Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …

This survey explores applications of explainable artificial intelligence in manufacturing and
industrial cyber–physical systems. As technological advancements continue to integrate …

This paper proposes a novel adversarial scheme for learning from data under harsh
learning conditions of partially labelled samples and skewed class distributions. This novel …

Bearing fault diagnosis in real-world applications has challenges such as insufficient
labeled data, changing working conditions of the rotary machinery, and missing data due to …

In recent years, with the advent of mature machine learning products like ChatGPT, Stable
Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid …

Domain adaptation technologies have been extensively explored and successfully applied
to machine fault diagnosis, aiming to address problems that target data are unlabeled and …

This paper investigates the attack isolation (AI) and attack location (AL) problems for a cyber-
physical system (CPS) based on the combination of the H-infinity observer and the zonotope …

The work described in this paper aims to detect and eliminate cyber-attacks in smart grids
that disrupt the process of dynamic state estimation. This work makes use of an …

Abstract In recent years, Generative Adversarial Network (GAN) and its variants have gained
great popularity in both academia and industry. In this chapter, we survey different state-of …

For over a century, induction furnaces have been used in the core of foundries for metal
melting and heating. They provide high melting/heating rates with optimal efficiency. The …