Zhen Guo | Automotive Robotics | Research Excellence Award

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Dr. Zhen Guo | Automotive Robotics | Research Excellence Award

Doctoral student |  Wuhan University of Technology |  China

Dr. Zhen Guo is an emerging researcher in intelligent fault diagnosis and industrial artificial intelligence, with work spanning advanced data-driven methods for complex mechanical and robotic systems. The academic profile reflects strong training in engineering research with a focus on applying deep learning and statistical learning to real-world reliability challenges. Professional experience includes active service as a peer reviewer for leading international journals and conferences in engineering informatics, artificial intelligence, measurement science, and mechanical systems, demonstrating recognition within the scholarly community. Research interests center on deep learning–based fault diagnosis, rotating machinery health monitoring, robotics, anomaly detection, imbalance learning, few-shot and incremental learning, and transfer learning, particularly for wind turbines, gearboxes, and autonomous systems. Research skills encompass convolutional and attention-based neural networks, adversarial learning, feature extraction, time-series analysis, imbalance data handling, and intelligent condition monitoring frameworks. Contributions are evidenced by publications in high-impact journals such as Renewable Energy, IEEE Transactions on Instrumentation and Measurement, Expert Systems with Applications, Measurement, Ocean Engineering, and Scientific Reports, addressing both theoretical modeling and practical deployment. Awards and honors are reflected through consistent publication in top-tier venues and trusted reviewer roles. Overall, the work demonstrates a coherent trajectory toward robust, scalable, and intelligent diagnostic solutions for next-generation industrial and transportation systems. He has achieved 152 Citations 11 Documents 7h-index.

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Featured Publications

Bao Xie | Electric Vehicles | Editorial Board Member

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Dr. Bao Xie | Electric Vehicles | Editorial Board Member

lecturer | Hefei University of Technology | China

Dr. Bao Xie is an accomplished researcher and lecturer specializing in Electrical Engineering with a strong academic foundation and extensive experience in renewable energy systems, grid-connected power generation, and inverter stability control. Currently serving as a lecturer and supervisor of master’s candidates at the Hefei University of Technology, he has established a solid reputation in the fields of power electronics, control theory, and grid integration of renewable energy sources. His research primarily focuses on the control and stability of renewable energy grid-connected power systems, addressing challenges related to weak grid conditions, harmonic resonance, and digital control of large-scale photovoltaic (PV) plants. Over the years, Dr. Xie has demonstrated exceptional technical acumen and problem-solving ability, contributing significantly to multiple national and provincial-level research projects, including the Anhui Provincial Natural Science Foundation and the National Key Research and Development Programs. His research skills encompass advanced modeling, control strategy design, resonance analysis, and power conversion optimization, supported by a profound understanding of grid dynamics and inverter interactions. Dr. Xie’s scholarly contributions include over 60 publications in prestigious journals such as IEEE Transactions on Energy Conversion, IET Power Electronics, and International Journal of Electrical Power and Energy Systems, showcasing innovative approaches to improving grid stability and renewable integration. His dedication to academic excellence has earned him recognition as a promising figure in the next generation of electrical engineers, with his work offering impactful insights for sustainable and intelligent energy systems. Through his continuous pursuit of innovation, collaboration, and mentorship, he exemplifies the integration of theory and practical engineering for real-world energy applications. He has achieved 653 Citations, 61 Documents, 15h-index.

Profile:   Scopus

Featured Publications 

  1. Xie, B., Zheng, W., Li, P., Shi, Y., & Su, J. (2025). Stability analysis and admittance reshaping for PQ inverters with different power control methods. International Journal of Electrical Power and Energy Systems.

  2. Xie, B., Zhang, Q., Liu, T., Zhou, L., & Hao, G. (2025). Research on multi-model LQR control strategy for grid-connected inverters under weak grid. Electric Power Systems Research.

  3. Xie, B., Guo, K., Mao, M., Zhou, L., Liu, T., & Zhang, Q. (2025). Optimization of energy storage capacity of village-level microgrid considering the orderly charging of electric vehicles. Sustainable Energy Grids and Networks.

  4. Xie, B., Zhou, L., Liu, T., Zhang, Q., & Hao, G. (2025). Topology and control method of interleaved parallel DC/DC converters with ripple compensation for fuel cell applications. Journal of Power Electronics.

  5. Xie, B., Mao, M., Liu, T., Zhou, L., & Zhang, Q. (2025). State prediction consistency secondary control strategy for microgrids with adaptive virtual impedance. Dianji Yu Kongzhi Xuebao (Electric Machines and Control).

    Dr. Bao Xie’s research advances the stability, efficiency, and intelligence of renewable energy integration within modern power grids. His innovative control strategies for inverters and microgrids foster sustainable energy transitions and resilient smart grid infrastructures. Through interdisciplinary research bridging academia and industry, his work supports global innovation in clean energy technologies and digital power systems.

