[1] Fan Xinguang. Study of fatigue crack initiation and propagation of railroad wheel under rolling contact[D]. Beijing: Beijing Jiaotong University, 2019. (in Chinese)
[2] Gong Ke, Wu Ming, Xie Fei, 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 [J]. Construction and Building Materials, 2020, 270: 124826.
[3] Zhou Zhixin. Overview of NDT methods for mechanical cracks [J]. Mechanical and Electrical Engineering, 2017, 34(10): 1138-1143. (in Chinese)
[4] Tang Changming, Zhong Jianfeng, Zhong Shuncong, et al. Ultrasound infrared thermography defect recognition based on improved adaptive genetic algorithm with two-dimensional maximum entropy [J]. Infrared Technoloy, 2020, 42(8): 801-808. (in Chinese) doi:  10.3724/SP.J.7102614865
[5] Ji Longxin, Feng Fuzhou, Min Qingxu. Ultrasonic infrared thermal image processing based on wavelet transform [J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2020, 43(4): 112-116, 128. (in Chinese)
[6] He Yunze, Deng Baoyuan, Wang Hongjin, et al. Infrared machine vision and infrared thermography with deep learning: A review [J]. Infrared Physics & Technology, 2021, 116: 103754.
[7] Chang Ying, Chang Dajun. Research on solder joint defect recognition algorithm based on improved convolutional neural network [J]. Laser Technology, 2020, 44(6): 779-783. (in Chinese)
[8] Liu Bingji, Xiong Bangshu, Ou Qiaofeng, et al. Fault diagnosis of rolling bearing based on time-frequency representations and CNN [J]. Journal of Nanchang Hangkong University (Natural Science Edition), 2018, 32(2): 86-91. (in Chinese)
[9] Renshaw J, Chen J C, Holland S D, et al. The sources of heat generation in vibrothermography [J]. NDT and E International, 2011, 44(8): 736-739.
[10] Min Qingxu, Zhu Junzhen, Feng Fuzhou, et al. Study on optimization method of test conditions for fatigue crack detection using lock-in vibrothermography [J]. Infrared Physics and Technology, 2017, 83: 17-23.
[11] Zhou Feiyan, Jin Linpeng, Dong Jun. Review of convolutional neural network [J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251. (in Chinese)
[12] Kang Chaomeng. Cloud detection in domestic high-resolution remote sensing image based deep neural networks[D]. Xi 'an: University of Chinese Academy of Sciences (Xi 'an Institute of Optics & Precision Mechanics, Chinese Academy of Sciences), 2018. (in Chinese)
[13] Zhang Anan, Huang Jinying, Ji Shuwei, et al. Bearin fault pattern recognition based on image classification with CNN [J]. Vibration and Impact, 2020, 39(4): 165-171. (in Chinese)
[14] Feng Fuzhou, Zhang Chaosheng, Song Aibin, et al. Probability of detection model for fatigue crack in ultrasonicinfrared imaging [J]. Infrared and Laser Engineering, 2016, 45(3): 0304005. (in Chinese)
[15] Xue Shan, Zhang Zhen, Lv Qiongying, et al. Image recognition method of anti UAV system based on convolutional neural network [J]. Infrared and Laser Engineering, 2020, 49(7): 20200154. (in Chinese)
[16] Zhang Xiangxiang. Reserach on convolutional code decoders based on deep learning under correlated noise[D]. Beijing: Beijing University of Posts and Telecommunications, 2019. (in Chinese)
[17] Wu Yunxia, Tian Yimin. A coal-rock recognition method based on max-pooling sparse coding [J]. Chinese Journal of Engineering, 2017, 39(7): 981-987. (in Chinese)
[18] Jiao Jinyang, Zhao Ming, Lin Jing, et al. A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes [J]. Knowledge-Based Systems, 2018, 160(15): 237-250.
[19] Zhu Wenbo, Webb Z T, Mao Kaitian, et al. A deep learning approach for process data visualization using t-distributed stochastic neighbor embedding [J]. Industrial & Engineering Chemistry Research, 2019, 58(22): 9564-9575.
[20] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [C]//International Conference on Neural Information Processing Systems, 2012: 1106-1114.
[21] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015: 1-8.
[22] Liu Li, Sun Liujie, Wang Wenju. Classification of fluorescent images in high-throughput dPRC gene chips based on SVM [J]. Packaging Engineering, 2020, 41(19): 223-229. (in Chinese)