文献类型：期刊文献

英文题名：A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention

作者：Wang, Huifeng[1];Yin, Jianchuan[1,2];Wang, Nini[3];Wang, Lijun[1,2]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang, Peoples R China;[2]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang, Peoples R China;[3]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang, Peoples R China

年份：2025

卷号：12

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001521309000001)、、Scopus(收录号：2-s2.0-105009815788)、WOS

基金：The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China under grants 52231014 and 52271361, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684, the Technology breakthrough plan project of Zhanjiang under Grant 2023B01024, and the Program for Scientific Research Start-Up Funds of Guangdong Ocean University.

语种：英文

外文关键词：ship rolling motion; multi-dimensional data-driven; principal component analysis; variational mode decomposition; temporal convolutional network; bidirectional gated recurrent unit; improved dung beetle optimization

外文摘要：Introduction The motion of a ship at sea is complex. This motion is affected by environmental factors such as wind, waves, and currents. These factors cause the ship's movement to be nonlinear, dynamic, and uncertain. Such complex motion can impact the ship's performance and pose a safety risk. This has become an urgent problem in maritime safety. This study aimed to improve the prediction of a ship's roll motion with high accuracy. As such, the study proposes a combined prediction model. This model integrates data decomposition, dimensionality reduction, deep learning, and optimization techniques.Methods The model uses the variational mode decomposition (VMD) method to break down the ship's roll motion data into components at different scales. This improves the smoothness of the data. Principal component analysis (PCA) is applied to reduce the dimensionality of the decomposed components. This step helps remove noise and redundant features that could affect the prediction results. The core of the model combines temporal convolutional networks (TCNs) and bidirectional gated recurrent units (BiGRUs). These deep learning techniques enable the model to extract both spatial features and temporal dependencies from the data. An attention mechanism is added to focus on the most important features,improving the prediction accuracy of the model. Finally,the improved dung beetle optimization (IDBO) algorithm is used to optimize the hyper-parameters of the model. This step further enhances the model performance.Results Simulation experiments were conducted using full-scale data from the Yukun ship. The results show that the proposed prediction model has a root mean square error reduction of about 78.25% and an increase of about 65.63% reliability compared with TCN.Discussion The model outperforms traditional methods in terms of accuracy and stability. This demonstrates its potential for improving the prediction of ship motion an attitude.