journal article·2025-12

A Dual-regularization Mechanism used for Ultrasound Signal Classification by Acoustic Velocity-guided Dropout and Squeeze-and-excitation Attention

Xingru Wang, Yang Zhao, Yufeng Huang

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OpenAlex topics: Flow Measurement and Analysis · Ultrasonics and Acoustic Wave Propagation · Fault Detection and Control Systems

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References (27)

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1. Cheng, L., Tian, G.Y.: Comparison of nondestructive testing methods on detection of delaminations in composites. J. Sens. 2012(1), 408437 (2012). https://doi.org/10.1155/2012/40843710.1155/2012/408437 ↗
2. Sun, H., Ramuhalli, P., Meyer, R.: An assessment of machine learning applied to ultrasonic nondestructive evaluation. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) (2023)
3. Mendikute, J., Carmona, I., Aizpurua, I., Bediaga, I., Castro, I., Galdos, L., et al.: Defect detection in wind turbine blades applying convolutional neural networks to ultrasonic testing. NDT & E International 154, 103359 (2025). https://doi.org/10.1016/j.ndteint.2025.10335910.1016/j.ndteint.2025.103359 ↗
4. Smagulova, D., Samaitis, V., Jasiuniene, E.: Machine learning based approach for automatic defect detection and classification in adhesive joints. NDT & E International 148, 103221 (2024). https://doi.org/10.1016/j.ndteint.2024.10322110.1016/j.ndteint.2024.103221 ↗
5. Tang, L., Li, Y., Bao, Q., Hu, W., Wang, Q., Su, Z., et al.: Quantitative identification of damage in composite structures using sparse sensor arrays and multi-domain-feature fusion of guided waves. Measurement 208, 112482 (2023). https://doi.org/10.1016/j.measurement.2023.11248210.1016/j.measurement.2023.112482 ↗
6. Xiao, J., Cui, F.: Machine learning enhanced characterization of surface defects using ultrasonic Rayleigh waves. NDT & E International 140, 102969 (2023). https://doi.org/10.1016/j.ndteint.2023.10296910.1016/j.ndteint.2023.102969 ↗
7. Wang, J., Schmitz, M., Jacobs, L.J., Qu, J.: Deep learning-assisted locating and sizing of a coating delamination using ultrasonic guided waves. Ultrasonics 141, 107351 (2024). https://doi.org/10.1016/j.ultras.2024.10735110.1016/j.ultras.2024.107351 ↗
8. Ji, C., Wang, H., Li, H.: Defects detection in weld joints based on visual attention and deep learning. NDT & E International 133, 102764 (2023). https://doi.org/10.1016/j.ndteint.2022.10276410.1016/j.ndteint.2022.102764 ↗
9. Cortes C, Mohri M, Rostamizadeh A.: L2 Regularization for Learning Kernels. (2012)
10. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
11. Prechelt, L.: Automatic early stopping using cross validation: quantifying the criteria. Neural Netw. 11(4), 761–767 (1998). https://doi.org/10.1016/S0893-6080(98)00010-010.1016/S0893-6080(98)00010-0 ↗
12. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds). Proceedings of the 32nd International Conference on Machine Learning. vol. 37 of Proceedings of Machine Learning Research. Lille, France: PMLR, pp. 448–456 (2015)
13. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019). https://doi.org/10.1186/s40537-019-0197-010.1186/s40537-019-0197-0 ↗
14. He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2019)10.1109/CVPR.2019.00065 ↗
15. Ghiasi, G., Lin, T.Y., Le, Q.V.: DropBlock: A regularization method for convolutional networks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc. (2018)
16. Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019). https://doi.org/10.1016/j.jcp.2018.10.04510.1016/j.jcp.2018.10.045 ↗
17. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2018)10.1109/CVPR.2018.00745 ↗
18. Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652–662 (2019)10.1109/TPAMI.2019.2938758 ↗
19. Ji, C., Wang, H., Li, H.: Defects detection in weld joints based on visual attention and deep learning. NDT & E International 133, 102764 (2023). https://doi.org/10.1016/j.ndteint.2022.10276410.1016/j.ndteint.2022.102764 ↗
20. Li, T., Chen, X., Zhu, F., Zhang, Z., Yan, H.: Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection. Neurocomputing 439, 256–270 (2021). https://doi.org/10.1016/j.neucom.2021.01.09710.1016/j.neucom.2021.01.097 ↗
21. Diogo, A.R., Moreira, B., Gouveia, C.A.J., Tavares, J.M.R.S.: A review of signal processing techniques for ultrasonic guided wave testing. Metals 12(6) (2022). https://doi.org/10.3390/met1206093610.3390/met12060936 ↗
22. : COMSOL Multiphysics® v. 6.2. Stockholm, Sweden. Available from: https://www.comsol.com
23. Di, X.: Research on defect identification technology of station process pipeline based on ultrasonic guided waves [in Chinese]. China University of Petroleum (Beijing). In Chinese (2021)
24. Rus, J., Grosse, C.: Local ultrasonic resonance spectroscopy: a demonstration on plate inspection. J. Nondestr. Eval. 39(2), 31 (2020)10.1007/s10921-020-00674-5 ↗
25. Pillarisetti, L.S.S., Lissenden, C.J., Shokouhi, P.: Control of low-frequency guided elastic wave modes in a hollow pipe using a meta-surface. AIP Adv. 12(8), 085027 (2022). https://doi.org/10.1063/5.009857610.1063/5.0098576 ↗
26. Murphy, W.F., III.: Sonic and ultrasonic velocities: theory versus experiment. Geophys. Res. Lett. 12(2), 85–88 (1985)10.1029/GL012i002p00085 ↗
27. Wang, H., Chen, J., Zheng, Y., Zeng, T.: Navigating beyond dropout: an intriguing solution towards generalizable image super resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 25532–25543 (2024)10.1109/CVPR52733.2024.02412 ↗