This paper reviews the latest development of fault diagnosis techniques of networked
systems. Considering various challenges raised by unreliable local sensors, signal …

Machine learning covers a large group of computational methods built upon past data and
experience. This chapter serves as a gentle introduction to machine learning fundamentals …

An early diagnosis of faults prevents financial loss and downtimes in the industry. In this
paper the authors presented the early fault diagnostic technique of stator faults of the single …

The performance of logistics highly depends on the time efficiency, and hence, plenty of
efforts have been devoted to ensuring the on-time delivery in modern logistics industry …

Clinical curative effect is the core value and fundamental pursuit of medicine. The data-
driven clinical quantitative evaluation model is an important research issue in clinical …

Effective fault diagnosis methods for dynamic systems are crucial in various industrial
applications. As systems become increasingly complex, traditional diagnostic frameworks …

Background: Through the analysis of the relevant data of industrial equipment, faults
diagnosis is helpful for system maintenance and reducing economic losses. Objective: This …

In this paper, we present an efficient equalizer based on random forest for channel
equalization in optical fiber communication systems. The results are experimentally …

Unknown environmental noise and varying operation conditions negatively affect gear fault
diagnosis (GFD) performance. In this paper, the sample/feature hybrid transfer learning (TL) …

The increasing complexity and high cost of integrated systems has placed immense
pressure on root-cause analysis and diagnosis. In light of artificial intelligent and machine …