A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network-Reference-Cited by-同舟云学术

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A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network

Published:2022-10-28 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Jiang Hongquan^ORCID,Yang Deyan,Zhi Zelin,Jing Qiangzheng,Gao Jianmin,Tao Chenyue,Cheng Zhixiang

Funder

National Key Research and Development Program of China

Research and Development Project of the TOFD Auxiliary Recognition System for Detecting Weld Defects in Spherical Tank

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-022-02041-9.pdf

Reference50 articles.

1. Abirami, A., Prakash, P., & Thangavel, K. (2018). Fractional diffusion equation-based image denoising model using CN–GL scheme. International Journal of Computer Mathematics, 95(6–7), 1222–1239. https://doi.org/10.1080/00207160.2017.1401707

2. Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2018). GANomaly: Semi-supervised anomaly detection via adversarial training. Asian Conference on Computer Vision (ACCV). https://doi.org/10.1007/978-3-030-20893-6_39

3. Amraee, S., Vafaei, A., Jamshidi, K., & Adibi, P. (2018). Abnormal event detection in crowded scenes using one-class SVM. Signal, Image and Video Processing, 12(6), 1115–1123. https://doi.org/10.1007/s11760-018-1267-z

4. Barari, A., & de Sales Guerra Tsuzuki, M., Cohen, Y., & Macchi, M. (2021). Editorial: Intelligent manufacturing systems towards industry 4.0 era. Journal of Intelligent Manufacturing, 32, 1793–1796. https://doi.org/10.1007/s10845-021-01769-0

5. Barcelo, F., Hierro, P., Llario, F., & Herraiz, J. (2018). Development of an ultrasonic weld inspection system based on image processing and neural networks. Nondestructive Testing and Evaluation, 33(2), 229–236. https://doi.org/10.1080/10589759.2017.1376056

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