Jiahao Luo | Smart Agriculture | Best Researcher Award

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Jiahao Luo | Smart Agriculture | Best Researcher Award

Graduate Student | Xihua University | China

Mr. Jiahao Luo is a graduate student at Xihua University specializing in multi-machine collaborative scheduling and path planning for intelligent agricultural mechanization. He holds a strong academic background in optimization algorithms and their applications to complex agricultural systems, focusing on methodological innovation and practical implementation. His professional experience includes leading research on traversal path planning and collaborative scheduling for corn harvesting and transportation in challenging hilly terrains, integrating Dijkstra’s algorithm with improved Harris Hawk Optimization to enhance efficiency and safety. Jiahao has published six SCI-indexed papers, including one in a Q1 journal, three in Q2, and two in Q3, showcasing a consistent record of impactful contributions to high-quality research. His work advances hybrid algorithms that combine evolutionary computation with local search, addressing real-world challenges such as terrain complexity, dynamic obstacles, and operational coordination, ultimately improving mechanization in agriculture. In addition to his research output, Jiahao has contributed to three consultancy or industry projects and holds three patents under process, reflecting the translational value of his work. His efforts significantly bridge the gap between theory and application, supporting sustainable, technology-driven farming practices with both academic and industrial relevance.

Profile: Scopus 

Featured Publication

Luo J.*, Intelligent Path Tracking for Single-Track Agricultural Machinery Based on Variable Universe Fuzzy Control and PSO-SVR Steering Compensation. Agriculture Switzerland, 2025.

Shiyu Liu | Feature extraction | Best Researcher Award

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Shiyu Liu | Feature extraction | Best Researcher Award

Lecturer | Hebei University | China

Dr. Shiyu Liu is a Lecturer and Postdoctoral Researcher at the College of Quality and Technical Supervision, Hebei University, specializing in spectral detection, artificial intelligence, and battery health monitoring. He earned his Ph.D. in Instrument Science and Technology from Yanshan University and served as a visiting Ph.D. student at the University of Huddersfield, where he worked on battery health monitoring using AI methods. His professional experience includes leading and contributing to multiple national and provincial research projects on spectral detection, lithium-ion battery state-of-health estimation, and environmental monitoring technologies. His research integrates chemometrics, machine learning, and deep learning to advance near-infrared spectroscopy for accurate detection of complex organic compounds and diesel quality management. Dr. Liu has authored numerous peer-reviewed articles in high-impact journals such as Chemometrics and Intelligent Laboratory Systems, Journal of Energy Storage, and Spectrochimica Acta Part A, and holds several patents related to NIR-based classification systems. He is skilled in big data analysis, ensemble learning, and deep learning algorithm development, with applications across energy storage systems, environmental monitoring, and process optimization. His academic contributions include presenting at international conferences, providing technical support, and publishing innovative methodologies. He has received multiple honors, including the Excellence Award at the Hebei Provincial Competition of the National Postdoctoral Innovation and Entrepreneurship Competition, a CSC Scholarship, and national and provincial “excellent graduate” titles, reflecting his significant impact on advancing intelligent measurement and monitoring technologies.

Profile: Scopus

Featured Publications

Liu, S., Fang, L., & Wang, S. (2025). Accurate determination of alcohol-based diesels using optimal chemical factors. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 326, 125140.

Liu, S., Fang, L., & Zhao, X. (2024). State-of-health estimation of lithium-ion batteries using a kernel support vector machine tuned by a new nonlinear gray wolf algorithm. Journal of Energy Storage, 102, 114052.

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.

Xiao Liang | Multi-Agent Control | Outstanding Scientist Award

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Prof. Xiao Liang | Multi-Agent Control | Outstanding Scientist Award

Associate Dean | Shenyang Aerospace University | China

Xiao Liang is a Professor at Shenyang Aerospace University and Associate Director of the Key Laboratory of Liaoning Province. He earned his B.S. in Automation from Northeastern University, followed by an M.S. in Control Theory and Control Engineering. He received his Ph.D. in Navigation, Guidance, and Control from Beihang University. With over 30 academic papers published in leading journals and five authorized patents, Liang has significantly advanced unmanned aerial and ground vehicle (UAV/UGV) research. His international engagement includes academic exchanges at Swansea University, UK. He has led numerous prestigious projects funded by the National Natural Science Foundation of China, the Aeronautical Science Foundation, and provincial foundations. His research integrates intelligent decision-making, mission planning, and heterogeneous multi-agent collaboration. Liang’s work bridges theory and practice, addressing challenges in robotics, aerospace, and autonomous systems.

Professional Profiles

ORCID | Scopus

Education 

Xiao Liang’s academic journey demonstrates consistent excellence and commitment to automation and control engineering. He completed his undergraduate studies in Automation at Northeastern University, where he distinguished himself by being recommended for postgraduate study without entrance examinations. He pursued a Master’s degree in Control Theory and Control Engineering, which he earned. His doctoral studies at Beihang University culminated in a Ph.D. in Navigation, Guidance, and Control, specializing in autonomous systems and flight control technologies. This educational trajectory provided Liang with a robust foundation in aircraft design, control theory, and computational modeling. His exposure to both theoretical and applied dimensions of control engineering has enabled him to integrate multidisciplinary perspectives into his research. An academic exchange visit to Swansea University further enriched his international outlook. His education laid the groundwork for his leadership in UAV/UGV cooperative systems and intelligent autonomous decision-making.

