• ETRI Journal
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• 제39권5호
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• pp.718-728
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• 2017

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.

• 응용통계연구
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• 제34권1호
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• pp.9-23
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• 2021

본 논문에서는 평균장 방법(mean-field methods)을 기반으로 사후 분포(posterior distribution)를 근사하는 방법인 변분 베이즈 방법(variational Bayes methods)에 대해 소개한다. 특히, 모수들을 실수공간으로 변환 후의 결합 사후분포를 가우시안 분포(Gaussian distribution)들의 곱(product)으로 근사하는 방법인 자동 미분 변분 추론(automatic differentiation variational inference)방법에 대해 자세히 소개하고, 환자에게 약물을 투여한 후 시간에 따라 약물의 흐름을 파악하는 연구인 약물동태학 모형(pharmacokinetic models)에 적용한다. 소개된 변분 베이즈 방법을 이용하여 자료분석을 실시하고 마코프 체인 몬테 카를로(Markov chain Monte Carlo)방법을 기초로한 자료분석의 결과와 비교한다. 알고리즘의 구현은 Stan을 이용한다.

• 한국위성정보통신학회논문지
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• 제6권1호
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• pp.12-18
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• 2011

고주파수 대역을 사용하는 위성통신 시스템의 링크 성능 저하의 가장 큰 요인 중의 하나가 강우 감쇠라고 할 수 있으며, 이러한 강우 감쇠를 보상하기 위한 가장 효율적인 방법으로써, 적응형 전송방식을 사용하고 있다. 강우 감쇠에 대처하기 위한 적응형 전송 방식을 개발하고 설계하는데 있어서 중요한 요소 중의 하나가 실제 발생하는 강우 감쇠에 대한 동적 시뮬레이션 모델이다. 본 논문에서는 초 단위 강우 감쇠 실측 데이터에 대한 통계치를 바탕으로 Markov 프로세스 모델을 이용하여 모델링하는 절차를 기술한다. 먼저 실측된 데이터의 통계적 특성을 추출하여 4가지 상태를 가지는 Markov 프로세스를 정의하고, 이를 이용하여 모델링된 데이터와 실측 데이터를 비교 분석한 결과를 제시한다.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2844-2853
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• 2023

A sophisticated Bayesian multilevel model for estimating group bias was developed to improve the utility of the ASTM E900-15 embrittlement trend curve (ETC) to assess the conditions of nuclear power plants (NPPs). For multilevel model development, the Baseline 22 surveillance dataset was basically classified into groups based on the NPP name, product form, and notch orientation. By including the notch direction in the grouping criteria, the developed model could account for TTS differences among NPP groups with different notch orientations, which have not been considered in previous ETCs. The parameters of the multilevel model and biases of the NPP groups were calculated using the Markov Chain Monte Carlo method. As the number of data points within a group increased, the group bias approached the mean residual, resulting in reduced credible intervals of the mean, and vice versa. Even when the number of surveillance test data points was less than three, the multilevel model could estimate appropriate biases without overfitting. The model also allowed for a quantitative estimate of the changes in the bias and prediction interval that occurred as a result of adding more surveillance test data. The biases estimated through the multilevel model significantly improved the performance of E900-15.

• Communications for Statistical Applications and Methods
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• 제21권6호
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• pp.521-528
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• 2014

A stochastic Gompertz diffusion model for tumor growth is a topic of active interest as cancer is a leading cause of death in Korea. The direct maximum likelihood estimation of stochastic differential equations would be possible based on the continuous path likelihood on condition that a continuous sample path of the process is recorded over the interval. This likelihood is useful in providing a basis for the so-called continuous record or infill likelihood function and infill asymptotic. In practice, we do not have fully continuous data except a few special cases. As a result, the exact ML method is not applicable. In this paper we proposed a method of parameter estimation of stochastic Gompertz differential equation via Markov chain Monte Carlo methods that is applicable for several data structures. We compared a Markov transition data structure with a data structure that have an initial point.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.61-77
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• 2017

The most challenging issues in the multi-channel MAC of the IEEE 1609.4 standard is how to handle the dynamic vehicular traffic condition with a high mobility, dynamic topology, and a trajectory change. Therefore, dynamic channel coordination schemes between CCH and SCH are required to provide the proper bandwidth for CCH/SCH intervals and to improve the quality of service (QoS). In this paper, we use a Markov model to optimize the interval based on the dynamic vehicular traffic condition with high mobility nodes in the multi-channel MAC of the IEEE 1609.4 standard. We evaluate the performance of the three-dimensional Markov chain based on the Poisson distribution for the node distribution and velocity. We also evaluate the additive white Gaussian noise (AWGN) effect for the multi-channel MAC coordination scheme of the IEEE 1609.4 standard. The result of simulation proves that the performance of the dynamic channel coordination scheme is affected by the high node mobility and the AWGN. In this research, we evaluate the model analytically for the average delay on CCHs and SCHs and also the saturated throughput on SCHs.

