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。

超声无损检测 声学信号处理 智能制造

担任IEEE Trans. Ultrason. Ferroelectr. Freq. Control，NDT E Int.等国际期刊审稿人

[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.