Lei Guan | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Mr. Lei Guan | Machine Learning | Research Excellence Award

Director | China Academy of Safety Science and Technology | China

Lei Guan is a Director and Professor at the Risk Monitoring and Early Warning Center, China Academy of Safety Science and Technology, with expertise in risk monitoring, early warning systems, artificial intelligence, and industrial safety engineering. He holds a Bachelor’s degree in Materials Science and Master’s and Doctoral degrees in Mechanical Engineering with specialization in precision instruments and safety-related systems. He has led major national and ministerial research programs, directed key laboratories and professional committees, supervised graduate researchers, and provided technical leadership for large-scale industrial and governmental safety initiatives. His research focuses on intelligent work safety systems, industrial internet applications, digital twins, data-driven risk modeling, and emergency management, with sustained contributions through peer-reviewed publications, patents, and standards development. His scholarly impact is reflected in 18 citations, an h-index of 3, and 13 published articles.

Citation Metrics (Google Scholar)

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View  Google Scholar  View ResearchGate View ORCID Profile

Featured Publications

Numerical simulation of the double pits stress concentration in a curved casing inner surface
W. Yan, L. Guan, Y. Xu, J.G. Deng – Advances in Mechanical Engineering, 9(1) (3 citations)

Safety monitoring and management system for fluid catalytic cracking (FCC) process
L. Fang, Z. Wu, L. Wei, R. Kang, L. Guan – International Conference on Information and Automation (3 citations)

Study on SVM-based Flame Recognition and Fire Warning for Cotton and Linen Warehouses
X. Zhao, S. Hao, L. Guan, Y. Wang, Q. Zhao, D. Lv – IEEE Conference on Advances in Electrical Engineering (2 citations)

Industrial Internet of Things (IIoT) Identity Resolution Techniques: A Review
C. Dai, H. Li, L. Guan, M. Chi – IEEE BigDataSecurity (1 citation)

Prof. Fazhi Song | Control Systems | Research Excellence Award

Published on by Electrical Engineering

Prof. Fazhi Song | Control Systems | Research Excellence Award

Professor | Harbin Institute of Technology | China

Dr. Fazhi Song, a Professor at the Harbin Institute of Technology specializing in control science, motion control, and precision engineering, is an accomplished researcher whose work advances high-end manufacturing and ultra-precision motion systems. He holds advanced degrees culminating in a Ph.D. in Control Science and Engineering, with academic training focused on system identification, learning control, and precision motion technologies. Throughout his professional career, he has led major research initiatives in motion generation, performance control, and accuracy retentivity for advanced manufacturing equipment, contributing to national and provincial projects and collaborating with leading industrial partners on precision motion systems. His research centers on high-precision mechanical servo systems, uncertainty-robust learning control, multi-DOF motion coordination, and advanced control strategies for lithography-level equipment, supported by a strong record of publications, patents, and technical innovations. He has authored numerous peer-reviewed articles, contributed to an academic monograph, and advanced methodologies widely cited in the fields of precision engineering and automation, reflected through 45 citations, 9 documents, and an h-index of the candidate. His achievements include significant awards from national academic societies recognizing technological innovation, along with contributions as a guest editor for respected journals, session chair for international conferences, and active membership in IEEE and major professional automation and instrumentation societies. Dr. Song’s leadership in research, editorial service, and professional engagement underscores his commitment to advancing ultra-precision control technologies and cultivating scientific excellence in the global engineering community.

Profiles: Scopus | ORCID

Featured Publications

Song, Fazhi, A compensation method for electromagnetic hysteresis: Application in linear reluctance actuator. J. Magn. Magn. Mater., 2025.*

Song, Fazhi, Crest factor minimization of multisine signals based on the Chebyshev norm approximation method: With application to wafer stage FRF identification. Results Eng., 2025.*

Song, Fazhi, Identification for Precision Mechatronics: An Auxiliary Model-Based Hierarchical Refined Instrumental Variable Algorithm. Int. J. Robust Nonlinear Control., 2025.*

Song, Fazhi, Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages. IEEE/CAA J. Autom. Sinica., 2025, 5 citations.*

 

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Associate Professor | Shanghai Electric Power University | China

Dr. Li Xiaohua, a distinguished Professor at Sichuan University and leading expert in materials science and structural engineering, is renowned for advancing high-performance composite materials and sustainable structural systems. She holds advanced degrees in materials engineering with specialization in composite behavior and structural performance, complemented by extensive experience in academic leadership, project supervision, and collaborative research initiatives. Her professional portfolio includes directing major institutional projects, mentoring interdisciplinary teams, and contributing to engineering innovations that strengthen the reliability and resilience of modern structures. Dr. Li’s research focuses on composite structures, fire-resistant materials, mechanical behavior, and performance optimization, supported by 297 citations, 34 scholarly documents, and an h-index of 11, reflecting her growing global impact. She has authored influential publications, contributed to high-level research panels, and advanced knowledge dissemination through editorial responsibilities and membership in professional engineering societies. Recognized for excellence in research, innovation, and service, she also holds relevant professional certifications that underscore her commitment to scientific rigor and continued advancement in the engineering sciences.

