• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.466-466
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• 2015

지역빈도해석은 수문학에서 오랜 역사를 갖고 있으며, 수년에 걸쳐 수문학적 변량의 정량적 추정을 위해 다양한 접근방법들이 제안되어 왔다. 그러나 제안된 방법들의 가설설정 수준이 높기 때문에 실제 적용에 제약이 많고, 적용 시에도 예측에 대한 불확실성이 높은 문제점이 있다. 본 연구에서는 이러한 문제점을 개선하기 위한 방법으로 계층적 베이지안 모델을 이용한 지역빈도해석 모형을 제안하고자 한다. 본 모형은 2개의 계층적 구조로 구성된다. 첫번째 계층은 재현기간별 GEV 분포의 매개변수를 정규화하여 주변분포로 설정하고, Kriging 기법을 이용하여 지형학적, 기상학적 정보들과 극치강수량 효과를 적합시켜 공간적 이질성과 미계측 유역에 대한 효과적인 보간을 가능하게 한다. 두번째 계층은 지점의 특성을 나타내는 매개변수들간의 공분산을 Bayesian 모델에 연계하여 매개변수들의 공간적 변동성을 나타낸다. 2개 계층의 결합확률분포는 MCMC 기법을 이용하여 예측값에 대한 불확실성을 정량적으로 분석하게 된다. 본 모형을 통해 홍수량 추정 시 필요한 시간 단위 극치강수량의 공간적 분포를 효과적으로 추정할 수 있을 것으로 판단된다.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.947-958
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• 2014

Understanding extreme precipitation events is very important for flood planning purposes. Especially, the r-year return level is a common measure of extreme events. In this paper, we present a spatial analysis of precipitation return level using hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitations and daily precipitation above a high threshold at 62 stations in Korea with generalized extreme value(GEV) and generalized Pareto distribution(GPD), respectively. The spatial dependence among return levels is incorporated to the model through a latent Gaussian process of the GEV and GPD model parameters. We apply the proposed model to precipitation data collected at 62 stations in Korea from 1973 to 2011.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.169-175
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• 2015

An effective way to understand the dynamic and time series that follows the passage of time, as valuation is to establish a model to analyze the phenomena of the system. Model of the decision process is efficient clustering information of the total mass of the time series data of the relevant population been collected in a particular number of sub-groups than to look at all a time to an understand of the overall data through each community-specific model determination. In this study, a sub-grouping of the group and the first of the two process model of each cluster by determining, in the following in sub-population characterized by a fusion with heuristic Bayesian clustering techniques proposed a process which can reduce calculation time and cost was confirmed by experiments using actual effectiveness valuation.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.206-206
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• 2016

최근 기상변동성 증가 및 기후변화로 인해 기존 한반도의 기상패턴과 다른 이상강우 현상이 증가하고 있다. 이러한 변동성 증가는 수자원 계획을 수립하는데 있어 불확실성을 가중시키기고 있다. 이러한 점에서 수문 시계열의 변화양상을 효과적으로 인지할 수 있으며, 유역단위에서 일관된 변화를 평가할 수 있는 변동성 분석 개발이 필요하다. 이에 본 연구에서는 기존 변동성 분석방법에 계층적 베이지안(Hierarchical Bayesian) 기법을 연계하여 유역단위에서 변동점 해석을 위한 모형을 개발하였다. 한강유역의 30년 이상의 강우 자료를 활용하여 연강우량 자료를 구축하였으며, 본 연구를 통해 개발된 모형의 적합성을 평가하였다. 분석결과, 약 2000년대를 기준으로 강우의 변화 양상을 확인할 수 있었으며, 과거에 비해 강우의 증가 특성을 효과적으로 평가할 수 있었다. 이와 같은 수문기상자료에 대한 변동성 분석은 미래에 발생 가능한 홍수나 가뭄과 같은 사상을 모의함에 있어 효율적으로 활용될 수 있을 것으로 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.138-150
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• 2012

The purpose of this study was to examine the regional disparity of ambulatory health care utilization considering spatio-temporal variation in South Korea during 1996-2008(precisely, in 1996, 1999, 2002, 2005, and 2008) using bayesian hierarchial spatio-temporal model. The spatial pattern uses an intrinsic gaussian conditional autoregressive (CAR) error component. Ornstein-Uhlenbeck method was applied to detect the temporal patterns. The results showed that substantial temporal-geographical variation depending on diseases exists in Korea. On the Contrary to the pattern of total outpatient utilizations, for example, the areas that chronic diseases distributed relatively high were most in rural where the proportion of elderly population was higher than in the urban. Chungcheongnam-do, Junlabuk-do, and Kyeongsangbuk-do had higher risks in hypertension, whereas arthritis was higher risk in the Kyeonggi-do, Chungcheongbuk-do, Junlanam-do, and Junlabuk-do. The results of this study suggested that the effective health intervention programmes needed to alleviate the regional variation of health care utilization. These outcomes also provided the foundation for further investigation of risk factors and interventions in these high-risk areas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2335-2342
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• 2006

The current web is accumulating abundant information. In particular, text based documents are a type used very easily and frequently by human. So, numerous researches are progressed to retrieve the text documents using many methods, such as probability, statistics, vector similarity, Bayesian, and so on. These researches however, could not consider both subject and semantic of documents. So, to overcome the previous problems, we propose the document similarity method for semantic retrieval of document users want. This is the core method of document clustering. This method firstly, expresses a hierarchy semantically of document content ut gives the important hierarchy domain of document to weight. With this, we could measure the similarity between documents using both the domain weight and concepts coincidence in the domain hierarchies.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.627-641
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• 2012

We consider a model estimation and model selection methods for the multi-way contingency table data with non-response or missing values. We also consider hierarchical Bayesian model in order to handle a boundary solution problem that can happen in the maximum likelihood estimation under non-ignorable non-response model and we deal with a model selection method to find the best model for the data. We utilized Bayes factors to handle model selection problem under Bayesian approach. We applied proposed method to the pre-election survey for the 2004 Korean National Assembly race. As a result, we got the non-ignorable non-response model was favored and the variable of voting intention was most suitable.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.133-148
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• 2022

In this study, a road pavement crack deterioration model was developed for a pavement road sections of the Sejong-city. Data required for model development were acquired using a deep learning-based road asset monitoring system. Road pavement monitoring was conducted on the same sections in 2021 and 2022. The developed model was analyzed by dividing it into a method for estimating the annual average amount of deterioration and a method based on Bayesian Markov Mixture Hazard model. As a result of the analysis, it was found that an analysis results similar to the crack deterioration model developed based on the data acquired from the Automatic pavement investigation equipmen was derived. The results of this study are expected to be used as basic data by local governments to establish road management plans.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.333-340
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• 2019

A combat system performs a given mission enduring various threats. It is important to analyze the reliability of combat systems in order to increase their ability to perform a given mission. Most of studies considered no threat or on threat and didn't analyze all the dependent relationships among the components. In this paper, we analyze the loss probability of the function of the combat system and use it to analyze the reliability. The proposed method is divided into two layers, A lower layer and a upper layer. In lower layer, the failure probability of each components is derived by using FTA to consider various threats. In the upper layer, The loss probability of function is analyzed using the failure probability of the component derived from lower layer and BBN in order to consider the dependent relationships among the components. Using the proposed method, it is possible to analyze considering various threats and the dependency between components.