Mr. Yuhang Meng | Control Awards | Best Researcher Award

Mr. Yuhang Meng | Control Awards | Best Researcher Award

Mr. Yuhang Meng | Control Awards | Nanjing University of Science and Technology | China

Mr. Yuhang Meng is a highly motivated and accomplished researcher in the field of Electronic Information, specializing in advanced control systems, fault-tolerant mechanisms, and unmanned vehicle technologies, with a growing record of impactful publications and international recognition. He received his Bachelor’s degree in Electrical Engineering from Suzhou City University, followed by a Master’s degree in Electronic Information from Jiangsu University of Science and Technology, and is currently pursuing his EngD in Electronic Information at Nanjing University of Science and Technology under the supervision of Professor Zhengrong Xiang. Throughout his academic career, Mr. Meng has gained extensive experience in switched systems, adaptive control, sliding mode control, and the development of advanced algorithms for autonomous systems, with a specific emphasis on unmanned surface and amphibious vehicles. His professional experience reflects active engagement in high-impact research projects, both theoretical and application-oriented, resulting in publications in leading international journals such as IEEE Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, Aerospace Science and Technology, and Applied Ocean Research, many of which are indexed in Scopus and widely cited within the research community. His expertise extends to designing robust trajectory-tracking controllers, developing hybrid amphibious platforms, and implementing artificial intelligence-based approaches such as bidirectional long short-term memory neural networks for adaptive control

Professional Profile: ORCID 

Selected Publications

  1. Design and Analysis of a Multimodal Hybrid Amphibious Vehicle, 2025 – Citations: 15

  2. Trajectory tracking control for unmanned amphibious surface vehicles with actuator faults, 2024 – Citations: 22

  3. An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing, 2024 – Citations: 18

  4. Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing, 2023 – Citations: 12

Prof Dr. Jong-Chen Chen | Intelligent Control Award | Best Researcher Award

Prof Dr. Jong-Chen Chen | Intelligent Control Award | Best Researcher Award

Prof Dr. Jong-Chen Chen, National YunLin University of Science and Technology, Taiwan

Jong-Chen Chen is a Full Professor in the Department of Information Management at the National Yunlin University of Science and Technology, Taiwan, where he has served since 2000. He earned his M.S. in Computer Science and Electrical Engineering from the University of Texas at Arlington in 1986 and his Ph.D. in Computer Science from Wayne State University in 1993. Dr. Chen is renowned for developing a neuromolecular model that abstracts biological structure-function relationships into a system’s structure, allowing for rich intraneuronal dynamics. This model enables the system to self-organize and learn, with applications in robotic maze navigation, image recognition, data differentiation, and more. His research spans areas such as evolvable hardware, brain-like computer simulation, bio-computing, evolutionary computation, and pattern recognition. He has published extensively in journals including Sensors, Applied Science, Biomimetics, Evolutionary Computation, and Neurocomputing.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award:

Dr. Jong-Chen Chen demonstrates strong qualifications for the “Best Researcher Award” due to his significant contributions
in various fields of computer science, robotics, and biomedical applications. He has extensive experience, having earned an
M.S. degree in computer science and electrical engineering and a Ph.D. in computer science. His academic journey,
particularly his tenure as a full professor since 2000, reflects a longstanding commitment to research and education.

Education:

  • M.S. in Computer Science and Electrical Engineering (1986)
    • University of Texas at Arlington, USA
  • Ph.D. in Computer Science (1993)
    • Wayne State University, USA

Work Experience:

  • Full Professor (2000 – Present)
    • Department of Information Management, National Yunlin University of Science and Technology, Taiwan
    • Developed a neuromolecular model (1993), abstracting biological structure-function relationships into system structure, specifically focusing on intraneuronal dynamics.

Key Contributions:

  • Applied neuromolecular models in fields like robotic maze navigation, image recognition, and biomimetic robotics.
  • Published research in prestigious journals including Sensors, Applied Science, Biomimetics, Algorithms, Evolutionary Computation, Neuro-computing, and BioSystems.

Research Interests:

  • Evolvable hardware
  • Brain-like computer simulation
  • Ecosystem simulation
  • Bio-computing
  • Artificial life
  • Molecular electronics
  • Evolutionary computation
  • Genetic programming
  • Pattern recognition

Publication top Notes:

Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients

Applying an Artificial Neuromolecular System to the Application of Robotic Arm Motion Control in Assisting the Rehabilitation of Stroke Patients—An Artificial World Approach

Bridging the Finger-Action Gap between Hand Patients and Healthy People in Daily Life with a Biomimetic System

Using Artificial Neuro-Molecular System in Robotic Arm Motion Control-Taking Simulation of Rehabilitation as an Example

Effectiveness of Companion Robot Care for Dementia: A Systematic Review and Meta-Analysis

Using Homemade Pressure Device to Improve Plantar Pressure-A Case Study on the Patient with Lower Limb Lymphedema