• Proceedings of the KSR Conference
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• 2005.11a
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• pp.246-251
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• 2005

Accident scenarios analysis is a course to understand, analyze, and describe a process of an accident and behavior pattern of the parties to an accident. The method of accident scenarios is that we described patterns represented between accidents and hazardous conditions, and then provide data to prevent the accident. We have carried out scenarios analysis in various fields so far, but it was not taking account of system. In this research, we made a study on technology of accident scenarios analysis using QFD (Quality Function Deployment) to analyze systematically and evaluate quantitatively types of hazards and scenarios of railway accident. And we analyses accident scenarios of a subject of work-related fatality accident to railway employee and conducted risk assessment for different scenarios. Also we defined relation between unsafe events and hazardous conditions caused to work-related fatality accident, and attempted to quantitatively assess work-related fatality accident and the parties to accidents. The results of this research will be used in analyzing for important causes and contributing factors of work-related fatality accidents at the step of risk assessment of railway system, and quantitatively assessing frequency of work-related accidents and risk.

• Journal of the Korean Society of Safety
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• v.18 no.3
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• pp.75-80
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• 2003

This paper presents a set of typical pseudo-accident scenarios related to major equipments in petrochemical plants, which would be useful for performing such quantitative risk analysis techniques as fault tree analysis, event tree analysis, etc. These typical scenarios address what the main hazard of each equipment might be and how the accident might develop from an "initiating event". The proposed set of accident scenarios consists of total thirteen (13) scenarios specific for five (5) major equipments like reactor, distillation column, etc., and has been determined and screened out of one hundred and twenty-five (125) potential accident scenarios that were generated by performing semi-quantitative risk analysis practically for twenty-five (25) petrochemical processes, considering advices from the operation experts. It is assumed that with simple consideration or incorporation of plant-specific conditions only, the proposed accident scenarios could be easily reorganized or adapted for the relevant process with less time and labor by the safety engineers concerned in the petrochemical industries.ndustries.

• IE interfaces
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• v.15 no.2
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• pp.114-125
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• 2002

The objective of this paper is to describe a systematic accident scenario analysis method(SASA) adept at creating accident scenarios for the design of safer products. This approach was inspired by the Quality Function Deployment(QFD) method, which is conventionally used in quality management. In this study, the QFD provides a formal and systematic scheme to devise accident scenarios while maintaining objectivity. SASA consists of three key stages to be broken down into a series of consecutive steps:(1) developing an accident analysis tableau,(2) devising the accident scenarios using the accident analysis tableau,(3) performing a feasibility test, a clustering process and a patterning process, and finally(4) performing quantitative evaluation of each accident scenario. The SASA was applied to a case study of child safety seats. The accident analysis tableau devised 2828(maximum) accident scenarios from all possible relationships between the hazard factors and situation characteristics. Among them, 270 scenarios were devised through the feasibility test and the clustering process. The patterning process reduced them to 29 patterns representative of all accident scenarios. Based on an intensive analysis of the accident patterns, design guidelines for a safer child safety seat were recommended. The implications of the study on the child safety seat case were then discussed.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3336-3346
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• 2022

In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Journal of Navigation and Port Research
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• v.41 no.3
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• pp.165-172
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• 2017

Current scenarios for marine spill accidents were developed based on probable maximum spill accidents. However,, accidents of similar scale to maximum spill accidents are virtually non-existent, and training or deployment of response equipment based on these scenarios can be cost prohibitive. Current scenarios require realism for practical use and need to be designed for purpose of use. In this study we developed scenarios that may replace current scenarios by using the HNS accident standard codes based on past accident cases. Scenarios were developed by modularizing the HNS accident standard code, that is classified into three scenarios: Maximum Frequency Scenario, Maximum Damage Scenario, and Maximum Vulnerability Scenario. The situation of an accident presented in each scenario developed in this process is much like a real accident, and therefore, it is has practical application.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.661-670
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• 2007

In this study, collision accident scenarios are derived for crashworthy design of rolling stocks because the detailed guidelines to complement domestic safety regulations with respect to collision accidents of rolling stocks are under preparation. Through this study, several collision accident scenarios are broadly investigated for those of advanced countries like USA, UK and EU. Next, the basic engineering considerations which are necessary to derive the collision accident scenarios are reviewed and analysed in some details. Finally, two collision accident scenarios are derived considering the circumstances of domestic railroads.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.95-103
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• 2022

Quick and accurate understanding of the situation in a severe accident is essential for conducting the appropriate accident management and response using the accident diagnosis information. This study employed deep learning technology to diagnose severe accidents through the major safety parameters transferred from a nuclear power plant (NPP) to AtomCARE. After selecting the major accident scenarios to consider, a learning database was established for particular scenarios affiliated with major scenarios by performing a large number of severe accident analyses using MAAP5 code. The severe accident diagnosis technology, which classifies detailed accident scenarios using the major safety parameters from NPPs, was developed by training it with the established database . Verification and validation were conducted by blind test and principal component analysis. The technology developed in this study is expected to be extended and applied to all severe accident scenarios and be utilized as a base technology for quick and accurate severe accident diagnosis.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.46-52
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• 2017

In this study, safety evaluation scenarios on "cut-in" situation are presented to assess the performance of automatic emergency braking systems. The ASSESS project in EU is surveyed for derive efficient test scenarios for cut-in situation. The TASS database are also analyzed to find representative accident scenarios in Korea. With the results of the ASSESS and TASS, the safety evaluation scenarios in cut-in situations are suggested and the scenarios are tested with simulation software PRESCAN.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.27-31
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• 2018

This paper reports an analysis of cyclist accident cases with respect to passenger vehicles on Korean roads. A typology based on Initiative for the Global Harmonization of Accident Data (iGLAD) code book is applied to a traffic accident analysis system(TAAS), which has the real-world crash data on Korea roads, to understand the accident scenarios in more detail and efficiently. Similarly this typology has been used for Germany In-Depth Accidents Study (GIDAS) as well. The accident data analysis with consideration of the typology of Korean road conditions may prioritize traffic safety issues regarding cyclists and is aimed to develop an Automatic Emergency Braking (AEB) system for cyclist. In summary, this paper characterizes and analyzes the scenarios of cyclist crashes with passenger car. The most common accident scenarios on Korean roads are Car-to-Bicyclist Nearside Adult (CBNA) and Car-to-Bicyclist Longitudinal Adult (CBLA), which are more than 86% of total accidents cases. Therefore, it is inferred that AEB cyclist system should include these accident types in the operational design domain to reduce more fatality in Korea.