IEEE

SSDCL: Semi-Supervised Denoising-Aware Contrastive Learning for Time Series Anomaly Detection in Cyber-Physical Systems

Abstract: Time series anomaly detection is crucial for improving the security and reliability of Cyber-Physical systems (CPS). While significant progress has been made, existing methods struggle to ...

Improved short-term sea level change predictions achieved with better AI training

Sea level can temporarily change for a variety of reasons—atmospheric pressure shifts and water accumulation from wind and ...
Tech Xplore

Novel framework for unsupervised point cloud anomaly localization developed

The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for ...
IEEE

Deep Learning for Anomaly Detection in Environmental Monitoring

Abstract: Anomaly detection in environmental datasets is critical for identifying unusual patterns that could signal potential hazards or changes in environmental conditions. Traditional methods often ...