Assoc. Prof. Dr. Mao Jun | Nonlinear Control | Best Researcher Award

Assoc. Prof. Dr. Mao Jun, China Jiliang University, China

Dr. Mao Jun is an Associate Professor at the School of Mechanical and Electrical Engineering, China University of Metrology. He earned his Ph.D. in Control Science and Engineering from Nanjing University of Science and Technology in 2019, following a Master’s and Bachelor’s degree in the same field from Yangzhou University. Since joining the university in 2019, he has progressed from Lecturer to Associate Professor in 2024. His primary research interests include sampled-data control, nonlinear systems, and time-delay systems. Dr. Mao has served as the principal investigator for a Youth Science Foundation project funded by the National Natural Science Foundation of China. His representative works, published in prestigious IEEE journals, focus on adaptive consensus, output feedback control, and stabilization of complex nonlinear systems. He is recognized as a sole or co-first author in several high-impact research articles.

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Summary of Suitability: Dr. Mao Jun – Candidate for Best Researcher Award

Dr. Mao Jun, an Associate Professor at the China University of Metrology, has demonstrated significant research achievements in control science and engineering, particularly in the areas of nonlinear system control, sampled-data systems, and finite-time stabilization. His consistent contributions to theoretical and applied control methodologies make him a strong candidate for the Best Researcher Award.

🎓 Education

📍 Ph.D. in Control Science and Engineering
Nanjing University of Science and Technology (2015.09 – 2019.06)
📍 Master’s in Control Science and Engineering
Yangzhou University (2012.09 – 2015.06)
📍 Bachelor’s in Electrical Engineering and Automation
Yangzhou University (2007.09 – 2011.06)

💼 Work Experience

👨‍🏫 Associate Professor
School of Mechanical and Electrical Engineering, China University of Metrology (2024.01 – Present)
👨‍🏫 Lecturer
Same institution (2019.07 – 2023.12)

🏆 Achievements & Honors

📚 Published multiple high-impact journal articles in IEEE Transactions on Systems, Man, and Cybernetics: Systems and IEEE Transactions on Fuzzy Systems
- 🥇 Sole First Author on 4 major IEEE journal papers
- 🤝 Joint First Author on an IEEE Fuzzy Systems publication (2024)
🔬 Successfully led a National Natural Science Foundation of China Youth Project
- Project: Finite-time Sampling Control of Nonlinear Systems and Its Applications
- Grant: ¥300,000
- Duration: 2022 – 2024

Publication Top Notes:

Adaptive finite-time stabilizing control of fractional-order nonlinear systems with unmodeled dynamics via sampled-data output-feedback

Adaptive Fuzzy Finite-Time Sampled-Data Control for a Class of Fractional-Order Nonlinear Systems

Observer-Based Finite-Time Sampled-Data Control for a Class of Nonlinear Time-Delay Systems

Stability of short memory fractional-order hybrid systems

Stabilization for A Class of Second-order Nonlinear Time-Delay Systems with Input Unmodeled Dynamics by Using Dynamic Gain

Finite-time sampled-data output feedback stabilization for a class of feed-forward non-holonomic systems

Practical Finite-Time Sampled-Data Output Feedback Stabilization for a Class of Upper-Triangular Nonlinear Systems with Input Delay

Self-Tuning Sliding Mode Control for an Uncertain Coaxial Octorotor UAV

Global Stabilization for a Class of Switched Nonlinear Time-Delay Systems via Sampled-Data Output-Feedback Control

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.

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