Dr. Mojtaba Ahmadiehkhanesar | Robotics | Best Researcher Award

Published on by Computer Scientists Awards

Mojtaba Ahmadiehkhanesar | Robotics | Best Researcher Award

University of Nottingham | United Kingdom

Dr. Mojtaba Ahmadieh Khanesar (PhD, MIET’20, SMIEEE’16, MASME’23) is a distinguished research fellow in optical metrology and machine learning at the Department of Mechanical, Materials, and Manufacturing Engineering, University of Nottingham, UK. With extensive international experience in Denmark, Turkey, Iran, and the UK, his research spans metrology, robotics, control systems, AI, and machine learning. He earned his Ph.D. in Control Engineering from K. N. Toosi University of Technology, Tehran, Iran, with a thesis on model reference interval type-2 fuzzy control of SISO nonlinear systems, following an M.Sc. on sliding mode fuzzy control of a rotary inverted pendulum and a B.Sc. in Control Engineering. Dr. Khanesar possesses strong technical expertise in MATLAB, Python, OpenCV, AVR, ARM, Arduino, and robotics platforms including UR5, Baxter, and Sawyer, as well as metrology tools such as laser trackers, laser interferometers, Sensofar, Zygo, and Polytec systems. He has contributed significantly to EPSRC-funded projects including Robodome, HARISOM, and Chattyfactories, supervising PhD and undergraduate students while developing high-accuracy robotic systems and virtual instruments. He also serves as associate editor for Human-Centric Intelligent Systems, Complex and Intelligent Systems, and Energies, contributing to special issues on robust control and electromechanical systems. Dr. Khanesar’s research output includes 110 documents cited 2,363 times, with an h-index of 25, reflecting his significant impact in the field. He has received multiple honors including the Collaborate to Innovate Award, top student awards, and top paper recognitions in IEEE and Robotics journals, and he maintains active memberships in IEEE, ASME, IET, and BCS, demonstrating leadership and influence in engineering and computational intelligence communities worldwide.

Profile : Scopus | ORCID | Google Scholar

Featured Publications

  • Khanesar, M. A., Kayacan, E., Teshnehlab, M., & Kaynak, O. (2011). Extended Kalman filter based learning algorithm for type-2 fuzzy logic systems and its experimental evaluation. IEEE Transactions on Industrial Electronics, 59(11), 4443–4455.

  • Khanesar, M. A., Kayacan, O., Yin, S., & Gao, H. (2015). Adaptive indirect fuzzy sliding mode controller for networked control systems subject to time-varying network-induced time delay. IEEE Transactions on Fuzzy Systems, 23(1), 205–214.

  • Sharafi, Y., Khanesar, M. A., & Teshnehlab, M. (2013). Discrete binary cat swarm optimization algorithm. In Proceedings of the 3rd IEEE International Conference on Computer, Control and Communication (pp. xx–xx). IEEE.

  • Kayacan, E., Kayacan, E., & Khanesar, M. A. (2015). Identification of nonlinear dynamic systems using type-2 fuzzy neural networks—A novel learning algorithm and a comparative study. IEEE Transactions on Industrial Electronics, 62(3), 1716–1724.

  • Camci, E., Kripalani, D. R., Ma, L., Kayacan, E., & Khanesar, M. A. (2018). An aerial robot for rice farm quality inspection with type-2 fuzzy neural networks tuned by particle swarm optimization-sliding mode control hybrid algorithm. Swarm and Evolutionary Computation, 41, 1–8.

 

Heung-Shik Lee | Robotics | Best Researcher Award

Published on by Computer Scientists Awards

Prof. Heung-Shik Lee | Robotics | Best Researcher Award

Professor, Joongbu University, South Korea

Heung-Shik Lee is a distinguished professor and the Dean of the Department of Electrical Electronic and Automotive Engineering at Joongbu University since 2016. 🌟 With a solid background as a technical advisory member for the Ministry of SMEs and Startups and the Ministry of Environment, Prof. Lee has made significant contributions to the field. Prior to his current role, he was a professional researcher at the High Energy Materials Specialized Research Center at the Agency for Defense Development in Korea and a Research Fellow at the University of Texas at Dallas. 🌐

Profile

Strengths for the Award:

  1. Innovative Research Focus: Prof. Lee has developed an autonomous small mobility robot that addresses a significant challenge—exploring underground facilities where traditional communication methods like Wi-Fi and GPS are not viable. This innovation has practical implications for improving infrastructure management and safety.
  2. Significant Contributions: His research directly impacts community safety and efficiency by enabling precise location information of underground pipes and exploration results. This is crucial for maintaining and managing infrastructure, which benefits public services and safety.
  3. Professional Expertise: Prof. Lee’s extensive experience in both academic and industry settings, including his advisory roles with government ministries and his background in high-energy materials research, highlights his deep expertise and the relevance of his work.
  4. Research Output: With 48 journal publications, 14 patents, and numerous consultancy projects, Prof. Lee demonstrates a high level of productivity and impact in his field. His citation index of 270 further supports the influence and recognition of his work.
  5. Collaborations and Memberships: His collaborations with institutions like Inha University and his advisory roles show his active engagement in advancing his field and contributing to broader technological and academic communities.

Areas for Improvement:

  1. Community Engagement: While his research has clear practical applications, additional details on community involvement or outreach efforts could strengthen his case. Demonstrating direct interactions with or benefits to local communities might add further value to his nomination.
  2. Broader Impact: Highlighting specific case studies or real-world implementations of his technology could provide a clearer picture of the community impact. Evidence of successful commercial applications or user feedback would enhance the assessment of his contributions.
  3. Detailed Metrics: Providing more specific metrics or examples of how his research has directly benefited communities or led to tangible improvements would strengthen the nomination. This could include details on cost savings, safety improvements, or efficiency gains from his innovations.

Education

Prof. Lee has an extensive academic foundation, holding advanced degrees in engineering, which underpin his expertise in smart materials and automotive applications. 📚

Experience

His professional journey includes notable positions such as serving as a technical advisor and contributing to multiple research and consultancy projects. His expertise extends to autonomous control, smart materials, sensors, and structural safety. 🚀

Research Interests

Prof. Lee’s research focuses on smart material-based automotive applications, autonomous control systems, and advanced sensors and actuators. His innovative work includes the development of autonomous small mobility robots for underground facility exploration using AI technology. 🤖

Awards

Prof. Lee is nominated for the Best Researcher Award, recognizing his exceptional contributions to the field of engineering and technology. 🏆

Publications

Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces

Analysis of Used Cars Price Determinant Factors Using IPA | IPA

Multiple IMU sensor fusion using resolution refinement method to reduce quantization error