• Proceedings of the KSRS Conference
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• v.2
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• pp.868-871
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• 2006

Mortality of domestic people from cardiovascular disease ranked second, which followed that of from cancer last year. Therefore, it is very important and urgent to enhance the reliability of medical examination and treatment for cardiovascular disease. Heart Rate Variability (HRV) is the most commonly used noninvasive methods to evaluate autonomic regulation of heart rate and conditions of a human heart. In this paper, our aim is to extract a quantitative measure for HRV to enhance the reliability of medical examination for cardiovascular disease, and then develop a prediction method for extracting multi-parametric features by analyzing HRV from ECG. In this study, we propose a hybrid Bayesian classifier called FP-based Bayesian. The proposed classifier use frequent patterns for building Bayesian model. Since the volume of patterns produced can be large, we offer a rule cohesion measure that allows a strong push of pruning patterns in the pattern-generating process. We conduct an experiment for the FP-based Bayesian classifier, which utilizes multiple rules and pruning, and biased confidence (or cohesion measure) and dataset consisting of 670 participants distributed into two groups, namely normal and patients with coronary artery disease.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.16-22
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• 2009

The inflection S-shaped software reliability growth model (SRGM) proposed by Ohba(1984) is one of the most commonly used models and has been discussed by many authors. The main purpose of this paper is to estimate the parameters of Ohba's SRGM within the Bayesian framework by applying the Markov chain Monte Carlo techniques. While the maximum likelihood estimates for these parameters are well known, the Bayesian method for the inflection S-shaped SRGM have not been discussed in the literature. The proposed methods can be quite flexible depending on the choice of prior distributions for the parameters of interests. We also compare the Bayesian methods with the maximum likelihood method numerically based on the real data.

• Journal of Applied Reliability
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• v.18 no.3
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• pp.271-279
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• 2018

Purpose: Probabilistic safety analysis was performed to enhance the safety and reliability of nuclear power plants because traditional deterministic approach has limitations in predicting the risk of failure by crack growth. The study introduces a probabilistic approach to establish a basis for probabilistic safety assessment of passive components. Methods: For probabilistic modeling of fatigue crack growth rate (FCGR), various FCGR tests were performed either under constant load amplitude or constant ${\Delta}K$ conditions by using heat treated X-750 at low temperature with adequate cathodic polarization. Bayesian inference was employed to update uncertainties of the FCGR model using additional information obtained from constant ${\Delta}K$ tests. Results: Four steps of Bayesian parameter updating were performed using constant ${\Delta}K$ test results. The standard deviation of the final posterior distribution was decreased by a factor of 10 comparing with that of the prior distribution. Conclusion: The method for developing a probabilistic crack growth model has been designed and demonstrated, in the paper. Alloy X-750 has been used for corrosion fatigue crack growth experiments and modeling. The uncertainties of parameters in the FCGR model were successfully reduced using the Bayesian inference whenever the updating was performed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.645-652
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• 2016

The statistical modeling of input random variables is necessary in reliability analysis, reliability-based design optimization, and statistical validation and calibration of analysis models of mechanical systems. In statistical modeling methods, there are the Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), and Bayesian method. Those methods basically select the best fitted distribution among candidate models by calculating their likelihood function values from a given data set. The number of data or parameters in some methods are considered to identify the distribution types. On the other hand, the engineers in a real field have difficulties in selecting the statistical modeling method to obtain a statistical model of the experimental data because of a lack of knowledge of those methods. In this study, commonly used statistical modeling methods were compared using statistical simulation tests. Their advantages and disadvantages were then analyzed. In the simulation tests, various types of distribution were assumed as populations and the samples were generated randomly from them with different sample sizes. Real engineering data were used to verify each statistical modeling method.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.1-9
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• 2003

In this paper we discuss Bayesian computation and model selection for Littlewood-Verrall model using Gibbs sampling. A numerical example with a simulated data is given.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.324-337
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• 2010

For the development of load and resistance factor design, reliability analysis is required to calibrate resistance factors in the framework of reliability theory. The distribution of measured-to-predicted pile resistance ratio was constructed based on only the results of load tests conducted to failure for the assessment of uncertainty regarding pile resistance and used in the conventional reliability analysis. In other words, successful pile load test (piles resisted twice their design loads without failure) results were discarded, and therefore, were not reflected in the reliability analysis. In this paper, a new systematic method based on Bayesian theory is used to update reliability index of driven steel pile piles by adding more pile load test results, even not conducted to failure, into the prior distribution of pile resistance ratio. Fifty seven static pile load tests performed to failure in Korea were compiled for the construction of prior distribution of pile resistance ratio. Reliability analyses were performed using the updated distribution of pile resistance ratio and the total load distribution using First-order Reliability Method (FORM). The challenge of this study is that the distribution updates of pile resistance ratio are possible using the load test results even not conducted to failure, and that Bayesian update are most effective when limited data are available for reliability analysis or resistance factors calibration.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.137.5-137
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• 2001

This paper proposes a method that combines diverse evidence relevant to the reliability to evaluate the reliability of complicated systems such as robots. In practice, reliability experts combine diverse evidences relevant to the reliability and infer the answers by using their own way that are mostly informal. The proposed method also combines diverse evidence and performs inferences but informal and quantitative way by using the benefits of Bayesian Belief Nets (BBN). Diverse evidences could be those from dassical analysis techniques, test results, quality assurance about the process of manufacturing, and the quality of the company or development team, etc. Some of these evidences are qualitative and others are quantitative. Both are ...

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.394-399
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• 2002

In this paper, Multisource data classification methods based on Bayesian formula are considered. For this decision fusion scheme, the individual data sources are handled separately by statistical classification algorithms and then Bayesian fusion method is applied to integrate from the available data sources. This method includes the combination of each expert decisions where the weights of the individual experts represent the reliability of the sources. The reliability measure used in the statistical approach is common to all pixels in previous work. In this experiment, the weight factors have been assigned to have different value for all pixels in order to improve the integrated classification accuracies. Although most implementations of Bayesian classification approaches assume fixed a priori probabilities, we have used adaptive a priori probabilities by iteratively calculating the local a priori probabilities so as to maximize the posteriori probabilities. The effectiveness of the proposed method is at first demonstrated on simulations with artificial and evaluated in terms of real-world data sets. As a result, we have shown that Bayesian statistical fusion scheme performs well on multispectral data classification.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1901-1910
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• 2023

The safety-class (1E) digital control system (DCS) of nuclear power plant characterized structural multiple redundancies, therefore, it is important to quantitatively evaluate the reliability of DCS in different degree of backup loss. In this paper, a reliability evaluation model based on T-S fuzzy fault tree (FT) is proposed for 1E DCS of nuclear power plant, in which the connection relationship between components is described by T-S fuzzy gates. Specifically, an output rejection control system is chosen as an example, based on the T-S fuzzy FT model, the key indicators such as probabilistic importance are calculated, and for a further discussion, the T-S fuzzy FT model is transformed into Bayesian Network(BN) equivalently, and the fault diagnosis based on probabilistic analysis is accomplished. Combined with the analysis of actual objects, the effectiveness of proposed method is proved.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.301-310
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• 2001

Lim et al.(1998) proposed the Bayesian Imperfect Repair Model, in which a failed system is perfectly repaired with probability P and is minimally repaired with probability 1 - P, where P is not fixed but a random variable with a prior distribution II(p). In this note, the steady state availability of the model is derived and the measure is obtained for several particular prior distribution functions.