Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
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Prediction of Barge Ship Roll Response Amplitude Operator Using Machine Learning Techniques

  • Lim, Jae Hwan (Ocean Science and Technology, Korea Maritime and Ocean University) ;
  • Jo, Hyo Jae (Division of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University)
  • Received : 2019.12.05
  • Accepted : 2020.05.21
  • Published : 2020.06.30
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Abstract

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.

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References

  1. 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
  2. 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
  3. 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. https://doi.org/10.3744/SNAK.2005.42.1.057
  4. Stathakis, D. (2009). How Many Hidden Layers and Nodes. International Journal of Remote Sensing, 30(8), 2133-2147. https://doi.org/10.1080/01431160802549278
  5. 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.
  6. Ham, D.G. (2016). A Study of Data-Mining Methodology in Offshore Plant's Outfittings Procurement Management (Master's thesis). Korea Maritime and Ocean University, Busan, Korea.
  7. Kim, G.H. (2017). Artificial Neural Network for Short-Term Load Forecasting Algorithm Considering Temperature Characteristics (Master's thesis). Korea Maritime and Ocean University, Busan, Korea.
  8. Kim, J.H. (2018). A Study on Machine Learning for Prediction of the Shipbuliding Lead Time (Master's thesis). Korea Maritime and Ocean University, Busan, Korea.
  9. Kim, Y.R., Park, J.-B., & Moon, S.-B. (2018). Prediction of Ship Roll Motion Using Machine Learning-based Surrogate Model. Journal of Korean Navigation and Port Research 42(6), 395-405. https://doi.org/10.5394/KINPR.2018.42.6.395
  10. Jeon, M.Y. (2019). Development of Predictive Model of Ship's Fuel Consumption Using Machine Learning Method (Master's thesis). Pusan National University, Busan, Korea.
  11. Kim, Y.R., (2019). Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship's Data (Master's thesis). Korea Maritime and Ocean University, Busan, Korea.
  12. Kim, Y.R., Park, J.-B., Park, J.-C., & Park, S.-K. (2019). Development of an Evaluation Procedure for Seakeeping Performance of High-speed Planing Hull Using Hybrid Method. Journal of the Society of Naval Architects of Korea, 56(3), 200-210. https://doi.org/10.3744/SNAK.2019.56.3.200