Peng Su | Machine Learning on Databases | Research Excellence Award

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Prof. Peng Su | Machine Learning on Databases | Research Excellence Award

Hebei University of Technology | China

Prof. Peng Su’s research centers on the advanced design, electromagnetic modeling, and performance optimization of permanent-magnet (PM) electrical machines, with a primary emphasis on flux-switching machine topologies for electric and hybrid-electric vehicle applications. With a citation record of 563 citations in total (431 since 2020), an h-index of 12 (11 since 2020), and an i10-index of 16 (12 since 2020), his contributions are well recognized within the electrical machine research community. His work significantly advances understanding of rotor-PM and stator-PM flux-switching architectures through rigorous analyses of operating principles, air-gap field modulation, hybrid-excitation mechanisms, and multi-phase configurations, enabling improved torque density, efficiency, and thermal robustness. Prof. Peng Su has delivered influential findings on PM eddy-current losses, stator-slot and rotor-pole selection, cogging-torque reduction strategies, and magnetization effects, offering practical design paths for minimizing parasitic losses and enhancing reliability under high-speed and vector-controlled drive conditions. His portfolio extends across diverse machine types—including axial-modular machines, multitooth structures, tubular PM generators, and toroidally wound direct-drive motors—demonstrating comprehensive expertise in advanced electromagnetic machine architectures. He also contributes to loss modeling in soft magnetic composites, fault behavior characterization, and performance evaluation methodologies tailored to transportation electrification requirements. Through systematic comparative studies, innovative structural proposals, and refined analytical models, Prof. Peng Su continues to shape the development of next-generation PM machines and high-efficiency energy-conversion technologies, reinforcing his position as a leading contributor to modern electrical machine engineering.

Profiles: Google Scholar | Orcid

Featured Publications

  • Hua, W., Su, P., Tong, M., & Meng, J. (2016). Investigation of a five-phase E-core hybrid-excitation flux-switching machine for EV and HEV applications. IEEE Transactions on Industry Applications, 53(1), 124–133.

  • Su, P., Hua, W., Wu, Z., Han, P., & Cheng, M. (2017). Analysis of the operation principle for rotor-permanent-magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 65(2), 1062–1073.

  • Su, P., Hua, W., Wu, Z., Chen, Z., Zhang, G., & Cheng, M. (2018). Comprehensive comparison of rotor permanent magnet and stator permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 66(8), 5862–5871.

  • Su, P., Hua, W., Hu, M., Chen, Z., Cheng, M., & Wang, W. (2019). Analysis of PM eddy current loss in rotor-PM and stator-PM flux-switching machines by air-gap field modulation theory. IEEE Transactions on Industrial Electronics, 67(3), 1824–1835.

  • Su, P., Hua, W., Hu, M., Wu, Z., Si, J., Chen, Z., & Cheng, M. (2019). Analysis of stator slots and rotor pole pairs combinations of rotor-permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 67(2), 906–918.*

Jingcheng Tong | Deep Learning | Best Researcher Award

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Mr. Jingcheng Tong | Deep Learning | Best Researcher Award

Beijing Institute of Graphic Communication | China

Mr. Jingcheng Tong, a postgraduate student at the Beijing Institute of Graphic Communication, China, is an emerging researcher whose work focuses on advancing artificial intelligence applications in industrial manufacturing through deep learning and computer vision technologies. As a student member actively contributing to this growing field, he has developed innovative object detection algorithms tailored for steel material identification and quality assessment, bridging the gap between advanced AI methods and traditional manufacturing practices. His notable research, including the publication CBH-YOLO: A steel surface defect detection algorithm based on cross-stage mamba enhancement and hierarchical semantic graph fusion in the SCI-indexed journal Neurocomputing, highlights his ability to design effective solutions that significantly improve defect detection accuracy, enhance efficiency, and reduce manual inspection costs. Mr. Tong’s interdisciplinary approach not only advances industrial automation and smart manufacturing initiatives but also demonstrates how applied artificial intelligence can modernize conventional production systems and elevate product quality standards. In addition to his technical expertise, he exhibits strong academic commitment and a forward-looking vision, aiming to extend his research toward broader industrial applications of AI that can support sustainable, intelligent, and globally competitive manufacturing. Through his scholarly contributions, practical innovations, and dedication to excellence, Mr. Jingcheng Tong exemplifies the promise and potential of the next generation of researchers committed to shaping the future of intelligent manufacturing technologies.

