Jinyang Guo | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Jinyang Guo | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Jinyang Guo, Beihang University, China.

Dr. Jinyang Guo is an Assistant Professor at Beihang University, China, specializing in efficient AI computing 🖥️. A recipient of China’s prestigious National Youth Talent Program 🇨🇳, he has published 40+ papers in top-tier venues like ICML and CVPR 📚. . His research excellence is complemented by patents, IEEE awards 🏅, and active roles in global AI conferences 🌐.

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🎓 Early Academic Pursuits

Dr. Jinyang Guo began his academic journey in Australia, earning a Bachelor of Engineering (Honors) in Electrical Engineering from The University of New South Wales with First Class Honours and placement on the Dean’s Award List (top 5%) from 2014 to 2017. He pursued a Ph.D. in Electrical and Information Engineering at The University of Sydney, focusing on efficient and scalable machine learning, where he was supported by prestigious scholarships including the USydIS and ARC-backed fellowships.

💼 Professional Endeavors

Currently serving as an Assistant Professor at the School of Artificial Intelligence, Beihang University, China, Dr. Guo specializes in efficient AI computing. He has emerged as a lead researcher in several national and industrial projects, overseeing more than 10 competitive grants and spearheading initiatives in AI deployment for UAVs, model compression, and human-machine hybrid systems. He also plays active roles in international academic circles through workshops, guest editorships, and conference organizations.

🔬 Contributions and Research Focus On Artificial Intelligence

Dr. Guo’s research focuses on model compression, AI efficiency, sparsity, and scalable learning, addressing real-world challenges in deploying AI on edge and embedded systems. His pioneering work includes multidimensional pruning frameworks, 3D action recognition, and language model compression, bridging fundamental AI theory with impactful applications. His contributions also span human-machine alignment, video diffusion, and efficient large language models.

🌍 Impact and Influence

With over 40 high-impact publications in IEEE Transactions and elite venues such as ICML, CVPR, AAAI, and NeurIPS, Dr. Guo has gained global recognition in AI and machine learning. His research has influenced both academic and industrial practices, contributing significantly to robust model design and resource-constrained AI deployment. His awards include the ICCV Doctoral Consortium Award and 2nd Place in the IEEE Autonomous UAV Challenge 2023, affirming his standing as a rising leader in the field.

🧠 Research Skills

Dr. Guo possesses robust skills in model pruning, quantization, and neural network optimization. He is adept at developing scalable solutions for real-world AI applications, such as UAVs and embedded systems. His expertise also spans human-machine alignment, point cloud processing, and transformer-based 3D object detection.

🏅 Awards and Honors

He is a recipient of the ICCV Doctoral Consortium Award, 2nd Place in the 2023 IEEE Autonomous UAV Chase Challenge, and multiple prestigious scholarships, including the University of Sydney International Scholarship, Postgraduate Research Supplementary Scholarship, and Engineering and IT Research Scholarship, amounting to over $147,000 USD in academic suppo

Chengjiang Zhou | Artificial Intelligence | Best Researcher Award

Published on by Best Achievements

Assoc. Prof. Dr. Chengjiang Zhou | Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr. Chengjiang Zhou, Yunnan normal university, China.

Dr. Chengjiang Zhou 🎓, an Associate Professor at Yunnan Normal University, specializes in AI-driven fault diagnosis for industrial systems. With 10+ years of research experience, he has published 40+ SCI papers, secured 9 patents, and led national/provincial projects. 🛰️ His innovations aid real-time monitoring in wind turbines and pipelines. As a mentor, he has guided students to 16 national awards 🏆. Dr. Zhou actively serves as a guest editor and reviewer for top journals. 🧠🔧

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🎓 Early Academic Pursuits

Dr. Chengjiang Zhou began his academic journey with a Ph.D. in Control Engineering, laying a strong foundation in intelligent systems and automation. His passion for signal processing and fault diagnosis was cultivated early in his studies, which led him to engage in high-impact research projects during his graduate years. Collaborating on four National Natural Science Foundation projects and participating in three infrastructure-based research initiatives, Dr. Zhou gained critical experience that shaped his future research trajectory.

💼 Professional Endeavors

As an Associate Professor at Yunnan Normal University, Dr. Zhou has dedicated over a decade to academic excellence and research innovation. Beyond classroom teaching, he plays a pivotal role in project management, student mentorship, and experimental center operations. His leadership in academia is evidenced by his guidance of students to win over 16 national competition awards and his coordination of university-wide research collaborations that uplift institutional research capacity.

🔬 Contributions and Research Focus On Artificial Intelligence 

Dr. Zhou’s research revolves around AI-driven intelligent fault diagnosis under variable and noisy environments, particularly in high-altitude regions like Yunnan. He developed novel algorithms like FRLSTSVM and EMGGDFuRDE, which address overfitting and signal noise issues in wind turbine and bearing fault detection. His innovations bridge AI theory and real-world utility, enabling accurate, cross-domain fault diagnostics. His work has been implemented in industry, significantly improving pipeline monitoring and turbine health systems in local enterprises.

🌍 Impact and Influence

Dr. Zhou’s research has had far-reaching impacts in both academia and industry. His diagnostic solutions are actively employed by companies such as Yunnan Dahongshan Pipeline Co., improving system reliability and energy sustainability. His interdisciplinary innovations enhance operational safety and efficiency in green energy infrastructure, affirming his role as a catalyst for smart industrial transformation in southwestern China.

