• 한국군사과학기술학회지
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• 제16권5호
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• pp.631-638
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• 2013

The probabilistic fatigue life analysis is one of the common methods to account the uncertainty of parameters on the structural failure. Frequently, the Bayesian approach has been demonstrated as a proper method to show the uncertainty of parameters. In this work, the application of probabilistic fatigue life prediction method for the aircraft structure was studied. This effort was conducted by using the PoF(Probability of Failure) based on Bayesian approach. Furthermore, numerical example was carried out to confirm the validation of the suggested approach. In conclusion, it was shown that the Bayesian approach can calculate the probabilistic fatigue lives and the quantitative value of PoF effectively for the aircraft structural component. Moreover the calculated probabilistic fatigue lives can be utilized to determine the optimized inspection period of aircraft structures.

• 응용통계연구
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• 제27권4호
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• pp.589-603
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• 2014

본 연구는 시간의 순서에 따라 순차적으로 발생한 신호 자료에 있어서, 변화점 검출을 위한 베이지안 방법을 개발하고자 한다. 특히, Reversible Jump MCMC를 이용하여, 차원이 정해지지 않은 모수 공간을 탐색할 수 있는 효율적인 베이지안 추론 모형을 개발한다. 신호가 각 구간에서 선형함수인 경우에 대한 모형과 이해가 용이한 모형을 제안하고, 추정을 위해 고유의 MCMC알고리즘을 개발하였다. 제안된 방법을 모의실험 자료에 적용함으로써 그 정확성 및 효율성을 검증하였고, 실제 자료에도 적용하여 보았다.

• Structural Engineering and Mechanics
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• 제63권1호
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• pp.47-53
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• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.911-919
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• 2011

최근 의학에서는 정신 질환과 관련된 위험 인자를 찾는 것이 중요해지고 있다. 인자들을 찾아서 인자들의 특성과 관련성을 파악하면 병을 사전에 예방 할 수 있다. 또한 이 연구는 의학 발전에 많은 도움을 줄 수 있다. 정신 질환에 대한 위험요인은 주로 로지스틱 회귀모형을 사용하여 찾아 왔다. 하지만 이 논문에서는 데이터마이닝 기법 중 CART, C5.0, 로지스틱, 신경망, 베이지안 네트워크 방법을 이용한다. 정신장애 질병인 섬망자료를 적용하여, 최적의 모형인 베이지안 네트워크 방법을 선택하였다. 이 베이지안 네트워크 기법을 위험 요소를 찾는데 사용하고, 이 위험인자 간의 관계를 방사형 그래프를 통해서 규명하였다.

• 한국환경과학회지
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• 제20권3호
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• pp.329-340
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• 2011

The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• 경영과학
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• 제34권2호
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• pp.15-33
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• 2017

This paper considers a Bayesian Network analysis for understanding the heterogeneous cross-category effects of different promotion activities and developing an efficient overall promotion strategy for a large retail store. More specifically we differentiate price reduction promotion and floor promotion and study their heterogeneous effect on consumer purchase behavior under a market basket setting. We then utilize Bayesian networks in identifying complex association structure in market basket dataset by analyzing the effects of different promotional activities and also include the effects of time, family income and size. We find from our Bayesian network analysis that the dominant cross-category promotion effect of price promotion is the indirect effect whereas the dominant cross-category promotion effect of floor promotion is the direct effect. Also, among the demographic variables we find that family size of the household is linked with more product categories compared to income and see that there are differences in the extent of the effects by product category. Finally, we also show the existence of products acting as a network hub and how they can be utilized by retailers faced with a limited marketing budget and suggest a more efficient promotion strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2370-2387
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• 2012

A new security management model based on Rough set and Bayesian learner is proposed in the paper. The model focuses on finding out malicious nodes and getting them under control. The degree of dissatisfaction (DoD) is defined as the probability that a node belongs to the malicious node set. Based on transaction history records local DoD (LDoD) is calculated. And recommended DoD (RDoD) is calculated based on feedbacks on recommendations (FBRs). According to the DoD, nodes are classified and controlled. In order to improve computation accuracy and efficiency of the probability, we employ Rough set combined with Bayesian learner. For the reason that in some cases, the corresponding probability result can be determined according to only one or two attribute values, the Rough set module is used; And in other cases, the probability is computed by Bayesian learner. Compared with the existing trust model, the simulation results demonstrate that the model can obtain higher examination rate of malicious nodes and achieve the higher transaction success rate.

• 한국인터넷방송통신학회논문지
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• 제9권3호
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• pp.31-36
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• 2009

베이지안망은 불확실한 상황 하에서 영역지식을 표현하고 예측하기 위한 좋은 도구로 알려져 있다. 그러나 변수가 많아졌을 때 학습이 어렵고 시간의 요구량이 늘어나게 되어 효율적이고 신회도 높은 탐색에 문제가 있다. 이를 해결하기 위해서 노드의 순서를 정하여 효율적인 구조학습이 가능하도록 한다. 본 논문에서는 각 상황에 따른 확률의 엔트로피를 계산하여 다양한 변수간의 관계나 상호의존적인 상황에서도 오차를 줄이고 신뢰도를 높일 수 있는 효과적인 분류학습모델을 제시한다. 베이지안망 학습 방법 중 일반적으로 널리 알려져 있는 K2알고리즘에서 각 노드의 엔트로피 수치를 계산하여 엔트로피가 낮은 노드의 순서를 결정하여 결과적으로 빠른 시간 안에 최적화된 베이지안망의 모델을 구성하는 효율적인 학습모델을 제시한다.

• 컴퓨터교육학회논문지
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• 제21권3호
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• pp.51-58
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• 2018

본 연구에서는 컴퓨터 오락추구 행동의 예측 분석을 목적으로 한국아동 청소년패널조사(KCYPS) 데이터를 대상으로 베이지안 추론을 사용하여 컴퓨터 오락추구 행동과 관련 변수들의 상호의존성과 인과관계를 조사하였다. 이를 위해 일반 베이지안 네트워크를 통한 마코프 블랭킷(Markov Blanket)을 추출하였다. 또한 변수들의 확률을 변화시켜 컴퓨터 오락추구 행동에 대한 변수들의 영향 정도를 분석하였다. 연구결과, 컴퓨터 오락추구 행동은 관련 변수들(학교학습활동, 비행-흡연, 비행-조롱, 팬덤활동, 학교규칙)의 값을 조정하였을 때 유의미하게 변화되는 것으로 나타났다. 본 연구의 결과로 베이지안 추론은 청소년의 컴퓨터 오락추구 행동을 예측하고 조절하는 등 교육 분야에서 활용될 수 있음을 제시하였다.