Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network
In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time sh...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/8/10/777 |
| _version_ | 1827704921849331712 |
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| author | Yumin Su Jianfeng Lin Dagang Zhao Chunyu Guo Chao Wang Hang Guo |
| author_facet | Yumin Su Jianfeng Lin Dagang Zhao Chunyu Guo Chao Wang Hang Guo |
| author_sort | Yumin Su |
| collection | DOAJ |
| description | In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model. |
| first_indexed | 2024-03-10T15:50:57Z |
| format | Article |
| id | doaj.art-b141a04443d844b7a73d662bc1df15f6 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T15:50:57Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-b141a04443d844b7a73d662bc1df15f62023-11-20T16:02:51ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-10-0181077710.3390/jmse8100777Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural NetworkYumin Su0Jianfeng Lin1Dagang Zhao2Chunyu Guo3Chao Wang4Hang Guo5College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, ChinaIn marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model.https://www.mdpi.com/2077-1312/8/10/777artificial intelligencerecursive neural networktime series predictionlarge-scale ship modelvertical acceleration |
| spellingShingle | Yumin Su Jianfeng Lin Dagang Zhao Chunyu Guo Chao Wang Hang Guo Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network Journal of Marine Science and Engineering artificial intelligence recursive neural network time series prediction large-scale ship model vertical acceleration |
| title | Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network |
| title_full | Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network |
| title_fullStr | Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network |
| title_full_unstemmed | Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network |
| title_short | Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network |
| title_sort | real time prediction of large scale ship model vertical acceleration based on recurrent neural network |
| topic | artificial intelligence recursive neural network time series prediction large-scale ship model vertical acceleration |
| url | https://www.mdpi.com/2077-1312/8/10/777 |
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