Label-Noise-Resistant Time-Series Classification With Self-Supervised Label Correction See

The reliable operation of industrial systems requires not only the prompt detection of faults but also their accurate classification into the appropriate categories. At present, numerous data-driven industrial fault detection and diagnosis models, which have been developed based on historical fau...

Industrial Anomaly Detection Based on Improved Diffusion Model: A Review See

As a class of highly effective generative models, diffusion models have attracted considerable attention in recent years and have been extensively applied to industrial anomaly detection tasks. In this review, a comprehensive discussion is presented on industrial anomaly detection based on i...

Probabilistic vs Deep Generative Models: A Fairness Centred Evaluation of Synthetic Healthcare Tabular Data See

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Flood Susceptibility for Storage Dams Locations to Reduce the Risk of Flash Floods and to Harvest Rainfall Utilizing a <scp>GIS</scp> Spatial Distribution Model and the Analytical Hierarchy Approach See

Flooding is a natural calamity that causes widespread devastation, including severe infrastructure destruction, significant economic consequences, and social disturbances around the world, particularly in the Sinai region. Wadi Ked is one of Sinai, Egypt's, most vulnerable districts to flood hazar...

Learning With Noisy Labels for Industrial Time Series Outlier Detection: A Transformer-Embedded Contrastive Learning Framework See

In many real-world industrial scenarios, acquiring accurately labeled data are often challenging due to limited resources or unexpected errors. Learning with noisy labels (LNL) has emerged as a significant research topic, aiming to develop reliable deep learning models using noisy-labeled trainin...

Anti-eavesdropping set-membership state estimation for networked systems: An encryption–decryption scheme See

In this paper, the secure set-membership state estimation problem is investigated for a class of networked linear systems, where the measurement data might be intercepted by potential eavesdroppers. To protect the privacy of system state from information leakage, an artificial-noise-assisted encr...

Privacy-Preserving Distributed Economic Dispatch of Microgrids Over Directed Networks via State Decomposition: A Fast Consensus Algorithm See

This article is concerned with the privacy-preserving distributed economic dispatch problem of microgrids. The main goal of this work is to develop a privacy-preserving distributed optimization algorithm over directed networks, aiming to achieve supply-demand balance at the lowest economic cost u...

Privacy-Preserving Distributed Energy Management for Battery Energy Storage Systems over Time-Varying Networks See

This article addresses the privacy-preserving energy management problem of battery energy storage systems (BESSs). An autonomous privacy-preserving distributed optimization (APPDO) scheme is developed over time-varying networks with the aim of regulating the power output of local BESS to fulfill the&#...

PID Containment Control for Multi-Agent Systems With Multi-Rate Measurements Under Sensor Resolution Constraints See

This article investigates the proportional-integral-derivative (PID) containment control problem for a class of linear MAS with multirate measurements under the constraint of sensor resolution. The sensors of agents are classified into two distinct groups, characterized by their relatively fast and&#x...