Huiming Wang | Nonlinear Control | Research Excellence Award

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Assoc. Prof. Dr. Huiming Wang | Nonlinear Control | Research Excellence Award

Chongqing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Huiming Wang is an Associate Professor at the School of Automation, Chongqing University of Posts and Telecommunications, China. He earned his Ph.D. in Measurement Technique and Automation Equipment from Southeast University, following an M.S. in Control Theory and Control Engineering and a B.S. in Automation. Assoc. Prof. Dr. Huiming Wang has professional experience as a postdoctoral researcher at Nanyang Technological University, Singapore, and serves as deputy director of key laboratories and departments. His research interests focus on anti-disturbance control, robotics, servo systems, and power electronics. His research skills include robust control, sliding-mode control, disturbance observers, and intelligent electromechanical systems. Assoc. Prof. Dr. Huiming Wang has received national and international awards for innovation and best papers, reflecting his strong academic impact and leadership in automation research.

Citation Metrics (Scopus)

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Citations
1,092

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h-index
17

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h-index


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Featured Publications

Generalized Disturbance Estimation Based Continuous Integral Terminal Sliding Mode Control for Magnetic Levitation Systems

IEEE Transactions on Automation Science and Engineering, 2025 · 20 Citations
Current-Constrained Finite-Time Control Scheme for Speed Regulation of PMSM Systems With Unmatched Disturbances

IEEE Transactions on Automation Science and Engineering, 2025 · 3 Citations
Active Disturbance Rejection with Continuous Sliding Mode Control for Lower Limb Rehabilitation Exoskeleton

Conference Paper · 0 Citations
A Current-Constrained Finite-Time Disturbance Rejection Control Scheme for Buck Converter

Conference Paper · 0 Citations
Sampled-Data Safety-Critical Control of Nonlinear Systems with Input Delay

Conference Paper · 0 Citations

Chao Li | Data Processing | Research Excellence Award

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Dr. Chao Li | Data Processing | Research Excellence Award

Qingdao Technical College | China

Dr. Chao Li is an engineering scholar and lecturer whose work bridges professional education and applied research in advanced sensing technologies. He holds a doctoral degree in engineering and completed his undergraduate studies in industrial equipment and control engineering, where he built a strong foundation in intelligent systems. Since 2019, he has focused extensively on indoor mapping and positioning, integrating theoretical innovation with engineering-driven problem-solving. His research experience includes serving as a core contributor to multiple provincial key R&D initiatives and collaborations with major technology enterprises, where he helped develop applied solutions for real-world industrial environments. He has published several SCI-indexed journal articles as a first or corresponding author and holds an invention patent that reflects the practical impact of his work. In addition to research, he is dedicated to teaching and curriculum development in professional courses, promoting hands-on learning and interdisciplinary thinking. His academic achievements demonstrate a commitment to advancing positioning technologies, enhancing industry–academia collaboration, and addressing emerging challenges in smart manufacturing and intelligent monitoring. Looking ahead, he aims to continue deepening his contributions to indoor mapping and positioning, driving innovation that supports both scientific development and technological progress.

Profile : Orcid

Featured Publications

Li, C., Chai, W., Zhang, M., Sun, Z., Shao, G., & Li, Q. (2023). “A novel visual-aided method to enhance the inertial navigation system of an intelligent vehicle in indoor environments.” IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/TIM.2023.3293884

Chai, W., Li, C., & Li, Q. (2023). “Multi-sensor fusion-based indoor single-track semantic map construction and localization.” IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2022.3226821

Li, C., Chai, W., Wu, Q., Li, J., Lin, F., Li, Z., & Li, Q. (2022). “A graph optimization enhanced indoor localization method.” In 2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE). https://doi.org/10.1109/cipae55637.2022.00055

Li, C., Chai, W., Yang, X., & Li, Q. (2022). “Crowdsourcing-based indoor semantic map construction and localization using graph optimization.” Sensors. https://doi.org/10.3390/s22166263

Chai, W., Li, C., Zhang, M., Sun, Z., Yuan, H., Lin, F., & Li, Q. (2021). “An enhanced pedestrian visual-inertial SLAM system aided with vanishing point in indoor environments.” Sensors. https://doi.org/10.3390/s21227428

Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

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Mr. Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

