جست‌وجوی استاد و پژوهشگر

• Control Systems
• Fault Detection and Isolation (FDI)
• Electric Drives and Electromechanical Systems
• +۳ مورد دیگر

Sanja Antić, PhD , is an Associate Professor in the Department of Electrical Engineering at the Faculty of Technical Sciences Čačak , University of Kragujevac , Serbia. She has been a faculty member since 2001, teaching a broad range of control-systems and electrical-engineering courses. Education PhD in Electrical Engineering, Faculty of Electrical Engineering, University of Belgrade (2016) Dissertation: “Application of Model-Based Failure Detection Methods in Electro-Mechanical Systems” MSc in System Control, Faculty of Technical Sciences Čačak (2009) Thesis: “Simulation and Realization of Voltage-Current Control of a DC Micro Motor” Dipl. Ing. in Industrial Power Engineering, Faculty of Technical Sciences Čačak (2000) Thesis: “Application of Fuzzy Logic in Estimating the Dynamic Model of a DC Motor” Research Interests Her research centres on model-based fault detection and isolation (FDI) for electromechanical systems, with particular emphasis on permanent-magnet DC motors and their associated power-electronic amplifiers. She develops structured and directional residual techniques combined with parameter-estimation algorithms to detect actuator, sensor and multiplicative faults. Additional interests include energy-efficient electric drives , digital control systems , torque-ripple reduction in induction motor drives , and the creation of remote and virtual laboratories for engineering education. Scientific Awards & Recognition “Vuk Karadžić” diploma for exceptional academic performance (primary & secondary school) Top graduate of the 1999/2000 academic year, Faculty of Technical Sciences Čačak Projects & Funding Principal Investigator, Serbian Ministry of Education project “Modernisation of three compulsory courses in Electrical Machines 2, Automatic Control and Electric Drives” (2020-2021) Team member, TEMPUS project “Building Network of Remote Labs for strengthening university-secondary vocational schools collaboration” (2013-2016) Participated in national technological-development projects on energy efficiency of electric motor drives (2011-2014) and prototype development of a 4-axis CNC welding machine (2011-2012) Teaching & Educational Contributions Dr Antić has designed and delivered courses in Automatic Control , Digital Control Systems , Control of Electromotive Drives , Fundamentals of Electrical Engineering and Electric Drives . She co-authored three textbooks and numerous workbooks and practicums that integrate remote-experiment platforms, thereby fostering hands-on learning aligned with Industry 4.0 concepts.

Harold W. Lewisمشاهده پروفایل

استاد

• Intelligent systems
• Fuzzy control
• Soft computing
• +۳ مورد دیگر

Harold W. Lewis is the Bartle Professor at the School of Systems Science and Industrial Engineering, Binghamton University. He holds a BS, MS, and PhD from Binghamton University. His research focuses on intelligent systems, fuzzy control, and soft computing, emphasizing applications in control systems and cognitive modeling. Key research interests include fuzzy clustering algorithms, neuro-fuzzy systems, and adaptive control methodologies. His work bridges theoretical foundations with practical implementations in automation and decision-making under uncertainty. Dr. Lewis received the Chancellor's Award for Excellence in Teaching (2013). His contributions span fuzzy logic, evidence theory, and systems engineering, with notable studies on aircraft landing scheduling and cognitive approaches to soft computing. His advising and grants involve advancing fuzzy set theory applications in interdisciplinary fields. Ongoing work explores hybrid systems and AI-driven control frameworks.

