Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data
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 nondestructive evaluation (NDE) applications. How...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control. - 1986. - 69(2022), 4 vom: 01. Apr., Seite 1485-1496
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| 1. Verfasser: |
Pyle, Richard J
(VerfasserIn) |
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| Weitere Verfasser: |
Bevan, Rhodri L T,
Hughes, Robert R,
Ali, Amine Ait Si,
Wilcox, Paul D |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control
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| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
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