Wenhe Shen , Xinjue Hu , Jialun Liu , Shijie Li , Hongdong Wang
{"title":"A pre-trained multi-step prediction informer for ship motion prediction with a mechanism-data dual-driven framework","authors":"Wenhe Shen , Xinjue Hu , Jialun Liu , Shijie Li , Hongdong Wang","doi":"10.1016/j.engappai.2024.109523","DOIUrl":null,"url":null,"abstract":"<div><div>The advancement of autonomous maritime surface ships has increased the need for accurate and rapid multi-step prediction of ship motion for decision-making, motion planning, and real-time control tasks. This paper proposes a multi-step prediction method based on Informer with a pre-trained strategy to achieve accurate and fast motion prediction for ships, which substitutes generative inference for rolling prediction to avoid the cumulative error caused by the increasing time horizon. Due to the difference in temporal features from long-term control actions and short-term state sequences, heterogeneous inputs of encoder and decoder are designed to respectively capture their information without information redundancy. To address the bottleneck between the high cost of real data acquisition and the high demand for deep learning methods for data, we propose a mechanism-data dual-driven framework. This framework utilizes a prior mechanism model to generate virtual data incorporating a range of excitation signals designed in accordance with the results of free-running model tests. To reduce the need for real data and increase interpretability, the improved Informer is pre-trained by virtual data from the mechanism model before being trained by real data. Our experiments for multi-step ship motion prediction demonstrate that the proposed method respectively reduces the error and time to 41.36% and 13.20% on average compared to state-of-the-art and classical methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624016816","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The advancement of autonomous maritime surface ships has increased the need for accurate and rapid multi-step prediction of ship motion for decision-making, motion planning, and real-time control tasks. This paper proposes a multi-step prediction method based on Informer with a pre-trained strategy to achieve accurate and fast motion prediction for ships, which substitutes generative inference for rolling prediction to avoid the cumulative error caused by the increasing time horizon. Due to the difference in temporal features from long-term control actions and short-term state sequences, heterogeneous inputs of encoder and decoder are designed to respectively capture their information without information redundancy. To address the bottleneck between the high cost of real data acquisition and the high demand for deep learning methods for data, we propose a mechanism-data dual-driven framework. This framework utilizes a prior mechanism model to generate virtual data incorporating a range of excitation signals designed in accordance with the results of free-running model tests. To reduce the need for real data and increase interpretability, the improved Informer is pre-trained by virtual data from the mechanism model before being trained by real data. Our experiments for multi-step ship motion prediction demonstrate that the proposed method respectively reduces the error and time to 41.36% and 13.20% on average compared to state-of-the-art and classical methods.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.