Page 27 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)

Van Vy and Hyungchul Yoon 11

8. S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural
network," in International Conference on Engineering and Technology (ICET), Antalya,
Turkey, 2017.
9. M. Barbosh, K. Dunphy, A. Sadhu, "Acoustic emission-based damage localization using
wavelet-assisted deep learning," Journal of Infrastructure Preservation and Resilience,
2022.
10. S. Sikdar, D. Liu, A. Kundu, "Acoustic emission data based deep learning approach for
classification and detection of damage-sources in a composite panel," Composites Part B:
Engineering, vol. 228, 2022.
11. D. Perfetto, A.D. Luca, M. Perfetto, G. Lamanna, and F. Caputo, "Damage Detection in
Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite
Element Method," Materials, 2021.
12. Y. Shi, Q. Ruan, "Continuous wavelet transforms," in Proceedings 7th International
Conference on Signal Processing, Beijing, China, 2004.
13. E. H. Bouchikhi, V. Choqueuse, M. E. H. Benbouzid, J. F. Charpentier, and G. Barakat,
"A Comparative Study of Time-frequency Representations for Fault Detection in Wind
Turbine," in IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics
Society, Melbourne, 2011.
14. Wavelet Analysis and Its Applications (vol. 8), Academic Press, 1998, pp. 167-190.
15. M. Sause, "Investigation of Pencil-Lead Breaks as Acoustic Emission Sources," Journal of
Acoustic Emission, vol. 29, pp. 184-196, 2011.
16. H. Chen, Z. Yang, "Arrival Picking of Acoustic Emission Signals Using a Hybrid
Algorithm Based on AIC and Histogram Distance," IEEE Transactions On Instrumentation
And Measurement, vol. 70, 2021.
17. Jierula, S. Wang, T.M. Oh, P. Wang, "Study on Accuracy Metrics for Evaluating the
Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with
Acoustic Emission Data," Applied Sciences, 2021.

ISBN: 978-604-80-8083-9 CITA 2023