• International Journal of Reliability and Applications
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• 제3권2호
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• pp.81-90
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• 2002

The importance of the reliability of software is growing more and more as more complicated digital computer systems are used for real-time control applications. To provide more reliable software, the testing period should be long enough, but not unnecessarily too long. In this study, we suggest a simple but effective stopping rule which can provide just proper amount of testing time. We take unique features of software into consideration and adopt non-homogeneous Poisson process model and Bayesian approach. A numerical example is given to demonstrate the validity of our stopping rule.

• Nuclear Engineering and Technology
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• 제22권2호
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• pp.128-138
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• 1990

본 논문에서는 원자로냉각재펌프 고장진단 전문가시스템 (ESRCP)에 대해 기술하였다. 이 시스템의 목적은 RCP의 고장진단과 함께 발전소 운전원에게 적절한 운전 조작 및 비상조치 사항 등을 알려주는데 있다. 진단을 위한 일차적 증상은 RCP 영역에 관련된 경보들이다. 경보처리는 Rule-based Deduction 또는 Priority Factor Operation에 의한다. 고장진단은 Rule-based Deduction이나 Bayesian Inference에 의해 수행된다. 각종 Sensor들의 측정값들은 정확한 원인을 진단하기 위해 필요로 하다 증상들이 부족하거나 불착실성을 나타낼 때는 Bayesian Inference로 고장을 진단한다.

• 품질경영학회지
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• 제29권1호
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• 한국전자거래학회지
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• 제17권1호
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• pp.53-74
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• 2012

본 연구에서는 비즈니스 프로세스 관리(Business Process Management, BPM) 환경에서 자원의 성능에 영향을 미치게 되는 여러 요소를 고려하여 인적자원을 선택하는 방법론을 개발한다. 스케줄링에 있어서 자원의 선택 문제는 작업 수행도에 직접적인 영향을 미치기 때문에 중요한 문제로 인식되어져 왔다. 비록 많은 문제에 있어서 전통적인 자원선택 방법론이 의미를 가져왔으나, 인적자원을 다루는데 있어서는 가장 좋은 방법론이라고 볼 수 없다. 인적자원은 작업부하, 작업소요시간, 작업간 시간 등의 다양한 요소에 의해서 영향을 받는 특이한 요소이며 본 연구는 이러한 다양한 요소를 고려하여 작업자를 선택하는 방법론을 제시한다. 이를 위해서 베이지안 네트워크를 사용하며, 앞서 기술한 여러 요소들을 한꺼번에 고려하기 위한 베이지안 선택규칙(Bayesian Selection Rule, BSR)을 도입하였다. 또한, 시뮬레이션을 통해서 본 연구에서 개발된 방법론이 대기시간, 작업수행시간과 사이클 타임을 줄일 수 있음을 보였다.

• 품질경영학회지
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• 제30권3호
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• pp.186-194
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• 2002

In this paper we introduce formulae for estimating the failure rate of a large scaled software by using the Bayesian rule when a black-box random testing which selects an element(test case) at random with equally likely probability, is performed. A program or software can be treated as a mathematical function with a well-defined (input)domain and range. For a large scaled software, their input domains can be partitioned into multiple subdomains and exhaustive testing is not generally practical. Testing is proceeding with selecting a subdomain, and then picking a test case from within the selected subdomain. Whether or not the proportion of selecting one of the subdomains is assumed probability, we developed the formulae either case by using Bayesian rule with gamma distribution as a prior distribution.

• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.311-330
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• 2023

The present work describes simulation studies to compare the performances in terms of averaged mean squared error of bayesian wavelet shrinkage methods in estimating component curves from aggregated functional data. Five bayesian methods available in the literature were considered to be compared in the studies: The shrinkage rule under logistic prior, shrinkage rule under beta prior, large posterior mode (LPM) method, amplitude-scale invariant Bayes estimator (ABE) and Bayesian adaptive multiresolution smoother (BAMS). The so called Donoho-Johnstone test functions, logit and SpaHet functions were considered as component functions and the scenarios were defined according to different values of sample size and signal to noise ratio in the datasets. It was observed that the signal to noise ratio of the data had impact on the performances of the methods. An application of the methodology and the results to the tecator dataset is also done.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1387-1395
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• 2006

This paper considers testing for the ratio of two exponential means. We propose a solution based on a Bayesian decision rule to this problem in which no subjective input is considered. The criterion for testing is the Bayesian reference criterion (Bernardo, 1999). We derive the Bayesian reference criterion for testing the ratio of two exponential means. Simulation study and a real data example are provided.

• Journal of the Korean Statistical Society
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• 제29권3호
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• pp.337-350
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• 2000

This paper suggests a new diagnostic measure and a stopping rule for detecting influential observations in multiple discriminant analysis (MDA). It is developed from a Bayesian point of view using a default Bayes factor obtained from the fractional Bayes factor methodology. The Bayes factor is taken as a discriminatory information in MDA. It is shown that the effect of an observation over the discriminatory information is fully explained by the diagnostic measure. Based on the measure, we suggest a stopping rule for detecting influential observations in a given training sample. As a tool for interpreting the measure a graphical method is sued. Performance of the method is used. Performance of the method is examined through two illustrative examples.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2474-2476
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• 2004

지폐 인식 자동화기기가 여러 분야에 보편화되면서 다양한 지폐를 고속으로 처리할 수 있는 고속지폐 인식 자동화 기기가 요구되고 있다. 하지만 대부분의 지폐 인식 자동화 기기가 고속화에 적합하지 않은 구조로 설계되어 있고 신권 추가가 용이하지 않다. 본 논문은 고속 Paper Currency 인식 시스템에 적합한 범용 하드웨어 시스템과 Bayes Rule 기반의 고속 인식 알고리즘을 제안한다. 제안된 범용 하드웨어 구조는 고속의 CIS(Contact Image Sensor)와 DSP(Digital Signal Processor) 그리고 Dual Memory System으로 구성되었다. Bayes Rule에 기초한 고속 인식 알고리즘은 기존의 Paper Currency 인식 시스템에 사용되었던 기계학습 방법에 비해 신권 추가가 쉽고 적은 연산으로 권종을 판별할 수 있어 고속 지폐 인식 자동화기기에 적합하다. 본 논문에서는 제안된 방법들을 실제 자동화기기로 구현하여 그 유용성을 검증한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.