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Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device
Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units (GPUs), various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e. g., temperature) and types of damages.
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