• Title/Summary/Keyword: 마코프분석

Search Result 175, Processing Time 0.026 seconds

Bayesian inference of longitudinal Markov binary regression models with t-link function (t-링크를 갖는 마코프 이항 회귀 모형을 이용한 인도네시아 어린이 종단 자료에 대한 베이지안 분석)

  • Sim, Bohyun;Chung, Younshik
    • The Korean Journal of Applied Statistics
    • /
    • v.33 no.1
    • /
    • pp.47-59
    • /
    • 2020
  • In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.

An Activity Recognition Algorithm using a Distributed Inference based on the Hidden Markov Model in Wireless Sensor Networks (WSN환경에서 은닉 마코프 모텔 기반의 분산추론 기법 적용한 행위인지 알고리즘)

  • Kim, Hong-Sop;Han, Man-Hyung;Yim, Geo-Su
    • Proceedings of the Korean Society of Computer Information Conference
    • /
    • 2009.01a
    • /
    • pp.231-236
    • /
    • 2009
  • 본 연구에서는 집이나 사무실과 같은 일상 공간에서 발생할 수 있는 연간의 일상생활행위 (ADL: Activities of Daily Living) 들을 인지하는 분산 모델을 제시한다. 사용자의 환경 정보, 위치 정보 및 행위 정보를 간단한 센서들이 부착된 가정용 기기들과 가구, 식기들을 통해 무선 센서 네트워크를 통해 수집하며 분석한다. 하지만 이와 같은 다양한 기기의 활용과 충분히 분석되어지지 않은 데이터들은 본 논문에서 제시하는 일상 환경에서 고차원의 ADL 모델을 구축하기 어렵게 한다. 그러나 ADL들이 생성하는 센서 데이터들과 센서 데이터들의 순서들은 어떤 행위가, 이루어지고 있는지 인지할 수 있도록 도와준다. 따라서 이 센서 데이터들의 순서를 특정 행위 패턴을 분석하는 데 활용하고, 이를 통해 분산 선형 시간 추론 알고리즘을 제안한다. 이 알고리즘은 센서 네트워크와 같은 소규모 시스템에서 행위를 인지하는 데 적절하다.

  • PDF

Reliability analysis of multi-state parallel system with a multi-functional standby component (다기능 대기부품을 갖는 다중상태 병렬시스템의 신뢰도 분석)

  • Kim, Dong-Hyeon;Lee, Suk-Hoon;Lim, Jae-Hak
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.20 no.4
    • /
    • pp.75-87
    • /
    • 2015
  • A redundant structure typically consists of primary component and standby component taking over the function of the primary component when the primary component fails. In this research, we consider a redundant structure in which a standby component can take over the function of more than one primary component when primary components fail. And we assume that the system has multi-state according to the states of components while all components have two states. This system is called as the multi-state redundant system with a multi-functional standby component. This type of redundant structure is frequently adapted by the system such as an aircraft in which the weight is an important design factor. In this paper, we propose new reliability model for this multi-state redundant system with a multi-functional standby component in order for evaluating the reliability of the system. Under the assumption that all components have constant failure rate, we evaluate the reliability of the system by applying Markov analysis method. And we investigate the effect of the multi-functional standby component by comparing reliabilities of the parallel system with multi-functional standby component and a simple parallel system and a parallel system with redundant structure.

Reliability Analysis of Repairable Systems Considering Failure Detection Equipments (고장감지장치를 고려한 수리가능 시스템의 신뢰도 분석)

  • Na, Seong-Ryong
    • The Korean Journal of Applied Statistics
    • /
    • v.24 no.3
    • /
    • pp.515-521
    • /
    • 2011
  • In this paper we consider failure detection equipment that which find failures in repairable systems and enable repair operations. In practical situations, failure detection equipment may come across troubles that can cause the omissions in detecting system failures and have a serious effect on system reliability. We analyze this effect through the appropriate modeling of Markov processes.

The Classification of the Schizophrenia EEG Signal using Hidden Markov Model (은닉 마코프 모델을 이용한 정신질환자의 뇌파 판별)

  • 이경일;김필운;조진호;김명남
    • Journal of Biomedical Engineering Research
    • /
    • v.25 no.3
    • /
    • pp.217-225
    • /
    • 2004
  • In this paper, a new automatic classification method for the normal EEC and schizophrenia EEC using hidden Markov model(HMM) is proposed. We used the feature parameters which are the variance for statistical stationary interval of the EEC and power spectrum ratio of the alpha, beta, and theta wave. The results were shown that high classification accuracy of 90.9% in the case of normal person, and 90.5% in the case of schizophrenia patient. It seems that proposed classification system is more efficient than the system using complicate signal processing process. Hence, the proposed method can be used at analysis and classification for complicated biosignal such as EEC and is expected to give considerable assistance to clinical diagnosis.