Profile : Orcid

Featured Publication

Tong, J. (2025). CBH-YOLO: A steel surface defect detection algorithm based on cross-stage mamba enhancement and hierarchical semantic graph fusion. Neurocomputing. Advance online publication.

Bo Gao | Deep Learning | Best Researcher Award

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Dr. Bo Gao | Deep Learning | Best Researcher Award

Beijing Institute of Graphic Communication | China

Dr. Gao Bo, PhD, is a Professor and Master’s Supervisor at the School of Information Engineering, Beijing Institute of Graphic Communication, following a distinguished tenure at Inner Mongolia University of Finance and Economics. His research encompasses nonlinear dynamics, cryptography, game theory, and computational mathematics, where he has made notable interdisciplinary contributions linking theory and practice. According to Scopus, Dr. Gao has published 32 documents, cited 593 times across 488 works, with an h-index of 14, reflecting both productivity and international recognition. His studies have been published in leading journals such as Physical Review E, Applied Mathematics and Computation, Chaos, and IEEE Transactions on Circuits & Systems II: Express Briefs, highlighting his expertise in applied and computational mathematics. Beyond research papers, he has authored academic monographs, secured multiple invention and utility model patents, and registered software copyrights, showcasing his commitment to innovation. He has also led several funded projects supported by national and regional foundations, addressing challenges in lightweight cryptography, wireless sensor networks, and Internet of Things security. In academic service, he reviews for respected journals including Nonlinear Dynamics, Applied Mathematics and Computation, PLOS ONE, and China Communications. He also contributes professionally as a member of the Education and Popular Science Committee of the Chinese Society of Cryptologism. Through sustained research, project leadership, and mentoring, Dr. Gao has advanced knowledge in information security, fostered innovation in engineering sciences, and contributed to training the next generation of researchers.

Profile: Scopus

Featured Publications

Gao B, Tao K, Mu C, et al. Asymmetry of individual activity promotes cooperation in the spatial prisoner’s dilemma game. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023, 33(9).

Gao B, Hong J, Guo H, et al. Cooperative evolution and symmetry breaking in interdependent networks based on alliance mechanisms. Physica A: Statistical Mechanics and its Applications, 2023, 609.

Lan Z Z, Dong S, Gao B, Shen Y J. Bilinear form and soliton solutions for a higher order wave equation. Applied Mathematics Letters, 2022, 134: 108340.

Dong S, Lan Z Z, Gao B, et al. Bäcklund transformation and multi-soliton solutions for the discrete Korteweg–de Vries equation. Applied Mathematics Letters, 2022, 125: 107747.

Gao B, Liu X, Lan Z Z, et al. The evolution of cooperation with preferential selection in voluntary public goods game. Physica A: Statistical Mechanics and its Applications, 2021, 584: 126267.

Gao B, Li B, Dong S, et al. Payoff-dependence learning ability resolves social dilemmas. International Journal of Modern Physics B, 2021, 35(08): 2150125.

Gao B, Liu X, Wu X, et al. The Stability of Nonlinear Feedback Shift Registers with Periodic Input. Computers, Materials & Continua, 2020, 62(2): 833-847.

Wang Q, Ren X, Gao B, et al. Heterogeneity reproductive ability promotes cooperation in spatial prisoner’s dilemma game. Chaos, Solitons & Fractals, 2020, 134: 109715.