{"title":"将多模态对比学习与原型领域对齐相结合,实现时间序列的无监督领域适应性调整","authors":"","doi":"10.1016/j.engappai.2024.109205","DOIUrl":null,"url":null,"abstract":"<div><p>Unsupervised domain adaptation (UDA) addresses the challenge of transferring knowledge from a labeled source domain to an unlabeled target domain. This task is particularly critical for time series data, characterized by unique temporal dynamics. However, existing methods often fail to capture these temporal dependencies, leading to domain discrepancies and loss of semantic information. In this study, we propose a novel framework for the unsupervised domain adaptation of time series (UDATS) that integrates Multimodal Contrastive Adaptation (MCA) and Prototypical Domain Alignment (PDA). MCA leverages image encoding techniques and prompt learning to capture complex temporal patterns while preserving semantic information. PDA constructs multimodal prototypes, combining visual and textual features to align target domain samples accurately. Our framework demonstrates superior performance across various application domains, including human activity recognition, mortality prediction, and fault detection. Experiments show our method effectively addresses domain discrepancies while preserving essential semantic content, outperforming state-of-the-art models.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating multimodal contrastive learning with prototypical domain alignment for unsupervised domain adaptation of time series\",\"authors\":\"\",\"doi\":\"10.1016/j.engappai.2024.109205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Unsupervised domain adaptation (UDA) addresses the challenge of transferring knowledge from a labeled source domain to an unlabeled target domain. This task is particularly critical for time series data, characterized by unique temporal dynamics. However, existing methods often fail to capture these temporal dependencies, leading to domain discrepancies and loss of semantic information. In this study, we propose a novel framework for the unsupervised domain adaptation of time series (UDATS) that integrates Multimodal Contrastive Adaptation (MCA) and Prototypical Domain Alignment (PDA). MCA leverages image encoding techniques and prompt learning to capture complex temporal patterns while preserving semantic information. PDA constructs multimodal prototypes, combining visual and textual features to align target domain samples accurately. Our framework demonstrates superior performance across various application domains, including human activity recognition, mortality prediction, and fault detection. Experiments show our method effectively addresses domain discrepancies while preserving essential semantic content, outperforming state-of-the-art models.</p></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-08-31\",\"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/S0952197624013630\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624013630","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}
Integrating multimodal contrastive learning with prototypical domain alignment for unsupervised domain adaptation of time series
Unsupervised domain adaptation (UDA) addresses the challenge of transferring knowledge from a labeled source domain to an unlabeled target domain. This task is particularly critical for time series data, characterized by unique temporal dynamics. However, existing methods often fail to capture these temporal dependencies, leading to domain discrepancies and loss of semantic information. In this study, we propose a novel framework for the unsupervised domain adaptation of time series (UDATS) that integrates Multimodal Contrastive Adaptation (MCA) and Prototypical Domain Alignment (PDA). MCA leverages image encoding techniques and prompt learning to capture complex temporal patterns while preserving semantic information. PDA constructs multimodal prototypes, combining visual and textual features to align target domain samples accurately. Our framework demonstrates superior performance across various application domains, including human activity recognition, mortality prediction, and fault detection. Experiments show our method effectively addresses domain discrepancies while preserving essential semantic content, outperforming state-of-the-art models.
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.