• Nuclear Engineering and Technology
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• v.52 no.4
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• pp.764-775
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• 2020

Periodic safety reviews (PSRs) are conducted on operating nuclear power plants (NPPs) and have been mandated also for research reactors in Korea, in response to the Fukushima accident. One safety review tool, the probabilistic safety assessment (PSA), aims to identify weaknesses in the design and operation of the research reactor, and to evaluate and compare possible safety improvements. However, the PSA for research reactors is difficult due to scarce data availability. An important element in the analysis of research reactors is the reactor protection system (RPS), with its functionality and importance. In this view, we consider that of the AGN-201K, a zero-power reactor without forced decay heat removal systems, to demonstrate a risk-informed safety improvement study. By incorporating risk- and safety-significance importance measures, and sensitivity and uncertainty analyses, the proposed method identifies critical components in the RPS reliability model, systematically proposes potential safety improvements and ranks them to assist in the decision-making process.

• The Journal of Industrial Distribution & Business
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• v.13 no.10
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.143-144
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• 2014

• Proceedings of the PSK Conference
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• 2005.04a
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• pp.116-118
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• 2005

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1284-1290
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• 2004

In market-basket analysis, weighted association rule(WAR) discovery can mine the rules which include more beneficial information by reflecting item importance for special products. However, when items are divided into more than one group and item importance for each group must be measured by different measurement or separately, we cannot directly apply traditional weighted association rule discovery. To solve this problem, we propose a novel methodology to discovery the weighted association rule in this paper In this methodology, the items should be first divided into sub-groups according to the properties of the items, and the item importance is defined or calculated only with the items enclosed to the sub-group. Our algorithm makes qualitative evaluation for network risk assessment possible by generating risk rule set for risk factor using network sorority data, and quantitative evaluation possible by calculating risk value using statistical factors such as weight applied in rule generation. And, It can be widely used for new model of more delicate analysis in market-basket database in which the data items are distinctly separated.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4809-4818
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• 2022

The decommissioning project of a nuclear facility is a large-scale process that is expected to take about 15 years or longer. The range of risks to be considered is large and complex, then, it is expected that various risks will arise in decision-making by area during the project. Therefore, in this study, the risk family derived from the Decommissioning Risk Management (DRiMa) project was reconstructed into a decommissioning project risk profile suitable for the Kori Unit 1. Two criteria of uncertainty and importance are considered in order to prioritize the selected 26 risks of decommissioning project. The uncertainty is scored according to the relevant laws and decommissioning plan preparation guidelines, and the project importance is scored according to the degree to which it primarily affects the triple constraints of the project. The results of risks are divided into high, medium, and low. Among them, 10 risks are identified as medium level and 16 risks are identified as low level. 10 risks, which are medium levels, are classified in five categories: End state of decommissioning project, Management of waste and materials, Decommissioning strategy and technology, Legal and regulatory framework, and Safety. This study is a preliminary assessment of the risk of the decommissioning project that could be considered in the preparation stage. Therefore, we expect that the project risks considered in this study can be used as an initial data for reevaluation by reflecting the detail project progress in future studies.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.125-138
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• 2022

Purpose: Failure Mode and Effect Analysis (FMEA) is a widely utilized technique to measure product reliability by identifying potential failure modes. Even though FMEA techniques have been studied, the form of Risk Priority Number (RPN) used to evaluate risk priority in FMEA is still questionable because of its shortcomings. In this study, we suggest common RPN(cRPN) to resolve shortcomings of the traditional RPN and show the extensibility of cRPN. Methods: We suggest cRPN which is based on Cobb-Douglas production function, and represent the various application on weighting risk factors, weighted RPN in a mathematical way, and show the possibility of statistical approach. We also conduct numerical study to examine the difference of the traditional RPN and cRPN as well as the potential application from the analysis on marginal effects of each risk factor. Results: cRPN successfully integrates previously suggested approaches especially on the relative importance of risk factors and weighting RPN. Moreover, we analyze the effect of corrective actions in terms of econometric analysis using cRPN. Since cRPN is rely on the reliable mathematical model, there would be numerous applications using cRPN such as smart factory based on A.I. techniques. Conclusion: We propose a reliable mathematical model of RPN based on Cobb-Douglas production function. Our suggested model, cRPN, resolves various shortcomings such as consideration of the relative importance, the effect of combinations among risk factors. In addition, by adopting a reliable mathematical model, quantitative approaches are expected to be applied using cRPN. We find that cRPN can be utilized to the field of industry because it is able to be applied without modifying the entire systems or the conventional actions.

• Journal of Contemporary Eastern Asia
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• v.20 no.2
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• pp.35-39
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• 2021

This article discusses the importance of investigating risk and crisis communication in the context of Asian digital societies. It introduces the remaining articles in this special issue.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.2
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• pp.206-216
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• 2001

This study was intended to identify the information search behavior according to the risk perception and clothing involvement. The data were collected via a questionnaire from 369 students of Semyung University in Checheon and data were analyzed by frequency, factor analysis, Cronbachs alpha and multiple regression. The results of this study were as follows; 1. Multiple regression revealed the fact that each type of involvement dimensions were influenced by the risk perception dimensions. Among four involvement dimensions, importance of clothing was the best influenced factor according to the risk perceptions. 2. Also information search behavior was influenced by risk perception and fashion involvement. Among four information search behaviors, industry providing search was the best influenced factor by the risk perception and clothing involvement.

• Journal of the Korea Institute of Building Construction
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• v.13 no.4
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• pp.424-433
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• 2013

There have been few attempts made to perform a systematic analysis of the various risk influence factors in building development projects. This study suggests an analysis of the risk influence factors in the process of feasibility studies for apartment building development projects. To reflect the voice of professionals, surveys were carried out. In addition, an FD-AHP method was applied to identify the importance of the risk influence factors. Through the surveys, major risk factors were separately identified as direct and/or indirect elements. An analysis of risk influence factors supports an effective feasibility study of apartment building development projects.