Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into
transfer learning (TL), has achieved excellent success in computer vision, text classification …

With the rapid development of the remote sensing monitoring and computer vision
technology, the deep learning method has made a great progress to achieve applications …

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …

Building on the recent progress of four-dimensional (4D) printing to produce dynamic
structures, this study aimed to bring this technology to the next level by introducing control …

Acquiring sufficient fault data labels for new tasks in rotating machinery fault diagnosis is
tricky. Accurately identifying faults in unlabeled scenarios is a critical and urgent practical …

Numerous methods are proposed for the Traffic Signal Control (TSC) tasks aiming to provide
efficient transportation and mitigate congestion waste. In recent, promising results have …

The increasing trend of studying the innate softness of robotic structures and amalgamating
it with the benefits of the extensive developments in the field of embodied intelligence has …

Residential-level short-term load forecasting (STLF) is significant for power system
operation. Data-driven forecasting models, especially machine-learning-based models, are …

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are
not capable of generalizing across different domains (ie, subjects, devices, or datasets) with …

Daily activity recognition between different smart home environments faces some
challenges, such as an insufficient amount of data and differences in data distribution …