Mohd Faizan l Diagnostic Analytics | Research Excellence Award

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Mr. Mohd Faizan l Diagnostic Analytics | Research Excellence Award

University of Lille | France

Mohd Faizan is a PhD Candidate in Control Systems at the University of Lille, France, specializing in resilience control, fault-tolerant systems, power electronics, and DC microgrids. He holds an M.Tech and B.E. in Electrical Engineering from Aligarh Muslim University with first-class distinction. His professional experience includes PhD research at CRIStAL Laboratory on RTTR-based performance degradation prediction and prior research at NTUST, Taiwan, on LSTM-based PV fault detection. His research interests span resilience control, fault diagnosis, DC microgrids, and ML for control, supported by strong skills in MATLAB, Python, power converters, and hardware prototyping. He has received multiple scholarships, academic honors, and IEEE publications, positioning him for impactful research and engineering roles.

Citation Metrics (Scopus)

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Citations
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Documents
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Featured Publications

Online Estimation of Remaining Time to Recovery to Enhance Resilience Using Bond Graph Based Power Loss Estimation


IFAC Journal of Systems and Control, 2026
| Mohd Faizan, Mahdi Boukerdja, Anne Lise Gehin, Belkacem Ould Bouamama, Sumit Sood

Non-Linear Control of Interleaved Boost Converter Using Disturbance Observer-Based Approach


IEEE Access, 2025
| Avinash Mishra, Sanjoy Mandal, Jean-Yves Dieulot, Mrinal R. Bachute, Mohd Faizan et al.

Long Short-Term Memory-Based Feedforward Neural Network Algorithm for Photovoltaic Fault Detection Under Irradiance Conditions


IEEE Transactions on Instrumentation and Measurement, 2024
| Nien-Che Yang, Mohd Faizan

 

Design of 31-Level Asymmetrical Inverter With Reduced Components


International Journal of Circuit Theory and Applications, 2022

Design of Single Phase Five Level Packed U-Cell Inverter for Standalone and Grid Connected Modes


IEEE SEFET 2022 – International Conference on Sustainable Energy and Future Electric Transportation

Daxiong Ji | Data Analysis | Research Excellence Award

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Dr. Daxiong Ji | Data Analysis | Research Excellence Award

Zhejiang University | China

Dr. Daxiong Ji is an Associate Professor at the Institute of Marine Electronics and Intelligent Systems, Ocean College, Zhejiang University, and a Senior Member of IEEE. He earned his Ph.D. in Pattern Recognition and Intelligent Systems from the University of Chinese Academy of Sciences and a B.S. in Automation from Wuhan Polytechnic University. Dr. Ji has held visiting scholar positions at the University of Melbourne, University of Plymouth, and University of Victoria, collaborating internationally on advanced marine robotics research. His expertise spans automatic control, artificial intelligence applications, autonomous marine robotics, electrical engineering, and intelligent systems. He has supervised numerous undergraduate, master’s, and doctoral students, leading courses on Marine Robot Design, Marine Intelligent Systems, and Professional Practices. Dr. Ji has received multiple honors, including the First Prize of Zhejiang Provincial Teaching Achievement Award, recognition as an Excellent Moral Education Mentor, and awards for scientific and technological achievements from the Chinese Academy of Sciences. He has led and participated in numerous national and international research projects, focusing on autonomous navigation, fault diagnosis, and data-driven control of underwater vehicles. Dr. Ji is also an accomplished inventor with multiple patents in underwater robotics and autonomous systems. His research has been widely published in top journals, reflecting significant contributions to marine intelligent systems.

Profiles : Orcid | Google Scholar

Featured Publications

Ji, D.; Ogbonnaya, S.G.; Hussain, S.; Hussain, A.F.; Ye, Z.; Tang, Y.; Li, S. Three-Dimensional Dynamic Positioning Using a Novel Lyapunov-Based Model Predictive Control for Small Autonomous Surface/Underwater Vehicles. Electronics, 2025, 14(3), 489.

Xu, L.; Ji, D. Online Fault Diagnosis Using Bioinspired Spike Neural Network. IEEE Transactions on Industrial Informatics, 2024.

Zhou, J.; Ye, Z.; Zhao, J.; Ji, D.; Peng, Z.; Lu, G.; Tadda, M.A.; Shitu, A.; Zhu, S. Multi-detector and Motion Prediction-Based High-Speed Non-Intrusive Fingerling Counting Method. Biosystems Engineering, 2024, 218, 1–15.

Ji, D.; Wang, R. Path Following of QAUV Using Attitude-Velocity Coupling Model. Ocean Engineering, 2024, 293, 116885.

Ji, D.; Cheng, H.; Zhou, S.; Li, S. Dynamic Model-Based Integrated Navigation for a Small and Low-Cost Autonomous Surface/Underwater Vehicle. Ocean Engineering, 2023, 273, 114091.

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.