Frontiers

Graph-Guided Neural Networks: Revolutionizing Anomaly Detection in Chemical Engineering

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...
IEEE

Short and Medium-Term Power Load Anomaly Detection Method Based on Convolutional Neural Network and EL-DCC

Abstract: Power load anomaly detection is critical to grid stability and security. With the increasing complexity of load patterns, it is difficult for traditional methods to meet the requirements of ...
IEEE

Knowledge Distillation-Based Anomaly Detection via Adaptive Discrepancy Optimization

Abstract: Knowledge distillation has emerged as a primary solution for anomaly detection, leveraging feature discrepancies between teacher–student (T–S) networks to locate anomalies. However, previous ...