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Abstract Design of a ship hull form is very complex and time consuming process. It starts from the selection of a parent ship, and a designer modifies the selected ship to meet the owner’s and Class requirements. When the hull form is modified, the hydrodynamic analysis is conducted. If the hydrodynamic performance is acceptable, the verification is done through the model test. If the hydrodynamic performance is not acceptable, a designer modifies the hull form manually until it satisfies a certain requirement. During this process, a lot of time is consumed, and it requires the designer’s experiences. The modification methods can be different, and the quality of the hull form can be varied from the designer’s proficiency. In this study, the optimization method is proposed to obtain the optimized hull form automatically using the reinforcement learning that is one of the deep learning methods. The smallest total resistance of a hull form is used as the reward in the reinforcement learning, but the other hydrodynamic performance values can be used as the rewards. The KVLCC2 tanker that is a public hull form is used to get the optimal hull form, and the result shows that the proposed method can generate the optimal hull form. It is expected that the proposed method can be used in the hull form design to reduce the time and enhance the performance.
Publication Date 2019-07-08

Min-Jae Oh, Myung-Il Roh, Young-Soo Seok, and Sung-Jun Lee, "Optimization of a Ship Hull Form using Deep Learning", Proceedings of ACDDE(Asian Conference on Design and Digital Engineering) 2019, Penang, Malaysia, 2019.07.07-10


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368 Domestic Conference 이준범, 노명일, 김기수, 김상엽, 한기민, 김대현, "딥 러닝 기법을 이용한 선박의 소요 마력 예측", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 523, 2019.10.24-26 file 2019-10-25
367 Conference Chairman Computational Design and Optimization 4, ACDDE(Asian Conference on Design and Digital Engineering) 2018, Okinawa, Japan, 2018.11.01-03 2018-11-02
366 Domestic Conference 이준범, 노명일, 김기수, 손명조, 한기민, 김대헌, "딥 러닝 기반 해기상 및 소요 마력 예측", 2019년도 한국CDE학회 하계학술발표회, 제주, pp. 235, 2019.08.19-22 file 2019-08-21
365 Domestic Conference 김진혁, 노명일, 공민철, "부분 재액화 시스템의 유지 및 보수를 위한 증강 현실 컨텐츠 개발", 2019년도 한국CDE학회 하계학술발표회, 제주, pp. 269, 2019.08.19-22 file 2019-08-21
364 Domestic Conference 이혜원, 노명일, 함승호, 전도현 "블록의 탑재 제어를 위한 갠트리 크레인의 제어기 설계 고도화", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 139, 2019.10.24-26 file 2019-10-24
363 Domestic Conference 전도현, 노명일, 이혜원, 하지상, 진은석, 김인일, "딥 러닝 기반 자율 운항 선박의 충돌 회피 방법 연구", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 429, 2019.10.24-26 file 2019-10-24
362 Domestic Conference 김진혁, 노명일, 공민철, "증강 현실 기술을 이용한 장비의 원격 운영 및 유지 보수 방법", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 196, 2019.10.24-26 file 2019-10-24
361 Domestic Conference 이원재, 노명일, 이성준, 하지상, 석영수, 오민재, "선박 운항 시 주변 인지를 위한 물체 탐지, 추적 및 거리 추정 방법", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 431, 2019.10.24-26 file 2019-10-24
360 Domestic Conference 하지상, 노명일, 이혜원, 은종호, 박종진, "실시간 AIS 데이터를 이용한 해상 충돌 회피 알고리즘 연구", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 430, 2019.10.24-26 file 2019-10-25
359 International Conference Luman Zhao, Myung-Il Roh, Hye-Won Lee, Do-Hyun Chun, Sung-Jun Lee, "A Collision Avoidance Method of Multi-ships Based on Deep Reinforcement Learning Considering COLREGs," Proceedings of ICCAS 2019, Rotterdam, Netherlands, pp. 85-88, 2019.09.24-26 file 2019-09-24
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