Chao Wen | Machine | Best Researcher Award

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Assoc. Prof. Dr. Chao Wen | Machine | Best Researcher Award

Assoc. Prof. Dr. Chao Wen, Shanxi University, Best Researcher Award

Assoc. Prof. Dr. Chao Wen is a distinguished academic at Shanxi University, serving with the Institute of Big Data Science and Industry. He earned his M.S. and Ph.D. degrees from Xidian University in 2013 and 2017, respectively. His expertise lies in intelligent signal processing, object detection, and graph machine learning. Dr. Wen is actively supported by the National Natural Science Foundation of China and serves as a peer-reviewed expert for the same. He is a reviewer for top-tier journals such as IEEE JSTSP and IOT and holds memberships in IEEE, ACM, and CCF. His notable research includes through-wall human pose estimation and cross-modal knowledge transfer. Dr. Wen has published over 10 peer-reviewed journal articles and holds more than 10 patents. He collaborates with the neuromorphic perception team at Xidian University and contributes to industry through projects like multimodal visual perception systems. His work is cited 130+ times, reflecting growing recognition in AI-driven machine perception.

Publication Profile

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Academic and Professional Background

Assoc. Prof. Dr. Chao Wen is a highly accomplished researcher currently serving at the Institute of Big Data Science and Industry, Shanxi University, China. He received both his M.S. and Ph.D. degrees from Xidian University in 2013 and 2017, respectively. His research interests are rooted in intelligent signal processing, object detection, and graph machine learning. Dr. Wen is a recipient of funding support from the National Natural Science Foundation of China and also serves as a peer-reviewed expert for the foundation. His academic influence extends further as he actively reviews for several top-tier journals, including IEEE Journal of Selected Topics in Signal Processing (JSTSP) and IEEE Internet of Things Journal (IOT). In addition to his research, he is a committed member of IEEE, ACM, and the China Computer Federation (CCF), where he contributes to the Technical Committee on Artificial Intelligence and Pattern Recognition. His academic journey reflects excellence in artificial intelligence and computing.

Research Focus

Assoc. Prof. Dr. Chao Wen’s research primarily focuses on Artificial Intelligence for Machine Perception, particularly in developing intelligent algorithms for signal processing, object detection, and graph-based learning systems. His work spans a range of advanced topics including graph neural networks, direction-of-arrival (DOA) estimation for MIMO and FDA-MIMO radar systems, variational sparse Bayesian learning, and domain adaptive object detection. Dr. Wen has made notable contributions in federated learning, cooperative multi-agent systems, and dense crowd localization using semantic graph models. His research demonstrates a fusion of theoretical modeling and real-world applications, such as through-wall human pose estimation and visual perception under complex environmental conditions. His publications in top-tier journals and conferences reflect deep engagement with emerging fields like self-supervised learning, cross-domain adaptation, and federated collaborative frameworks. Dr. Wen’s interdisciplinary work integrates AI with signal systems, reinforcing his leadership in machine perception, sensor intelligence, and graph-based representation learning in distributed and complex environments.

Publication Top Notes

📘 Active and Semi-Supervised Graph Neural Networks for Graph Classification – Cited by 52, Published in 2022 🧠📊
📘 Estimation of Directions of Arrival of Multiple Distributed Sources for Nested Array – Cited by 33, Published in 2017 🎯📡
📘 A Tensor Generalized Weighted Linear Predictor for FDA-MIMO Radar Parameter Estimation – Cited by 21, Published in 2022 📶📘
📘 Off-grid DOA Estimation under Nonuniform Noise via Variational Sparse Bayesian Learning – Cited by 19, Published in 2017 📉🔍
📘 A Unitary ESPRIT Scheme of Joint Angle Estimation for MOTS MIMO Radar – Cited by 5, Published in 2014 📐📡
📘 Fourier Feature Decorrelation Based Sample Attention for Dense Crowd Localization – Cited by 3, Published in 2024 👥📍
📘 Consensus Graph Filter Learning for Multiple Graph Clustering – Cited by 1, Published in 2025 🧩🔗
📘 FGSS: Federated Global Self-Supervised Framework for Large-Scale Unlabeled Data – Cited by 1, Published in 2023 🌐🤖
📘 MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets – Cited by 1, Published in 2020 🛰️📡
📘 Foreground-Aware Universe Graph Matching for Domain Adaptive Object Detection – Citation data pending, Published in 2025 🎯🧠
📘 Prompt-Based Invertible Mapping Alignment for Unsupervised Domain Adaptation – Citation data pending, Published in 2025 🔄🌍

Xinfeng Shao | Control Theory | Best Researcher Award

Published on by Global Academic Awards

Mr. Xinfeng Shao | Control Theory | Best Researcher Award

Lecturer at Liaoning University of Technology, China

Ph.D. in Control Science and Engineering from Northeastern University (2023), Dr. Xinfeng Shao is a Lecturer & Master’s Supervisor at Liaoning University of Technology (LUT). His research focuses on adaptive intelligent control, cyber-physical system security, event-triggered control, and fault-tolerant control. He has led multiple national-level projects, including the National Youth Science Foundation Project, and contributed to 10+ papers in top journals, with two highly cited in IEEE Transactions on Fuzzy Systems. 📝 His technical skills include MATLAB/Simulink and Python, and he is an active member of the Chinese Association of Automation and IEEE. 🌐

