白龙（Bai Long，Associate Researcher）, 华中科技大学机械学院副研究员，博士生导师，湖北省高层次人才。2010年、2013年先后于北京大学元培学院、北京大学工学院获得学士学位与硕士学位，2018年1月毕业于英国布里斯托大学机械工程系，获博士学位。2018年至2020年在英国布里斯托大学超声无损检测实验室从事博士后研究工作，于2020年5月进入华中科技大学。

       主要研究方向为超声无损检测、智能制造、智能机器人等，在IEEE Trans. Ultrason. Ferroelectr. Freq. Control，NDT E Int.，J. Acoust. Soc. Am.等期刊上发表论文10余篇。主持国家重点研发计划课题、国家自然科学基金青年基金项目、湖北省重点研发计划项目各一项。

科研项目

[1] 国家重点研发计划课题，“工业机器人离线编程与免示教部署技术研究”，462.5万，2022—2024（负责人）
[2] 国家自然科学基金青年项目，“基于超声散射矩阵的小缺陷表征技术”，24万，2021—2023（负责人）
[3] 湖北省重点研发计划项目，“面向航天核心部件亚表层损伤的新型相控阵检测技术与系统研究”，100万，2023—2025（负责人）
[4] 欧盟地平线2020计划项目，“Advanced Inspection of Complex Structures”，2017—2020（参与人）
[5] 英国EPSRC项目，“UK Research Council in Non-Destructive Evaluation”，2014—2017（参与人）

论文专著与专利
代表性著作：
[1] L. Bai, Q. Yang, X. Cheng, Y. Ding, J. Xu*. A hybrid physics-data-driven surface roughness prediction model for ultra-precision machining. Science China Technological Sciences 2023, in press.

[2] L. Bai, X. Cheng, Q. Yang, J. Xu*. Predictive model of surface roughness in milling of 7075Al based on chatter stability analysis and back propagation neural network. The International Journal of Advanced Manufacturing Technology 2023, 126: 1347-1361.

[3] C. Guo, J. Ren, J. Xu, L. Bai*. Ultrasonic defect characterization using Bayesian inversion and scattering matrix denoising neural networks. NDT&E International 2023, 136:102813.

[4] L. Bai, F. Xu, X. Chen, X. Su, F. Lai, J. Xu*. A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components. Frontiers of Mechanical Engineering 2022, 17(3):32.

[5] L. Bai, N. Liu, C. Guo, J. Xu*. Ultrasonic defect characterization using time-domain scattering matrices and convolutional sparse coding. NDT&E International 2022, 131:102699.

[6] L. Bai, M. Liu, N. Liu, X. Su, F. Lai, J. Xu*. Dimensionality reduction of ultrasonic array data for characterization of inclined defects based on supervised locality preserving projection. Ultrasonics 2022, 119:106625.

[7] L. Bai*, J. Zhang, A. Velichko, B. W. Drinkwater. The use of full-skip ultrasonic data and Bayesian inference for improved characterisation of crack-like defects. NDT&E International 2021, 121:102467.

[8] L. Bai*, F. Le Bourdais, R. Miorelli, P. Calmon, A. Velichko, B. W. Drinkwater. Ultrasonic defect characterization using the scattering matrix: A performance comparison study of Bayesian inversion and machine learning schemas. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2021, 68(10): 3143-3155.

[9] L. Bai*, A. Velichko, A. T. Clare, P. Dryburgh, D. Pieris, B. W. Drinkwater. The effect of distortion models on characterisation of real defects using ultrasonic arrays. NDT&E International 2020, 113:102263. 

[10] L. Bai*, A. Velichko, B. W. Drinkwater. Grain scattering noise modeling and its use in the detection and characterization of defects using ultrasonic arrays. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2019, 66(11): 1798-1813.

[11] L. Bai*, A. Velichko, B. W. Drinkwater. Ultrasonic defect characterisation-Use of amplitude, phase, and frequency information. Journal of the Acoustical Society of America 2018, 143(1):349-360.

[12] L. Bai*, A. Velichko, B. W. Drinkwater. Characterization of defects using ultrasonic arrays: A dynamic classifier approach. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2015, 62(12): 2146-2160.

[13] L. Bai*, A. Velichko, B. W. Drinkwater. Ultrasonic characterization of crack-like defects using scattering matrix similarity metrics. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2015, 62(3): 545-559.

[14] L. Bai, X. Huang*. Observer-based beamforming algorithm for acoustic array signal processing. Journal of the Acoustical Society of America 2011, 130(6):3803-3811.

荣誉获奖

[1] University of Bristol Postgraduate Research Scholarship     2013-2017
[2] 北京市优秀毕业生               2013
[3] 硕士研究生国家奖学金          2012