Author

Marios Impraimakis

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#machine learning Open access Oct 2026

Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft

The signal generation capabilities of a novel data augmentation framework are examined in aerospace structures. First, the framework learns class-conditional temporal and spectral characteristics of the measured structural responses. It uses conditional generative adversarial network training and Monte Carlo dropout un...

Alvaro Peiro, Marios Impraimakis · Aerospace Science and Techno... · 0 citations

#artificial intelligence Review Open access Mar 2026

YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception

The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here, and it offers a powerful tool for transparent and practical perception component for autonomous and multimodal artificial intelligence applications.

Marios Impraimakis, Daniel Vazquez, Fei Zhou · Scientific Reports · 0 citations