Rasool Ghobadian | Automobile Engineering | Best Researcher Award

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Prof. Dr. Rasool Ghobadian | Automobile Engineering | Best Researcher Award

University Professor  |  Razi university  |  Iran

Dr. Rasoul Ghobadian is a distinguished academic and researcher in the field of water engineering, specializing in advanced hydraulics, sediment transport, flood modeling, and computational hydraulics. As a professor at the Department of Water Engineering, Faculty of Agriculture, Razi University of Kermanshah, he has contributed extensively to both academia and industry through innovative research, design expertise, and teaching excellence. His professional experience spans over two decades, including collaboration with leading consulting firms such as Saman Abraha Tose, Alborz Studies, and Dezab Consulting Engineers, where he participated in large-scale irrigation and hydraulic infrastructure projects involving surface irrigation networks, diversion dams, and intake systems. Dr. Ghobadian’s research integrates theoretical modeling with practical applications, focusing on seepage loss estimation, flood routing, and the hydraulic performance of river confluences and hydraulic structures, with publications in reputed international journals such as Journal of Hydrologic Engineering, Water SA, and Alexandria Engineering Journal. His expertise encompasses computational fluid dynamics, numerical modeling, and water resource system design. Recognized for his excellence, he has received multiple awards including Best Researcher and Exemplary Teaching Professor from the Faculty of Agriculture and the Department of Water Engineering. His research has greatly influenced sustainable water resource management and hydraulic engineering education. Dr. Ghobadian’s scholarly achievements continue to advance the global understanding of hydraulic systems and water infrastructure optimization. He has achieved 136 Citations,  30 Documents, 6 h-index.

Profiles: Google Scholar  |  ORCID  |  Scopus

Featured Publications 

  1. Ghobadian, R., & Bajestan, M. S. (2007). Investigation of sediment patterns at river confluence. [Unpublished study]. Citations: 51

  2. Ghobadian, R., & Mohammadi, K. (2011). Simulation of subcritical flow pattern in 180° uniform and convergent open-channel bends using SSIIM 3-D model. Water Science and Engineering, 4(3), 270–283. Citations: 35

  3. Ghobadian, R., & Meratifashi, E. (2012). Modified theoretical stage-discharge relation for circular sharp-crested weirs. Water Science and Engineering, 5(1), 26–33. Citations: 21

  4. Ghobadian, R. (2007). Investigation of flow, scouring and sedimentation at river-channel confluences. Ph.D. Thesis, Department of Hydraulic Structures, Shahid Chamran University of Ahvaz. Citations: 13

  5. Mobara, S. E. H., Ghobadian, R., Rouzbahani, F., & Đorđević, D. (2021). Numerical simulation of submarine non-rigid landslide by an explicit three-step incompressible smoothed particle hydrodynamics. Engineering Analysis with Boundary Elements, 130, 196–208. Citations: 12

Dr. Rasoul Ghobadian’s pioneering research in hydraulic and sediment transport modeling enhances the understanding of river dynamics, erosion, and flood management systems. His work bridges scientific innovation with engineering practice, contributing to the sustainable design of hydraulic structures, water resource optimization, and disaster resilience for communities and industries worldwide.

 

Jiayin Tang | Automotive Artificial Intelligence | Excellence in Research Award

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Dr. Jiayin Tang | Automotive Artificial Intelligence  |  Excellence in Research Award

Associate professor  |  Southwest Jiaotong university  |  China

Prof. Jiayin Tang is a distinguished academic and researcher whose work bridges the fields of manufacturing, reliability engineering, and intelligent systems, with a strong focus on mechanical, electrical, and automation technologies. His scholarly pursuits emphasize reliability assessment, degradation modeling, fault diagnosis, and intelligent prediction within industrial systems, contributing to both theoretical innovation and practical applications in smart manufacturing and system health management. His research encompasses areas such as accelerated life testing, reliability inference under multiple stress factors, and fault detection using deep learning and advanced signal processing. Prof. Tang’s notable publications in leading international journals including IEEE Transactions on Instrumentation and Measurement, Quality and Reliability Engineering International, PLOS ONE, and IEEE Sensors Journal demonstrate his mastery of reliability modeling and intelligent diagnostic algorithms. He has developed advanced methodologies such as Wiener process-based models, complex attention transformers, and graph attention networks to enhance predictive maintenance and system dependability in modern industrial environments. With expertise in automation, transportation systems, and electronic reliability, he continues to contribute significantly to the advancement of smart industrial solutions and sustainable engineering practices. Prof. Tang’s research skills include data-driven modeling, machine learning, statistical analysis, and sensor-based fault detection, reflecting his interdisciplinary strength and innovative vision. His dedication to academic excellence and impactful research has earned him recognition within the international reliability and automation research communities. He has acheived 250 Citations, 39 Documents, 8 h-index.

Profiles:  ORCID  |  Scopus

Featured Publication

  1. Gan, W., & Tang, J. (2024). Multi-Performance Degradation System Reliability Analysis with Varying Failure Threshold Based on Copulas. Symmetry, 16(1), 57.