Research on a Real-Time motion forecasting model for marine cargo elevators based on EMD-KPCA-LSTM

Research on a Real-Time motion forecasting model for marine cargo elevators based on EMD-KPCA-LSTM | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on a Real-Time motion forecasting model for marine cargo elevators based on EMD-KPCA-LSTM Yuquan Yan, Hailan Liu, Haoyu Song, Yu Chen, Yuming Du, Feihong Yun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8194831/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Currently, traditional single Long Short-Term Memory(LSTM) models exhibit reduced prediction accuracy and increased errors when handling nonlinear data influenced by complex maritime environments, particularly during periods of short-term, intense fluctuations in data points. To address these challenges, a novel EMD-KPCA-LSTM composite model is proposed. This model integrates Empirical Mode Decomposition (EMD) and Kernel Principal Component Analysis (KPCA) to optimize LSTM performance, enabling real-time motion prediction for marine cargo elevators during operational navigation conditions. Simulation results indicate that compared to the single LSTM model, the composite model exhibits significantly reduced prediction errors relative to actual values. Based on the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) evaluation metrics, the errors decreased by 29.3% and 38.2%, respectively. The EMD-KPCA-LSTM model significantly enhances the prediction accuracy and stability of time-series data in complex marine environments, effectively overcoming the sensitivity of single LSTM model to short-term sharp fluctuations. This model provides a reliable solution for processing high-noise, nonlinear marine environmental data and holds significant engineering application value in the field of marine equipment condition monitoring and fault early warning. Characteristic forecast Real-time forecast Empirical Modal Decomposition Kernel Principal Component Analysis Long Short-Term Memory Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8194831","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557315242,"identity":"8e9600c3-f50e-4ed2-ad5b-644b72eb63fc","order_by":0,"name":"Yuquan Yan","email":"","orcid":"","institution":"China Shipbuilding Industry Corporation (China)","correspondingAuthor":false,"prefix":"","firstName":"Yuquan","middleName":"","lastName":"Yan","suffix":""},{"id":557315243,"identity":"40b5934c-f83e-4a61-8eb7-e289f1ab3198","order_by":1,"name":"Hailan 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