• 한국경영과학회지
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• 제22권4호
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• pp.151-165
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• 1997

We propose a method for estimating the probability of perfect PM from successive failure times of a repairable system. The system under study is maintained preventively at periodic times, and it undergoes minimal repair at failure. We consider Brown-Proschan imperfect PM model in which the system is restored to a condition as good as new with probability P and is otherwise restored to its condition just prior to failure. We discuss the identifiability problem when the PM modes are not recorded. The expectation-maximization principle is employed to handle the incomplete data problem. We assume that the lifetime distribution belongs to a parametric family with increasing failure rate. For the two parameter Weibull lifetime distribution, we propose a specific algorithm for finding the maximum lifelihood estimates of the reliability parameters : the probability of perfect PM (P), as well as the distribution parameters. The estimation method will provide useful results for maintaining real systems.

• 한국멀티미디어학회논문지
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• 제20권6호
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• pp.865-872
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• 2017

Recent advance in electronics and scintillators makes it possible to utilize the time-of-flight (TOF) information in improving image reconstruction of positron emission tomography(PET). In this paper, we propose a TOF-based fast image reconstruction method for PET. The proposed method uses the deconvolution of TOF data for each angle view and the rotational averaging of deconvolved images. Simulation results show an improved performance of the proposed method, as compared with filtered backprojection (FBP) method, TOF-FBP, and TOF version of expectation-maximization(EM) methods. Simulation results also show a great potentiality of the proposed method in limited angle tomography applications.

• 음성과학
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• 제15권3호
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• pp.7-15
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• 2008

This paper proposes the method to extract new feature vectors using the difference between the cepstrum for static characteristics and delta cepstrum for dynamic characteristics in speaker recognition (SR). The difference vector (DV) which it proposes from this paper is containing the static and the dynamic characteristics simultaneously at the intermediate characteristic vector which uses the deference between the static and the dynamic characteristics and as the characteristic vector which is new there is a possibility of doing. Compared to the conventional method, the proposed method can achieve new feature vector without increasing of new parameter, but only need the calculation process for the difference between the cepstrum and delta cepstrum. Experimental results show that the proposed method has a good performance more than 2.03%, on average, compared with conventional method in speaker identification (SI).

• 한국통신학회논문지
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• 제24권8A호
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• pp.1156-1162
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• 1999

본 논문에서는 무선 협대역 채널에서 다이버시티 수신 알고리즘을 제안하였다. 수신 알고리즘은 통계학분야에서 Maximum-Likelihood Sequence Estimation의 근사 추정치를 계산하는데 활용되는 Expectation-Maximization(EM) 알고리즘을 기본으로 유도하였다. 알고리즘의 특성은 파일럿 심볼을 이용하여 반복적으로 블록 디코딩을 수행하며 시뮬레이션 결과를 기존의 파일럿 심볼을 이용하는 방식(PSI)에 비교하여 매우 우수한 성능을 보였다. 수신 알고리즘의 성능은 컴퓨터 시뮬레이션을 이용하여 검증하였으며 다양한 차수의 다이버시티 수신단과 Trellis Coded Modulation (TCM)을 이용한 시스템에 알고리즘을 적용하였다.

• 한국전기전자재료학회논문지
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• 제26권2호
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• pp.151-157
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• 2013

In this paper, properties of pattern using LBG (Linde-Buzo-Gray) Algorithm was explored including the exactness of K-means algorithm and process time of EM (Expectation Maximization) algorithm in order to develop analysis algorithm of partial discharge pattern in a cable using acoustic data analysis system. Partial discharge was measured by generating inner fault due to lamination of XLPE which is used for cable insulation material. Discharge pattern was analysed by changing the number of swarm article to 2, 4, and 6 in order to interpret swarm structure and properties.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.110.1-110
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• 2002

We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.96.2-96
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• 2002

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 scheme that represents the information from observations as a set of random variables in the form 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 p...

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.381-384
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• 2000

This paper proposes a method of estimating the lifetime distribution at use condition for constant stress accelerated lift tests when extrinsic failure mode as well as intrinsic one exists. A mixture of two log-normal distributions is introduced to describe these failure modes and it is assumed that a linear relation exists between the location parameter and stress. An estimation procedure using the expectation and maximization algorithm is proposed and a numerical example is given.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.167-170
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• 2003

We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: an approximate crossvalidation scheme to estimate the smoothing parameter inherent in regression splines, as well as Monte Carlo Expectation Maximization (MCEM) and Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM and Bayesian approaches using simulation, showing that they appear approximately equally efficient, with the approximate cross-validation method being computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.217-222
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• 2009

In this paper, a new approach is proposed for the segmentation of Computed Tomography (CT) head images. The approach consists of two-stage segmentation with each stage contains two different segmentation techniques. The ultimate aim is to segment the CT head images into three classes which are abnormalities, cerebrospinal fluid (CSF) and brain matter. For the first stage segmentation, k-means and fuzzy c-means (FCM) segmentation are implemented in order to acquire the abnormalities. Whereas for the second stage segmentation, modified FCM with population-diameter independent (PDI) and expectation-maximization (EM) segmentation are adopted to obtain the CSF and brain matter. The experimental results have demonstrated that the proposed system is feasible and achieve satisfactory results.