Forest fires are the main cause of desertification, and they have a disastrous impact on
agricultural and forest ecosystems. Modern fire detection and warning systems rely on …

Bearing fault diagnosis is important to ensure safe operation and reduce loss for most
rotating machinery. In recent years, deep learning (DL) has been widely used for bearing …

Maintenance of mechanical elements is considered to be the most crucial task in industry
and a® ects the economics, predominantly. There are many techniques available for fault …

A bearing is a key component in rotating machinery. The prompt monitoring of a bearings'
condition is critical for the reduction of mechanical accidents. With the rapid development of …

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised
defect in a REB typically results in periodic impulses in vibration signals at bearing …

This paper proposes a novel approach to predicting the useful life of rotating machinery and
making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural …

This article presents a robust visible light localization (VLL) technique for wireless sensor
networks, with 2-D indoor positioning (IP) capabilities, based on embedded machine …

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on
time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention …

In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-
based accelerometer sensors is nowadays increasingly widespread. Such sensors allow …

Anomaly detection problems in industrial control systems (ICSs) are always tackled by a
network traffic monitoring scheme. However, traffic-based anomaly detection systems may …