In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …

Emerging wireless networks may suffer severe security threats due to the ubiquitous access
of massive wireless devices. Specific emitter identification (SEI) is considered as one of the …

Intelligent fault diagnosis (IFD) plays a significant function in ensuring the reliable operation
of mechanical equipment. However, most existing IFD methods are trained in batch learning …

Chunk-level real-time safety assessment of dynamic systems is a critical component of
industrial processes, which is essential to prevent hazards and reduce the risk of injury or …

This paper presents a novel approach for DC faults diagnosis in renewables based DC-ring
microgrid (DC-RM). The proposed novel approach consists of a second-order derivative …

The random vector functional link (RVFL) neural network has shown the potential to
overcome traditional artificial neural networks' limitations, such as substantial time …

A novel neural network, namely, broad learning system (BLS), has shown impressive
performance on various regression and classification tasks. Nevertheless, most BLS models …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

In an intelligent transportation system, accurate traffic flow prediction can provide significant
help for travel planning. Even though some methods are proposed to do so, they focus on …

Abstract Intelligent Fault Diagnosis (IFD) models are all trained in one-time learning way,
lacking the incremental learning capability for continually incoming samples and newly …