Chongqing University of Technology | China

Dr. Xiaoxia Yu is a scholar in mechanical engineering whose work advances intelligent diagnostics and predictive maintenance for large-scale rotating machinery, particularly wind turbines. With a Ph.D. in Mechanical Engineering and earlier degrees in Vehicle Engineering and Armored Vehicle Engineering, she has built a strong interdisciplinary foundation that integrates mechanical systems knowledge with advanced computational modeling. Her research spans fault diagnosis, health assessment, digital twin systems, graph neural networks, reinforcement learning, and signal processing, supported by a growing publication record that includes 29 documents, 477 citations by 448 documents, and an h-index of 7. As a Lecturer, she leads research projects funded by regional scientific agencies and has contributed to national-level R&D initiatives related to machinery health management. Her work appears in high-impact journals, and she has secured patents focused on structural health monitoring, image recognition, and intelligent fault detection. Recognized with competitive grants and academic honors, she continues to influence the fields of renewable energy reliability and smart manufacturing. Through her commitment to innovation, research leadership, and engineering application, she is emerging as a key contributor to the development of intelligent, data-driven mechanical health monitoring systems.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Yu, X., Tang, B., & Zhang, K. (2021). Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks. IEEE Transactions on Instrumentation and Measurement, 70, 1–14.

Yu, X., Tang, B., & Deng, L. (2023). Fault Diagnosis of Rotating Machinery Based on Graph Weighted Reinforcement Networks Under Small Samples and Strong Noise. Mechanical Systems and Signal Processing, 186, 109848.

Zhang, K., Tang, B., Deng, L., & Yu, X. (2021). Fault Detection of Wind Turbines by Subspace Reconstruction‑Based Robust Kernel Principal Component Analysis. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

Li, B., Tang, B., Deng, L., & Yu, X. (2020). Multiscale Dynamic Fusion Prototypical Cluster Network for Fault Diagnosis of Planetary Gearbox Under Few Labeled Samples. Computers in Industry, 123, 103331.

Xiong, P., Tang, B., Deng, L., Zhao, M., & Yu, X. (2021). Multi‑block Domain Adaptation with Central Moment Discrepancy for Fault Diagnosis. Measurement, 169, 108516.

Somnath Nandi | Data-informed Decision Making | Best Researcher Award

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Mr. Somnath Nandi | Data-informed Decision Making | Best Researcher Award

Jadavpur University | India

Mr. Somnath Nandi is a PhD Research Scholar at the Additive Manufacturing Research Group (AMRG), CSIR-Central Mechanical Engineering Research Institute (CMERI), India, affiliated with the Academy of Scientific and Innovative Research (AcSIR). His research focuses on functionally graded material (FGM) development, mechanical and tribological characterization, and process optimization in Wire Arc Additive Manufacturing (WAAM). He has contributed to several funded projects, including the design of SS-Ni transition metals-based electrodes for green hydrogen generation, bimetallic structure development for Tata Steel, and remanufacturing of railway brake shoes. Mr. Nandi holds an M.E. in Production Management from Jadavpur University and a B.Tech in Mechanical Engineering from the Government College of Engineering and Textile Technology, Berhampore. His published works in reputed SCI-indexed journals such as Progress in Additive Manufacturing, Materials Letters, and Journal of Materials Engineering and Performance highlight his expertise in optimization, digital twins, and sustainable manufacturing. His research interests span metal additive manufacturing, tribology, hybrid manufacturing, and digital monitoring systems. A GATE-qualified engineer (2021, 2022), he is an active member of professional societies including IIM and IAENG. Mr. Nandi has authored 10+ scientific documents, achieving an h-index of 3 with over 45 citations, reflecting his growing impact in the field of advanced manufacturing.

Profiles : Orcid | Google Scholar

Featured Publications

Biswas, S., Nandi, S., Hussain, M. S., Mandal, A., & Mukherjee, M. (2025). “Influence of heat input on the interfacial characteristics of SS316L–In718 bi-metallic deposition for wire arc additive manufacturing.” Materials Letters.

Nandi, S., Biswas, S., & Mukherjee, M. (2025). “Optimization of Wire Arc Additive Manufacturing Parameters for Steel–Aluminum Bimetallic Interface: A Comparative Study of Metaheuristic and Machine Learning Approaches.” Journal of Materials Engineering and Performance, 35(6).

Ahammed, A. A. C. P., Nandi, S., Paul, A. R., Sreejith, B., MJ, J., & Mukherjee, M. (2025). “On the Characteristic Evaluation of Bimetallic Interface of Carbon Steel (EN31) and Aluminium (AA4043) for Wire Arc Additive Manufacturing.” Metals and Materials International, 1–18.

Pendokhare, D., Nandi, S., & Chakraborty, S. (2025). “A comparative analysis on parametric optimization of abrasive water jet machining processes using foraging behavior-based metaheuristic algorithms.” OPSEARCH, 1–42.