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耿国盛
Associate Professor
Supervisor of Master's Candidates
School/Department: College of Engineering
Education Level:With Certificate of Graduation for Doctorate Study
Degree:Doctoral Degree in Engineering
Professional Title:
Associate Professor
Alma Mater:
南京航空航天大学
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Paper Publications
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Paper Publications
Song Xinmin,Wei Weihua,Zhou Junbo,Ji Guojun,Hussain Ghulam,Xiao Maohua,Geng Guosheng.Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis,SENSORS,2023,23(11)(Co corresponding author)
Geng Guosheng,Jiang Feng,Chai Chao,Wu Jianming,Zhu Yejun,Zhou Guiguan,Xiao Maohua.Design and experiment of magnetic navigation control system based on fuzzy PID strategy,MECHANICAL SCIENCES,2022,13(2):921-931(First Author)
汪洋,吕石祥,肖茂华,朱虹,汪小旵,耿国盛.面向鱼塘养殖的水质检测装置研究与性能试验,黑龙江粮食,2022,2022(2022年04期):35-37(Participating authors)
耿国盛,汪洋,张梁,肖茂华.Optimization of cutting parameters in double-excitation ultrasonic elliptical vibration cutting of 630 stainless steel,INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,2021,114(44385):2169-2183(First Author)
肖茂华,廖亚兵,Bartos Petr,Filip Martin,耿国盛,姜紫薇.Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm,MULTIMEDIA TOOLS AND APPLICATIONS,2021,:-(Participating authors)
周爽,肖茂华,Bartos Petr,Filp Martin,耿国盛.Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network,SHOCK AND VIBRATION,2020,2020:-(Participating authors)
肖茂华,张存义,温凯,熊龙飞,耿国盛,吴丹.Bearing fault feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise,Journal of Vibroengineering,2018,20(7):2622-2631(Participating authors)
肖茂华,陈婧文,王月文,耿国盛,张静,芦新华.Starting control strategy of HMCVT based on fuzzy control algorithm,International Journal of Mechatronics and Applied Mechanics,2018,2(4):56-60(Participating authors)
耿国盛,李国红,肖茂华,周康,翁玮,李昊.高速精密冲床可靠性的Monte Carlo模拟与试验验证,机械设计,2017,34(6):41-46(First Author)
张伟超,邹凡,李源,肖茂华,耿国盛,张海军,聂信天,杨文安.Research on tool wear prediction based on Archard agglomeration scavenging theory,ADVANCES IN MECHANICAL ENGINEERING,2017,9(10):-(Participating authors)
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