• 대한교통학회지
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• 제28권3호
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• pp.119-130
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• 2010

교통사고의 재구성 해석은 도로와 사고흔적, 자동차 손상 등 다양한 자료들을 분석함으로서 이루어진다. 대부분의 자료들은 사고 해석에서 변수로 작용하며, 측정으로부터 구해지는 자료들은 조사자와 도구, 주어진 환경 등에 의해 측정 오차가 발생된다. 따라서 사고해석에서는 측정 오차에서 비롯되는 불확실성이 항상 존재한다. 본 연구는 불확실성이 존재할 가능성이 매우 높은 도로 기하구조와 타이어 흔적 등 길이와 마찰계수 등에 대해 반복 측정 실험을 함으로서 교통 사고해석에서의 불확실성을 정량화하였다. 또한 자동차 충돌 변형량의 사진 계측에 대한 불확실성에 대해서도 해석 결과를 제시하였다. 이러한 통계학적 분포들은 사고 재구성 불확실성을 추정하기 위해 입력 계수의 적절한 범위를 결정하는 것을 도울 수 있다.

• 한국안전학회지
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• 제32권5호
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• pp.129-134
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• 2017

This study aims to develop the traffic accident models by circular intersection type using count data model. The number of accident, the number of fatal and injured persons(FSI), and EPDO are calculated from the traffic accident data of TAAS. The circular intersection accident models are developed through Poisson and negative binomial regression analysis. The main results of this study are as follows. First, the null hypotheses that there are differences in the number of traffic accidents, FSI and EPDO by type of circular intersections are rejected. Second, the scale of intersection(median, large), number of approach road, mean width and length of exit road, area of the circulating roadway and central island are selected as factors influencing the number of traffic accidents, FSI and EPDO in rotary. Third, the scale of intersection(median), guide signs(limited speed, direction, roundabout), number of approach road, entry angle, area of the intersection and central island are adopted as factors influencing the number of traffic accidents, FSI and EPDO in roundabout. Finally, transferring from rotary to roundabout could be expected to make the accident decrease.

• Nuclear Engineering and Technology
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• 제46권3호
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• pp.373-380
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• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.

• 대한안전경영과학회지
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• 제4권4호
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• pp.63-71
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• 2002

Accident analyses are used to identify common factors contributing to occupational accidents and to give recommendations for accident prevention. This study concerns with the accident characteristics and prevention in the telecommunication service industry. To investigate the accident characteristics, we used workers' compensation reports and employers' accident analysis reports. Three hundred and forty-five injury accidents which results in more than 4 days absence were surveyed. These data were used to investigate the accident characteristics in terms of company size, injured person's age, work experience, accident time, activity at time of accident, accident type, injured body part, and accident agency. We propose the accident prevention policy based on the accident characteristics. These results can be used to develop more effective occupational safety management policies in the telecommunication service industries.

• 한국대기환경학회지
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• 제24권1호
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• pp.1-15
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• 2008

Chemical Accident Response Information System (CARIS) which has been designed for the efficient emergency response of chemical accidents produces the real-time atmospheric fields through the Regional Atmospheric Modeling System, RAMS. The previous studies were emphasized that improving an initial input data had more effective results in developing prediction ability of atmospheric model. In a continuous effort to improve an initial input data, we replaced the land-use dataset using in the RAMS, which is a high resolution USGS digital data constructed in April, 1993, with the latest land-use data of the Korea Ministry of Environment over the South Korea and simulated atmospheric fields for developing a real-time prediction in dispersion of chemicals. The results showed that the new land-use data was written in a standard RAMS format and shown the modified surface characteristics and the landscape heterogeneity resulting from land-use change. In the results of sensitivity experiment we got the improved atmospheric fields and assured that it will give more reliable real-time atmospheric fields to all users of CARIS for the dispersion forecast in associated with hazardous chemical releases as well as general air pollutants.

• 대한인간공학회지
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• 제36권2호
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• pp.157-168
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• 2017

Objective: This study aims to understand the motorcycle accident characteristics of food delivery workers and to present basic guidelines on accident prevention through accident. Background: It is known that food delivery workers have a high ratio of self-employed and youth workers, and occupations with frequent disasters. Therefore the occupation is known to really be in need of accident prevention policy. Method: This study analyzed the data of motorcycle crashes of 1,310 food delivery workers that have been approved as on-duty industrial crashes since 2015. The accident characteristics were examined by dividing them into driver related factors and accident related factors. Results: Among the motorcycle crashes of food delivery workers, 99.2% of the victims were males, 82.6% had less than six months of work experience. 76.2% of the victims were employed by the companies with less than five workers. In addition, there was a difference in accident characteristics according to age, type of cuisine, accident time of the day, injured organs and injured body part. Conclusion: The results of this study can be used as baseline data to devise systematic measures to prevent motorcycle crashes of food delivery workers. Application: Preventative measures for novice young part time workers including safety education/training need to be established.

• 한국학교ㆍ지역보건교육학회지
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• 제12권1호
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• pp.71-89
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• 2011

Objectives: The purpose of this study is to identify risk behavior related to the school accident between male and female elementary school students. Methods: 838 School accident data provided by Seoul School Safety Council were analyzed by gender. Based on the results above, survey questionnaires on characteristics of school accident were developed. Self-reported data were collected from a sample population of 433 students in grade 5 to 6 students attending 4 elementary schools in Seoul. Results: The students who answered they experienced the accident in school for the past 1 year, accounts 60.5% of male and 39.5% of females students, which has statistically significant difference. The male's cases happened most around corridor/door, while female's cases happened most in the playground/gymnasium. As for the accident risk behavior, male students had the risk behavior by using the personal belongings/toys, while the female students had much risk behavior related to physical facility/playground. When classifying the characteristics of risk behaviors according to the accident causes, male students showed higher score in the accident risk behaviors related to play/fight than in those of the female students(p<0.05). Conclusions: Health care providers should develop school safety programs by characteristics of risk behavior between male and female elementary school students.

• 한국안전학회지
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• 제30권1호
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• pp.104-110
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• 2015

This study deals with the roundabout accidents. The goal of this study is to develop the sideswipe accident models at roundabout. In the pursuing the above, this study gives particular attentions to collecting the data of geometric structure and accidents of 54 roundabouts in Korea and developing the Poisson and negative binomial regression models. The main results are as follows. First, sideswipe accident is analyzed to be the highest frequency that is 39.5% of total accident data. Second, Poisson models which is statistically significant is developed. Finally, traffic volume per approach($X_1$), number of circulatory roadway($X_3$), operation of parking lot($X_4$) and width of circulatory roadway($X_6$) are adopted as the common variables. This study might be expected to give some implications to the accident research on the roundabout.

• 한국안전학회지
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• 제32권5호
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• pp.122-128
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• 2017

Korean government announced long-term railway safety investment plan for the safety improvement by 2020. But no research have been done about differential analysis on railroad safety investment and safety improvement. In this study, recent 10 year data on safety investments and accident data are analysed for the differential analysis. Three main safety investments are analysed on regard to accident rate and accident fatalities. Three safety measures include level crossing accident, platform fatalities, and track trespass fatalities. About 90% of railway accident fatalities are caused by these three kind of accidents. Differential analysis shows about 4 to 6 years delay after railroad safety investment and safety improvement. This result can be utilized for the decision making on safety measures and safety target. Which required long term approach.

• Nuclear Engineering and Technology
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• 제54권9호
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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.