Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

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Prof. Dr. Yongyi Yan | Computer Science and Artificial Intelligence | Best Researcher Award

Henan University of Science and Technology | China

Prof. Dr. Yongyi Yan, Professor of Control Science at Henan University of Science and Technology, has devoted over 15 years to advancing robust control and intelligent optimization for industrial and autonomous systems, demonstrating a rare blend of theoretical excellence and practical impact. He has authored 65 publications, including influential articles in IEEE Transactions on Automation Science and Engineering and SCIENCE CHINA Information Sciences, accumulating 506 citations with an h-index of 10, reflecting his work’s high recognition and application in both academia and industry. Professor Yan has led multiple National Natural Science Foundation of China projects (U1804150, 62073124, 12571584), supervised three PhD graduates, and served as associate editor for Control Engineering Practice. His pioneering contributions include an adaptive robust control algorithm that reduces oscillation by 40% in high-precision machining and an AI-driven optimization framework (patents ZL2021 1 0419210.4 and ZL202110418903.1) that decreases energy consumption by 18% in automotive assembly lines, now adopted by leading manufacturers. His collaborations with Luoyang Sanwu Cable Group have resulted in significant breakthroughs in high-conductivity aluminum stranded wire cable production, integrating multi-machine coordination, dynamic compensation, and laser-guided visual positioning to enhance efficiency, product quality, and equipment control. By bridging fundamental research with industrial applications, Professor Yan has advanced smart manufacturing, autonomous navigation, and process optimization. His sustained innovation, leadership, and scholarly achievements exemplify research excellence, making him highly deserving of the Best Researcher Award for transformative contributions to control science and engineering.

Profile: Scopus

Featured Publications

  • Li, X., Yan, Y., Yue, J., & Zhang, S. (2025, September). Algebraic insight into universal logic functions and implications for logical system modeling.

  • An, Z., Yan, Y., Yue, J., & Li, X. (2025, May). Construction of automaton observer based on matrix semi-tensor product. In Conference proceedings.

  • Wang, X., Yan, Y., Yue, J., & An, Z. (2025, May). Construction and synchronization analysis of state power set automata based on algebraic methods. In Conference proceedings.

  • Dong, C., Yan, Y., Li, H., & Yue, J. (2024, November). Semi-tensor product approach to controllability, reachability, and stabilizability of extended finite state machines.

  • Zhang, S., Yan, Y., Hao, P., & Yue, J. (2024, October). Structural simplification of finite state machines using pruning operators based on semi-tensor product of matrices. In Conference proceedings.

  • Zhang, S., Yan, Y., Wang, C., & Yue, J. (2024, October). Implementation of automaton product networks: From formal language to algebraic models. In Conference proceedings.

  • Yan, Y., Hao, P., Yue, J., & Feng, J. (2024, October). An STP look at logical blocking of finite state machines: Formulation, detection, and search.

  • Yan, Y., Dong, C., Li, H., & Yue, J. (2024, July). Algebraic implementation of extended finite state machine networks.

Xiaogang Song | Computer Science | Best Researcher Award

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Prof. Xiaogang Song | Computer Science | Best Researcher Award

School of Computer Science and Engineering | Xi ‘an University of Technology | China

Dr. Xiaogang Song is an Associate Professor at the School of Computer Science and Engineering, Xi’an University of Technology. He earned his Ph.D. from Northwestern Polytechnical University and is a member of IEEE. His research focuses on computer vision and the autonomous navigation of unmanned systems. Throughout his career, Dr. Song has led several significant projects and has an extensive publication record in esteemed journals and conferences.

Profile

Scopus

Orcid

Strengths for the Awards

  • Strong Academic Background – Dr. Xiaogang Song holds a Ph.D. from Northwestern Polytechnical University and serves as an Associate Professor and Associate Dean at the School of Computer Science and Engineering, Xi’an University of Technology.
  • Significant Research Contributions – His expertise in computer vision and autonomous navigation is demonstrated through extensive research, including national and provincial-level funded projects.
  • Publications in High-Impact Journals – He has authored over 30 papers in prestigious IEEE journals and other well-known international conferences, which reflect the quality and impact of his research.
  • Innovative Research Work – The development of the Spatial and Channel Enhanced Self-Attention Network (SCESN) and the Global Self-Attention Module (GSM) shows his contributions to advancing AI and machine learning.

