AI 딥러닝 기반 친환경 선박에서 가스누출에 따른 최적대피경로 분석 = Analysis of the Optimal Evacuation Plan by Gas Leakage in Eco-Friendly Vessels Based on AI Deep Learning

다국어 초록 (Multilingual Abstract)

As the need for eco-friendly fuel arises, the use of fossil fuels is decreasing.
Accordingly, the korea coast guard are striving to convert the fuel system by building the marine pollution control ship with LNG ships. However, when highly flammable fuels such as LNG and hydrogen are used as ship fuel, it is necessary to prepare a plan for a leakage accident. Against this background, research on the use of eco-friendly fuels and the development and safety of advanced marine mobility technologies is being actively conducted. Therefore, in this study, a post-processing system module based on a convolutional neural network was constructed, and the optimal evacuation route according to the number of people, passage width, and hull trim was derived using a deep learning model and path search algorithm based on a deep neural network. As a result of comparing it with the classical pathfinding method under the assumed conditions, the maximum population density by area was improved from 5.4 per/㎡ to 2.6 per/㎡ through deep learning. In addition, the total evacuation time decreased from 1,030 seconds to 248 seconds, and the estimated number of deaths decreased by 67, improving the survival rate by 35%. Therefore, the program developed in this study is expected to be used as decision support data to secure the safety of passengers and crew by deriving the optimal evacuation route in a disaster situation.

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