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第 41 卷 第 2 期                高子洋等: 卷积神经网络的缺陷类型识别分析                                           309


                 trasonics NDE[J]. NDT & E International, 2002, 35(8):  [16] Khumaidi A, Yuniarno E M, Purnomo M H. Welding de-
                 567–572.                                          fect classification based on convolution neural network
              [8] Veiga J L B C, Carvalho A A D, de Silva I C, et al.  (CNN) and Gaussian kernel[C]. IEEE, 2017: 261–265.
                 The use of artificial neural network in the classification of  [17] Munir N, Kim H J, Song S J, et al. Investigation of deep
                 pulse-echo and TOFD ultra-sonic signals[J]. Journal of the  neural network with drop out for ultrasonic flaw classifi-
                 Brazilian Society of Mechanical Sciences and Engineering,  cation in weldments[J]. Journal of Mechanical Science and
                 2005, 27(4): 394–398.                             Technology, 2018, 32(7): 3073–3080.
              [9] Sambath S, Nagaraj P, Selvakumar N. Automatic de-  [18] Munir N, Kim H J, Park J, et al. Convolutional neu-
                 fect classification in ultrasonic NDT using artificial in-
                                                                   ral network for ultrasonic weldment flaw classification in
                 telligence[J]. Journal of Nondestructive Evaluation, 2011,
                                                                   noisy conditions[J]. Ultrasonics, 2019, 94: 74–81.
                 30(1): 20–28.
                                                                [19] 张重远, 岳浩天, 王博闻, 等. 基于相似矩阵盲源分离与卷积
             [10] Filho E F S, Silva M M, Farias P C M A, et al. Flexi-
                                                                   神经网络的局部放电超声信号深度学习模式识别方法 [J]. 电
                 ble decision support system for ultrasound evaluation of
                                                                   网技术, 2019, 43(6): 1900–1907.
                 fiber–metal laminates implemented in a DSP[J]. NDT &
                                                                   Zhang Chongyuan, Yue Haotian, Wang Bowen, et al.
                 E International, 2016, 79: 38–45.
                                                                   Pattern recognition of partial discharge ultrasonic signal
             [11] Cruz F C, Simas Filho E F, Albuquerque M C S, et al. Ef-
                                                                   based on similar matrix bss and deep learning CNN[J].
                 ficient feature selection for neural network based detection
                                                                   Power System Technology, 2019, 43(6): 1900–1907.
                 of flaws in steel welded joints using ultrasound testing[J].
                                                                [20] Munir N, Park J, Kim H J, et al. Performance enhance-
                 Ultrasonics, 2017, 73: 1–8.
                                                                   ment of convolutional neural network for ultrasonic flaw
             [12] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learn-
                                                                   classification by adopting autoencoder[J]. NDT & E In-
                 ing applied to document recognition[J]. Proceedings of the
                                                                   ternational, 2020, 111: 102218.
                 IEEE, 1998, 86: 2278–2324.
                                                                [21] Nair V, Hinton G E. Rectified linear units improve
             [13] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classi-
                                                                   restricted boltzmann machines[C]. Proceeding, Twenty-
                 fication with deep convolutional neural networks[J]. Com-
                 munications of The ACM 2017, 60(6): 84–90.        Seventh International Conference on Machine Learning
             [14] 施成龙, 师芳芳, 张碧星. 利用深度神经网络和小波包变换进                   (ICML 2010), Haifa, Israel, 2010: 807–814.
                 行缺陷类型分析 [J]. 声学学报, 2016, 41(4): 499–506.       [22] Maas A L, Hannun A Y, Ng A Y. Rectifier nonlineari-
                 Shi Chenglong, Shi Fangfang, Zhang Bixing. Analysis on  ties improve neural network acoustic models[C]. Atlanta,
                 defect classification by deep neural networks and wavelet  Georgia, USA: Proceedings of the 30 th  International Con-
                 packet transform[J]. Acta Acustica, 2016, 41(4): 499–506.  ference on Machine Learning, 2013.
             [15] Meng M, Chua Y J, Wouterson E, et al. Ultrasonic signal  [23] Santurkar S, Tsipras D, Ilyas A, et al. How does batch
                 classification and imaging system for composite materials  normalization help optimization?[C]. Montréal, Canada:
                 via deep convolutional neural networks[J]. Neurocomput-  32nd Conference on Neural Information Processing Sys-
                 ing, 2017, 257: 128–135.                          tems(NeurIPS), 2018.
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