Publication Profile

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🎓 Educational Background

Dr. Xinfeng Shao earned his Ph.D. in Control Science and Engineering from Northeastern University in 2023. His research is focused on adaptive intelligent control, cyber-physical system security, event-triggered control, and fault-tolerant control. These areas are critical for enhancing the reliability and security of modern automation systems, especially in the context of cyber-physical systems (CPS). His innovative work aims to develop intelligent control frameworks that ensure optimal performance even under uncertain and adversarial conditions. Dr. Shao’s expertise contributes significantly to the advancement of automation and security technologies.

🔬 Research Projects

Dr. Xinfeng Shao has been a Principal Investigator for several key research initiatives, including the National Youth Science Foundation Project (Category C), the Liaoning Provincial Education Department Youth Cultivation Project, and the LUT Doctoral Research Startup Fund. These projects reflect his leadership in advancing research in control systems and cybersecurity. Additionally, Dr. Shao is a Key Participant in two National Natural Science Foundation of China (NSFC) Projects and the prestigious Liaoning “Xing Liao Talent Program” Leading Talent Project. His involvement in these projects highlights his significant contribution to the scientific community.

📚 Research Achievements

Dr. Xinfeng Shao has authored over 10 papers in prestigious journals like IEEE Transactions, with 2 papers being ESI Top 1% Highly Cited. His research has introduced novel control frameworks for ensuring the security of multi-agent systems (MASs) and improving nonlinear fault tolerance. These groundbreaking contributions are recognized internationally for advancing the fields of adaptive control and cybersecurity. His work continues to shape the future of control systems and automation, making a significant impact in both theoretical and applied research.

🧠 Research Focus

Dr. Xinfeng Shao’s research is centered around adaptive intelligent control, cyber-physical system (CPS) security, fault-tolerant control, and event-triggered control. His work explores the resilience and security of multi-agent systems (MASs), focusing on attack-resistant control frameworks in the presence of malicious disturbances like FDI attacks and DoS attacks. Dr. Shao also investigates nonlinear systems and distributed control strategies, developing secure formation control and adaptive fuzzy control techniques. His contributions are instrumental in enhancing the robustness and security of automated systems, improving fault tolerance and performance under uncertain conditions

Conclusion

Mr. Xinfeng Shao is highly suitable for the Research for Best Researcher Award. His innovative research in intelligent and secure control systems, impressive publication record, project leadership, and recognized academic contributions present a strong case for this honor. He exemplifies the qualities of a top-tier researcher advancing critical technologies in automation and cyber-physical security.

📚 Publications Top Notes

  • “Dynamic‐Event‐Based Predefined‐Time Secure Formation Control for Nonlinear Multiagent Systems Against FDI Attacks” – International Journal of Robust and Nonlinear Control, 2025. DOI: 10.1002/rnc.8017 🔒

  • “Adaptive Fault-Tolerant Consensus Tracking Control of Stochastic High-Order MASs Under FDI Attacks” – IEEE Transactions on Fuzzy Systems, 2024. DOI: 10.1109/TFUZZ.2024.3352076 🔄

  • “Event-based distributed resilient control strategy for microgrids subject to disturbances and hybrid attacks” – Applied Mathematics and Computation, 2023. DOI: 10.1016/j.amc.2023.128273 🌍

  • “Robust adaptive dynamic memory‐event‐triggered attitude control for nonlinear multi‐UAVs resist actuator hysteresis” – International Journal of Robust and Nonlinear Control, 2023. DOI: 10.1002/rnc.6821 🚁

  • “Event-based adaptive fuzzy fixed-time control for nonlinear interconnected systems with non-affine nonlinear faults” – Fuzzy Sets and Systems, 2022. DOI: 10.1016/j.fss.2021.08.005 ⚙️

  • “Neural-network-based adaptive secure control for nonstrict-feedback nonlinear interconnected systems under DoS attacks” – Neurocomputing, 2021. DOI: 10.1016/j.neucom.2021.03.087 🧠

  • “Fuzzy Adaptive Event-Triggered Secure Control for Stochastic Nonlinear High-Order MASs Subject to DoS Attacks and Actuator Faults” – IEEE Transactions on Fuzzy Systems, 2020. DOI: 10.1109/tfuzz.2020.3028657 🛡️

  • “Adaptive Fuzzy Prescribed Performance Control of Non-Triangular Structure Nonlinear Systems” – IEEE Transactions on Fuzzy Systems, 2019. DOI: 10.1109/tfuzz.2019.2937046 🔧

  • “Adaptive Fuzzy Prescribed Performance Control for MIMO Stochastic Nonlinear Systems” – IEEE Access, 2018. DOI: 10.1109/ACCESS.2018.2882634 🔄

  • “Adaptive prescribed performance decentralized control for stochastic nonlinear large-scale systems” – International Journal of Adaptive Control and Signal Processing, 2018. 📊