Sun Guangmin

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Personal profile

NameSun Guangmin

GenderMale

DegreesPh.D.

TitleProfessor

E-mailgmsun@bjut.edu.cn

Current Professional Societies

Senior member of China Electronics Society

Research Areas

lArtificial Intelligence,

lPattern Recognition,

lSignal and Image Processing,

lDeep Learning and Neural Networks.

Honors

lThe Second Grade Advancement Award of Science and Technology by the Weaponry Ministry of China in 1997.

lExcellent Youth Teacher of Beijing in 1998.

Publications

1. Li Zibo,Sun Guangmin*, He Cunfu, Multi-variable regression methods using modified Chebyshev polynomials of class 2,Journal of Computational and Applied Mathematics, V.346, p 609-619, 2019, SCI(JCR:1)

2.Sun Guangmin, Wang Chenyang, Ma Beichuan, An improved SIFT algorithm for infringement retrieval,MULTIMEDIA TOOLS AND APPLICATIONS, V.77, N.12, p 14745-14765, 2018,SCI(JCR:2)

3.Sun Guangmin, Liu Hao, He Cunfu, A Novel Prediction Method for Hardness Using Auto-regressive Spectrum of Barkhausen Noise,Journal of Nondestructive Evaluation, V. 37, N. 4, 2018,SCI(JCR:2)

4. Li Zibo,Sun Guangmin*, He Cunfu, Prediction of the hardness of X12m using Barkhausen noise and component analysis methods, JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, V. 478, p 59-67, 2019, SCI(JCR:2)

5. Zhu Zhongyang,Sun Guangmin*, He Cunfu, Prediction of the tensile force applied on surface-hardened steel rods based on a CDIF and PSO-optimized neural network,MEASUREMENT SCIENCE AND TECHNOLOGY,V. 29,N. 11,2018,SCI(JCR:2)

6. Zhu Zhongyang,Sun Guangmin, He Cunfu, An intelligent approach for simultaneously performing material type recognition and case depth prediction in two types of surface-hardened steel rods using a magnetic hysteresis loop, MEASUREMENT SCIENCE AND TECHNOLOGY, V. 30, N. 10, 2019, SCI(JCR:2)

7. Li Zibo,Sun Guangmin*,He Cunfu, Non-Destructive Residual Strain Prediction Using the Binary Pattern of Eddy Current,IEEE TRANSACTIONS ON MAGNETICS,V. 53, N. 8, 2017,SCI(JCR:3)

8. Zhu Zhongyang,Sun Guangmin*, Wu Bin, He Cunfu, Li Yu, A Temperature Compensation Method for Magneto-Elastic Tension Sensor in Rod-Like Structure Tension Measurement,IEEE TRANSACTIONS ON MAGNETICS, V. 54, N. 4, 2018,SCI(JCR:3)

9. Li Zibo,Sun Guangmin*, Zhang Fan, Smartphone-based fatigue detection system using progressive locating method, IET INTELLIGENT TRANSPORT SYSTEMS, V. 10, N. 3, p148-156, 2016,SCI(JCR:3)

10. Chen Guandong, Li Yu,Sun Guangmin, Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images,APPLIED SCIENCES-BASEL , V.7, N.10, 2017,SCI(JCR:2)

11. Zheng Kun, Wang Wenpeng,Sun Guangmin, An Effective Recognition Method for Road Information Based on Mobile Terminal,MATHEMATICAL PROBLEMS IN ENGINEERING, V. 2014, 2014,SCI(JCR:3)

12. Zheng Kun, Wei Mengfei,Sun Guangmin, Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images, ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, V. 8, N. 9, 2019, SCI.

Personal Statement

In recent years, Artificial Neural Networks, especially Deep Neural Networks, has been widely applied to the function approximation, nonlinear mapping, optimization calculation, prediction and pattern recognition problem. ANNs are effective in these applications because of their learning capabilities.

I have been conducting researches in the fields of specialization such as artificial intelligence, pattern recognition, deep neural networks and applications, signal and image processing. My research subjects include human face recognition based on machine learning methods, image recognition based on deep learning models, nondestructive testing of materials, fatigue driving monitoring and recognition, tissue classification based on gene expression spectrum data, human motion analysis based on neural networks, biometrics and bioinformatics, optimization calculation and algorithm simulation, system modeling and numerical analysis, etc.

I have been in charge of over forty research projects and authored or co-authored over one hundred and fifty papers in these fields by now. And some models and algorithms of neural networks have been researched out, which have been used in many fields such as industrial design, biomedicine engineering, mechanical engineering, chemical engineering and material science etc..