• Safety and Health at Work
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• v.8 no.3
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• pp.306-314
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• 2017

Background: In order to obtain a deeper understanding of an incident, it needs to be investigated to "peel back the layers" and examine both immediate and underlying failures that contributed to the event itself. One of the key elements of an effective accident investigation is recording the scene for future reference. In recent years, however, there have been major advances in survey technology, which have provided the ability to capture scenes in three dimension to an unprecedented level of detail, using laser scanners. Methods: A case study involving a fatal incident was surveyed using three-dimensional laser scanning, and subsequently recreated through virtual and physical models. The created models were then utilized in both accident investigation and legal process, to explore the technologies used in this setting. Results: Benefits include explanation of the event and environment, incident reconstruction, preservation of evidence, reducing the need for site visits, and testing of theories. Drawbacks include limited technology within courtrooms, confusion caused by models, cost, and personal interpretation and acceptance in the data. Conclusion: Laser scanning surveys can be of considerable use in jury trials, for example, in case the location supports the use of a high-definition survey, or an object has to be altered after the accident and it has a specific influence on the case and needs to be recorded. However, consideration has to be made in its application and to ensure a fair trial, with emphasis being placed on the facts of the case and personal interpretation controlled.

• Journal of the Korean Institute of Gas
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• v.15 no.2
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• pp.33-39
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• 2011

This study presented quantitative risk analysis in case of transporting explosive materials by railway. Accident types were classified into accidents of in station and in transit. And the study presented an initial value of accident frequency through derailment accident and crushing one according to each type, and drew the results of accident frequency through event tree analysis. Damage impact evaluation used TNT equivalent method and probit analysis method. As the result of risk evaluation, railway transportation of explosive materials passing through areas which are high in population density is appeared to be able to cause a large number of personnel injury when occurring accidents. Specially, the accident of explosive transportation combined with petroleum was forecasted as easily resulting in large explosive accident. Consequently, there is a necessity to reduce consequences by decreasing passage through areas where are high in population density, and take measures for lessening the risks in case of transporting dangerous explosive materials.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.223-232
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• 2012

Recently, the leakage of personal information and privacy piracy increase. The victimized case of the malicious object rapidlies increase. Most of users use the windows operating system. Recently, the Windows 7 operating system was announced. Therefore, we need to study for the intrusion response technique at the next generation operate system circumstances. The accident response technique developed till now was mostly implemented around the Windows XP or the Windows Vista. However, a new vulnerability problem will be happen in the breach process of reaction as the Windows 7 operating system is announced. In the windows operating system, the system incident event needs to be efficiently analyzed. For this, the event information generated in a system needs to be visually analyzed around the time information or the security threat weight information. Therefore, in this research, we analyzed visually about the system event information generated in the Windows 7 operating system. And the system analyzing the system incident through the visual event information analysis process was designed and implemented. In case of using the system developed in this study the more efficient accident analysis is expected to be possible.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.45-52
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• 2018

This study identifies the cause of the accident and presents a new concept for safe urban stream management by numerical simulating the flood event of Cheonggyecheon on August 17, 2017, using rain data measured through a dense weather observation network. In order to simulate water retention in the CSO channel listed as one of the causes of the accident, a reliable urban runoff model(XP-SWMM) was used which can simulate various channel conditions. Rainfall data measured through SK Techx using SK Telecom's cell phone station was used as rain data to simulate the event. The results of numerical simulations show that rainfall measured through AWSs of Korea Meteorological Administration did not cause an accident, but a similar accident occurred under conditions of rainfall measured in SK Techx, which could be estimated more similar to actual phenomena due to high spatial density. This means that the low spatial density rainfall data of AWSs cannot predict the actual phenomenon occurring in Cheonggyecheon and safe river management needs high spatial density weather stations. Also, the results of numerical simulation show that the residual water in the CSO channel directly contributed to the accident.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.9-15
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• 2010

As catenary supply electric power directly to the railway system, it is very important to prevent an accident of a catenary for appropriate train operation. This paper proposed the assessment the outage data for "British Catenary Safety Analysis Report" and Korean data to compare the reliability of the railway system. The analyzed data were applied to Event Tree and Fault Tree algorithm to calculate the reliability indices of railway system. Event tree is created and gate results of fault tree analysis are used as the source of event tree probabilities. Fault tree represents the interaction of failures and basic events within a system. Event Tree and Fault Tree analysis result is helpful to assess the reliability to interpreted. The reliability indices can be used to determine the equipment to be replaced for the entire system reliability improvement.

• Radiation Oncology Journal
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• v.31 no.2
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• pp.88-96
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• 2013

Purpose: This study identifies accident sequences from the past accidents in order to help the risk analysis application to the external radiotherapy. Materials and Methods: This study reviews 59 accidental cases in two retrospective safety analyses that have collected the incidents in the external radiotherapy extensively. Two accident analysis reports that accumulated past incidents are investigated to identify accident sequences including initiating events, failure of safety measures, and consequences. This study classifies the accidents by the treatments stages and sources of errors for initiating events, types of failures in the safety measures, and types of undesirable consequences and the number of affected patients. Then, the accident sequences are grouped into several categories on the basis of similarity of progression. As a result, these cases can be categorized into 14 groups of accident sequence. Results: The result indicates that risk analysis needs to pay attention to not only the planning stage, but also the calibration stage that is committed prior to the main treatment process. It also shows that human error is the largest contributor to initiating events as well as to the failure of safety measures. This study also illustrates an event tree analysis for an accident sequence initiated in the calibration. Conclusion: This study is expected to provide sights into the accident sequences for the prospective risk analysis through the review of experiences.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.313-326
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• 2017

The systematic management of plant risk is crucial for enhancing the safety of nuclear power plants and for designing new nuclear power plants. Accident sequence precursor (ASP) analysis may be able to provide risk significance of operational experience by using probabilistic risk assessment to evaluate an operational event quantitatively in terms of its impact on core damage. In this study, an ASP methodology for two operation mode, full power and low power/shutdown operation, has been developed and applied to significant accident precursors that may occur during the operation of nuclear power plants. Two operational events, loss of feedwater and steam generator tube rupture, are identified as ASPs. Therefore, the ASP methodology developed in this study may contribute to identifying plant risk significance as well as to enhancing the safety of nuclear power plants by applying this methodology systematically.

• Nuclear Engineering and Technology
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• v.28 no.3
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• pp.247-256
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• 1996

This paper presents a new approach for assessing accident management strategies using containment event trees (CETs) developed during an individual plant examination (IPE) for a reference plant (CE type, 950 MWe PWR). Various accident management strategies to reduce risk have been proposed through IPE. Three strategies for the station blackout sequence are used as an example : 1) reactor cavity flooding only, 2) primary system depressurization only, and 3) doing both. These strategies are assumed to be initiated at about the time of core uncovery. The station blackout (SBO) sequence is selected in this paper since it is identified as one of the most threatening sequences to safety of the reference plant. The effectiveness and adverse effects of each accident management strategy are considered synthetically in the CETs. A best estimate assessment for the developed CETs using data obtained from NUREG-1150, other PRA results, and the MAAP code calculations is performed. The strategies are ranked with respect to minimizing the frequencies of Various containment failure modes. The proposed approach is demonstrated to be very flexible in that it can be applied to any kind of accident management strategy for any sequence.