• Title/Summary/Keyword: 내항성 안전모듈

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16000톤급 여객선 항해 중 탱크변화량 패턴 분석에 관한 연구

  • Jeong, U-Ri;Mun, Seong-Bae;Jeong, Eun-Seok
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2018.05a
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    • pp.38-40
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    • 2018
  • 선박 침수사고 발생시, (De)Ballasting 작업은 기본적인 대응방안 중 하나이다. 16000톤급 여객선의 항해 중 탱크 변화량의 패턴을 분석하여 내항성 안전모듈 대응가이던스에서 정량적인 평가방법을 통해 침수사고 발생 시 최적탱크를 선정하여 제시할 수 있도록 실제 상황에 적용 가능한 알고리즘(안)을 개발하였다.

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Case Study of S2 Service Response Guidance in case of Passenger Ship H Abnormal Condition (여객선 H호 선내이상 알람 발생시 대응가이던스 사례연구)

  • Yoo, Yun-Ja;Song, Chae-Uk;Yea, Byeong-Deok
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2018.05a
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    • pp.45-46
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    • 2018
  • S2 module, which is one of the Korean type e-Navigation services, is a service concept that monitors the situation onboard and provides an emergency level determination and response guidance to the ship when an alarm occurs. S2 module is divided into fire/ seakeeping / navigation safety sub-module. In this paper, the concept of S2 service based on actual ship is explained through the response guidance case study of navigation safety module in case of abnormal condition in the passenger ship H.

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Prediction of Ship Roll Motion using Machine Learning-based Surrogate Model (기계학습기반의 근사모델을 이용한 선박 횡동요 운동 예측)

  • Kim, Young-Rong;Park, Jun-Bum;Moon, Serng-Bae
    • Journal of Navigation and Port Research
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    • v.42 no.6
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    • pp.395-405
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    • 2018
  • Seakeeping safety module in Korean e-Navigation system is one of the ship remote monitoring services that is employed to ensure the safety of ships by monitoring the ship's real time performance and providing a warning in advance when the abnormal conditions are encountered in seakeeping performance. In general, seakeeping performance has been evaluated by simulating ship motion analysis under specific conditions for its design. However, due to restriction of computation time, it is not realistic to perform simulations to evaluate seakeeping performance under real-time operation conditions. This study aims to introduce a reasonable and faster method to predict a ship's roll motion which is one of the factors used to evaluate a ship's seakeeping performance by using a machine learning-based surrogate model. Through the application of various learning techniques and sampling conditions on training data, it was observed that the difference of roll motion between a given surrogate model and motion analysis was within 1%. Therefore, it can be concluded that this method can be useful to evaluate the seakeeping performance of a ship in real-time operation.