[1] Bonin T A, Choukulkar A, Brewer W A , et al. Evaluation of turbulence measurement techniques from a single Doppler lidar [J]. Atmospheric Measurement Techniques, 2017, 10(8): 1-26.
[2] Leung M Y T, Zhou Wen, Shun Chiming, et al. Large-scale circulation control of the occurrence of low-level turbulence at Hong Kong international airport [J]. Advances in Atmospheric Sciences, 2018, 35(4): 435-444. doi:  10.1007/s00376-017-7118-y
[3] Wildmann N, Pschke E, Roiger A, et al. Towards improved turbulence estimation with Doppler wind lidar velocity-azimuth display (VAD) scans [J]. Atmospheric Measurement Techniques, 2020, 13(8): 4141-4158. doi:  10.5194/amt-13-4141-2020
[4] Dyk R V, Pariseau D H, Dodson R E, et al. Systems integration of unmanned aircraft into the national airspace: Part of the federal aviation administration next generation air transportation system[C]//IEEE Symposium on Systems and Information Engineering Design, SIEDS, 2012:1-25, 34.
[5] Organization I. Meteorological Service for International Air Navigation: Annex 3 to the Convention on International Civil Aviation[M]. Chicago: International Civil Aviation Organization, 1998.
[6] Chan P W. Validating the turbulence parameterization schemes of a numerical model using eddy dissipation rate and turbulent kinetic energy measurements in terrain-disrupted airflow [J]. Meteorology & Atmospheric Physics, 2010, 108(3-4): 95-112.
[7] Jiang Lihui, Gao Zhiguang, Xiong Xinglong, et al. Study on type recognition of low attitude wind shearbased on lidar image processing [J]. Infrared and Laser Engineering, 2012, 41(12): 3410-3415. (in Chinese) doi:  10.3969/j.issn.1007-2276.2012.12.049
[8] Jiang Lihui, Chen Hong, Zhuang Zibo, et al. Recognition on low-level wind shear of wavelet invariant moments [J]. Infrared and Laser Engineering, 2014, 43(11): 3783-3787. (in Chinese) doi:  10.3969/j.issn.1007-2276.2014.11.048
[9] Chen Xiaoqing, Ma Junguo, Fu Qiang, et al. Target recognition using singular value feature for laser imaging radar [J]. Infrared and Laser Engineering, 2011, 40(9): 1801-1805. (in Chinese) doi:  10.3969/j.issn.1007-2276.2011.09.041
[10] Xu Qiwei, Wang Peipei, Zeng Zhenjia, et al. Extracting atmospheric turbulence phase using deep convolutional neural network [J]. Acta Physica Sinica, 2020, 69(1): 286-296. (in Chinese)
[11] Lan Zhangli, Kuang Heng, Li Zhan, et al. Study on CNN-based turbulence image degradation intensity classification [J]. Computer Systems & Applications, 2019, 28(4): 199-204. (in Chinese)
[12] Yin Xiaoli, Guo Yilin, Cui Xiaozhou, et al. Method of mode recognition for Multi-OAM multiplexing based on convolutional neural network [J]. Journal of Beijing University of Posts and Telecommunications, 2019, 42(1): 47-52. (in Chinese)
[13] Vasudevan S. Mutual information based learning rate decay for stochastic gradient descent training of deep neural networks [J]. Entropy, 2020, 22(5): 560. doi:  10.3390/e22050560
[14] Keskar N S, Saon G. A nonmonotone learning rate strategy for SGD training of deep neural networks[C]// IEEE International Conference on Acoustics. IEEE, 2015.
[15] Qu Jingyi, Zhu Wei, Wu Renbiao. Image classification for dual-channel neural networks based on attenuation factor [J]. Systems Engineering and Electronics, 2017, 39(6): 1391-1399. (in Chinese)
[16] Davies F, Collier C G, Pearson G N, et al. Doppler lidar measurements of turbulent structure function over an urban area [J]. Journal of Atmospheric & Oceanic Technology, 2003, 21(5): 753-761.
[17] 张兆顺, 崔桂香, 许春晓. 湍流理论与模拟[M]. 清华大学出版社, 2005.

Zhang Zhaoshun, Cui Guixiang, Xu Chunxiao. Theory and Modeling of Turbulence[M]. Beijing: Tsinghua University Press, 2005. (in Chinese)
[18] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507.
[19] 吴佳全. 基于神经网络的ECG分类算法及高能效架构研究[D]. 浙江大学, 2020.

Wu Jiaquan. Research on neural network based ECG classification algorithm and energy-efficient architecture[D]. Hangzhou: Zhejiang University, 2020. (in Chinese)
[20] Huang Xu, Ling Zhigang, Li Xiuxin. Discriminative deep feature learning method by fusing linear discriminant analysis for image recognition [J]. Journal of Image and Graphics, 2018, 23(4): 510-518. (in Chinese)
[21] Luo Chang, Wang Jie, Wang Shiqiang, et al. General deep transfer features based high resolution remote scene classification [J]. Systems Engineering and Electronics, 2018, 40(3): 682-691. (in Chinese)