Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

Operating conditions reflect the mission evolution of rotating machinery in specific
application scenarios. The monitoring data under different operating conditions exhibit …

Federated learning (FL) has attracted increasing attention to building models without
accessing raw user data, especially in healthcare. In real applications, different federations …

Domain generalization (DG) aims to learn transferable knowledge from multiple source
domains and generalize it to the unseen target domain. To achieve such expectation, the …

Machinery fault diagnosis based on deep learning methods is cost-effective to guarantee
safety and reliability of mechanical systems. Due to the variability of machinery working …

Domain generalization (DG) approaches typically use the hypothesis learned on source
domains for inference on the unseen target domain. However, such a hypothesis can be …

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs),
various spatial filtering methods based on individual calibration data have been proposed to …

The study of transfer learning in rotating equipment fault diagnosis helps overcome the
problem of low sample marker data and accelerates the practical application of diagnostic …

This research addresses the challenge of generalizing deep learning models for different
CFRP composite structures in the task of fatigue damage detection. To overcome this …

Abstract Domain Generalization (DG), designed to enhance out-of-distribution (OOD)
generalization, is all about learning invariance against domain shifts utilizing sufficient …