Guillaume SANDOUمشاهده پروفایل

استاد

• Model Predictive Control
• Optimization
• Autonomous Systems
• +۹ مورد دیگر

Guillaume SANDOU is a Professor at CentraleSupélec, University Paris-Saclay, leading the Control Department and affiliated with the Signals and Systems Laboratory (L2S). He holds a Ph.D. (2005) and HDR (2012) from University Paris-Sud XI. His research focuses on optimization-based control methodologies, including model predictive control (MPC), metaheuristics, and energy systems. He has supervised numerous PhD students, contributing to advancements in autonomous systems, wind farm control, and smart grid technologies. Key research areas include robust control, multi-agent systems, and sensorless control for electric drives. He actively contributes to education, redesigning curricula to integrate modern control trends and industry needs, impacting over 800 students annually. His work spans applications in energy optimization, autonomous driving, and industrial automation. Publications emphasize innovative control strategies, with recent work on game-theoretic autonomous driving frameworks and energy management systems. He has secured patents in wind farm control and electric machine systems. Current and past students reflect his interdisciplinary approach, addressing both theoretical and applied challenges in control engineering.

Миодраг Д. Спасићمشاهده پروفایل

دانشیار

• Control Systems
• Model Predictive Control
• Orthogonal Functions
• +۴ مورد دیگر

Миодраг Д. Спасић is an Associate Professor at the Department of Automatics, Faculty of Electronic Engineering, University of Niš, appointed in 2021. His academic journey includes completing his undergraduate studies at the same institution in 2010, followed by earning his PhD from the Norwegian University of Science and Technology in Trondheim in 2019. Dr. Spasić's research focuses on advanced control systems, particularly in the areas of model predictive control, orthogonal functions, neural networks, and nonlinear systems. His work demonstrates a strong emphasis on practical implementations for electromechanical systems, particularly DC motor control and magnetic levitation systems. He has developed innovative approaches combining orthogonal polynomial networks with traditional control methodologies to address challenges in robustness and real-time implementation. His publication record shows consistent productivity with 13 papers in journals with impact factors. Analysis of his recent publications reveals a clear research trajectory focused on enhancing control system performance through orthogonal function-based approaches, with increasing sophistication in handling nonlinearities and uncertainties in complex systems. His work bridges theoretical control concepts with practical engineering applications. Dr. Spasić is actively involved in research projects, currently participating in 2 national and 1 international projects. His collaborative work with colleagues at the University of Niš demonstrates strong institutional research connections within the Department of Automatics.

Evren Mert Turanمشاهده پروفایل

مدرس

• Process Systems Engineering
• Machine Learning
• Optimization
• +۳ مورد دیگر

Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Hui Fang is a researcher at Loughborough University , Department of Computer Science, UK. Their work spans Artificial Intelligence , Machine Learning , and Remote Sensing , with recent focus on Medical Imaging , Natural Language Processing , and Urban Mobility . Publications highlight innovations in MRI Reconstruction , Legal Judgment Prediction , and Video SAR Analysis . Research Interests include Graph Neural Networks , Transformer Models , and Data Mining applied to domains like Agricultural Technology , Healthcare Systems , and Smart Cities . Methodologies emphasize Interpretable AI , Federated Learning , and Multimodal Integration . Key 2024-2025 Trends involve Transformer-Based Architectures for Medical Imaging , Information Bottleneck in Spatio-Temporal Networks , and Bootstrapping Techniques for Information Extraction . Collaborative projects with Dongdong Weng , Zhu Xiao , and Yong He demonstrate interdisciplinary applications in Transportation , Healthcare , and Agriculture . Recent Co-authored Publications span IEEE Transactions , Neurocomputing , and Remote Sensing Journals , addressing Dynamic Phasor Modeling , 4D Facial Capture , and Virtual Reality Usability .

Mehmet Bodurمشاهده پروفایل

دانشیار

• Data Mining
• Decision Making Models
• Computational Intelligence
• +۶ مورد دیگر

Assoc. Prof. Dr. Mehmet Bodur is a faculty member in the Computer Engineering Department at Eastern Mediterranean University . His research focuses on Data Mining, Robotics, Computational Intelligence, and Control Systems. He holds a Ph.D. in Electrical and Electronics Engineering from Middle East Technical University (1991) and has been affiliated with IEEE and ACM as a Senior Member since 2010 and 2020, respectively. His teaching interests include Embedded Systems, Robotics, and Artificial Intelligence. Recent research highlights include satellite image-based obstacle recognition for agricultural vehicles (2015–2016), fuzzy logic modeling (2006–2009), and autonomous vehicle control systems (2010–2012). He has contributed to over 20 publications spanning robotics, control systems, and data analysis. Prof. Bodur has supervised numerous theses at the graduate level and actively participates in academic committees and editorial roles. His work bridges theory and practice, with applications in smart home systems, precision agriculture, and mobile robotics.

