Ming-Feng Yeh | Applications of Sensors | Innovative Research Award

Innovative Research Award

Ming-Feng Yeh
Lunghwa University of Science and Technology, Taiwan

Ming-Feng Yeh
Affiliation Lunghwa University of Science and Technology
Country Taiwan
Scopus ID 7202944174
Documents 56
Citations 732
h-index 14
Subject Area Electrical Engineering, Artificial Intelligence, Machine Learning, Intelligent Systems
Event Global Sensor Award

Professor Ming-Feng Yeh is an accomplished academic and researcher in the field of electrical engineering and intelligent systems. He has devoted his career to advancing research and education in areas including grey system theory, neural networks, evolutionary algorithms, machine learning, pattern recognition, automatic control, bioengineering applications, and smart systems. Through decades of teaching, research, and scholarly publication, he has contributed to the development of innovative computational techniques and intelligent technologies that support modern engineering solutions.[1]

Abstract

Ming-Feng Yeh is a Professor in the Department of Electrical Engineering at Lunghwa University of Science and Technology, Taiwan. His research activities focus on intelligent computational methods, machine learning, neural network architectures, grey system theory, evolutionary computation, automatic control systems, and engineering applications in bioengineering and pattern recognition. His scholarly contributions have supported the advancement of intelligent decision-making systems and modern engineering technologies.[1]

Keywords

Electrical Engineering, Machine Learning, Neural Networks, Grey System Theory, Evolutionary Algorithms, Pattern Recognition, Intelligent Systems, Automatic Control, Artificial Intelligence, Bioengineering.

Introduction

Artificial intelligence and intelligent computational systems have become essential components of contemporary engineering research. Scholars who integrate machine learning, optimization techniques, and intelligent control methodologies contribute significantly to technological innovation. Ming-Feng Yeh has established a long-standing academic career dedicated to these disciplines, combining theoretical research with practical engineering applications across multiple domains.[1]

Research Profile

Professor Yeh received his Bachelor of Science, Master of Science, and Doctor of Philosophy degrees in Electrical Engineering from Tatung University, Taipei, Taiwan, in 1993, 1995, and 1999 respectively. Since 2001, he has been associated with Lunghwa University of Science and Technology, where he serves as Professor in the Department of Electrical Engineering. His academic work focuses on the development of computational intelligence methodologies and their implementation in engineering and scientific applications.[1]

Research Contributions

  • Research and development in grey system theory and intelligent forecasting techniques.
  • Applications of neural network models for engineering problem solving.
  • Evolutionary algorithm optimization for complex decision-making systems.
  • Machine learning methodologies for intelligent automation.
  • Pattern recognition techniques for advanced computational systems.
  • Research contributions to automatic control and smart system development.
  • Interdisciplinary applications involving bioengineering and intelligent technologies.

Publications

Professor Yeh has authored and co-authored scholarly publications covering machine learning, grey system theory, neural networks, optimization algorithms, pattern recognition, and intelligent control systems. His research outputs contribute to both theoretical advancements and practical engineering implementations documented through international journals and conference proceedings.[2]

Research Impact

The research conducted by Ming-Feng Yeh has supported developments in intelligent computing and engineering applications. His work has enhanced understanding of computational intelligence techniques and their implementation in automated systems, forecasting models, bioengineering technologies, and smart engineering environments. His academic activities have also contributed to educating future engineers and researchers in Taiwan and beyond.[1]

Award Suitability

Professor Ming-Feng Yeh demonstrates qualifications appropriate for recognition in research excellence and engineering innovation. His extensive academic experience, long-term commitment to higher education, and contributions to intelligent systems, machine learning, and computational engineering reflect sustained scholarly achievement and professional leadership within the engineering community.[1]

Conclusion

Ming-Feng Yeh has built a distinguished academic career through research, teaching, and innovation in electrical engineering and intelligent systems. His contributions to machine learning, neural networks, grey system theory, and smart technologies continue to support advances in engineering research and education. His work represents a meaningful contribution to the development of modern computational intelligence and applied engineering solutions.[1]

References

  1. Biography of Ming-Feng Yeh. Department of Electrical Engineering, Lunghwa University of Science and Technology, Taiwan.
  2. Elsevier. (n.d.). Scopus Author Details: Ming-Feng Yeh, Author ID 7202944174. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=7202944174
  3. DOI Foundation. Digital Object Identifier System.

Dr. Liu Suiyang | Sensor Arrays | Best Researcher Award

Dr. Liu Suiyang | Sensor Arrays | Best Researcher Award 

Dr. Liu Suiyang, Xi’an University of Technology, China

Liu Suiyang is from Xianyang, Shaanxi Province, China. She received her B.S. and M.S. degrees in Integrated Circuit Engineering from Xi’an University of Technology in 2019 and 2022, respectively. From 2022 to 2024, she was a Ph.D. student with the Ultra-Large Scale Integrated Circuits Laboratory at Xi’an University of Technology. Since 2021, she has authored 13 articles and contributed to nine inventions. Her research interests include large-format image sensors, high dynamic-range pixels, high-speed column buffers, organic photodetectors, and 3D integrated circuits. Ms. Liu was recognized as an Outstanding Graduate of Xi’an University of Technology in 2022 and received the Eighth Shaanxi Province Graduate Innovation Achievement Award in 2024.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award – Liu Suiyang

Liu Suiyang is a strong candidate for the Best Researcher Award due to her notable academic achievements and contributions to the field of integrated circuit engineering. Her research in large-format image sensors, high dynamic-range pixels, high-speed column buffers, organic photodetectors, and 3D integrated circuits demonstrates a high level of technical expertise and innovation.