• 융합보안논문지
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• 제8권4호
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• pp.173-181
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• 2008

최근 웜에 의한 사이버 위협이 증가함에 따라 웜의 확산 특성을 분석하기 위한 전파 모델이 연구되고 있다. 대표적인 예로 수학적 모델링 기법인 Epidemic(SI), KM(Kermack-MeKendrick), Two-Factor, AAWP(Analytical Active Worm Propagation)등의 모델 기법들이 제시되었다. 하지만, 기존 모델 방법들은 대부분 코드레드와 같은 네트워크를 대상으로 하는 랜덤 스캐닝 기법에 대해서만 모델링이 가능하다. 또한 거시적인 분석만 가능하고 특정 위협에 대해 예측하는데 한계점을 가지고 있다. 따라서 본 논문에서는 과거의 위협 발생 데이터를 근거로 하여 Mass SQL Injection 같은 사이버위협에 적용 가능한 마코브 체인(markov chain) 기반 예측 방법을 제시한다. 이를 통하여 각 위협별 발생 확률 및 발생빈도를 예측할 수 있다.

• 한국전산구조공학회논문집
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• 제36권3호
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• pp.203-211
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• 2023

층상 반무한체에서의 확률론적 완전파형역산을 위한 Markov chain Monte Carlo (MCMC) 모사 기법을 정식화한다. Thin-layer method를 사용하여 조화 수직 하중이 작용하는 층상 반무한체의 지표면에서 추정된 동적 응답과 관측 데이터와의 차이 및 모델 변수의 사전 정보와의 차이를 최소화하도록 목적함수와 모델 변수의 사후 확률밀도함수를 정의한다. 목적함수의 기울기에 기반하여 MCMC 표본을 제안하기 위한 분포함수와 이를 수락 또는 거절할지 결정하는 수락함수를 결정한다. 기본 진동모드 뿐만이 아니라 고차 진동모드가 우세한 경우를 포함하여 다양한 층상 반무한체의 전단파 속도 추정에 제안된 MCMC 모사 기법을 적용하고 그 정확성을 검증한다. 제안된 확률론적 완전파형역산을 위한 MCMC 모사 기법은 층상 반무한체의 전단파 속도와 같은 재료 특성의 확률적 특성을 추정하는 데 적합함을 확인할 수 있다.

• 대한산업공학회지
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• 제16권2호
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• pp.1-9
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• 1990

The control of deterioration processes for which only incomplete state information is available is examined in this study. When the deterioration is governed by a Markov process, such processes are known as Partially Observable Markov Decision Processes (POMDP) which eliminate the assumption that the state or level of deterioration of the system is known exactly. This research investigates a two state partially observable Markov chain in which only deterioration can occur and for which the only actions possible are to replace or to leave alone. The goal of this research is to develop a new jump algorithm which has the potential for solving system problems dealing with continuous state space Markov chains.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1043-1050
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• 2022

Markov chains and Monte Carlo Simulation were applied to account for the probabilistic nature of pavement deterioration over time using data collected in the field. The primary purpose of this study was to evaluate pavement network performance of Western Australia (WA) by applying the existing pavement management tools relevant to WA road construction networks. Two approaches were used to analyze the pavement networks: evaluating current pavement performance data to assess WA State Road networks and predicting the future states using past and current pavement data. The Markov chains process and Monte Carlo Simulation methods were used to predicting future conditions. The results indicated that Markov chains and Monte Carlo Simulation prediction models perform well compared to pavement performance data from the last four decades. The results also revealed the impact of design, traffic demand, and climate and construction standards on urban pavement performance. This study recommends an appropriate and effective pavement engineering management system for proper pavement design and analysis, preliminary planning, future pavement maintenance and rehabilitation, service life, and sustainable pavement construction functionality.