Dr Rich Pyle
Meng, PhD
Current positions
Research Associate
School of Electrical, Electronic and Mechanical Engineering
Contact
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Research interests
NDE, Ultrasonics, Deep Learning, Domain Adaptation, Data Compression
Publications
Recent publications
24/02/2023Interpretable and Explainable Machine Learning for Ultrasonic Defect Sizing
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Potential and limitations of NARX for defect detection in guided wave signals
STRUCTURAL HEALTH MONITORING
Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Deep Learning for Ultrasonic Crack Characterization in NDE
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Thesis
Application of machine learning to ultrasonic nondestructive evaluation
Supervisors
Award date
24/01/2023
Related people
Professor Anthony Croxford
Professor of Ultrasonics and Dynamics
School of Electrical, Electronic and Mechanical Engineering
Professor Paul Wilcox
Professor of Dynamics
School of Electrical, Electronic and Mechanical Engineering
Dr Robert Hughes
Senior Lecturer
School of Electrical, Electronic and Mechanical Engineering