• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.2
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• pp.179-194
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• 2020

A Bayesian approach was introduced to improve the belief of prior distributions of input parameters for the probabilistic safety assessment of radioactive waste repository. A GoldSim-based module was developed using the Markov chain Monte Carlo algorithm and implemented through GSTSPA (GoldSim Total System Performance Assessment), a GoldSim template for generic/site-specific safety assessment of the radioactive repository system. In this study, sequential Bayesian updating of prior distributions was comprehensively explained and used as a basis to conduct a reliable safety assessment of the repository. The prior distribution to three sequential posterior distributions for several selected parameters associated with nuclide transport in the fractured rock medium was updated with assumed likelihood functions. The process was demonstrated through a probabilistic safety assessment of the conceptual repository for illustrative purposes. Through this study, it was shown that insufficient observed data could enhance the belief of prior distributions for input parameter values commonly available, which are usually uncertain. This is particularly applicable for nuclide behavior in and around the repository system, which typically exhibited a long time span and wide modeling domain.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.331-344
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• 2016

Whole Crop Barley (WCB) is a representative self-sufficient winter annual forage crop, along with Italian Ryegrass (IRG), in Korea. In this study, we examined the path relationship between WCB yield and climate factors such as temperature, precipitation, and sunshine duration using a structural equation model. A Bayesian approach was considered to overcome the limitations of the small WCB sample size. As prior distribution of parameters in Bayesian method, standard normal distribution, the posterior result of structural equation model for WCB, and the posterior result of structural equation model for IRG (which is the most popular winter crop) were used. Also, Heywood case correction in prior distribution was considered to obtain the posterior distribution of parameters; in addition, the best prior to fit the characteristics of winter crops was identified. In our analysis, we found that the best prior was set by using the results of a structural equation model to IRG with Heywood case correction. This result can provide an alternative for research on forage crops that have hard to collect sample data.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.747-757
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• 2021

Quantification of extreme rainfall is very important in establishing a flood protection plan, and a general measure of extreme rainfall is expressed as an T-year return level. In this study, a method was proposed for quantifying spatial distribution and uncertainty of daily rainfall depths with various return periods using a hierarchical Bayesian model combined with climate and geographical information, and was applied to the Seoul-Incheon-Gyeonggi region. The annual maximum daily rainfall depth of six automated synoptic observing system weather stations of the Korea Meteorological Administration in the study area was fitted to the generalized extreme value distribution. The applicability and reliability of the proposed method were investigated by comparing daily rainfall quantiles for various return levels derived from the at-site frequency analysis and the regional frequency analysis based on the index flood method. The uncertainty of the regional frequency analysis based on the index flood method was found to be the greatest at all stations and all return levels, and it was confirmed that the reliability of the regional frequency analysis based on the hierarchical Bayesian model was the highest. The proposed method can be used to generate the rainfall quantile maps for various return levels in the Seoul-Incheon-Gyeonggi region and other regions with similar spatial sizes.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.1-11
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• 2017

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS : Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS:The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.195-198
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• 2005

최근 실내 환경에서 영상 정보를 사용하여 로봇이 서비스를 제공하기 위한 연구가 활발하다. 과거 영상 처리 접근 방법은 산업 환경과 같은 예측 가능한 환경을 바탕으로 미리 정의된 기하학적 모델을 통해 상황을 인식하였기에, 이를 실내 환경과 같은 가변적인 환경에 적용할 시 성능이 저하된다. 이에 지식을 기반으로 불확실성을 해결하여 정확도를 향상 시킴으로써 영상 인식 성능을 높이기 위한 다양한 연구가 진행되어 왔다. 본 논문에서는 실내에서 활동하는 서비스 로봇의 물체인식 성능을 향상시키기 위해, 대상 물체가 다른 물체에 의해서 가려져 있는 경우 대상 물체의 존재 여부를 추론하기 위한 베이지안 네트워크 모델링 방법을 제안한다. 제안하는 방법은 작은 단위로 설계된 베이지안 네트워크들을 상황에 따라 결합하여 추론 모델이 구성되게 하였고 물체간의 관계를 효과적으로 표현하고 초기 확률 값을 단일하게 유지하기 위해 제안된 확률 값 설정 방법을 사용하였다. 실험은 물체 관계를 추론하는 모듈의 성능을 검증하기 위해 수행되었는데, 5가지 장소에서 82.8$\%$의 정확도를 보여주었다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.125-136
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• 2011

Ground-level ozone, an air pollutant that is monitored by the Environmental Protection Agency (EPA), damages human health by irritating the respiratory system, reducing lung function, damaging lung cells, and aggravating asthma and other chronic conditions. In March 2008, the EPA strengthened ozone standards by lowering acceptable limits from 84 parts per billion to 75 parts per billion. Here epidemiologic data is used to study the effects of ozone regulation on human health and assessed how various regulatory standards for ozone may affect nonaccidental mortality, including respiratory-related deaths during ozone season. The assessment uses statistical methods based on hierarchical Bayesian models to predict the potential effects of the different regulatory standards. It also analyzes the variability of the results and ho they are impacted by different modeling assumptions. We focused on the technical an statistical approach to assessing relationship between new ozone regulations and mortality while other researches have detailed the relationship between ozone and human mortality. We shows a statistical correlation between ozone regulations and mortality, with lower limits of acceptable ozone linked to a decrease in deaths, and projects that mortality is expected to decrease by reducing ozone regulatory standards.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.292-294
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• 2005

대사증후군은 당뇨병, 고혈압, 복부 비만, 고지혈증 등의 질병이 한 개인에게 동시에 발현하는 것을 말한다. 미국에서는 $25\%$ 이상의 성인이 대사성 증후군인 것으로 알려져 있으며, 경제 여건의 향상 및 식생활 습관의 변화와 함께 최근 우리나라에서도 심각한 문제가 되고 있다. 한편 불확실성의 처리를 위해 많이 사용되고 있는 베이지안 네트워크는 사람이 분석 가능한 확률 기반의 모델로 최근 의학 분야에서 지식 발견, 데이터 마이닝을 위한 도구로 유용하게 사용되고 있다. 본 논문에 서 는 대사증후군을 예측하는 문제를 다루며, 베이지안 네트워크와 의학 지식을 이용한 대사증후군의 예측 모델을 제안한다. 제안하는 모델을 통해 1993년의 데이터를 가지고 1995년의 상태를 예측하는 분류 실험을 수행하였으며, 실험 결과 다층 신경망, k-최근접 이웃 등의 분류기 보다 높은 $81.5\%$의 예측율을 보였다.

• ETRI Journal
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• v.38 no.3
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• pp.510-517
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• 2016

Recent developments in the field of separation of mixed signals into music/voice components have attracted the attention of many researchers. Recently, iterative kernel back-fitting, also known as kernel additive modeling, was proposed to achieve good results for music/voice separation. To obtain minimum mean square error (MMSE) estimates of short-time Fourier transforms of sources, generalized spatial Wiener filtering (GW) is typically used. In this paper, we propose an advanced music/voice separation method that utilizes a generalized weighted ${\beta}$-order MMSE estimation (WbE) based on iterative kernel back-fitting (KBF). In the proposed method, WbE is used for the step of mixed music signal separation, while KBF permits kernel spectrogram model fitting at each iteration. Experimental results show that the proposed method achieves better separation performance than GW and existing Bayesian estimators.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.81-96
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• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.