Automatic Quantification of Subsurface Defects by Analyzing Laser Ultrasonic Signals Using Convolutional Neural Networks and Wavelet Transform
The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining conv...
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Détails bibliographiques
| Publié dans: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control. - 1986. - 68(2021), 10 vom: 09. Okt., Seite 3216-3225
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| Auteur principal: |
Guo, Shifeng
(Auteur) |
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| Autres auteurs: |
Feng, Haowen,
Feng, Wei,
Lv, Gaolong,
Chen, Dan,
Liu, Yanjun,
Wu, Xinyu |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2021
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| Accès à la collection: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control
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| Sujets: | Journal Article
Research Support, Non-U.S. Gov't |
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