7 citations · 32 across the 18 of their papers we have counts for
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cs.CV2023★ 1 cited
An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators
Laya Das, Blazhe Gjorgiev, Giovanni Sansavini
Inspection of insulators is important to ensure reliable operation of the power system. Deep learning is being increasingly exploited to automate the inspection process by leveragi…
cs.LG2023★ 3 cited
Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines
Laya Das, Blazhe Gjorgiev, Giovanni Sansavini
Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data…