Hakan Terzioğlu | Engineering | Best Researcher Award

Assist. Prof. Dr. Hakan Terzioğlu | Engineering | Best Researcher Award

Assist. Prof. Dr. Hakan Terzioğlu at Selçuk University, Turkey

Dr. Hakan Terzioğlu is a distinguished academic in Electrical and Electronics Engineering, currently serving at Selçuk University. He earned his Ph.D. in 2016 with research focused on switched reluctance motors. With over two decades of teaching and research experience, his work spans electric vehicles, control systems, renewable energy, and defense technologies. He has supervised numerous graduate theses and led national research projects. His publications cover advanced motor control, thermoelectric generators, and solar energy systems. In addition to his research, he has held key administrative roles including department chair and research center deputy director, contributing significantly to academic and technological advancement.

Professional Profile

Google Scholar

🎓 Education Background

Dr. Hakan Terzioğlu holds a strong academic foundation in Electrical and Electronics Engineering. He completed his Ph.D. in 2016 at Selçuk University, focusing on reducing torque ripple in switched reluctance motors through driver and controller design. He earned his M.Sc. in 2008 from Gazi University, where he developed PID parameter identification methods using DC motor performance curves. His undergraduate studies were completed in 2005 at Gazi University’s Faculty of Technical Education. With this solid educational background, Dr. Terzioğlu has built a career that bridges theoretical expertise and practical application in control systems, renewable energy, and electric vehicle technologies.

🏢 Professional Experience

Dr. Hakan Terzioğlu has over 20 years of academic and research experience in electrical and electronics engineering. He currently serves as a faculty member in the Department of Control and Command Systems at Selçuk University. Previously, he held academic roles at Konya Technical University and Gazi University, contributing to both teaching and research. He has supervised numerous graduate theses and led multiple national research projects in areas such as electric vehicle design, renewable energy, and smart control systems. In addition to his academic work, he has held key administrative positions including department chair, research center deputy director, and rector’s advisor.

🏆 Awards and Honors

Dr. Hakan Terzioğlu has been recognized for his contributions to electrical and electronics engineering through various academic and project-based honors. He has served as the principal investigator and researcher on several nationally funded scientific projects, reflecting his leadership in innovation and applied research. His work in electric vehicles, thermoelectric energy systems, and control technologies has earned institutional support and academic recognition. Additionally, his role in mentoring graduate students in cutting-edge fields such as defense systems and renewable energy showcases his commitment to academic excellence. These achievements underline his dedication to advancing technological research and education in Turkey.

🔬 Research Focus

Dr. Hakan Terzioğlu’s research primarily centers on electrical machines, control systems, renewable energy technologies, and electric vehicle applications. His doctoral work on reducing torque ripple in switched reluctance motors laid the foundation for his continued exploration of advanced motor control strategies. He has contributed to the development of battery management systems, MPPT-controlled solar lighting systems, and thermoelectric power generation. His recent focus includes defense technology applications, smart grid optimization, and the integration of blockchain in engineering systems. Dr. Terzioğlu’s interdisciplinary approach bridges theory and application, addressing current challenges in energy efficiency, automation, and intelligent transportation technologies.

📚 Top Publications with Details

Dijkstra algorithm using UAV path planning
📅 Year: 2020 | Cited by: 51

Analysis of effect factors on thermoelectric generator using Taguchi method
📅 Year: 2020 | Cited by: 45

Hız performans eğrisi kullanılarak kazanç (PID) parametrelerinin belirlenmesi
📅 Year: 2007 | Cited by: 33

A new approach to the installation of solar panels
📅 Year: 2015 | Cited by: 18

Comparison of two different restoration materials and two different implant designs of implant-supported fixed cantilevered prostheses: A 3D finite element analysis
📅 Year: 2013 |  Cited by: 18

📌 Conclusion

Dr. Hakan Terzioğlu is a highly accomplished academic and researcher in the fields of Electrical Engineering, Control Systems, and Sustainable Technology Development, making him an excellent candidate for the Best Researcher Award. With a Ph.D. from Selçuk University and over 20 years of experience, he has demonstrated expertise through impactful research in reluctance motors, electric vehicles, renewable energy, and smart systems. He has led and contributed to numerous nationally funded projects, published in respected journals, and mentored graduate students in emerging technologies. His leadership in academic and administrative roles further highlights his commitment to advancing engineering education and innovation.

 

Dr. Shengfei Ji | Mechanical | Best Researcher Award

Dr. Shengfei Ji | Mechanical | Best Researcher Award

Dr. Shengfei Ji , China University of Mining and Technology, China

Shengfei Ji is a dedicated Ph.D. candidate in Mechatronic Engineering at the School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, P.R. China. With a strong academic and technical background, he focuses on developing intelligent systems to enhance the operational reliability of hydraulic machinery. His passion for research and innovation in fault diagnosis and predictive modeling has led to several impactful publications in renowned journals.

Publication Profile

Scopus

ORCID

🎓 Education Background

Shengfei Ji is currently pursuing his Ph.D. in Mechatronic Engineering at China University of Mining and Technology, where he is engaged in advanced studies on intelligent condition monitoring systems. His academic foundation includes rigorous training in machine learning, system dynamics, and hydraulic machinery.

💼 Professional Experience

As a Ph.D. researcher, Shengfei has collaborated with a multidisciplinary team on projects involving construction machinery and intelligent fault detection. His work involves both theoretical research and practical application, integrating AI technologies like graph convolutional networks and LSTM models with mechanical systems. He has co-authored research with industry and academic experts, further expanding his expertise in smart diagnostics.

🏆 Awards and Honors

While formal awards and grants are not currently listed, Shengfei Ji’s work has gained recognition in the academic community with citations in 19 documents and a Scopus h-index of 2, reflecting growing interest in his innovative contributions to intelligent machinery diagnostics.

🔬 Research Focus

Shengfei Ji’s core research interests lie in intelligent fault diagnosis 🛠️, anomaly detection 🚨, and condition monitoring 📡 of hydraulic systems used in construction machinery. His work primarily applies deep learning and graph-based methods to create predictive models that enhance machine efficiency and reliability.

📝 Conclusion

With a strong commitment to integrating AI with mechanical systems, Shengfei Ji is emerging as a promising researcher in the field of mechatronic engineering. His scientific contributions reflect a unique intersection of engineering insight and computational intelligence, positioning him for continued academic and industrial impact 🌐.

📚 Top Publications

  1. Multivariate Prediction Soft Sensor Model for Truck Cranes Based on Graph Convolutional Network and Random Forest
    Published in: Actuators, 2024

  2. A Soft Sensor Model for Predicting the Flow of a Hydraulic Pump Based on Graph Convolutional Network–Long Short-Term Memory
    Published in: Actuators, 2024

  3. Bucket Teeth Detection Based on Faster Region Convolutional Neural Network
    Published in: IEEE Access, 2021
    Cited by: 19 articles