Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

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Mr. Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

Phd Student | Oum Elbouaghi University | Algeria

Ali Yahia-Cherif is a seasoned Algerian electrical and automation engineer born in 1991, with extensive expertise in predictive control systems, renewable energy, and electronic system diagnostics. He holds a Master’s in Industrial Automation and a Bachelor’s in Automatic Control from the University of Mentouri Constantine, and has been pursuing a Ph.D. in Electrical and Automatic Engineering since 2014 at the University of Larbi Ben M’hidi, focusing on the development of experimental platforms for shunt active filter systems. His professional experience spans over a decade, including roles as a principal engineer and project manager in high and low-current installations—covering solar energy systems, surveillance, access control, and fire safety networks. He also worked as an engineer in security systems and has teaching experience in programming and electrical engineering at the university level. His leadership in the F.E.A.T scientific electronics club demonstrates his early commitment to innovation. Ali has authored over 12 scientific publications in reputable journals such as IET Renewable Power Generation, International Journal of Circuit Theory and Applications, and European Journal of Electrical Engineering, addressing advanced topics in predictive control, hybrid energy storage, and photovoltaic systems. He has presented his work at key international conferences including GPECOM, IREC, and ICEEAC.

Profile: Google Scholar 

Featured Publications

1. Meddour, S., Rahem, D., Yahia Cherif, A., Hachelfi, W., & Hichem, L. (2019). A novel approach for PV system based on metaheuristic algorithm connected to the grid using FS-MPC controller. Energy Procedia, 162, 57–66. 
Cited by: 32

2. Remache, S. E. I., Yahia Cherif, A., & Barra, K. (2019). Optimal cascaded predictive control for photovoltaic systems: Application based on predictive emulator. IET Renewable Power Generation, 13(15), 2740–2751. 
Cited by: 29

3. Yahia Cherif, A., Remache, S. E. I., Barra, K., & Wira, P. (2019). Adaptive model predictive control for three phase voltage source inverter using ADALINE estimator. In 2019 1st Global Power, Energy and Communication Conference (GPECOM) (pp. 164–169). IEEE. 
Cited by: 7

4. Yahia Cherif, A., Hicham, L., & Kamel, B. (2018). Implementation of finite set model predictive current control for shunt active filter. In 2018 9th International Renewable Energy Congress (IREC) (pp. 1–6). IEEE. 
Cited by: 6

5. Meddour, S., Rahem, D., Wira, P., Laib, H., Yahia Cherif, A., & Chtouki, I. (2022). Design and implementation of an improved metaheuristic algorithm for maximum power point tracking algorithm based on a PV emulator and a double-stage grid-connected system. European Journal of Electrical Engineering, 24(3), 423–430.
Cited by: 5

Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

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Mr. Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

Ali Yahia Cherif | Oum El Bouaghi University | Algeria

Dr. Ali Yahia-Cherif is a Principal Engineer and accomplished researcher in Electrical and Automatic Engineering at the University of Larbi Ben M’hidi, specializing in predictive control, renewable energy systems, and power electronics. He holds a Doctoral candidacy in Electrical and Automatic Engineering, a Master’s degree in Industrial Automation and Human Systems, and a Licence in Automatic Systems from the University of Mentouri Constantine, building a strong academic foundation in advanced control and automation. His professional career encompasses roles as project manager for solar energy, fire prevention, and security system networks, engineer of study and repair in electronic and security systems, university lecturer in programming and applied electrical sciences, and founder of the F.E.A.T. scientific electronics club. Dr. Yahia-Cherif’s research contributions focus on model predictive control, photovoltaic systems, and metaheuristic algorithms, with impactful publications in prestigious journals and international conferences such as Energy Procedia, IET Renewable Power Generation, and the European Journal of Electrical Engineering. His works include novel approaches to PV system optimization, cascaded predictive control, adaptive model predictive strategies, and matrix converter algorithms for wind energy systems. In addition to his academic output, he has demonstrated leadership in multidisciplinary projects, successfully integrating theory and practice in renewable energy applications, electronic system diagnostics, and predictive control innovations. He has also served as a reviewer for international journals and conferences, underscoring his recognition within the academic community. Through his diverse professional activities and scholarly achievements, Dr. Yahia-Cherif exemplifies excellence in advancing research and engineering practice in renewable energy and automatic control. 55 Citations, 9 Documents, 4 h-index.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

Meddour S., Rahem D., Yahia-Cherif A., Hachelfi W., Hichem L., A novel approach for PV system based on metaheuristic algorithm connected to the grid using FS-MPC controller. Energy Procedia, 2019, 162: 57–66. (32 citations)

