• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.94-99
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• 2020

Bikers can be subjected to injuries from unexpected accidents even if they wear basic helmets. A properly designed airbag can efficiently protect the critical areas of the human body. This study introduces a wearable smart airbag system using machine learning techniques to protect human neck and shoulders. When a bicycle accident happens, a microprocessor analyzes the biker's motion data to recognize if it is a critical accident by comparing with accident classification models. These models are trained by a variety of possible accidents through machine learning techniques, like k-means and SVM methods. When the microprocessor decides it is a critical accident, it issues an actuation signal for the gas inflater to inflate the airbag. A protype of the wearable smart airbag with the machine learning techniques is developed and its performance is tested using a human dummy mounted on a moving cart.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.45-46
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• 2019

Accident rate in domestic construction industry has been increased rapidly in every year. In particular, the rate of death has been shown very high compared with other industries. It means that safety activities performed by government is not effective in reducing the rate of accident. To solve these problems, the risk factors should be predicted in advance, controlled, monitored and managed from start of project to end of project. However, most studies have been conducted by using frequency of occurrence of accident and only listed the importance of risk. Therefore, the objective of this study is to provide basic material to develop risk quantifying model for human accidents on construction site in South Korea. In the future, it is expected to be used as a reference of study on developing safety mangement checklist in construction industry and model for forecasting accident.

• Journal of Navigation and Port Research
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• v.26 no.3
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• pp.329-336
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• 2002

Recently, in the whole fields of industry including shipping business, the conception and importance of Human Factor is very highly and frequently emphasized. But the domestic crew disaster including injury and illness in our shipping business shows higher accident rate than other shipping countries. Therefore the developing of measures to prevent/reduce the accident is highly required as the marine accident including crew disaster causes enormous loss of property and human life in size and scale. But, because the domestic data regarding the crew accident are relatively insufficient and rule, the developing of those measures has many difficulties. Therefore, this study is to make the concerning data for the useful reference by showing the trend and current situation of crew accidents, assorted by the crew's rank, type of the accident, injured part of the body, cause of the accident, type of ships and type of the works when the accident occurred, by using the each ocean-going shipping company's recent 5 years('95∼'99) data in korea.

• Korean Journal of Culture and Social Issue
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• v.16 no.3
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• pp.267-287
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• 2010

This study aimed to improve the Traffic Accident Management System's validity and reliability, so the system could help classify and judge the human factors that correlate with traffic accidents. We took traffic accident research and analysis systems of United States and United Kingdom and certain related, former studies into account in building our test system. Next, we used the test system's criteria to re-analyze 502 Gyeonggi province accident records from 2008. We compared the results to existing systems' results to verify the test system's validity and reliability. These results indicated the necessity of removing some uncertain items from the existing systems and adding in some new items from the test system. This should help improve understanding of what happens at traffic accident scenes and of the sources of drivers' abnormal, reckless behavior. We introduce suggestions for improving the Traffic Accident Management System and research concepts for further studies.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2021-2028
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• 2010

Rail human factors research has grown rapidly in both quantity and quality of output over the past few years. Human factors, also, still plays a significant part in many railway accidents. In this paper we review categorized performance shaping factors of human errors associated with railway accidents within and out of the country. This paper deals with the selection of the important performance shaping factors under accident management situations in railway for use in the assessment of human errors. The purpose of this study is to classify which human error would be selected for accident analysis. Therefore, the classification of human errors suggested in this study may be useful to enhance the Korean railway system safety.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.111-114
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• 2018

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.562-569
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• 2018

Recently, human errors have very rarely occurred during power generation at nuclear power plants. For this reason, many countries are conducting research on smart support systems of nuclear power plants. Smart support systems can help with operator decisions in severe accident occurrences. In this study, a smart support system was developed by integrating accident prediction functions from previous research and enhancing their prediction capability. Through this system, operators can predict accident scenarios, accident locations, and accident information in advance. In addition, it is possible to decide on the integrity of instruments and predict the life of instruments. The data were obtained using Modular Accident Analysis Program code to simulate severe accident scenarios for the Optimized Power Reactor 1000. The prediction of the accident scenario, accident location, and accident information was conducted using artificial intelligence methods.

• Journal of the Ergonomics Society of Korea
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• v.34 no.2
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• pp.103-113
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• 2015

Objective: This study proposes a systematic process to present the analysis methods and solutions of organizational root causes to human errors on the railroad. Background: In fact, organizational root cause such as organizational culture is an important factor in the safety concerns on human errors in the nuclear power plant, railroad and aircraft. Method: The proposed process is as follows: 1) define analysis boundary 2) select human error taxonomy 3) perform accident analysis 4) draw root causes with FGI 5) review root causes analysis with survey 6) chart analysis of root causes, and 7) propose alternatives and solutions. Results: As a result, root causes of the organizations like railroad and nuclear power plant came from the educational problems, violations, payoff system, safety culture and so forth. Conclusion: The proposed process does predict potential railroad accident through retrospect error analysis by building new human error taxonomies and problem solution. Application: This study would contribute to examination of the relationship between human error-based accidents and organizational root causes.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.195-196
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• 2017

The number of human accidents in the construction industry is increasing every year, and it constitute the highest percentage among industry. This means that activities performed to prevent safety accidents in the country are not efficient to reduce the rate of accidents in the construction industry. In order to solve this issue, research has been conducted from various perspectives. But, research regarding to quantification model of human accidents is insufficient. the objective of this study is to conduct a basic study on quantification model development of human accidents. To achieve the objective, first, Cause of accident is defined the through literature review. Second, a basic statistic analysis is conducted to determine the characteristics of the accident causes. Third, the analysis is conducted after dividing into four categories : accumulate rate, season, total construction cost, and location. In the future, this study can be used as a reference for developing the safety management checklist for safety management in construction site and development of prediction models of human accident.

• Journal of Environmental Health Sciences
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• v.46 no.5
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• pp.588-598
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• 2020

Objectives: Human indices were developed to determine returning point of residents and damage restoration after the chemical accident Methods: To determine the returning point of residents after the chemical accident, a new concept, the standard man model was introduced as a human index, in which both H-code and its acute effects were main idea. To evaluate the applicability, a hydrogen fluoride leakage accident in Gumi was applied. The returning point were suggested as the conservative remission period of acute effects among relevant hazard effects and compared with actual returning point. The coverage of each age group were considered with reflecting average daily dose expected for actual residents. In addition, a relief-index as a social-scientific approach was reflected as well to apply the damage restoration Results: Actual returning point of residents in Gumi was 88 days; and that of standard man model suggested was 84 days. The expected amount of exposure at aged 12 or under was at least 2.35 times greater than that of this model, 40s, theoretically. However, their population ratio was less than 1%, so 99% of residents could be applied when the standard man model was applied. The relief-index was as an objective and quantitative methodology to apply the qualitative aspect. Conclusions: Although evaluated as a relatively positive result, there was a limitation such as the number of accident applied to the verification of standard man model. The relief index was also considered, but further research should be carried out to find threshold level for the relief.