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A Study on the Initial Stability Calculation of Small Vessels Using Deep Learning Based on the Form Parameter Method

Form Parameter 기법을 활용한 딥러닝 기반의 소형선박 초기복원성 계산에 관한 연구

  • Dongkeun Lee (KOMSA, Korea Maritime Transportation Safety Authority) ;
  • Sang-jin Oh (Dept. of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Chaeog Lim (Research Institute of Industrial Technology, Pusan National University) ;
  • Jin-uk Kim (Dept. of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Sung-chul Shin (Dept. of Naval Architecture and Ocean Engineering, Pusan National University)
  • 이동근 (한국해양교통안전공단) ;
  • 오상진 (부산대학교 조선해양공학과) ;
  • 임채옥 (부산대학교 생산기술연구소) ;
  • 김진욱 (부산대학교 조선해양공학과) ;
  • 신성철 (부산대학교 조선해양공학과)
  • Received : 2024.01.11
  • Accepted : 2024.01.23
  • Published : 2024.02.28

Abstract

Approximately 89% of all capsizing accidents involve small vessels, and despite their relatively high accident rates, small vessels are not subject to ship stability regulations. Small vessels, where the provision of essential basic design documents for stability calculations is omitted, face challenges in directly calculating their stability. In this study, considering that the majority of domestic coastal small vessels are of the Chine-type design, the goal is to establish the major hull form characteristic data of vessels, which can be identified from design documents such as the general arrangement drawing, as input data. Through the application of a deep learning approach, specifically a multilayer neural network structure, we aim to infer hydrostatic curves, operational draft ranges, and more. The ultimate goal is to confirm the possibility of directly calculating the initial stability of small vessels.

Keywords

Acknowledgement

이 논문은 2023년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원의 지원과(P0023684, 2023년 산업혁신인재성장지원사업), 해양수산부 재원으로 해양수산과학기술진흥원의(20220210, AI 기반 스마트 어업관리시스템 개발) 지원을 받아 수행된 연구임.

References

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