Seyedali Mirjaliliمشاهده پروفایل

استاد

• Optimization
• Swarm Intelligence
• Evolutionary Algorithms
• +۶ مورد دیگر

Seyedali Mirjalili is a Professor of Artificial Intelligence and Director of the Centre for Artificial Intelligence Research and Optimization (AIRO) at Torrens University Australia. He holds distinguished professorships at VSB Technical University of Ostrava, Óbuda University, and De La Salle University, and is an adjunct researcher at Griffith University and Yonsei University. His research focuses on optimization algorithms, swarm intelligence, evolutionary computation, and machine learning. Education: PhD in Computer Science (Griffith University, 2016). Research interests include optimization techniques, swarm intelligence algorithms (e.g., Grey Wolf Optimizer, Whale Optimization Algorithm), machine learning applications, and robust optimization frameworks. He has published over 600 papers with an H-index of 110 and is among the most cited researchers in optimization fields globally. Awards include the Pro-Vice Chancellor Research Special Award (2019) and recognition as a top 1% highly cited researcher (since 2019). He serves as an associate editor for journals like Neurocomputing and Applied Soft Computing. Labs/Teams: Leads the AIRO Centre at Torrens University, focusing on AI-driven solutions for complex optimization problems.

• Artificial Intelligence
• Optimization
• Evolutionary Algorithms
• +۳ مورد دیگر

Dr. Ali Jamali is a Research Fellow at RMIT University's School of Engineering. He previously held roles as a visiting professor at Kyungpook National University (South Korea) and associate professor at the University of Guilan (Iran). His research focuses on artificial intelligence, optimization, evolutionary algorithms, control systems, and energy systems. He has supervised multiple doctoral and master's students and teaches courses in complex systems modeling, dynamics, automatic control, and advanced vibration. His teaching interests include soft computing, game theory, and evolutionary algorithms, reflecting his expertise in interdisciplinary applications of AI and optimization. He has contributed to projects like enhancing EV charging infrastructure and intelligent transportation systems. His work often integrates computational intelligence with real-world problems in engineering and automation. Dr. Jamali has published extensively in journals such as IEEE Transactions on Vehicular Technology and Engineering Applications of Artificial Intelligence , addressing topics like adaptive control systems, energy management, and machine learning applications in engineering. He currently collaborates on research projects at RMIT and maintains international academic connections.

• Solar Energy Optimization
• Control Systems
• Remote Laboratories
• +۷ مورد دیگر

Fariba Moghaddam is an Ordinary Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, leading the Power & Control orientation under the Institut Systèmes industriels. She specializes in renewable energy systems, control engineering, and educational technology, with a focus on solar energy optimization, remote laboratories, and sustainable development initiatives in the Global South. Her work spans academic leadership, research, and pedagogical innovation. Education: PhD in Control Engineering from École Polytechnique Fédérale de Lausanne (EPFL), 1996. Additional academic background in electrical and mechanical engineering. Research Interests: Solar tracking systems, photovoltaic-thermal (PVT) integration, machine learning applications in energy systems, remote experimentation platforms, and collaborative educational infrastructure. Recent projects include the Solar Energy Optimization via Remote Platforms and DEAR MENA (Digital Education & Research for MENA). Grants & Projects: Solar Energy Optimization (2022-2026): Swiss National Science Foundation-funded project developing cost-effective sun tracking solutions. DEAR MENA (2019-2024): Cluster fostering digital education partnerships between Swiss and MENA institutions. Sustainable Laboratories (2020-2021): Platform for redistributing lab equipment to emerging economies. MOOLs (2018-2021): Massive Open Online Laboratories for global engineering education. Labs & Infrastructure: Spearheaded the Remote Real-Time Research Platform for control engineering, enabling global access to lab equipment via secure web interfaces. Collaborates on IoT-integrated systems for remote experimentation and industrial automation. Awards: None explicitly listed, but recognized for contributions to sustainable energy and educational equity through numerous funded projects.

