• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1076-1082
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• 2013

The frequency bands were discovered which maximize the slopes of autocovariances of speech signals in frequency domain to increase the possibility of segregation between speech signals and background noise signal. A speech signal is divided into blocks which include multiples of sampled data, then those blocks are transformed to frequency domain using Fast Fourier Transform(FFT). To find linear equation by Linear Regression, the coefficients of autocovariance within blocks of some frequency band are used. The slope of the linear equation which is called the slope of autocovariance is varied from band to band according to the characteristics of the speech signal. Using speech signals of a man which consist of 200 files, the coefficients of the slopes of autocovariances are analyzed and compared from band to band.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.103-117
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• 2017

In longitudinal data analysis, the serial correlation of repeated outcomes must be taken into account using covariance matrix. Modeling of the covariance matrix is important to estimate the effect of covariates properly. However, It is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome the restrictions, several Cholesky decomposition approaches for the covariance matrix were proposed: modified autoregressive (AR), moving average (MA), ARMA Cholesky decompositions. In this paper we review them and compare the performance of the approaches using simulation studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2093-2099
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• 2011

Speech signal and car noise signal such as muffler noise are segregated from the one which has both signals mixed using statistical method. To classify speech signal from the other in segregated signals, FFT coefficients were obtained for all segments of a signal where each segment consists of 128 elements of a signal. For several coefficients of FFT corresponding to the low frequencies of a signal, autocovariances are calculated between coefficients of same order of all segments of a signal. Then they were averaged over autocovariances. Linear equation was eatablished for the those autocovariances using the linear regression method for each siganl. The coefficient of the slope of the line gives reference to compare and decide what the speech signal is. It is what this paper proposes. The results show it is very useful.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.340-343
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• 2011

To distinguish between a group of speech signals and nonspeech signals, you can use several features in domains like frequency, quefrency and time. It is very important to use features that differentiate two signal groups. As a feature to separate two signal groups, autocorrelation method was proposed and the variances between groups were studied. Autocovariances were just calculated for the time domain signal. Signals were divided into segments which consist of 128 data to be transformed to the frequency and quefrency domains. Autocovariances between each coefficient of segments in FFTs and quefrencies were found and they were averaged over wide spectrum. It is clear that the autocovariances in frequency domain show great differences between groups of signals.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.211-219
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• 2015

Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.489-496
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• 2018

This article is to examine the influence of occupational stress, socio-psychological factors and quality of sleep on fatigue symptoms from firefighters in fire service. The correlation coefficients were obtained by a tool of Pearson analysis, and covariance structure analysis was performed on the factors affecting the level of fatigue symptoms. This result suggests that the level of firefighters' fatigue symptoms in fire service. has a causal effect with occupational stress, socio-psychological factors and quality of sleep. Therefore, it is necessary to improve the work environment and to increase organizational support to deal with firefighters' fatigue in fire service.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.24-29
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• 2012

The sounds of exhaust emissions of automobiles are independent sound sources which are nothing to do with voices. We have no information for the sources of voices and exhaust sounds. Accordingly, Independent Component Analysis which is one of the Blind Source Separaton methods was used to segregate two source signals from each mixed signals. Maximum Likelyhood Estimation was applied to the signals came through the stereo microphone to segregate the two source signals toward the maximization of independence. Since there is no clue to find whether it is speech signal or not, the coefficients of the slope was calculated by the autocovariances of the signals in frequcency domain. Noise remover for speech signals was implemented by coupling the two algorithms.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• The Journal of Korean Association of Computer Education
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• v.12 no.4
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• pp.23-34
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• 2009

The main purpose of this study was to develop a mobile platform that supported college students to become self-regulated learners, and to examine its effects on students' academic achievement and self-regulated learning abilities. For this purpose, a mobile platform was designed and developed through the steps of systems approach. All the sub-steps were monitored and pilot-tested. The mobile platform incorporated a number of features designed specifically for the self-regulation components, with the intention of enhancing students' academic achievement and self-regulated learning abilities. Finally, research results suggested that students were taking advantage of the mobile platform that supported students' self-regulated learning. There were statically significant differences in academic achievement according to the type of mobile platform, F(1,133)=1767.202, P<.001

• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.2
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• pp.129-140
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• 1999

추계론적 해석은 구조계 내의 해석인수에 존재하는 공간적 또는 시간적 임의성이 구조계 반응에 미치는 영향에 대한 고찰을 목적으로 한다. 확률장은 구족계 내에서 특정한 확률분포를 가지는 것으로 가정된다. 구조계 반응에 대한 이들 확률장의 영향 평가를 위하여 통계학적 추계론적 해석과 비통계학적 추계론적 해석이 사용되고 있다. 본 연구에서는 비통계학적 추계론적 해석방법 중의 하나인 가중적분법을 제안하였다. 특히 구조계의 공간적 임의성이 큰 특성을 가지고 있는 반무한영역에 대한 적용 예를 제시하고자 한다. 반무한영역의 모델링에는 무한요소를 사용하였다. 제안된 방법에 의한 해석 결과는 통계학적 방법인 몬테카를로 방법에 의한 결과와 비교되었다. 제안된 가중적분법은 자기상관함수를 사용하여 확률장을 고려하므로 무한영역의 고려에 따른 해석의 모호성을 제거할 수 있다. 제안방법과 몬테카를로 방법에 의한 결과는 상호 잘 일치하였으며 공분산 및 표준편차는 무한요소의 적용에 의하여 매우 개선된 결과를 나타내었다.