• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.82-88
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• 2024

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.

• Proceedings of KOSOMES biannual meeting
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• 2003.11a
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• pp.7-11
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• 2003

Ship is being operated under a highly dynamic environments and many factors are related with ship's collision and those factors are interacting. So, an analysis on the ship's collision causes is very important to prepare countermeasures which will ensure the safe navigation. And the analysis confirmed that ship's collision is occurred most frequently and the cause is closely related with human factor. The main purpose of this study is to build a model of human factors in ship's collision cause using SD(System Dynamics} approach and to measure a effect which is risk control countermeasures of ship's collision. To achieve this aim, the structure analysis on the causes of ship's collision using FSM are performed, and the structure was changed by quantitative, qualitative factors and their feedback loops in casual map. This model was performed over 20 years(1993-2012) in a standard simulation model and 8 policy simulation models.

• Journal of Navigation and Port Research
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• v.27 no.5
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• pp.493-498
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• 2003

Ship is being operated under a highly dynamic environments and many factors are related with ship's collision and those factors are interacting. So, An analysis on the ship's collision muses is very important to prepare countermeasures which will ensure the safe navigation. And the analysis confirmed that ship's collision is occurred most frequently and the muse is closely related with human factor. The main purpose of this study is to build a model of human factors in ship's collision muse using SD(System Dynamics} approach and to measure a effect which is risk control countermeasures of ship's collision. To achieve this aim, the structure analysis on the muses of ship's collision using FSM are performed, and the structure was changed by quantitative, qualitative factors and their feedback loops in casual map. This model was performed over 20 years(1993-2012) in a standard simulation model and 8 policy simulation models.

• International Journal of Highway Engineering
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• v.9 no.2 s.32
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• pp.1-11
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• 2007

The rapid growths of economy and automobiles since the 1970's have caused serious traffic jams and environmental disruption in urban areas. To relieve these problems caused by urbanization, there should be considered alternative means of transportation modes. Many developed countries have accepted bicycles as a so called "Green Mode" for environmentally oriented strategies to increase the qualities of urban lives. Korea have also attempted various means to raise bicycle usages. In this research, significant factors affecting bicycle crashes at signalized intersections in urban areas were studied. The model results showed that Poisson regression is the best fit methodology for data modeling and revealed that traffic volume, a number of driveways, configuration of the ground, presence of bicycle path, school, and bus stop, residential area, size of intersection are significant factors affecting the bicycle crashes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.10 no.2 s.21
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• pp.17-22
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• 2004

Ship is bring operated under a highly dynamic environments and many factors are related whit marine accident and those factors are interacting. An analysis on the marine accident is very important to prepare countermeasures which will ensure the safe navigation. This paper aims to build a model of the causes and improved policy for marine accident using SD(System Dynamics} approach and to measure a effect which is risk control countermeasures of marine accident. The methodology of this paper is to perform the causes and improved policy for marine accident using Brainstorming method and was to changed by quantitutive, qualitative factors and their feedback loops in casual map. This model was performed over 23 years($1997\~2020$) in a standard simulation model and 4 policy simulation models.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.190-198
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• 2009

This study shows the developing process of the risk assessment models for railway casualty accidents. To evaluate the risks of these accidents, the hazardous events and the hazardous factors were identified by the review of the accident history and engineering interpretation of the accident behavior. The frequency of each hazardous event was evaluated from the historical accident data and structured expert judgments by using the Fault Tree Analysis (FTA) technique. In addition, to assess the severity of each hazardous event, the ETA (Event Tree Analysis) technique and other safety techniques were applied. The risk assessment models developed can be effectively utilized in defining the risk reduction measures in connection with the option analysis.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.936-943
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• 2009

In this study, a risk assessment model based on the ETA (Event Tree Analysis) and FTA (Fault Tree Analysis) is developed according to the procedure of hazard analysis and risk assessment in order to estimate the risk quantitatively. The FTA technique is applied to estimate the branch probability (frequency) and the ETA technique is applied to estimate the consequence for each branch path on the ET (Event Tree). A risk assessment model is developed by the combination of those ETA and FTA. In addition, the reliability and the validity of the risk assessment model are verified by comparing the risk estimated through the developed model with the actual equivalent fatality.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.7-9
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• 2016

점차적으로 확대되는 ECDIS의 강제화에 따라서 반복적으로 발생되고 있는 좌초 및 충돌사고의 예방을 위해서는 효과적인 ECDIS 교육과 현장에서의 올바른 운용이 필수적이다. 이 연구에서는 ECDIS의 운용과 관련된 사고사례를 조사하여 식별된 문제점을 바탕으로 ECDIS 교육 모델의 개선 방향성을 제시하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.539-540
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• 2023

최근 물체 인식 모델의 성능을 개선하기 위한 다양한 연구가 진행 중이다. 본 논문에서는 K-means 기반 앵커박스 선정 기법을 적용한 새로운 물체 인식 모델 성능 개선 방법을 제안한다. 제안된 방법은 항만 내 설치된 컨테이너 사고를 예방하기 위한 컨테이너 사고위험도 분류 모델에 적용하여 성능 평가를 하였다. 특히, 컨테이너 사고위험도 분류 모델은 작은 물체를 인식해야 하며 이런 환경에서는 기존 물체 인식 모델 성능이 낮게 나타난다. 본 논문에서는 제안한 K-means 기반 앵커박스 선정 기법을 적용하여 물체 인식 모델 성능이 개선됨을 확인하였디.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.77-85
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• 2012

Most of Automatic Accident Detection Algorithm has a problem of detecting an accident as traffic congestion. Actually, center's managers deal with accidents depend on watching CCTV or accident report by drivers even though they run the Automatic Accident Detection system. It is because of the system's detecting errors such as detecting non-accidents as accidents, and it makes decreasing in the system's overall reliability. It means that Automatic Accident Detection Algorithm should not only have high detection probability but also have low false alarm probability, and it has to detect accurate accident spot. The study tries to verify and evaluate the effectiveness of using Gaussian Mixture Model and individual vehicle tracking to adapt Accident Detection Algorithm to Center Management System by measuring accident detection probability and false alarm probability's frequency in the real accident.