School of Artificial Intelligence, Peking University, No.5 Yiheyuan Road, Haidian District, Beijing 100871, China.
Sensors (Basel). 2022 Jul 23;22(15):5507. doi: 10.3390/s22155507.
Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios, , using labeled data in the source domain and unlabeled data in the target domain to improve the performance of models in the target domain. This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field.
传感器是输出用于感测物理现象的信号的设备，广泛应用于我们社会生产活动的各个方面。物理参数的连续记录允许对被监测系统的运行状态进行有效分析，并预测未知风险。得益于深度学习的发展，传感器收集的时间信号的分析能力得到了极大的提高。然而，由于存在领域差距，在源域中训练的模型在目标域中的表现并不理想。近年来，许多研究人员使用深度无监督领域自适应技术来解决不同场景下传感器收集的信号之间的领域差距，利用源域中的标记数据和目标域中的未标记数据来提高目标域中模型的性能。本调查首先总结了最近对时间序列传感器数据的无监督领域自适应研究的背景、使用的传感器类型、源域和目标域之间的领域差距以及常用的数据集。然后，根据自适应方式对不同的无监督领域自适应方法进行分类和比较，并根据源域和目标域的数量总结不同的自适应设置。最后，本调查讨论了当前研究的挑战，并对未来的工作进行了展望。本调查系统地回顾和总结了最近关于时间序列传感器数据的无监督领域自适应的研究，为读者提供了对该领域的系统理解。
