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Abstract ute that minimizes the ship’s required power, it is necessary to predict the sea level and the ship’s required power accordingly. The weather forecasting companies such as the European Center for Medium-Range Weather Forecast (ECMWF) and Hybrid Coordinate Ocean Model (HYCOM), which provide ocean environmental data, typically make short-term forecasts of around six weeks. Therefore, when a long-term prediction is needed, it is necessary to predict ocean environmental data on its own. In the case of the prediction of ship’s required power, a numerical method using model test results is traditionally used. However, this method is difficult to accurately predict the ship’s required power due to the model test’s uncertainty. To solve this problem, an onboard test must be conducted, but this is expensive and time-consuming. Therefore, in this study, the ocean environmental data and ship’s required power were predicted using deep learning.
In relation to the prediction of ocean environmental data, many studies have used deep learning. However, most of them made predictions for limited ocean environmental data such as wave height, wave period, and wave direction. In addition, there was a limitation in that prediction was made for a specific sea area rather than the entire sea area. Therefore, in this study, the ocean environmental data of the entire sea area was imaged for prediction. In addition, convolutional LSTM suitable for training time-series image data was utilized. In addition, AutoEncoder was used to solve delayed prediction that mainly occurs in the problem of prediction of time series data. Also, to find the optimal model, the performance of the model was evaluated by changing the size of input data (look back step) and prediction time of output data (look forward step). Finally, based on this model, the entire sea area was divided into 12 areas, and the prediction of ocean environmental data was carried out.
Regression analysis was used in many studies related to the prediction of a ship’s required power. However, the regression analysis showed limitations in the accuracy of prediction in the case of complex problems such as the prediction of the ship’s required power affected by maritime and ship operating conditions. Therefore, in this study, deep feedforward neural network (DFN), a deep learning model suitable for numerical prediction, was used. In addition, various methods were used to increase prediction accuracy. First, in the case of wind and wave-related data in ocean environmental data, the effect was confirmed by performing pre-processing with relative values for the ship. Second, the effect was confirmed by changing the DFN model structure according to the characteristics of the DFN input data. Third, we analyzed the prediction accuracy according to the combination of five hyperparameters: number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer for the DFN learning model. Fourth, k-means clustering was conducted to develop an independent model for predicting the ship’s required power according to a sea state and ship operational conditions. As described above, the performance of various prediction models was compared and analyzed.
Publication Date 2021-02-26

June-Beom Lee, "Development of Prediction Models of Ship Power and Ocean Environmental Data Based on Deep Learning", M.Sc. Thesis, Seoul National University, 2021.02.26

이준범, "딥 러닝을 이용한 해기상 및 소요 마력 예측 모델 개발", 석사학위논문, 서울대학교, 2021.02.26


List of Articles
번호 분류 제목 Publication Date
23 Ph.D. Thesis Jisang Ha, "Optimal Arrangement Method of Equipment and Pipes in the Engine Room of a Ship", Ph.D. Thesis, Seoul National University, 2023.08.29 file 2023-08-29
22 M.Sc. Thesis Dong-Geun Jeong, "A Method for Hierarchical Route Planning of Small ships in Coastal Area Using Quadtree Chart", M.Sc. Thesis, Seoul National University, 2023.02.24 2023-02-24
21 M.Sc. Thesis Jeong-Ho Park, "A Method for Detection and Tracking of Maritime Obstacles Based on Multi-Video", M.Sc. Thesis, Seoul National University, 2023.02.24 file 2023-02-24
20 M.Sc. Thesis Hamin Song, "Optimization of Crew Manning Considering Operation Scenarios of a Naval Ship", M.Sc. Thesis, Seoul National University, 2023.02.24 file 2023-02-24
19 M.Sc. Thesis Yeongmin Jo, "A Method for the Path Tracking of Surrounding Ships Using Multiple Sensors", M.Sc. Thesis, Seoul National University, 2022.02.25 file 2022-02-25
18 M.Sc. Thesis Won-Jae Lee, "Image-based Object Detection and Tracking Method for the Awareness around the Ship", M.Sc. Thesis, Seoul National University, 2021.02.26 file 2021-02-26
» M.Sc. Thesis June-Beom Lee, "Development of Prediction Models of Ship Power and Ocean Environmental Data Based on Deep Learning", M.Sc. Thesis, Seoul National University, 2021.02.26 file 2021-02-26
16 Ph.D. Thesis Hye-Won Lee, "Wire Rope Contact Model and Crane Control Method for the Advanced Simulation and Automation of Block Erection", Ph.D. Thesis, Seoul National University, 2020.02.26 file 2020-02-26
15 Ph.D. Thesis Ki-Su Kim, "Assessment Method of the Fitness of Initial Arrangement Design of a Naval Ship", Ph.D. Thesis, Seoul National University, 2019.08.29 file 2019-08-29
14 M.Sc. Thesis Sung-Woo Park, "Data Mining Method for Offshore Structures based on Big Data Technology", M.Sc. Thesis, Seoul National University, 2019.02.26 file 2019-02-26
13 Ph.D. Thesis Luman Zhao, "Simulation Method to Support Autonomous Navigation and Installation Operation of an Offshore Support Vessel", Ph.D. Thesis, Seoul National University, 2019.02.26 file 2019-02-01
12 M.Sc. Thesis Joo-Pil Lee, "Design of Wreck Removal Considering Safety and Economy", M.Sc. Thesis, Seoul National University, 2019.02.26 file 2019-02-26
11 Ph.D. Thesis Seung-Ho Ham, "Integrated Simulation Method Based on Multibody Dynamics for Production Design Verification in Ships and Offshore Structures", Ph.D. Thesis, Seoul National University, 2018.08.29 file 2018-08-29
10 M.Sc. Thesis Sang-Hyun Lee, "Integrated Method for Layout Design of LNG FPSO Based on Optimization Technique and Expert System", M.Sc. Thesis, Seoul National University, 2018.02.26 file 2018-02-26
9 M.Sc. Thesis Seong-Hoon Kim, "A Study on the Method for the Estimation of Energy Efficiency Operational Indicator of a Ship Based on Technologies of Big Data and Deep Learning", M.Sc. Thesis, Seoul National University, 2018.02.26 file 2018-02-26
8 M.Sc. Thesis Sung-Min Lee, "A Study on the Method of Simultaneous Determination of Path and Speed for Ship Route Planning", M.Sc. Thesis, Seoul National University, 2017.08.25 file 2017-08-25
7 M.Sc. Thesis Sun-Kyung Jung, "Optimal Arrangement Method of a Naval Surface Ship Considering Stability, Operability, and Survivability", M.Sc. Thesis, Seoul National University, 2017.02.24 file 2017-02-24
6 M.Sc. Thesis Dong-Hoon Jeong, "A Method of Engagement Simulation of Engineering Level Considering the Detection and Maneuvering Performance", M.Sc. Thesis, Seoul National University, 2017.02.24 file 2017-02-24
5 M.Sc. Thesis Ju-Sung Kim, "Quasi-static Flooding Analysis Method of a Damaged Ship Considering Oil Spill and Cargo Load", M.Sc. Thesis, Seoul National University, 2017.02.24 file 2017-02-24
4 M.Sc. Thesis Sung-Kyoon Kim, "Optimal Layout Method of an Offshore Plant Topside Based on Expert System", M.Sc. Thesis, Seoul National University, 2016.02.26 file 2016-02-26
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