Remache S.E.I., Yahia-Cherif A., Barra K., Optimal cascaded predictive control for photovoltaic systems: application based on predictive emulator. IET Renewable Power Generation, 2019, 13(15): 2740–2751. (29 citations)

Yahia-Cherif A., Remache S.E.I., Barra K., Wira P., Adaptive model predictive control for three-phase voltage source inverter using ADALINE estimator. Proc. 1st Global Power, Energy and Communication Conference (GPECOM), 2019: 164–169. (7 citations)

Yahia-Cherif A., Hicham L., Kamel B., Implementation of finite set model predictive current control for shunt active filter. Proc. 9th Int. Renewable Energy Congress (IREC), 2018: 1–6. (6 citations)

Meddour S., Rahem D., Wira P., Laib H., Yahia-Cherif A., Chtouki I., Design and implementation of an improved metaheuristic algorithm for maximum power point tracking based on a PV emulator and a double-stage grid-connected system. Eur. J. Electrical Engineering, 2022. (5 citations)

Reza Faraji | Electrical Engineering | Best Researcher Award

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Dr. Reza Faraji | Electrical Engineering | Best Researcher Award

Reza Faraji | University of Science and Culture | Iran

Dr. Reza Faraji is a Ph.D. candidate in Electrical and Computer Engineering at Islamic Azad University, with additional academic affiliation at the University of Science and Culture, specializing in nanoelectronics and Quantum-dot Cellular Automata (QCA). He holds a Master’s degree in QCA Design, where his work centered on low-power, high-performance digital circuits. His professional experience spans research assistance and participation in industry-oriented projects, with a focus on energy-efficient architectures for future 6G-enabled IoT systems and semiconductor devices. Faraji’s research expertise encompasses nanoscale circuit design, reversible computing, QCA-based arithmetic logic unit (ALU) and full-adder design, and nanoscale device modeling, including HEMTs and MOSHEMTs. He has published influential work such as the development of a novel reversible multilayer full adder in QCA technology, a compact multilayer ALU achieving ultra-low power dissipation, and a multilayer reversible ALU (RALU) integrating Fredkin and HN gates for optimized area and power efficiency. He has also contributed to advanced modeling of AlN/β- and ε-Ga₂O₃ tri-gate MOSHEMTs for high-power and RF applications, providing theoretical insight into next-generation device performance. His contributions have been cited in multiple international journals, earning recognition for advancing low-power nanoelectronics bridging QCA computing and semiconductor technologies, making him a strong candidate for prestigious technology awards. He has 18 citations, 5 publications with an h-index of 3.

Profile: Scopus

Featured Publications

1. Faraji R., A novel reversible multilayer full adder circuit design in QCA technology. Facta Univ. Ser. Electron. Energ., 2024, 37(3), 437–453.

2. Faraji R., Design of a multilayer reversible ALU in QCA technology. J. Supercomput., 2024, 80(12), 17135–17158.

3. Khodabakhsh A.*, Faraji R., Tandem evaluation of AlN/β- and ε-Ga₂O₃ tri-gate MOSHEMTs. IEEE Trans. Electron Devices, 2025, 72(7), 3452–3460.

Xu Zhang | Motor control | Best Researcher Award

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Dr. Xu Zhang | Motor control | Best Researcher Award

Lecturer | North China Electric Power University | China

Xu Zhang is a productive researcher and lecturer specializing in advanced electrical drive systems, with a research record focused on flux-weakening control, sensorless control, model predictive control, and high-speed AC motor performance. He has authored over forty academic papers, with many appearing in top-tier journals such as IEEE Transactions on Power Electronics, IEEE Transactions on Industrial Electronics, and IEEE Transactions on Transportation Electrification; he is frequently the first or corresponding author. Xu has led competitive research projects funded by national and provincial agencies and served as a guest editor and reviewer for major journals. His work blends theoretical control design, algorithm development, and experimental validation, resulting in recognized contributions to torque quality improvement, overmodulation suppression, and rotor-parameter estimation techniques. Colleagues and collaborators note his consistent research leadership, strong grant-winning record, and commitment to translating advanced control methods toward practical motor-drive implementations.