Experience 

Xiao Liang is a Professor at Shenyang Aerospace University’s School of Automation, where he supervises master’s students and directs cutting-edge research. He also serves as Associate Director of the Key Laboratory of Liaoning Province. His research leadership includes hosting major projects funded by the National Natural Science Foundation of China, the Aeronautical Science Foundation of China, and multiple provincial agencies. Liang’s projects span UAV/UGV path planning, dynamic tracking, collaborative sensing, and obstacle avoidance, with applications in rescue missions and aerospace systems. He has published widely in journals such as Intelligent Service Robotics, Robotics and Autonomous Systems, and Aerospace Science and Technology. Beyond academia, his work includes five authorized patents on UAV control systems and path planning methods, underscoring his role in translating research into technological innovations. Liang’s international experience, including research exchange at Swansea University, has further shaped his multidisciplinary approach to intelligent decision-making and multi-agent collaboration.

Research Focus 

Xiao Liang’s research lies at the intersection of control theory, artificial intelligence, and aerospace systems. His primary focus is the design and optimization of heterogeneous multi-agent systems, particularly UAV/UGV collaboration in dynamic and complex environments. His contributions span navigation, guidance, and control (GNC), pursuit-evasion strategies, multi-agent decision-making, and robust target-tracking algorithms. Liang has introduced innovative approaches such as visual SLAM in dynamic environments, semantic octree mapping, and adaptive path planning methods under uncertain conditions. His work also explores mission planning under adversarial and rescue scenarios, emphasizing robustness and fault tolerance. Liang integrates hardware-software design into UAV/UGV systems, ensuring practical applicability. His pioneering contributions extend beyond aerospace into areas such as smart shopping systems for unmanned supermarkets, reflecting the adaptability of his methods. With a balance of theoretical advancement and applied innovation, his research continues to push the boundaries of intelligent multi-agent control and autonomous robotics.

Awards and Honors 

Xiao Liang has been recognized with multiple prestigious awards for his scientific and technological contributions. He received the Science and Technology Award from the Chinese Society of Astronautics, highlighting his impact in aerospace innovation. His mentorship and leadership have also led student teams to achieve top recognition: Second Prize in the 16th “Challenge Cup” National Undergraduate Academic Science and Technology Works, and First Prize in both the 13th and 14th China Graduate Electronic Design Competitions. He was selected for the Liaoning BaiQianWan Talents Program and recognized among the High-level Talents of Shenyang in the same year. His earlier contributions earned the Progress in Science and Technology Award from both Liaoning Province and Shenyang City. Collectively, these honors reflect Liang’s sustained excellence, leadership, and innovation in aerospace engineering, intelligent systems, and multi-agent control research, positioning him as a leading scientist of his generation.

Publication Top Notes

Title: Design and Development of Full Self-Service Smart Shopping System for Unmanned Supermarket
Year: 2025

Title: STSLAM: Robust Visual SLAM in Dynamic Scenes via Image Segmentation and Instance Tracking
Year: 2025

Title: Real-Time Semantic Octree Mapping under Aerial-Ground Cooperative System
Year: 2025

Title: Collaborative Pursuit-Evasion Game of Multi-UAVs Based on Apollonius Circle in the Environment with Obstacle
Year: 2023

Title: Design and Development of Ground Station for UAV/UGV Heterogeneous Collaborative System
Year: 2021

Title: Fault-Tolerant Control for the Multi-Quadrotors Cooperative Transportation under Suspension Failures
Year: 2021

Title: Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
Year: 2021

Title: Collaborative Pursuit-Evasion Strategy of UAV/UGV Heterogeneous System in Complex Three-Dimensional Polygonal Environment
Year: 2020

Title: Moving Target Tracking Method for Unmanned Aerial Vehicle/Unmanned Ground Vehicle Heterogeneous System Based on AprilTags
Year: 2020

Title: Moving Target Tracking of UAV/UGV Heterogeneous System Based on Quick Response Code
Year: 2019

Title: Real-Time Moving Target Tracking Algorithm of UAV/UGV Heterogeneous Collaborative System in Complex Background
Year: 2019

Title: A Geometrical Path Planning Method for Unmanned Aerial Vehicle in 2D/3D Complex Environment
Year: 2018

Conclusion

Xiao Liang demonstrates a strong combination of scholarly excellence, technological innovation, and leadership in the field of unmanned systems and intelligent robotics. His contributions to UAV/UGV heterogeneous collaboration, robust control, and path planning strategies position him as a highly competitive candidate for the Research for Outstanding Scientist Award. With further efforts toward global collaboration and broader application of his research outcomes, his profile will continue to strengthen and align well with the award’s standards of excellence.