RESUMEN
Fruit quality attributes interrelate with their dielectric properties, but such interrelationships in sea buckthorn berries under differing freezing regimes remain uninvestigated. Sea buckthorn (Hipophae rhamnoides L., cv. Shenqiuhong) berries were frozen at different temperatures (-13, -30, -35 and -40 °C) and stored for different periods (15, 30, 45, 60, 75 and 90 d). Seven quality attributes and nine dielectric parameters were measured to evaluate the effect of different frozen storage regimes on those attributes and parameters. The results showed that shorter time and lower temperature contributed to the preservation of berries quality. The dielectric parameters values increased with decreasing temperature and with the increase of freezing duration. The quality prediction models were established by the principal component analysis of the dielectric properties at characteristic frequency. The results are expected to provide a way to evaluate quality of frozen sea buckthorn berries by dielectric properties.
RESUMEN
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, i.e., 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.