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

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.*