Deep learning has made substantial breakthroughs in many fields due to its powerful
automatic representation capabilities. It has been proven that neural architecture design is …

Globally, the external internet is increasingly being connected to industrial control systems.
As a result, there is an immediate need to protect these networks from a variety of threats …

Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …

In recent years, data-driven methods have been widely used in rolling bearing fault
diagnosis with great success, which mainly relies on the same data distribution and massive …

Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing
attention. It is necessary to train a model only on source domain (SD) and directly …

Discrepancy representation metric completely determines the transfer diagnosis
performance of deep domain adaptation methods. Maximum mean discrepancy (MMD) …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

The tremendous success of deep learning and transfer learning broadened the scope of
fault diagnosis, especially the latter further improved the diagnosis accuracy under multiple …

Most domain adaptation (DA) methods in cross-scene hyperspectral image classification
focus on cases where source data (SD) and target data (TD) with the same classes are …

Time series has wide applications in the real world and is known to be difficult to forecast.
Since its statistical properties change over time, its distribution also changes temporally …