• Mechanical Engineering
• Thermal Systems
• Fluid Dynamics
• +۷ مورد دیگر

Sławomir Alabrudziński is an Assistant Professor at the Institute of Mechanical Engineering , part of the Faculty of Civil Engineering, Mechanics and Petrochemistry at Warsaw University of Technology (Poland). His work focuses on thermal systems, fluid dynamics, and heat exchanger optimization. He has authored 41 publications, supervised 51 promoted theses, and contributed to 5 research projects. His h-index stands at 7 (Scopus) and 6 (Web of Science). Research Interests: Dr. Alabrudziński specializes in two-phase flow dynamics, heat transfer enhancement, fouling mitigation in heat exchangers, and PID control strategies for industrial processes. His work frequently integrates computational fluid dynamics (CFD) and numerical modeling to address challenges in thermal management and energy efficiency. Article Trends: Recent publications (2022–2025) emphasize hybrid heat-integrated distillation systems, fouling impact analysis, and optimization of PID-controlled processes. Key themes include improving heat exchanger performance under fouling conditions, energy efficiency in industrial setups, and sustainable development of energy-water-environment systems. Labs/Teams: His research is anchored in mechanical engineering and petrochemical systems, with collaborations on thermal systems and industrial process optimization. Active participation in conferences like the SDEWES 2023 highlights his engagement with interdisciplinary sustainability challenges.

• Building Physics
• HVAC (Heating, Ventilation, and Air Conditioning)
• Energy Efficiency
• +۴ مورد دیگر

Mohsen Soleimani Mohseni is a Senior Lecturer at Halmstad University , affiliated with the School of Business, Innovation and Sustainability . He serves as Programme Director for the Master's Programme in Energy Smart Innovation in the Built Environment. His research focuses on Building Physics , HVAC Systems , and Energy Efficiency , with a particular emphasis on thermal performance optimization, control strategies for heating systems, and the application of artificial intelligence in climate control. Key research interests include: Thermal modeling of building components and systems Energy-efficient HVAC design and retrofitting Artificial neural networks for climate prediction and control Renovation technologies for sustainable buildings His publications span over 20 years, addressing topics such as transient radiator performance, low-energy building controls, and hybrid heating systems. He has authored technical handbooks like Formelsamling i energiteknik and contributed to international conferences on building automation and energy engineering. Mohsen collaborates on projects integrating smart technologies into building design, emphasizing practical solutions for climate-smart infrastructure.

Kemal Uçakمشاهده پروفایل

دانشیار

• Control Engineering
• Nonlinear Systems
• Machine Learning
• +۳ مورد دیگر

Kemal Uçak serves as an Associate Professor in the Department of Control and Automation Engineering at Istanbul Technical University, specializing in advanced control systems and machine learning integration. His research bridges theoretical control engineering with practical applications in nonlinear dynamical systems. Research Interests: Development of adaptive controllers for nonlinear systems using machine learning techniques Integration of support vector regression with classical control frameworks Fuzzy logic applications in MIMO control systems Novel observer designs for system state estimation Hybrid optimization approaches combining evolutionary algorithms and gradient-based methods His publication trend demonstrates consistent innovation in control theory, with recent works focusing on Runge-Kutta based adaptive controllers, LSSVR-integrated optimal control, and fuzzy PID systems. These contributions primarily advance nonlinear control methodologies applicable to industrial automation and robotics. Current Research Activity: Principal Investigator for the active project "Runge-Kutta and Machine Learning Based Adaptive Controller Architecture for Nonlinear Systems" (2024-2026) Collaborations spanning computational intelligence and control engineering domains Development of model-free control strategies reducing dependency on system identification

