Cited By
View all- Li XWang KTang MQin JWu PYang TZhang H(2022)Marine Drifting Trajectory Prediction Based on LSTM-DNN AlgorithmWireless Communications & Mobile Computing10.1155/2022/70994942022Online publication date: 1-Jan-2022
Study on Prediction Model of Magnetic Field Intensity of Submarine Power Cable Based on LSTM
Authors: 
Jianping Wu, Xi Yang, Huaan Wang, Jianping Chen, Quanzhong Zhao, Bo XiaoAuthors Info & Claims
Jianping Wu, Xi Yang, Huaan Wang, Jianping Chen, Quanzhong Zhao, Bo XiaoAuthors Info & ClaimsPages 107 - 112
Abstract
After the submarine power cables are laid, the parameters such as distance between cables and surface of the sea (H), distance between two submarine power cables placed in parallel (d) are constant. It is difficult to study the magnetic field intensity distribution of submarine power cable under different H and d, because the submarine cable can't be placed randomly. In order to solve this problem, firstly the influence of different groups of H and d on the distribution of submarine cable magnetic field intensity is studied, and the magnetic field intensity of submarine cable generated by different groups of H and d are used as training samples and testing samples. Then the training samples are used to construct the submarine power cable magnetic field prediction model based on Long Short Term Memory (LSTM) neural network, and the submarine power cable magnetic field is predicted with the testing samples as the input. Finally, the simulation results show that there is good prediction ability of LSTM prediction model for submarine cable magnetic field data.
References
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Index Terms
- Study on Prediction Model of Magnetic Field Intensity of Submarine Power Cable Based on LSTM
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October 2020
566 pages
ISBN:9781450375535
DOI:10.1145/3421766
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Published: 26 October 2020
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AIAM2020
AIAM2020: 2nd International Conference on Artificial Intelligence and Advanced Manufacture
October 15 - 17, 2020
Manchester, United Kingdom
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View all- Li XWang KTang MQin JWu PYang TZhang H(2022)Marine Drifting Trajectory Prediction Based on LSTM-DNN AlgorithmWireless Communications & Mobile Computing10.1155/2022/70994942022Online publication date: 1-Jan-2022
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