• ETRI Journal
• /
• 제5권1호
• /
• pp.10-16
• /
• 1983

복잡한 시스템의 신뢰도를 해석하기 위해서는 가용도 (Availability) 및 각종 고장간격 등의parameter를 계산하여야 한다. 따라서 이들 parameter를 정의하고 이의 적용성에 대하여 언급하였으며, Markov process를 적용하여 각종 고장간격을 계산하는 기법을 유도하였다. 본 Markov process에 의한 신뢰도 해석기법은 제반 시스템의 상태확률을 계산하여 각종parameter를 구하게 되므로 다양한 시스템의 상태를 해석할 수 있으며, 시스템의 신뢰도 예측은 물론 정비계획을 수행하는 데에도 광범위하게 응용할 수 있다.

• Communications for Statistical Applications and Methods
• /
• 제25권1호
• /
• pp.29-42
• /
• 2018

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.

• 한국정보통신학회논문지
• /
• 제26권2호
• /
• pp.317-320
• /
• 2022

This paper studies real-time error in the context of monitoring a symmetric binary information source over a delay system. To obtain the average real-time error, the delay system is modeled and analyzed as a discrete time Markov chain with a finite state space. Numerical analysis is performed on various system parameters such as state transition probabilities of information source, transmission times, and transmission frequencies. Given state transition probabilities and transmission times, we investigate the relationship between the transmission frequency and the average real-time error. The results can be used to investigate the relationship between real-time errors and age of information.

• 대한의용생체공학회:의공학회지
• /
• 제27권3호
• /
• pp.101-109
• /
• 2006

It has been known that there is strong relationship between human voices and the movements of the articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The EMG signals were acquired from three articulatory facial muscles. Preliminary, 10 Korean digits were used as recognition variables. The various feature parameters including filter bank outputs, linear predictive coefficients and cepstrum coefficients were evaluated to find the appropriate parameters for EMG-based speech recognition. The sequence of the EMG signals for each word is modelled by a hidden Markov model (HMM) framework. A continuous word recognition approach was investigated in this work. Hence, the model for each word is obtained by concatenating the subword models and the embedded re-estimation techniques were employed in the training stage. The findings indicate that such a system may have a capacity to recognize speech signals with an accuracy of up to 90%, in case when mel-filter bank output was used as the feature parameters for recognition.

• 대한기계학회논문집A
• /
• 제35권4호
• /
• pp.393-400
• /
• 2011

본 연구에서는 현장의 축적된 피로 수명 시험 데이터를 바탕으로 유한요소해석(Finite Element Analysis)을 이용하여 스프링의 피로 수명 파라미터를 역 추정(Inverse Estimation)하는 연구를 수행하였다. 베이지안 접근법(Bayesian Approach)을 이용하여 불확실성 피로 수명 파라미터의 사후분포(Posterior distribution)를 구하였고, 마코프체인몬테카를로(Markov Chain Monte Carlo) 기법을 이용하여 역 추정된 파라미터의 샘플 데이터를 생성하였다. 얻어진 샘플링 데이터를 기반으로 피로 수명을 예측한 결과 신뢰 수준 내에서 실제 수명 시험 결과가 예측한 범위 내에 잘 포함되고 있음을 알 수 있었다.

• 한국농공학회지
• /
• 제31권4호
• /
• pp.31-49
• /
• 1989

It is of the most urgent necessity to get hydrological time series of long duration for the establishment of rational design and operation criterion for the Agricultural hydraulic structures. This study was conducted to select best fitted frequency distribution for the monthly runoff and to simulate long series of generated flows by multi-season first order Markov model with comparison of statistical parameters which are derivated from observed and sy- nthetic flows in the five watersheds along Geum river basin. The results summarized through this study are as follows. 1. Both two parameter gamma and two parameter lognormal distribution were judged to be as good fitted distributions for monthly discharge by Kolmogorov-Smirnov method for goodness of fit test in all watersheds. 2. Statistical parameters were obtained from synthetic flows simulated by two parameter gamma distribution were closer to the results from observed flows than those of two para- meter lognormal distribution in all watersheds. 3. In general, fluctuation for the coefficient of variation based on two parameter gamma distribution was shown as more good agreement with the observed flow than that of two parameter lognormal distribution. Especially, coefficient of variation based on two parameter lognormal distribution was quite closer to that of observed flow during June and August in all years. 4. Monthly synthetic flows based on two parameter gamma distribution are considered to give more reasonably good results than those of two parameter lognormal distribution in the multi-season first order Markov model in all watersheds. 5. Synthetic monthly flows with 100 years for eack watershed were sjmulated by multi- season first order Markov model based on two parameter gamma distribution which is ack- nowledged to fit the actual distribution of monthly discharges of watersheds. Simulated sy- nthetic monthly flows may be considered to be contributed to the long series of discharges as an input data for the development of water resources. 6. It is to be desired that generation technique of synthetic flow in this study would be compared with other simulation techniques for the objective time series.

• 한국전산구조공학회논문집
• /
• 제23권6호
• /
• pp.641-649
• /
• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• 한국농공학회지
• /
• 제38권4호
• /
• pp.110-124
• /
• 1996

This study was conducted to simulate long seres of synthetic monthly flows by multi-season first order Markov model with selection of best fitting frequency distribution, harmonic synthetic and harmonic regression models and to make a comparison of statistical parameters between observes and synthetic flows of five watersheds in Geum river system. The results obtained through this study can be summarized as follow. 1. Both gamma and two parameter lognormal distributions were found to be suitable ones for monthly flows in all watersheds by Kolmogorov-Smirnov test. 2. It was found that arithmetic mean values of synthetic monthly flows simulated by multi-season first order Markov model with gamma distribution are much closer to the results of the observed data in comparison with those of the other models in the applied watersheds. 3. The coefficients of variation, index of fluctuation for monthly flows simulated by multi-season first order Markov model with gamma distribution are appeared closer to those of the observed data in comparison with those of the other models in Geum river system. 4. Synthetic monthly flows were simulated over 100 years by multi-season first order Markov model with gamma distribution which is acknowledged as a suitable simulation modal in this study.

• 대한기계학회논문집A
• /
• 제40권10호
• /
• pp.895-900
• /
• 2016

리튬 이온 배터리의 잔존수명 추정은 품질보증, 운전계획, 교체주기 파악 등을 위해 활용된다는 점에서 그 필요성이 점점 커지고 있다. 본 논문에서는 에너지 저장 장치용 배터리의 잔존 수명을 단일지수 용량열화 모델과 마코프체인 몬테카를로(MCMC) 방법을 이용하여 추정한 결과를 제시한다. MCMC방법은 사전 정보가 제대로 주어지지 않았을 때, 추정결과가 모델 초기값과 입력 설정값에 따라 크게 변하게 되는 단점이 있어, 실제 현장에서 배터리 모델과 추정법에 익숙하지 않은 사용자가 활용하는데 어려움이 있다. 이러한 어려움을 극복하기 위해, 본 논문에서는 베이지안 추론법의 이론식을 전역 탐색하여 구한 이론값과 MCMC 추정값을 비교해서, 초기값과 설정값을 결정하는 과정을 제안한다.

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2003년도 추계 학술발표회 논문집
• /
• pp.161-166
• /
• 2003

In this paper, a new form of generalized linear models is proposed. The proposed models consist of a distribution function of the mean response and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models. Markov chain Monte Carlo methods are used to estimate the parameters within a Bayesian framework.