Tuniyazi Abudoureheman | Intelligent Systems | Research Excellence Award

Published on by Environmental Scientists

Dr. Tuniyazi Abudoureheman | Intelligent Systems | Research Excellence Award

Dr. Tuniyazi Abudoureheman | Intelligent Systems | Hiroshima University | Japan

Dr. Tuniyazi Abudoureheman is a dedicated researcher and Ph.D. student at Hiroshima University whose work focuses on advanced sensing, machine vision, and robotic system diagnostics, contributing meaningfully to the fields of high-frame-rate (HFR) imaging, vibration analysis, and automated detection systems. Dr. Tuniyazi Abudoureheman began his academic journey with foundational studies that eventually led him to pursue graduate-level research, culminating in his current doctoral studies where he continues to expand his expertise in robotics and intelligent sensing technology. Throughout his professional experience, Dr. Tuniyazi Abudoureheman has actively engaged in collaborative research projects, working alongside multidisciplinary teams to design, implement, and validate methods involving HFR video, wing-beat frequency detection, and robot health monitoring across multiple postures. His early work also includes contributions to multi-target tracking using Kalman Filtering in complex environments, demonstrating both versatility and technical depth even before entering advanced doctoral research. The core research interests of Dr. Tuniyazi Abudoureheman include high-speed imaging, robotic vibration analysis, automated industrial inspection, bio-inspired detection systems, and machine vision algorithms, all of which align with the evolving demands of next-generation intelligent robotics. His research skills span HFR camera-based data acquisition, signal processing, vibration modeling, robotic motion evaluation, and applied machine learning, supported by strong analytical ability and experience with experimental system design. Dr. Tuniyazi Abudoureheman has also developed valuable competencies in publishing scientific results, presenting at conferences, and contributing to collaborative engineering investigations, which collectively strengthen his academic and professional profile. Although early in his academic career, Dr. Tuniyazi Abudoureheman has already earned recognition through peer-reviewed publications, citations, and participation in reputable conferences such as IEEE SENSORS, positioning him as an emerging scholar in robotics and sensing technology. His work has received growing scholarly attention, reflected in increasing citation counts and inclusion in respected journals covering robotics and mechatronics. In conclusion, Dr. Tuniyazi Abdurrahman continues to advance as a promising researcher whose technical contributions, methodological rigor, and commitment to innovation place him on a strong path toward future academic excellence and impactful scientific discovery.

Academic Profile: ORCID | Google Scholar

Featured Publications:

  1. Li, J., Shimasaki, K., Tuniyazi, A., Ishii, I., & Ogihara, M. (2023). HFR video-based hornet detection approach using wing-beat frequency analysis. 3 citations.

  2. Abudoureheman, T., Wang, F., Shimasaki, K., & Ishii, I. (2025). HFR-video-based vibration analysis of a multi-jointed robot manipulator. 1 citation.

  3. Abudoureheman, T., Otsubo, H., Wang, F., Shimasaki, K., & Ishii, I. (2025). High-frame-rate camera-based vibration analysis for health monitoring of industrial robots across multiple postures.

 

Dengxiang Liang | Robotics | Best Scholar Award

Published on by Environmental Scientists

Mr. Dengxiang Liang | Robotics | Best Scholar Award

Ph.D. Student, School of Automation Science and Engineering, South China University of Technology, ChinašŸ†

Dengxiang Liang is a dedicated Ph.D. student at the School of Automation Science and Engineering, South China University of Technology. His research focuses on control science and engineering, specializing in dynamic learning control, multi-agent cooperative control, and electric vehicle virtual energy storage. With a strong academic foundation, he has actively contributed to national research projects and has published multiple high-impact SCI papers. His achievements have been recognized with prestigious awards, including the National Scholarship and the Outstanding Graduate Student Award. Additionally, his industry experience, gained through an internship at the China Electric Power Research Institute, has strengthened his expertise in real-world energy storage applications.

Profile

Scopus

Education šŸŽ“

Dengxiang Liang holds a bachelorā€™s degree in Measurement and Control Technology & Instruments and a masterā€™s degree in Control Engineering from North China Electric Power University. Currently, he is pursuing a Ph.D. in Control Science and Engineering at South China University of Technology. His academic journey has equipped him with in-depth knowledge of automation, dynamic system modeling, and optimization control strategies, which he applies to his cutting-edge research in learning-based control methods for nonlinear systems.

Experience šŸ¢

Dengxiang Liang has extensive research and professional experience in control engineering. As a Ph.D. researcher, he has been actively involved in the study of deterministic learning control for discrete nonlinear systems, focusing on improving transient control performance and robustness in sampled-data environments. His expertise extends to industry applications, demonstrated through his internship at the China Electric Power Research Institute, where he researched the role of electric vehicles as energy storage units within smart grids. His contributions in both academia and industry have played a vital role in advancing energy-efficient solutions and intelligent control mechanisms.

Research Interests šŸ”¬

Dengxiang Liang’s research interests lie in dynamic learning control of sampled nonlinear systems, multi-agent cooperative learning control, and robust tube-model predictive control (MPC). He is also deeply engaged in visual control of mechanical arms, dynamic pattern recognition, and parameter identification. His work extends to multi-objective optimization strategies for electric vehicle virtual energy storage and the development of battery capacity decay models. His interdisciplinary approach aims to bridge theoretical advancements with practical applications in automation and intelligent control.

Awards šŸ…

Dengxiang Liang has been honored with multiple awards for his outstanding academic and research contributions. He received the prestigious National Scholarship, recognizing his excellence in control engineering research. Additionally, he was named an Outstanding Graduate Student for his exceptional academic performance and impactful research publications. His contributions to the field of automation and control science have earned him recognition at both institutional and national levels.

Publications šŸ“š

An optimization scheduling method of electric vehicle virtual energy storage
International Journal of Energy Research, 2020, Cited by 20+ articles.
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Masterā€slave game optimization method of smart energy systems
International Journal of Energy Research, 2021, Cited by 15+ articles.
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Adaptability assessment method of energy storage working conditions
Journal of Energy Storage, 2020, Cited by 25+ articles.
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Neural learning control for sampledā€data nonlinear systems
International Journal of Robust and Nonlinear Control, 2023.
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Disturbance Observer-Based Dynamic Learning Control
China Automation Congress (CAC), IEEE, 2023.
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Rapid dynamical learning from neural control of sampled-data nonlinear systems
Journal of the Franklin Institute.
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Rapid dynamical pattern modeling for sampled-data nonlinear systems
Nonlinear Dynamics.
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Conclusion

Dengxiang Liang is a highly deserving candidate for the Best Research Scholar Award due to his strong academic record, impactful research, and significant contributions to automation and energy storage. With continued focus on industry collaboration and innovation, his work will further shape the field of control engineering and sustainable energy solutions.