한국해양공학회지 (Journal of Ocean Engineering and Technology)

  • 제36권5호
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  • Pages.295-302
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  • 2022
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  • 1225-0767(pISSN)
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  • 2287-6715(eISSN)
한국해양공학회 (Korean Society of Ocean Engineers)
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Ship Motion-Based Prediction of Damage Locations Using Bidirectional Long Short-Term Memory

  • Son, Hye-young (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Kim, Gi-yong (Department of Naval Architecture and Ocean Engineering, Pusan National University) ;
  • Kang, Hee-jin (Alternative Fuels and Power System Research Division, Korea Research Institute of Ships and Ocean Engineering) ;
  • Choi, Jin (Autonomous & Intelligent Maritime Systems Research Division, Korea Research Institute of Ships and Ocean Engineering) ;
  • Lee, Dong-kon (Advanced Ship Research Division, Korea Research Institute of Ships and Ocean Engineering) ;
  • Shin, Sung-chul (Department of Naval Architecture and Ocean Engineering, Pusan National University)
  • 투고 : 2022.08.08
  • 심사 : 2022.09.08
  • 발행 : 2022.10.31
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초록

The initial response to a marine accident can play a key role to minimize the accident. Therefore, various decision support systems have been developed using sensors, simulations, and active response equipment. In this study, we developed an algorithm to predict damage locations using ship motion data with bidirectional long short-term memory (BiLSTM), a type of recurrent neural network. To reflect the low frequency ship motion characteristics, 200 time-series data collected for 100 s were considered as input values. Heave, roll, and pitch were used as features for the prediction model. The F1-score of the BiLSTM model was 0.92; this was an improvement over the F1-score of 0.90 of a prior model. Furthermore, 53 of 75 locations of damage had an F1-score above 0.90. The model predicted the damage location with high accuracy, allowing for a quick initial response even if the ship did not have flood sensors. The model can be used as input data with high accuracy for a real-time progressive flooding simulator on board.

키워드

과제정보

This research was supported by the 'Development of Autonomous Ship Technology (20200615)' funded by the Ministry of Oceans and Fisheries (MOF, Korea).

참고문헌

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