Prof. Dr. Xiaojun Tang | Electrical Sensors | Best Researcher Award

Prof. Dr. Xiaojun Tang, Xi’an Jiaotong University, China

Professor Xiaojun Tang is a leading expert in smart sensors and instrumentation at Xi’an Jiaotong University, where he directs the Institute of Intelligent Detection Technology and Systems. With a Ph.D. in instrumentation science, he has made significant contributions to spectral analysis, gas detection, and coal mine safety monitoring. He has led over 50 major research projects, authored 7 books, published more than 60 journal articles, and holds 50 patents. A member of IEEE and editorial board member of Applied Science and Technology, Professor Tang’s innovations have earned him national recognition, including multiple science and technology awards across China.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award – Professor Xiaojun Tang

Professor Xiaojun Tang is a highly qualified candidate for the Best Researcher Award, with a distinguished career dedicated to advancing smart sensing and gas detection technologies. His leadership at Xi’an Jiaotong University’s Institute of Intelligent Detection Technology and Systems is marked by groundbreaking innovations in FTIR spectroscopy, spectral analysis, and real-time gas logging.

🎓 Education & Academic Background

Ph.D. in Instrumentation Science and Technology – Specialized in smart sensors, spectral analysis, and gas detection.
Based at the School of Instrumentation Science and Technology, Xi’an Jiaotong University, China.

🧑‍🏫 Work Experience

Professor at Xi’an Jiaotong University
Director, Institute of Intelligent Detection Technology and Systems
Research Leader for over 50+ national and provincial projects, including:
- National Key Scientific Instrument and Equipment Project
- National Key R&D Program
- NSFC General Program
Editorial Committee Member – Applied Science and Technology
Collaborations with major industrial entities like PetroChina, CNOOC, State Grid Corporation, and others.

🏆 Achievements & Contributions

🧪 50 Patents (Published/Under Process)
📘 7 Books with ISBN
📝 60+ SCI/Scopus-indexed Journal Publications
🔬 Developed the first FTIR-based gas analyzer capable of 12-second composition analysis directly at wellheads
📈 Scopus h-index: 16
🤝 Led 10 industry consultancy projects
🧠 Innovator of multi-component spectral analysis methodology and spectral ID protocols for oil/gas/water zone differentiation.

🎖️ Awards & Honors

🥈 Second Prize, Shaanxi Science and Technology Achievement Award
🥇 First Prize, Liaoning Science and Technology Progress Award
🏅 Innovation Award, China Instrument and Control Society
🛠️ Recognized for impactful contributions in gas detection and smart instrumentation.

Publication Top Notes:

Adaptive event-triggered H∞ fault-tolerant control for aero-engine distributed systems

Water vapor absorption peaks in infrared spectroscopic gas logging and correlation with water layers

Characteristic Gases Detection Technology and Application of Buffer Layer Ablation Defects in XLPE Cables Based on FTIR

Topology and Control Strategy of Multi-Port DC Power Electronic Transformer Based on Soft Switching

Design of Double-Loop Current Transformer Based on Magnetic Field Incomplete Cancellation

Hybrid TDOA/DOA Multiple PD Localization Using Two Arrays With KPCA-mnc-FastICA and Sparrow Search Algorithm

Fourier Transform Infrared Spectrum Analysis of Free Gas in Power Transformer

Species discrimination of β-phenylethylamine, NaCl and NaOH based on Ultraviolet spectroscopy and principal component analysis combined with improved clustering by fast search and find of density peaks algorithm

Detection Method for Short-circuit Fault of Flexible DC Transmission System Based on Virtual Power Ratio

Dynamic event-triggered dual-channel quantized sliding mode control for uncertain networked control systems under DoS attacks

Dr. QingYang Ding, Beijing Union University, China

Ding Qingyang is a lecturer and Master’s Advisor with a distinguished career as a senior machine learning engineer. He earned his Ph.D. from the Central University of Finance and Economics in Beijing, China. An active member of the China Computer Association, the Higher Education Association, and the Blockchain Committee of the China Mobile Communication Association, Ding’s research interests span the smart Internet of Things, blockchain technology and its applications, as well as data mining and analysis. He has successfully led a general science and technology project funded by the Beijing Municipal Education Commission and contributed to several provincial and ministerial projects, including national key research and development initiatives and funding from the National Social Science Fund, Beijing Social Science Fund, and Beijing Natural Science Fund. With over 20 academic publications, one monograph, and applications for three national invention patents and two software copyrights, Ding Qingyang has made significant contributions to his fields of expertise.

Professional Profile:

Summary of Suitability for Best Researcher Award

Dr. Qingyang Ding is a lecturer and Master’s advisor with a strong academic background and professional experience in machine learning, blockchain, and the Internet of Things (IoT). He earned his Ph.D. from the Central University of Finance and Economics, Beijing, and is actively involved in various professional organizations such as the China Computer Federation and Blockchain Committee of China Mobile Communication Association.

Work Experience:

Lecturer and Master’s Advisor with a focus on technology and engineering.

Senior Machine Learning Engineer with extensive industry and academic experience in AI and data technologies.

Professional Member of:

China Computer Association
Higher Education Association
Blockchain Committee of China Mobile Communication Association

Publication top Notes:

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

Impact of Perceived Value and Community Attachment on Smart Renovation Participation Willingness for Sustainable Development of Old Urban Communities in China