Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder-Reference-Cited by-同舟云学术

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Performance enhancement of convolutional neural network for ultrasonic flaw classification by adopting autoencoder

Published:2020-04 Issue: Volume:111 Page:102218
ISSN:0963-8695
Container-title:NDT & E International
language:en
Short-container-title:NDT & E International

Author:

Munir Nauman,Park Jinhyun,Kim Hak-Joon,Song Sung-Jin,Kang Sung-Sik

Funder

Nuclear Safety Research Program of the Korea Foundation of Nuclear Safety

Nuclear Safety and Security Commission

Publisher

Elsevier BV

Subject

Mechanical Engineering,Condensed Matter Physics,General Materials Science

Reference40 articles.

1. Frequency invariant classification of ultrasonic weld inspection signals;Polikar;IEEE Trans Ultrason Ferroelectr Freq Control,1998

2. Minimum counterbore length and taper angle criteria for transition welds;Dekhane,2016

3. Ultrasonic flaw classification in weldments using probabilistic neural networks;Song;J Nondestruct Eval,1992

4. Neural network classification of flaws detected by ultrasonic means;Masnata;NDT E Int,1996

5. The use of neural networks in ultrasonic flaw detection;Margrave;Measurement,1999

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