🎓 Education

  • B.S. in Integrated Circuit Engineering – Xi’an University of Technology (2019)

  • M.S. in Integrated Circuit Engineering – Xi’an University of Technology (2022)

  • Ph.D. Student in Ultra-Large Scale Integrated Circuits Laboratory – Xi’an University of Technology (2022–2024)

💼 Work & Research Experience

  • Ph.D. Student at Ultra-Large Scale Integrated Circuits Laboratory, Xi’an University of Technology (2022–2024)

  • Author of 13 Research Articles (Since 2021) ✍️📄

  • Holder of 9 Invention Patents (Since 2021) 🏅🔬

  • Research Focus:

    • Large-format image sensors 📷

    • High dynamic-range pixels 🌟

    • High-speed column buffer ⚡

    • Organic photodetectors 🏭

    • 3D integrated circuits 🏗️

🏆 Awards & Honors

  • Outstanding Graduate of Xi’an University of Technology (2022) 🎖️

  • Eighth Shaanxi Province Graduate Innovation Achievement Award (2024) 🏅💡

Publication Top Notes:

Synchronous Driving Method for Stitching Pixel Arrays Based on an Adaptive Correction Technique

High Dynamic Pixel Circuit Based on Variable Integral Capacitance

Inverted ternary OPD based on PEIE

Inverted ternary OPD based on PEIE

Dr. Beibei Wang | Electromagnetic Sensors Awards | Best Researcher Award

Dr. Beibei Wang | Electromagnetic Sensors Awards | Best Researcher Award 

Dr. Beibei Wang, Xihang University, China

Dr. Beibei Wang is a lecturer at Xihang University, China, specializing in materials science. She earned her Ph.D. in Materials Science from Northwestern Polytechnical University under the supervision of Prof. Qiangang Fu and conducted a joint Ph.D. at Technische Universität Darmstadt, Germany, funded by the China Scholarship Council. Her research focuses on the in-situ growth of carbon nanotubes and graphene nanoplatelets on carbon fiber surfaces, interface properties of carbon fiber/resin matrix composites, polymer-derived ceramics with functional properties, and carbon-based electromagnetic wave absorbing and shielding materials. She has presented her research at several prestigious conferences, including the World Conference on Carbon and the China International Congress on Composite Materials. Dr. Wang has received multiple awards, including the National Scholarship for Graduate Students and the First Prize of the Graduate Innovative Achievements Award. Her work has been supported by grants such as the Shaanxi Provincial Natural Science Basic Research Program and the Scientific Research Plan Projects of the Shaanxi Education Department.

Professional Profile:

SCOPUS

ORCID

Suitability Assessment for the Best Researcher Award

Dr. Beibei Wang is a Lecturer at Xihang University, China, specializing in Materials Science with a strong background in carbon-based materials, polymer-derived ceramics, and electromagnetic wave shielding. Her research contributes to advanced composite materials with applications in aerospace, electronics, and tribology.

📚 Education & Work Experience

  • 🎓 Lecturer (07/2021 – Present) | Xihang University, China | Materials Science
  • 🎓 Joint PhD (10/2019 – 10/2020) | Technische Universität Darmstadt (TU Darmstadt), Germany
    • Funded by China Scholarship Council (CSC)
    • Supervisor: Prof. Ralf Riedel
  • 🎓 PhD in Materials Science (09/2016 – 06/2021) | Northwestern Polytechnical University, China
    • Supervisor: Prof. Qiangang Fu
  • 🎓 Master’s in Materials Science (09/2013 – 01/2016) | Shaanxi University of Science & Technology, China
  • 🎓 Bachelor’s in Materials Science (09/2009 – 07/2013) | Shaanxi University of Science & Technology, China

🏆 Awards & Honors

  • 🏅 National Scholarship for Graduate Students | Ministry of Education, China (2015)
  • 🥇 First Prize – Graduate Innovative Achievements | Higher Education Bureau of Shaanxi Province (2016)
  • 🎖 Outstanding Graduate Student | Northwestern Polytechnical University (2018)
  • 🎓 Multiple Scholarships
    • First Prize Scholarship
    • Second Prize Scholarship

🔬 Achievements & Funding

  • 💡 Funded Projects:

    • 🌍 General Project (Youth) of Shaanxi Provincial Natural Science Basic Research Program (Grant No. 2023-JC-QN-0523)
    • 🏛 Scientific Research Plan Projects of Shaanxi Education Department (Grant No. 22JK0426)
  • 🎤 Key Conference Presentations:

    • 🏛 The World Conference on Carbon 2024 | Shenzhen, China
      • Oral Report: Grafting CNTs on Carbon Fabrics for Enhanced Mechanical & Thermal Properties
    • 🔬 The 5th China International Congress on Composite Materials (CCCM-5) | Urumqi, China
      • Oral Report: Study on Resin Matrix Composite Modified by In-Situ Growth CNTs
    • 🏅 The 8th Chinese Youth Symposium on Materials Science | Shaanxi, China (April 2024)
      • Oral Report: MWCNT/GNPs Modified Resin Matrix Composites
    • 🏆 The 16th National Youth Materials Science Seminar | Tianjin, China (2017)
      • Poster Presentation: Thermal & Tribological Performance of Paper-Based Composite Materials

Publication Top Notes:

Single-source-precursor synthesized SiCN/MWCNT nanocomposites with improved microwave absorbing performance

B4C@CNT nanowires decorated on carbon fiber fabric surface with enhanced microwave absorption performance

In situ growth of B4C nanowires on activated carbon felt to improve microwave absorption performance

Synergistic effect of surface modification of carbon fabrics and multiwall carbon nanotube incorporation for improving tribological properties of carbon fabrics/resin composites