Prediction of Marine Accident Frequency Using Markov Chain Process (마코프 체인 프로세스를 적용한 해양사고 발생 예측)

  • Jang, Eun-Jin;Yim, Jeong-Bin
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
    • /
    • 2019.11a
    • /
    • pp.266-266
    • /
    • 2019
  • Marine accidents are increasing year by year, and various accidents occur such as engine failure, collision, stranding, and fire. These marine accidents present a risk of large casualties. It is important to prevent accidents beforehand. In this study, we propose a modeling to predict the occurrence of marine accidents by applying the Markov Chain Process that can predict the future based on past data. Applying the proposed modeling, the probability of future marine accidents was calculated and compared with the actual frequency. Through this, a probabilistic model was proposed to prepare a prediction system for marine accidents, and it is expected to contribute to predicting various marine accidents.

  • PDF

A Study of the Prediction of Incidence of Crime using Markov process (마코프 프로세스를 적용한 범죄 발생 예측 방법에 관한 연구)

  • Chung, Young-Suk;Jung, Jin-Young
    • Journal of the Korea Society of Computer and Information
    • /
    • v.17 no.3
    • /
    • pp.95-103
    • /
    • 2012
  • Modern society is experiencing a variety of crimes, and to prevent crime is being studied. Existing studies related to the crime of crimes that occur on spatial analysis and geographic information, or to analyze the type of criminal offense of studies have been conducted, However the existing studies of the geographical and psychological crime that occurs throughout the study area and by analyzing the motives for the crime prevention research is the most. In this paper, we introduce Markov processor model for predicting the crime is present. Of several crimes, murder, government official crimes, the incidence of violent crime has occurred over time by using the predicted incidence of crime. Presented in this paper, predictive modeling is used in a crime occurred in the average duration of the overall average number of crimes that occurred in the one-year average, which recently labeled as the average prediction was compared to if you can increase the likelihood, recent average to apply to increase the probability of the prediction that crime have been investigated.

A Study on the Hydrologic Decision-Making for Drought Management : 1. An Analysis on the Stochastic Behavior of PDSI using markov chain (가뭄관리를 위한 수문학적 의사결정에 관한 연구 : 1. 마코프연쇄를 이용한 PDSI의 추계학적 거동분석)

  • Kang, In-Joo;Yoon, Yong-Nam
    • Journal of Korea Water Resources Association
    • /
    • v.35 no.5
    • /
    • pp.583-595
    • /
    • 2002
  • The purposes of this study are to perform the management and monitoring of droughts for Mokpo area via the monthly Palmer index(PDSI), the data is obtained from the Mokpo meteorological station, and the used data are in the period of 1906 to 1999. Monthly Palmer index is classified into 7 stochastic classes and its dynamic change of monthly transition probability estimated by Markov chain is investigated. We also estimate the steady state probability of the classified PDSI. The 4th class shows the highest frequency of 49.6% out of 7 classes and the 7th class which is the most extreme drought show that a stochastic transition probability is more or less larger than an empirical one. Also, we found that the monthly steady state probability could be used for the forecasting of changing pattern of drought magnitude for the study area.

Unsupervised Korean Word Sense Disambiguation using CoreNet (코어넷을 활용한 비지도 한국어 어의 중의성 해소)

  • Han, Kijong;Nam, Sangha;Kim, Jiseong;Hahm, YoungGyun;Choi, Key-Sun
    • Annual Conference on Human and Language Technology
    • /
    • 2017.10a
    • /
    • pp.153-158
    • /
    • 2017
  • 본 논문은 한국어 어휘 의미망인 코어넷(CoreNet)을 활용한 비지도학습 방식의 한국어 어의 중의성 해소(Word Sense Dsiambiguation)에 대한 연구이다. 어의 중의성 해소의 실질적인 응용을 위해서는 합리적인 수준으로 의미 후보를 나눌 필요성이 있다. 이를 위해 동형이의어와 코어넷의 개념체계를 활용하여 의미 후보를 나누어서 진행하였으며 이렇게 나눈 것이 실제 활용에서 의미가 있음을 실험을 통해 보였다. 접근 방식으로는 문맥 속에서 서로 영향을 미치는 어휘의 의미들을 동시에 고려하여 중의성 해소를 할 수 있도록 마코프랜덤필드와 의존구조 분석을 바탕으로 한 지식 기반 모델을 사용하였다. 이 과정에서도 코어넷의 개념체계를 활용하였다. 이 방식을 통해 임의의 모든 어휘에 대해 중의성 해소를 하도록 직접 구축한 데이터 셋에 대하여 80.9%의 정확도를 보였다.

  • PDF