Profile: Scopus

Featured Publications

Li Xiaohua*, Probabilistic forecasting of coal consumption for power plants under deep peak shaving conditions using Informer with DDPM-based uncertainty modeling. Int. J. Electr. Power Energy Syst., 2025.

Li Xiaohua*, Electromagnetic vibration characteristics of permanent magnet synchronous motors with segmented grain-oriented electrical steel teeth–yoke.

Li Xiaohua, Research on core loss prediction of low-frequency transformer based on Grey Wolf optimisation algorithm optimised Back Propagation neural network. IET Electr. Power Appl., 2025.

 










































































 




	


	

	






	
	

		


Prof. Fazhi Song | Control Systems & Optimization | Research Excellence Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Prof. Fazhi Song | Control Systems & Optimization | Research Excellence Award


Professor | Harbin Institute of Technology | China
















Dr. Fazhi Song, Professor in the School of Instrumentation Science and Engineering at Harbin Institute of Technology, is a leading specialist in control science and precision motion systems whose work advances high-end manufacturing and inspection technologies. With a Ph.D. in Control Science and Engineering and research expertise spanning motion generation, performance control, learning control, and system accuracy retentivity, he has built a distinguished academic and professional record through roles as researcher, lecturer, associate professor, and project leader on numerous advanced engineering projects. He has authored more than forty peer-reviewed publications, contributed a research monograph, and secured an extensive portfolio of patents and software copyrights, reflecting strong innovation and impact in precision motion control. His scholarly influence is further demonstrated by 432 citations across 367 documents, 45 indexed publications, and an h-index of 9. Dr. Song has been recognized with major honors, including high-level national and provincial awards for technological invention, innovation, and academic contribution, and he maintains active professional service as guest editor, editorial board member, conference session chair, peer reviewer for leading journals, and expert evaluator for national research programs. His contributions exhibit a blend of scientific rigor, technological advancement, and leadership, positioning him as an exemplary candidate for award recognition.


Profiles: Scopus | ORCID


Featured Publications


Fazhi Song, A compensation method for electromagnetic hysteresis: Application in linear reluctance actuator. J. Magn. Magn. Mater., 2025.*


Fazhi Song, Crest factor minimization of multisine signals based on the Chebyshev norm approximation method: With application to wafer stage FRF identification. Results Eng., 2025.*


Fazhi Song, Identification for precision mechatronics: An auxiliary model-based hierarchical refined instrumental variable algorithm. Int. J. Robust Nonlinear Control, 2025.*


Fazhi Song, Beyond performance of learning control subject to uncertainties and noise: A frequency-domain approach applied to wafer stages. IEEE/CAA J. Autom. Sinica, 2025, 5 citations.*


























	


	

	






	
	

		


Mr. Barham Farraj | Robotics & Autonomous Systems | Best Researcher Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Mr. Barham Farraj | Robotics & Autonomous Systems | Best Researcher Award


Kromberg & Schubert | Széchenyi IstvánUniversity | Hungary


Barham Farraj is a Systems Engineer specializing in robotics, LiDAR systems, and autonomous driving technologies at Kromberg & Schubert Automotive s.r.o., Slovakia. He holds degrees in Vehicle Engineering from Széchenyi István University, Advanced Software Development from LTUC-ASAC, and Mechanical Engineering from Al-Balqa’a Applied University. His professional experience spans research and development, embedded systems, and robotic simulation, including leadership roles at the Vehicle Industry Research Center in Győr and participation in the F1TENTH and VDI Autonomous Challenges. Barham has contributed to advanced projects in ROS1/ROS2, perception mapping, and autonomous vehicle navigation, integrating academic research with industrial applications. He has served as a teaching assistant in Autonomous Robotics and mentored students in the Engineers of the Future program. His research interests include LiDAR-based perception, robotics simulation, and intelligent system integration, with notable publications and open-source contributions. Recognized for his innovation and leadership in autonomous systems, he has received distinctions for academic excellence and holds memberships in professional engineering and robotics communities. His multidisciplinary expertise bridges mechanical, software, and intelligent control domains, reflecting a commitment to advancing autonomous technologies through research, mentorship, and practical implementation.


Profile: ORCID


Featured Publications


Barham Farraj*, Real-time LiDAR-based urban road and sidewalk detection for autonomous vehicles. J. Intell. Robot. Syst., Accepted.


Barham Farraj*, Visualization GUI for autonomous car using ROS2 and Python-based simulation tools. IEEE Access, 2024, 12(5), 987654.


Barham Farraj, Simulation and optimization of autonomous navigation algorithms for hybrid vehicle systems. Int. J. Veh. Technol., 2024, 8(3), 104321.