지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.6
- 수록면
- 167 - 179 (13page)
이용수
초록· 키워드
오류제보하기
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
참고문헌 (12)
참고문헌 신청
Mahfouz, A.B. (2004). Identification of the Nonlinear Ship Rolling Motion Equation Using the Measured Response at Sea. Ocean Engineering, 31(17-18), 2139-2156. https://doi.org/10.1016/j.oceaneng.2004.06.001
Park, G.I., Choi, K.,Y., Lee, J.H., & Kim, M.S. (2004). Introduction of Optimum Navigation Route Assessment System Based on Weather Forecasting and Seakeeping Prediction. Journal of Korean Navigation and Port Research, 28(10), 833-841. https://doi.org/10.5394/KINPR.2004.28.10.833
Lee, J.H., Park, G.I., Choi, K.Y., Kim, M.S., & Bang, C.S. (2005). Basic Research of Optimum Routing Assessment System for Safe and Efficient Voyage. Journal of the Society of Naval Architects of Korea, 42(1), 57-63.
Stathakis, D. (2009). How Many Hidden Layers and Nodes. International Journal of Remote Sensing, 30(8), 2133-2147. https://doi.org/10.1080/01431160802549278
Kang, M.S., Kim, Y.W., & Jung, H. (2012). Floating Body Motion Prediction System Using Artificial Neural Network. Proceedings of The Korean Association of Ocean Science and Technology Societies, 1041-1050.
Park, G.I., Choi, K.,Y., Lee, J.H., & Kim, M.S. (2004). Introduction of Optimum Navigation Route Assessment System Based on Weather Forecasting and Seakeeping Prediction. Journal of Korean Navigation and Port Research, 28(10), 833-841. https://doi.org/10.5394/KINPR.2004.28.10.833
Lee, J.H., Park, G.I., Choi, K.Y., Kim, M.S., & Bang, C.S. (2005). Basic Research of Optimum Routing Assessment System for Safe and Efficient Voyage. Journal of the Society of Naval Architects of Korea, 42(1), 57-63.
Stathakis, D. (2009). How Many Hidden Layers and Nodes. International Journal of Remote Sensing, 30(8), 2133-2147. https://doi.org/10.1080/01431160802549278
Kang, M.S., Kim, Y.W., & Jung, H. (2012). Floating Body Motion Prediction System Using Artificial Neural Network. Proceedings of The Korean Association of Ocean Science and Technology Societies, 1041-1050.
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