Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Richard J. Pyle"'
Publikováno v:
Pyle, R, Hughes, R R & Wilcox, P D 2023, ' Interpretable and Explainable Machine Learning for Ultrasonic Defect Sizing ', IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 70, no. 4, pp. 277-290 . https://doi.org/10.1109/TUFFC.2023.3248968
Despite its popularity in literature, there are few examples of machine learning (ML) being used for industrial nondestructive evaluation (NDE) applications. A significant barrier is the ‘black box’ nature of most ML algorithms. This paper aims t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5a459d6c1e32da7fb0536b0ab1f4dcf
https://doi.org/10.1109/tuffc.2023.3248968
https://doi.org/10.1109/tuffc.2023.3248968
Publikováno v:
Pyle, R, Hughes, R R, Ait Si Ali, A & Wilcox, P D 2022, ' Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization ', IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 7, pp. 2339-2351 . https://doi.org/10.1109/TUFFC.2022.3176926
Deep learning for Non-Destructive Evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::099acd381e0d8b6d1dbfba66c714057f
https://research-information.bris.ac.uk/en/publications/76bd081f-e725-4533-a9b0-edcf6b2e9f0e
https://research-information.bris.ac.uk/en/publications/76bd081f-e725-4533-a9b0-edcf6b2e9f0e
Publikováno v:
Pyle, R J, Bevan, R L T, Hughes, R R, Ali, A A S & Wilcox, P D 2022, ' Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data ', IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 4, pp. 1485-1496 . https://doi.org/10.1109/TUFFC.2022.3151397
Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2984e3b947e81de522e5c08c7250a62
https://hdl.handle.net/1983/84a27e1f-8e30-40c9-86ef-21e536aaa631
https://hdl.handle.net/1983/84a27e1f-8e30-40c9-86ef-21e536aaa631
Autor:
Rosen K. Rachev, Paul D. Wilcox, Robert Hughes, Amine Ait Si Ali, Richard J. Pyle, Rhodri L. T. Bevan
Publikováno v:
Pyle, R J, Bevan, R L T, Hughes, R R, Rachev, R K, Ali, A A S & Wilcox, P D 2021, ' Deep Learning for Ultrasonic Crack Characterization in NDE ', IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 68, no. 5, 9298790, pp. 1854-1865 . https://doi.org/10.1109/TUFFC.2020.3045847
Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4e7cee67f1cc79f8978d4c021fdbf24
https://pubmed.ncbi.nlm.nih.gov/33338015
https://pubmed.ncbi.nlm.nih.gov/33338015