Page 101 - 《应用声学》2023年第3期
P. 101
第 42 卷 第 3 期 刘骁等: 基于深度学习的材料超声回波衰减预测方法 539
[2] 饶志锋. 未来航空发动机材料面临的挑战与发展趋向 [J]. 科 [9] Goodfellow I, Bengio Y, Courville A. Deep learning[M].
研, 2016(4): 32. Massachusetts: MIT Press, 2016.
Rao Zhifeng. Challenges and development trends of future [10] Zhang X, Zhao J, Lecun Y. Character-level convolutional
aero-engine materials[J]. Scientific Research, 2016(4): 32. networks for text classification[C]. Proceedings of the 28th
[3] Willems H, Goebbels K. Ultrasonic attenuation measure- International Conference on Neural Information Process-
ment using backscattering technique[M]. Berlin: Springer, ing Systems, 2015.
1989. [11] Chollet F. Deep learning with Python[M]. Greenwich:
[4] Thompson R B, Margetan F J, Haldipur P, et al. Scatter- Manning Publications, 2021.
ing of elastic waves in simple and complex polycrystals[J]. [12] Cho K, Merrienboer B V, Gulcehre C, et al. Learning
Wave Motion, 2008, 45(5): 655–674. phrase representations using RNN encoder-decoder for
[5] Li J, Rokhlin S I. Characterization of polycrystals by statistical machine translation[C]//Conference on Empir-
ultrasonic attenuation-to-back scattering ratio measure- ical Methods in Natural Language Processing (EMNLP),
ments[J]. The Journal of the Acoustical Society of Amer- 2014.
ica, 2012, 132(3): 1961. [13] Bengio Y. Deep learning of representations: looking
[6] Zhang A, Lipton Z C, Li M, et al. Dive into deep learn- forward[C]//International Conference on Statistical Lan-
ing[J]. arXiv Preprint, arXiv: 2106.11342, 2021. guage and Speech Processing, 2013.
[7] Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit- [14] Sermanet P. A deep learning pipeline for image under-
learn: machine learning in Python[J]. The Journal of Ma- standing and acoustic modeling[D]. New York: New York
chine Learning Research, 2011, 12: 2825–2830. University, 2014.
[8] Paszke A, Gross S, Massa F, et al. Pytorch: an impera- [15] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature,
tive style, high-performance deep learning library[C]. Pro- 2015, 521(7553): 436.
ceedings of the 33rd International Conference on Neural [16] Schmidhuber J. Deep learning in neural networks: an
Information Processing Systems, 2019. overview[J]. Neural Networks, 2015, 61: 85–117.