[1] [1] Fan Xinguang. Study of fatigue crack initiation propagation of railroad wheel under rolling contact[D]. Beijing: Beijing Jiaotong University, 2019. (in Chinese)

[2] Ke Gong, Ming Wu, Fei Xie, et al. Effect of dry/wet ratio and pH on the stress corrosion cracking behavior of rusted X100 steel in an alternating dry/wet environment. Construction and Building Materials, 270, 124826(2020).

[3] Zhixin Zhou. Overview of NDT methods for mechanical cracks. Mechanical and Electrical Engineering, 34, 1138-1143(2017).

[4] Changming Tang, Jianfeng Zhong, Shuncong Zhong, et al. Ultrasound infrared thermography defect recognition based on improved adaptive genetic algorithm with two-dimensional maximum entropy. Infrared Technoloy, 42, 801-808(2020).

[5] Longxin Ji, Fuzhou Feng, Qingxu Min. Ultrasonic infrared thermal image processing based on wavelet transform. Journal of Changchun University of Science and Technology (Natural Science Edition), 43, 112-116, 128(2020).

[6] Yunze He, Baoyuan Deng, Hongjin Wang, et al. Infrared machine vision and infrared thermography with deep learning: A review. Infrared Physics & Technology, 116, 103754(2021).

[7] Ying Chang, Dajun Chang. Research on solder joint defect recognition algorithm based on improved convolutional neural network. Laser Technology, 44, 779-783(2020).

[8] Bingji Liu, Bangshu Xiong, Qiaofeng Ou, et al. Fault diagnosis of rolling bearing based on time-frequency representations and CNN. Journal of Nanchang Hangkong University (Natural Science Edition), 32, 86-91(2018).

[9] J Renshaw, J C Chen, S D Holland, et al. The sources of heat generation in vibrothermography. NDT and E International, 44, 736-739(2011).

[10] Qingxu Min, Junzhen Zhu, Fuzhou Feng, et al. Study on optimization method of test conditions for fatigue crack detection using lock-in vibrothermography. Infrared Physics and Technology, 83, 17-23(2017).

[11] Feiyan Zhou, Linpeng Jin, Jun Dong. Review of convolutional neural network. Chinese Journal of Computers, 40, 1229-1251(2017).

[12] [12] Kang Chaomeng. Cloud detection in domestic highresolution remote sensing image based deep neural wks[D]. Xi ''an: University of Chinese Academy of Sciences (Xi ''an Institute of Optics & Precision Mechanics, Chinese Academy of Sciences), 2018. (in Chinese)

[13] Anan Zhang, Jinying Huang, Shuwei Ji, et al. Bearin fault pattern recognition based on image classification with CNN. Vibration and Impact, 39, 165-171(2020).

[14] Fuzhou Feng, Chaosheng Zhang, Aibin Song, et al. Probability of detection model for fatigue crack in ultrasonicinfrared imaging. Infrared and Laser Engineering, 45, 0304005(2016).

[15] Shan Xue, Zhen Zhang, Qiongying Lv, et al. Image recognition method of anti UAV system based on convolutional neural network. Infrared and Laser Engineering, 49, 20200154(2020).

[16] [16] Zhang Xiangxiang. Reserach on convolutional code decoders based on deep learning under crelated noise[D]. Beijing: Beijing University of Posts Telecommunications, 2019. (in Chinese)

[17] Yunxia Wu, Yimin Tian. A coal-rock recognition method based on max-pooling sparse coding. Chinese Journal of Engineering, 39, 981-987(2017).

[18] Jinyang Jiao, Ming Zhao, Jing Lin, et al. A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes. Knowledge-Based Systems, 160, 237-250(2018).

[19] Wenbo Zhu, Z T Webb, Kaitian Mao, et al. A deep learning approach for process data visualization using t-distributed stochastic neighbor embedding. Industrial & Engineering Chemistry Research, 58, 9564-9575(2019).

[20] [20] Krizhevsky A, Sutskever I, Hinton G E. Image classification with deep convolutional neural wks [C]International Conference on Neural Infmation Processing Systems, 2012: 11061114.

[21] [21] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions [C]2015 IEEE Conference on Computer Vision Pattern Recognition (CVPR). IEEE, 2015: 18.

[22] Li Liu, Liujie Sun, Wenju Wang. Classification of fluorescent images in high-throughput dPRC gene chips based on SVM. Packaging Engineering, 41, 223-229(2020).