• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.589-594
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• 2007

This paper is shown new method for durability assesment and design have been noticed to be very valuable has been successfully applied to predict concrete structures. This paper provides that a new approach for predicting the corrosion durability of reinforced concrete structures exposed to chloride attack. In this method, the prediction can be updated successive1y by the Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures under chloride attack environments.

• Journal of the Korean Data and Information Science Society
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• 제27권1호
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• pp.245-254
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• 2016

Many surveys provide categorical data and there may be one or more missing categories. We describe a nonignorable nonresponse model for the analysis of two-way contingency tables from small areas. There are both item and unit nonresponse. One approach to analyze these data is to construct several tables corresponding to missing categories. We describe a hierarchical Bayesian model to analyze two-way categorical data from different areas. This allows a "borrowing of strength" of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the small areas. Also we use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data on thirteen states to obtain the finite population proportions.

• 한국군사과학기술학회지
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• 제19권2호
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• pp.177-184
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• 2016

This paper presents a strike velocity estimation using the recursive Bayesian filter that operates both correction and prediction models to probabilistically remove noises of sensors and accurately estimate the strike velocity during the real-time experiments. Four different types of bullets such as 5.56 mm M193, 7.62 mm M80, 5.45 mm 7N10 and 7.62 mm MSC were used to validate the proposed method. Compared to the existing method, the proposed method statistically results in higher stability of the strike velocity estimation as well as its reliability for the ballistic limit velocity computation.

• 한국측량학회지
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• 제6권1호
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• pp.35-41
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• 1988

지상사진에 의한 삼차원변형해석을 하는데 있어서 변위양계산의 정확도를 향상시키기 위해 반복경증률 상사변환법이 사용되었으며, 변위점검출에서는 Bayesian Inference가 적용되었고, 변위형태해석을 위해 변위방정식을 이용하는 방법을 제시하였다. 그 결과 변위양계산에서는 최소절대법($\Sigma$$\mid$d$\mid$⇒min)에 의한 경중률조건이 정확도를 향상시켰으며, 또한 Bayesian Inference을 적용하므로써 정확한 변위점검출을 할 수 있었다. 변위형태해석에서는 최적변위방정식을 이용하여 대상들의 전체 또는 부분적인 움직임을 해석할 수 있었다.

• 한국환경과학회지
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• 제27권7호
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.723-726
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

• Structural Engineering and Mechanics
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• 제69권4호
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• pp.361-369
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• 2019

The determination of Paris' law parameters based on crack growth experiments is an important procedure of fatigue life assessment. However, it is a challenging task because it involves various sources of uncertainty. This paper proposes a novel probabilistic method, termed the S-N Paris law (SNPL) method, to quantify the uncertainties underlying the Paris' law parameters, by finding the best estimates of their statistical parameters from the S-N curve data using a Bayesian approach. Through a series of steps, the SNPL method determines the statistical parameters (e.g., mean and standard deviation) of the Paris' law parameters that will maximize the likelihood of observing the given S-N data. Because the SNPL method is based on a Bayesian approach, the prior statistical parameters can be updated when additional S-N test data are available. Thus, information on the Paris' law parameters can be obtained with greater reliability. The proposed method is tested by applying it to S-N curves of 40H steel and 20G steel, and the corresponding analysis results are in good agreement with the experimental observations.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.304-313
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• 2021

Nickel base Alloy X-750, which is used as fastener parts in light-water reactor (LWR), has experienced many failures by environmentally assisted cracking (EAC). In order to improve the reliability of passive components for nuclear power plants (NPP's), it is necessary to study the failure mechanism and to predict crack growth behavior by developing a probabilistic failure model. In this study, The Bayesian inference was employed to reduce the uncertainties contained in EAC modeling parameters that have been established from experiments with Alloy X-750. Corrosion fatigue crack growth rate model (FCGR) was developed by fitting into Paris' Law of measured data from the several fatigue tests conducted either in constant load or constant ΔK mode. These parameters characterizing the corrosion fatigue crack growth behavior of X-750 were successfully updated to reduce the uncertainty in the model by using the Bayesian inference method. It is demonstrated that probabilistic failure models for passive components can be developed by updating a laboratory model with field-inspection data, when crack growth rates (CGRs) are low and multiple inspections can be made prior to the component failure.

• 한국컴퓨터정보학회논문지
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• 제14권7호
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• pp.1-8
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• 2009

본 연구에서는 소프트웨어 제품을 개발하여 테스팅을 거친 후 사용자에게 인도하는 시기를 결정하는 방출문제에 대하여 연구하였다. 따라서 최적 소프트웨어 방출 정책은 소프트웨어 요구 신뢰도를 만족시키고 소프트웨어 개발 및 유지 총비용을 최소화 시키는 정책을 수용해야 한다. 본 논문에서는 로그포아송 실행시간모형에 대하여 베이지안 모수 추정법(마코브체인 몬테칼로(MCMC) 기법 중에 하나인 깁스 샘플링과 메트로폴리스 알고리즘을 이용한 근사기법)이 사용되었다. 본 논문의 수치적인 예에서는 Musa의 T1 자료를 적용하여 최우수추정법과 베이지안 모수 추정과의 관계를 빅교하고 또한 최적 방출시기를 추정하였다.

• 품질경영학회지
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• 제24권1호
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• pp.10-19
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• 1996

In order to build a model to predict accidents in a complicated man-machine sytem, human errors and mechanical reliability can be viewed as the most important factors. Such factors are explicitly included in a generic model. Another point to keep in mind is that the model should be constructed so that the data in a type of accident can be utilized to predict other types of accidents. Based on such a generic prediction model, we analyze the effects of system reliability. When we improve the system reliability, in other words, when there are changes in model parameters, the predicted time to next accidents should be modified influencing the effects of system reliability improvements. We apply Bayesian approach and finds the formula to explain how a change on the machine reliability or human error probability influences the time to next accident.