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논문 기본 정보

자료유형
학술저널
저자정보
Jae Hwan Lim (Korea Maritime and Ocean University) Hyo Jae Jo (Korea Maritime and Ocean University)
저널정보
한국해양공학회 한국해양공학회지 한국해양공학회지 제34권 제3호(통권 제154호)
발행연도
2020.6
수록면
167 - 179 (13page)

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초록· 키워드

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Recently, the increasing importance of artificial intelligence (AI) technology has led to its increased use in various fields in the shipbuilding and marine industries. For example, typical scenarios for AI include production management, analyses of ships on a voyage, and motion prediction. Therefore, this study was conducted to predict a response amplitude operator (RAO) through AI technology. It used a neural network based on one of the types of AI methods. The data used in the neural network consisted of the properties of the vessel and RAO values, based on simulating the in-house code. The learning model consisted of an input layer, hidden layer, and output layer. The input layer comprised eight neurons, the hidden layer comprised the variables, and the output layer comprised 20 neurons. The RAO predicted with the neural network and an RAO created with the in-house code were compared. The accuracy was assessed and reviewed based on the root mean square error (RMSE), standard deviation (SD), random number change, correlation coefficient, and scatter plot. Finally, the optimal model was selected, and the conclusion was drawn. The ultimate goals of this study were to reduce the difficulty in the modeling work required to obtain the RAO, to reduce the difficulty in using commercial tools, and to enable an assessment of the stability of medium/small vessels in waves.

목차

ABSTRACT
1. Introduction
2. Research Process
3. Learning Result and Discussion
4. Conclusions
References

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