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http://dx.doi.org/10.7837/kosomes.2019.25.7.851

A Machine Learning-Based Method to Predict Engine Power  

KIM, Dong-Hyun (Korea Marine Equipment Research Institute)
HAN, Seung-Jae (Pukyong National University)
JUNG, Bong-Kyu (Department of Maritime Police & Production System, Gyeongsang National University)
Han, Seung-Hun (Department of Mechanical System Engineering, Gyeongsang National University)
LEE, Sang-Bong (Lab021)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.25, no.7, 2019 , pp. 851-857 More about this Journal
Abstract
This study is about ship horsepower prediction of machine learning method using the big data of ship. Currently, new ships use the ISO15016 method to predict external environmental resistance through mathematical equations but due to complicated equations and requires many input variables so it is less applicable to be used in ship. In this recent research, we propose a model capable of predicting ship performance with high performance using SVM (Support Vector Machine) algorithm which shows excellent performance in recent prediction and recognition. The proposed predictive model has the advantage of being able to predict better performance than ISO15016 only if secured big data is used. In this study, we compared the ISO15016 technique and the SVM algorithm-based horsepower analysis method using the 178K bulk carrier's voyage data to reduce ship model data preparation, which is a disadvantage of ISO15016, and improve inaccurate horsepower prediction performance.
Keywords
Ship; Resistance; Prediction; SVM; ISO15016;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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