Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction  

作　　者：SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang 

机构地区：[1]Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Shandong Computer Science Center(National Supercomputer Center in Jinan),Qilu University of Technology(Shandong Academy of Sciences),Jinan 250000,China [2]Shandong Provincial Key Laboratory of Computer Networks,Shandong Fundamental Research Center for Computer Science,Jinan 250000,China

出　　处：《Journal of Ocean University of China》2025年第2期377-386,共10页中国海洋大学学报(英文版)

基　　金：supported by the Major Innovation Project for the Integration of Science,Education,and Industry of Qilu University of Technology(Shandong Academy of Sciences)(Nos.2023HYZX01,2023JBZ02);the Open Project of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)(No.2023ZD007);the Talent Research Projects of Qilu University of Technology(Shandong Academy of Sciences)(No.2023RCKY136);the Technology and Innovation Major Project of the Ministry of Science and Technology of China(No.2022ZD0118600);the Jinan‘20 New Colleges and Universities’Funded Project(No.202333043)。

摘　　要：Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关 键 词：time series prediction wind speed correction comparative learning shipborne sensor 

分 类 号：U675.7[交通运输工程—船舶及航道工程]