Müjde Güzelkayaمشاهده پروفایل

استاد

• Control Systems
• Fractional Order Control
• Fuzzy Logic Control
• +۷ مورد دیگر

Müjde Güzelkaya is a Professor in the Department of Control and Automation Engineering at Istanbul Technical University (ITU), Faculty of Electrical and Electronic Engineering. She has been a prominent figure in the field of control systems since at least 1983, with over 108 research outputs and an h-index of 30. Her research spans fractional-order control, intelligent systems, and optimization algorithms. She leads multiple active research projects funded by ITU's BAP program and serves as principal investigator on initiatives involving federated learning, robust control, and uncertainty quantification. Her research interests are deeply rooted in control theory and automation , particularly in the design and optimization of controllers using fractional calculus , fuzzy logic , and metaheuristic algorithms like the Big Bang–Big Crunch method. She applies these techniques to diverse domains such as robotics, structural protection, and industrial automation. Her work emphasizes both theoretical development and practical implementation, as seen in applications to quadcopters, building seismic protection, and DC motor control. The most recent articles reflect a strong trend toward integrating fractional calculus with machine learning and distributed intelligence , particularly in system identification and adaptive learning frameworks. Her publications consistently focus on enhancing control performance, robustness, and adaptability in complex, nonlinear systems. Scientific Awards and Recognition: No specific awards are listed in the provided text. Advising and Grants: She is actively mentoring graduate students, with 50 theses currently in progress under her supervision. She has secured multiple competitive research grants, including: Federated Learning-Based System Identification and Uncertainty Quantification (2025–2027) Robust and Adaptive Loss Functions Using Fractional Calculus (2024–2025) Intelligent Systems and Fractional Order Controller Design (2023–2026) Controller Design for Fractional Order Systems (2018–2021) These projects highlight her leadership in cutting-edge control research. Labs and Research Teams: While specific lab names are not mentioned, her role as PI on multiple projects and her extensive collaboration network suggest she leads a research group focused on intelligent control and fractional-order systems at ITU. Her work involves interdisciplinary collaboration across engineering domains.

• Power Electronics
• Control Systems
• Microgrids
• +۷ مورد دیگر

Antonio Camacho Santiago is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems, Automation, and Industrial Informatics. He is a key member of the SEPIC (Sistemes Electrònics de Potència i de Control) and AGROTECH-UPC research groups, contributing to advancements in power electronics, microgrid control, and industrial automation. His work spans multiple campuses including Vilanova i la Geltrú and Diagonal Sud, and he collaborates extensively within UPC's engineering schools. His research focuses on control strategies for distributed generation, voltage support in microgrids, power quality enhancement, and fault ride-through capabilities of inverters. He has made significant contributions to the design of control systems under unbalanced grid conditions and the integration of renewable energy sources into smart grids. His research also extends to agrivoltaics and intelligent traffic systems, reflecting interdisciplinary innovation. The analysis of his recent publications reveals a strong trend in the development of advanced control algorithms for power electronic systems, particularly in islanded and grid-connected microgrids. His work emphasizes robustness, efficiency, and compliance with grid standards, often employing sliding mode control, complex power sharing, and resonant control techniques. There is a clear trajectory toward intelligent, adaptive, and distributed control architectures for future energy systems. Antonio Camacho Santiago has supervised doctoral research, including the thesis of José Ignacio Iñiguez Amigot, and has participated in numerous competitive R&D projects funded by national and European programs such as HORIZON EUROPE and the Spanish State Research Plans. His collaborative network includes prominent researchers like Miguel Castilla, Jaume Miret, Manuel Velasco, and Josep Maria Guerrero, indicating active engagement in large-scale research initiatives. He is involved in laboratory development for real-time control systems and has contributed to educational innovation projects, such as enhancing interactive teaching in the ATENEA platform. His work bridges theoretical control design with practical implementation in networked and embedded systems, supporting both academic training and industrial applications.