PSI - Issue 37
Claudia Barile et al. / Procedia Structural Integrity 37 (2022) 307–313 Author name / Structural Integrity Procedia 00 (2019) 000 – 000
313
7
References
Vary, A., 1988. The acousto-ultrasonic approach. Acousto-ultrasonics, pp.1-21. Barile, C., Casavola, C., Pappalettera, G. and Vimalathithan, P.K., 2019. Acousto-ultrasonic evaluation of interlaminar strength on CFRP laminates. Composite Structures, 208, pp.796-805. Meng, M., Chua, Y.J., Wouterson, E. and Ong, C.P.K., 2017. Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks. Neurocomputing, 257, pp.128-135. Nasiri, A., Bao, J., Mccleeary, D., Louis, S.Y.M., Huang, X. and Hu, J., 2019. Online Damage Monitoring of SiC f-SiC m Composite Materials Using Acoustic Emission and Deep Learning. IEEE Access, 7, pp.140534-140541. Lu, L., Wang, X., Carneiro, G. and Yang, L. eds., 2019. Deep learning and convolutional neural networks for medical imaging and clinical informatics . Springer International Publishing. Meng, H., Yan, T., Yuan, F. and Wei, H., 2019. Speech emotion recognition from 3D log-mel spectrograms with deep learning network. IEEE access , 7 , pp.125868-125881. Chuang, W.Y., Tsai, Y.L. and Wang, L.H., 2019, March. Leak detection in water distribution pipes based on CNN with mel frequency cepstral coefficients. In Proceedings of the 2019 3rd International Conference on Innovation in Artificial Intelligence (pp. 83-86).
Made with FlippingBook Ebook Creator