer with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Before calculating the eigenvalue, each line segment is separated from the edge image where several line segments are overlapped to increase the accuracy of the line detection. Additionally, unnecessary line segments are eliminated by the number of pixels and the directional information of the detected line edges. The respects of the experiments emphasize that the proposed algorithm outperforms the existing algorithm which uses small eigenvalue analysis.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.321-328
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• 2009

In regression analysis, the ordinary least squares estimates of regression coefficients become poor, when the correlations among predictor variables are high. This phenomenon, which is called multicollinearity, causes serious problems in actual data analysis. To overcome this multicollinearity, many methods have been proposed. Ridge regression, shrinkage estimators and methods based on principal component analysis (PCA) such as principal component regression (PCR) and latent root regression (LRR). In the last decade, many statisticians discussed sensitivity analysis (SA) in ordinary multiple regression and same topic in PCR, LRR and logistic principal component regression (LPCR). In those methods PCA plays important role. Many statisticians discussed SA in PCA and related multivariate methods. We introduce the method of PCR and LRR. We also introduce the methods of SA in PCR and LRR, and discuss the properties of SA in PCR and LRR.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.515-516
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• 2011

송 배전 및 정보통신 설비, 건축물 등 모든 분야의 설비 및 인명 보호를 위해서는 반드시 필요하고 중요한 것이 접지이며, 접지 설계 시 대상 부지의 정확한 대지고유저항을 측정, 분석하는 것은 가장 중요하다고 할 수 있다. 본 대지고유저항 분석시스템은 이렇듯 중요한 대지고유저항을 기존의 수작업 분석방법이 아닌 시스템 반복계산에 의한 최적 curve 분석방법을 도입, 측정값의 신뢰도 평가 등을 이용한 측정값 분석으로, 분석값의 정확성을 높이고 분석시간을 단축함으로서 보다 정확하고 신속한 접지설계를 가능하도록 한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.162-170
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• 2015

Commute time embedding involves computing the spectral decomposition of the graph Laplacian. It requires the computational burden proportional to $o(n^3)$, not suitable for large scale dataset. Many methods have been proposed to accelerate the computational time, which usually employ the Nystr${\ddot{o}}$m methods to approximate the spectral decomposition of the reduced graph Laplacian. They suffer from the lost of information by dint of sampling process. This paper proposes to reduce the errors by approximating the spectral decomposition of the graph Laplacian using that of the affinity matrix. However, this can not be applied as the data size increases, because it also requires spectral decomposition. Another method called approximate commute time embedding is implemented, which does not require spectral decomposition. The performance of the proposed algorithms is analyzed by computing the commute time on the patch graph.

• Korean Journal of Food Science and Technology
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• v.33 no.1
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• pp.24-27
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• 2001

This study was performed to analyse aroma patterns of Nagaimo, Ichoimo and Tsukuneimo by the electronic nose with 32 conducting polymer sensors. Response by the electronic nose was analysed by the principal component analysis(PCA). Sensory evaluation also for organoleptic taste and odor of Nagaimo, Ichoimo and Tsukuneimo was performed. Nagaimo was very crunchy and sweet. Tsukuneimo was roasted nutty, hard, viscid taste and sticky. Ichoimo had intensive unique yam flavor and moderate hardness between Nagaimo and Ichoimo. Intensity of Ichoimo for unique yam flavor by the electronic nose was the strongest. The quality factor(QF) of PCA for normalized pattern by thirty two sensors showed less than 2, and so aroma pattern of three yam cultivars had no difference. But when the PCA was performed for normalized pattern by eight selected sensitive sensors, the QF for Nagaimo and Tsukuneimo is 2.057. Thus aroma pattern between Nagaimo and Tsukuneimo could be distinguished.