Professional Profile

ORCID

Education 

Xu Zhang completed a progressive program of formal training in electrical engineering. He earned his Bachelor of Engineering in Electrical Engineering, followed by a Master’s degree and then a Ph.D. in Electrical Engineering. His postgraduate training emphasized electric drives, power electronics, and control theory, and his doctoral research concentrated on high-speed flux-weakening control and dynamic current control for AC and induction motors. His graduate studies included coursework and research in control systems, power converters, electrical machines, and signal processing for sensorless drives. The academic training was complemented by research placements and collaborations at institutions active in power-electronics research, enabling him to couple rigorous theoretical foundations with experimental and simulation-based validation. His formal education prepared him to design advanced control architectures and to publish in high-impact venues in power electronics and industrial electronics.

Experience 

Xu Zhang’s professional experience combines academic appointment, doctoral research, and project leadership. He currently serves as a Lecturer in Electrical Engineering, where he teaches courses and supervises student research while leading laboratory experiments on motor-drive control. Previously he completed his Ph.D., during which he executed multiple research projects on flux-weakening, sensorless control, and predictive control. He has been principal investigator or project lead on grants from national postdoctoral programs and from provincial and university funding agencies. His duties include designing control algorithms, directing prototype testing, preparing grant proposals, and coordinating collaborators across institutions. He has chaired conference sessions, served as guest editor for journals, and regularly reviews submissions for leading IEEE journals. This combination of teaching, laboratory leadership, grant management, and scholarly service underpins his capacity to deliver research that is both rigorous and application-ready.

Research focus 

Xu Zhang’s research centers on control strategies and practical techniques that improve the performance of electric motor drives under constrained-voltage and high-speed conditions. Key focus areas include flux-weakening control for PMSMs and induction motors, sensorless position estimation and back-EMF methods, overmodulation and torque ripple mitigation, and model predictive control tailored to finite-control-set converters. He also investigates online parameter estimation (e.g., rotor time-constant estimation) to maintain performance across operating conditions and develops methods for improved current dynamic response and torque quality under voltage saturation and hexagon voltage extension. His work integrates analytical modeling, adaptive/robust control design, and experimental validation on drive testbeds, aiming to increase torque output, reduce harmonic distortion, and enhance position estimation accuracy without relying on mechanical sensors. Research directions increasingly include bridging advanced control methods to real-time implementation and industrial testbed validation.

Publication Top Notes

Title: Accuracy Improvement of Back-EMF-Based Position Sensorless Control of PMSM Drives Based on Virtual Current Sampling
Year: 2025

Title: A Model Reference Adaptive System-Based Online Rotor Time Constant Estimation Method for Induction Motor Field-Weakening Control Utilizing Dot Product of Stator Voltage and Stator Current
Year: 2024

Title: Analysis and Optimization of Current Coupling Control in Flux-Weakening Region of PMSM
Year: 2024

Title: Optimization of Current Dynamic Performance and Torque Harmonic for Induction Motor Field-Weakening Control Under Hexagon Voltage Extension
Year: 2024

Title: Overmodulation Harmonic Modeling and Suppression for Induction Motor Field-Weakening Control With Extended Voltage Tracking Method
Year: 2023

Title: Torque Adaptive Hexagon Voltage Extension Method for PMSM Flux-Weakening Control Based on Dual PI Cascade Structure
Year: 2023

Title: Minimum-Nonlinear-Voltage Method for Torque Ripple Suppression in Induction Motor Overmodulation and Field-Weakening Control
Year: 2022

Title: Overmodulation Index Optimization Method for Torque Quality Improvement in Induction Motor Field-Weakening Control
Year: 2021

Title: Unified Complex Vector Field-Weakening Control for Induction Motor High-Speed Drives
Year: 2021

Conclusion

Xu Zhang is a highly promising and impactful researcher whose work bridges theoretical innovation with practical applications in electric motor drive systems. His strong publication record, consistent funding acquisition, leadership roles in top conferences, and recognition through prestigious awards position him as a deserving candidate for the Best Researcher Award. With continued efforts toward expanding interdisciplinary collaboration and exploring next-generation control strategies, Xu Zhang is poised to become a leading figure in the field of advanced motor control systems.