Education 🎓

Dr. Song completed his doctoral studies at Northwestern Polytechnical University, where he specialized in areas that laid the foundation for his future research in computer vision and autonomous systems. His academic journey equipped him with the expertise to contribute significantly to these fields.

Experience 🏫

Currently serving as an Associate Professor at Xi’an University of Technology, Dr. Song has been instrumental in advancing research in computer science and engineering. His role involves both teaching and leading cutting-edge research projects, fostering innovation and knowledge dissemination within the academic community.

Research Interests On Computer Science🔍

Dr. Song’s research interests encompass:

  • Machine Learning
  • Multimodal Learning
  • Computer Vision

He is particularly focused on developing advanced algorithms and models that enhance the capabilities of autonomous systems and improve image processing techniques.

Awards 🏆

Dr. Song has been recognized for his contributions to the field, including:

  • Leading projects funded by the National Natural Science Foundation of China.
  • Securing grants from the National Key Research and Development Program of China.
  • Receiving support from the Key Research and Development Program of Shaanxi Province.

These accolades underscore his commitment to advancing research and innovation in computer science.

Publications 📚

  1. “Spatial and Channel Enhanced Self-Attention Network for Efficient Single Image Super-Resolution”
    • Authors: Song, X.; Tan, Y.; Pang, X.; Lu, X.; Hei, X.
    • Publication Year: 2025
    • Citations: 0
  2. “Single Image Super-Resolution with Lightweight Multi-Scale Dilated Attention Network”
    • Authors: Song, X.; Pang, X.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2025
    • Citations: 0
  3. “Local Motion Feature Extraction and Spatiotemporal Attention Mechanism for Action Recognition”
    • Authors: Song, X.; Zhang, D.; Liang, L.; He, M.; Hei, X.
    • Publication Year: 2024
    • Citations: 0
  4. “Salient Object Detection With Dual-Branch Stepwise Feature Fusion and Edge Refinement”
    • Authors: Song, X.; Guo, F.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 4
  5. “TransBoNet: Learning Camera Localization with Transformer Bottleneck and Attention”
    • Authors: Song, X.; Li, H.; Liang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 5
  6. “A Universal Multi-View Guided Network for Salient Object and Camouflaged Object Detection”
    • Authors: Song, X.; Zhang, P.; Lu, X.; Hei, X.; Liu, R.
    • Publication Year: 2024
    • Citations: 0
  7. “Self-Supervised Monocular Depth Estimation Method for Joint Semantic Segmentation”
    • Authors: Song, X.; Hu, H.; Ning, J.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 0
  8. “PSNS-SSD: Pixel-Level Suppressed Nonsalient Semantic and Multicoupled Channel Enhancement Attention for 3D Object Detection”
    • Authors: Song, X.; Zhou, Z.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 1
  9. “Unsupervised Monocular Estimation of Depth and Visual Odometry Using Attention and Depth-Pose Consistency Loss”
    • Authors: Song, X.; Hu, H.; Liang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 4
  10. “Image Super-Resolution with Multi-Scale Fractal Residual Attention Network”
  • Authors: Song, X.; Liu, W.; Liang, L.; Lu, X.; Hei, X.
  • Publication Year: 2023
  • Citations: 5

Conclusion 📝

Dr. Xiaogang Song is a distinguished scholar in computer science, with a focus on machine learning, multimodal learning, and computer vision. His extensive research, numerous publications, and leadership in significant projects highlight his dedication to advancing technology and contributing to the academic community.

Yuan Rao | Computer Science | Best Researcher Award

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Dr. Yuan Rao | Computer Science | Best Researcher Award

Lecturer | School of Artificial Intelligence, Guangzhou University | China

Yuan Rao is a dedicated researcher and lecturer specializing in media forensics and AI security. She has contributed significantly to the field of image forgery detection, focusing on self-supervised learning and domain adaptation techniques. Currently a Lecturer at the School of Artificial Intelligence, Guangzhou University, Yuan Rao combines academic excellence and practical expertise, earning recognition through various awards and high-impact publications.

