Rolling bearings are the core transmission components of large-scale equipment. Once a
fault occurs, the consequences may be catastrophic, posing a serious threat to life and the …

Abstract Machine learning models are the backbone of smart grid optimization, but their
effectiveness hinges on access to vast amounts of training data. However, smart grids face …

This paper focuses on the problem of computation offloading in a high-mobility Internet of
Vehicles (IoVs) environment. The goal is to address the challenges related to latency …

Pattern recognition systems always misclassify anomalies, which can be dangerous for
uninformed users. Therefore, anomalies must be filtered out from each classification. The …

In this work, we propose a disruptively frugal LiDAR perception dataflow that generates
rather than senses parts of the environment that are either predictable based on the …

This study introduces a novel methodology for fault detection and cause identification within
the Tennessee Eastman Process (TEP) by integrating a Bidirectional Long Short-Term …

Bearing fault detection is a critical task in predictive maintenance, where accurate and timely
fault identification can prevent costly downtime and equipment damage. Traditional attention …

This paper addresses the challenging problem of energy-efficient and uncertainty-aware
pose estimation in insect-scale drones, which is crucial for tasks such as surveillance in …