Lei Tian | Embedded Systems | Best Paper Award

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Assoc Prof. Dr. Lei Tian | Embedded Systems | Best Paper Award

Laboratory Director at Xi’an University of Posts and Telecommunications | China

Lei Tian is a laboratory director at Xi’an University of Posts & Telecommunications whose work spans embedded systems, new semiconductor materials, and optoelectronic interconnection. He has focused on the analysis, modeling, and design of photoelectric coupling systems, including conversion‑efficiency optimization and noise‑reduction modeling. He has led and completed provincial and municipal R&D projects, contributed to State Grid initiatives, and authored both a monograph and a ministry‑planned textbook. His publication record includes more than sixty papers across SCI, EI, and core journals, with recent articles in the International Journal of Hydrogen Energy, Diamond & Related Materials, Physica Status Solidi B, and on power‑management circuits. Tian’s recent research advances 2D/Janus heterostructures for water splitting and gas sensing, and investigates device‑level co‑design strategies where materials inform embedded hardware architectures. His work targets sustainable energy, intelligent sensing, and robust, low‑noise, high‑efficiency systems suitable for real‑world deployment.

Professional Profile

Scopus

Education 

Lei Tian earned a Ph.D. in Circuits and Systems from Xidian University, emphasizing the intersection of signal integrity, noise modeling, and device‑level architectures for mixed‑signal and optoelectronic systems. Postdoctoral training at the Institute of Modern Physics, Northwest University, strengthened his first‑principles and multi‑physics modeling toolkit, including density‑functional workflows that bridge material properties to circuit‑level specifications. This background shaped a research style that connects quantum‑scale material parameters with embedded‑system requirements such as power budgets, spectral response, and noise floors. Coursework and mentoring activities have centered on semiconductor devices, optoelectronic interfaces, embedded firmware for instrumentation, and algorithm‑hardware co‑optimization. Tian’s graduate and postdoctoral path fostered collaborations across materials science, device physics, and systems engineering, informing a translational approach from theory to prototypes. The resulting expertise supports end‑to‑end pipelines—from ab initio predictions and sensor stack design to embedded control, calibration routines, and system‑level validation for power, reliability, and real‑time performance.

Experience 

As Laboratory Director at Xi’an University of Posts & Telecommunications, Lei Tian leads a group focused on optoelectronic interconnection and embedded hardware–software co‑design. The team develops modeling frameworks for photoelectric conversion efficiency, designs low‑noise coupling schemes, and validates concepts through simulations and targeted prototypes. He has steered key provincial R&D programs and municipal science projects, as well as multiple State Grid engagements, delivering deployable insights for power and sensing infrastructure. Tian’s portfolio extends from novel 2D/Janus heterostructures and graphene‑based stacks to practical power‑management ICs such as high‑voltage, low‑quiescent‑current LDOs with stability‑oriented impedance buffers. He regularly collaborates with materials scientists and circuit designers to translate computed properties into embedded constraints, addressing latency, energy, thermal limits, and field robustness. Alongside publications and books, his experience includes curriculum and lab development, fostering hands‑on training that connects material innovation with firmware, drivers, diagnostics, and system bring‑up.

Research Focus

Tian’s research targets the convergence of embedded systems with novel semiconductor and 2D materials. The thrusts include first‑principles discovery of van der Waals and Janus heterojunctions optimized for hydrogen evolution and gas sensing  photoelectric conversion analysis and noise‑reduction modeling for optoelectronic coupling embedded co‑design, where device physics informs circuit topologies, firmware routines, and on‑board diagnostics; and power‑management solutions such as high‑voltage LDOs with ultra‑low quiescent current for edge instrumentation. A defining feature is the “materials‑to‑metrics” pipeline—mapping band alignments, excitonic effects, and defect physics to embedded KPIs like SNR, dynamic range, and power efficiency. This enables predictive selection of sensor stacks and control algorithms prior to fabrication, accelerating time‑to‑prototype. Recent studies on MoSSe‑based heterostructures for water splitting exemplify this approach, linking catalytic descriptors to embedded monitoring strategies and stability management for scalable, field‑ready hydrogen‑generation systems.

Publication Top Notes

Title: Z-scheme WSTe/MoSSe van der Waals heterojunction as a hydrogen evolution photocatalyst: First-principles predictions
Year: 2025

Title: First-principles exploration of hydrogen evolution ability in MoS₂/hBNC/MoSSe vdW trilayer heterojunction for water splitting
Year: 2025

Title: Research of Power Inspection Based on Intelligent Algorithm
Year: 2025.

Conclusion

Lei Tian’s research exhibits high originality, technical depth, and relevance to global energy challenges, making the candidate a strong contender for the Best Paper Award. The contributions to hydrogen evolution photocatalysts using novel van der Waals heterojunctions represent valuable advancements in computational materials science. With further emphasis on experimental validation and broader impact demonstration, the works could achieve even greater recognition. Overall, the candidate’s publications align well with the award’s objectives, and the research output shows significant promise for long-term influence in sustainable energy technologies.