Profile

Scopus

Google Scholar

Strengths for the Awards

  • Outstanding Research Contributions 
    • Yuan Rao has published numerous high-impact research articles in leading journals and conferences such as:
      • IEEE Transactions on Pattern Analysis and Machine Intelligence
      • Pattern Recognition
      • IEEE International Conference on Computer Vision (ICCV)
    • Her work on JPEG-resistant image forgery detection and AI security vulnerabilities reflects groundbreaking innovation in media forensics.
  • Leadership in Projects 
    • She has successfully undertaken four research projects as Principal Investigator (PI), funded by prestigious bodies such as:
      • National Natural Science Foundation of China
      • Guangdong Basic and Applied Research Foundation
      • Science and Technology Foundation of Guangzhou

🎓 Education

  • PhD in Information and Communication Engineering (2014.9 – 2021.6)
    • Sun Yat-sen University
    • Supervisor: Professor Ni Jiangqun
  • Master’s in Communication and Information Systems (2011.9 – 2014.6)
    • Jinan University
    • Supervisor: Professor Junkai Huang
  • Bachelor’s in Telecommunications Engineering and Management (2007.9 – 2011.6)
    • Beijing University of Posts and Telecommunications

 Work Experience

  • Lecturer (2021.6 – Present)
    • School of Artificial Intelligence, Guangzhou University
    • Yuan Rao conducts research, supervises projects, and inspires students in the evolving fields of AI and media forensics.

🔍 Research Interests On Computer Science

Yuan Rao’s primary research interests focus on:

  • Media Forensics: Detecting and localizing image forgeries using deep learning and self-supervised techniques.
  • AI Security: Exploring vulnerabilities and robustness of AI models in practical scenarios.

🏆 Awards

  • 🥇 Champion and 3rd Place in “Guangzhou Pazhou Algorithm Competition” – Algorithm Safety Track, 2023
  • 🥇 First Prize in the National “Challenge Cup” Black Science and Technology Special Competition, 2023
  • 🏅 6th Rank out of 1471 in “Secure AI Challenger Program: Tamper Detection for Forged Images,” 2020

📚 Publications

1. A Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2016
  • Citations: 590

2. Deep Learning Local Descriptor for Image Splicing Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Zhao
  • Publication Year: 2020
  • Citations: 104

3. Multi-semantic CRF-based Attention Model for Image Forgery Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Xie
  • Publication Year: 2021
  • Citations: 53

4. Block-based Convolutional Neural Network for Image Forgery Detection

  • Authors: J. Zhou, J. Ni, Y. Rao
  • Publication Year: 2017
  • Citations: 46

5. Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2021
  • Citations: 29

6. Towards JPEG-Resistant Image Forgery Detection and Localization via Self-Supervised Domain Adaptation

  • Authors: Y. Rao, J. Ni, W. Zhang, J. Huang
  • Publication Year: 2022
  • Citations: 13

7. High-accuracy Current Sensing Circuit with Current Compensation Technique for Buck–Boost Converter

  • Authors: Y. Rao, W.L. Deng, J.K. Huang
  • Publication Year: 2015
  • Citations: 5

8. A Trigger-Perceivable Backdoor Attack Framework Driven by Image Steganography

  • Authors: W. Tang, J. Li, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

9. Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography

  • Authors: W. Tang, H. Yang, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

10. Exploring the Vulnerability of Self-supervised Monocular Depth Estimation Models

  • Authors: R. Hou, K. Mo, Y. Long, N. Li, Y. Rao
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

11. MKD: Mutual Knowledge Distillation for Membership Privacy Protection

  • Authors: S. Huang, Z. Liu, J. Yu, Y. Tang, Z. Luo, Y. Rao
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

12. Deep Multi-image Hiding with Random Key

  • Authors: W. Zhang, W. Tang, Y. Rao, B. Li, J. Huang
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

Research Projects

Yuan Rao has led multiple projects as Principal Investigator (PI):

  1. National Natural Science Foundation of China: Research on Self-supervised Transfer Learning Based Image Forgery Forensics.
  2. Guangdong Basic and Applied Basic Research Foundation: Image Forgery Localization Combining Self-supervised Learning and Knowledge Distillation.
  3. Science and Technology Foundation of Guangzhou: Verifiable Robustness of Object Detection Models Using Domain Knowledge.
  4. Science and Technology Foundation of Guangzhou: Short-term Heavy Precipitation Forecasting with Super-resolution Reconstruction.

📝 Conclusion

Yuan Rao is a prominent researcher and educator in media forensics and AI security. With a robust academic background, impressive research achievements, and numerous accolades, she is committed to advancing technologies that ensure the integrity and security of digital media.