A four-part functional decomposition method replicates nonstationary cyclical trends and variance structures across membrane water treatment processes to support autonomous ...
A graph structure inference framework achieved a 96.3 percent detection rate and high discriminative power when identifying anomalies in wind ...
An AI framework combining supervised learning, autoencoder anomaly detection and a multi-agent system detected known and unseen faults in a ...
A new open access study proposes a four-layer framework combining machine learning forecasting, uncertainty-aware optimization, and automated fault redispatch for ...
Two unsupervised machine-learning methods trained on SuperKEKB beam position monitor data matched each other's detection performance and flagged nine unreported ...
A simulation-only proof of concept shows a compact residual-based neural system can distinguish severe faults across a nanosatellite electrical power ...
A new AI framework combining attention-enhanced YOLOv8 and Bald Eagle Optimization achieved 98.94% accuracy in detecting transmission and distribution faults ...
Researchers combined a low-cost IoT monitoring prototype with a Bayesian-optimized XGBoost model that achieved the highest accuracy and fastest computation ...
Researchers have developed a self-supervised deep learning model, SA-S2S, that removes random noise from 3D seismic data without requiring clean ...
A hybrid framework that turns voltage and current measurements into resistance-based statistical features enabled Random Forest to classify electrical faults ...
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