🧠 Research Skills

Dr. Zhou exhibits deep expertise in signal processing, intelligent fault diagnosis, machine learning, and entropy-based modeling. His ability to integrate AI algorithms into hardware systems demonstrates strong practical and theoretical skills. He is proficient in developing cross-domain transfer diagnosis methods and specializes in working with noisy, uncertain datasets, making his work both technically challenging and practically valuable.

🏅 Awards and Honors

Dr. Zhou has been honored with multiple accolades, including the prestigious Yunnan Natural Science Prize, in recognition of his scientific contributions. He has successfully secured five competitive grants as Principal Investigator, including a major project from the National Natural Science Foundation of China (NSFC). These accomplishments underscore his leadership in high-impact research and innovation.

📖 Editorial Appointments & Reviewer Roles

Dr. Zhou serves as a Guest Editor for Mathematical Problems in Engineering and a reviewer for journals such as Expert Systems with Applications (19 reviews), Information Fusion (18 reviews), and Measurement (7 reviews), among others. His rigorous peer-review contributions help uphold the quality of global scientific discourse.

🔧 Industry Projects & Consultancy

Dr. Zhou has successfully led multiple industry-academic collaborations, including:

  • Development of real-time detection systems for electric shock risks (Yunnan Power Grid).

  • Smart pipeline and vehicle terminal systems (Liancheng Technology).

  • AI-based “Transparent Kitchen” inspection platforms (Hunan KeChuang).

  • High-pressure pump diagnostics (Yunnan Dahongshan).

These projects highlight his role in transferring academic breakthroughs into scalable, real-world applications.

🤝 Collaborations

Dr. Zhou has collaborated with leading industrial and academic partners including:

  • Yunnan Power Grid Co., Ltd.

  • Hunan Skyleaf Information Technology Co.

  • Liancheng Technology Group

  • Yunnan Dahongshan Pipeline Co.

  • National and provincial government-funded research agencies

These collaborations have led to tangible products, publications, and enhanced research ecosystems.

🏛️ Legacy and Future Contributions

Dr. Zhou is poised to leave a lasting legacy through his continued dedication to AI for industrial resilience and safety. His upcoming research in wind turbine fault tracking under uncertain labels aims to push the boundaries of unsupervised learning. As a mentor, innovator, and leader, he is shaping the next generation of engineers while contributing to China’s green and intelligent infrastructure. His future work is set to expand internationally, enhancing global standards in fault diagnostics and smart systems.

Publications Top Notes

  1. A fault diagnosis approach using improved multiscale fluctuation Rényi dispersion entropy and optimized binary tree LSTSVM
    Expert Systems with Applications, 2025 🛠️🧠

  2. A fault diagnosis method using decomposition denoising improved multiscale weighted permutation entropy and one-versus-one least squares twin SVM
    Measurement, 2025 🔍📉

  3. A Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic Graph LSTSVM
    Entropy, 2024 🌀📊

  4. Fuzzy regular least squares twin support vector machine and its application in fault diagnosis
    Expert Systems with Applications, 2023 🧩⚙️

  5. A Mechanical Part Fault Diagnosis Method Based on Improved Multiscale Weighted Permutation Entropy and Multiclass LSTSVM
    Measurement, 2023 ⚙️📈

  6. A Novel Fault Diagnosis Method Based on NEEEMD-RUSLP Feature Selection and BTLSTSVM
    IEEE Access, 2023 🛠️🖥️

  7. A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD
    Entropy, 2023 🔧🧮

  8. A FCEEMD Energy Kurtosis Mean Filtering-Based Fault Feature Extraction Method
    Coatings, 2022 🛠️📐

  9. Fault Diagnosis of Check Valve Based on KPLS Optimal Feature Selection and Kernel Extreme Learning Machine
    Coatings, 2022 🔩📉

  10. Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy
    Entropy, 2022 ⚙️🧠

  11. Research on Fault Diagnosis Method of Rolling Bearing Based on Feature Optimization and Self-Adaptive SVM
    Mathematical Problems in Engineering, 2022 🔍🔄

📷 Image Processing & Target Detection

  1. A noise-robust CNN architecture with global attention and gated convolutional Kernels for bearing fault detection
    Measurement Science and Technology, 2024 🤖🎯

  2. AER-Net: Adaptive Feature Enhancement and Hierarchical Refinement Network for Infrared Small Target Detection
    IEEE Transactions on Instrumentation and Measurement, 2024 🌌🎯

  3. AT-GAN: A generative adversarial network with attention and transition for infrared and visible image fusion
    Information Fusion, 2023 🌉🧠

  4. Boosting target-level infrared and visible image fusion with regional information coordination
    Information Fusion, 2023 🔦🤝

  5. DCFusion: A Dual-Frequency Cross-Enhanced Fusion Network for Infrared and Visible Image Fusion
    IEEE Transactions on Instrumentation and Measurement, 2023 🔁🖼️

🚁 UAV Systems & Remote Sensing

  1. A Dual UAV Cooperative Positioning System With Advanced Target Detection and Localization
    IEEE Access, 2024 🚁📡

  2. Scene-aware refinement network for unsupervised monocular depth estimation in ultra-low altitude oblique photography of UAV
    ISPRS Journal of Photogrammetry and Remote Sensing, 2023 🛰️🏞️

  3. A Transmission Line Defect Detection Method Based on YOLOv7 and Multi-UAV Collaboration Platform
    Journal of Electrical and Computer Engineering, 2023 ⚡🚁

  4. Research on Insulator Defect Detection Based on Improved YOLOv7 and Multi-UAV Cooperative System
    Coatings, 2023 ⚙️🔍

💡 Other Applications

  1. A New Hyperspectral Image Identification Method Based on LSDA and OSELM
    Modelling and Simulation in Engineering, 2024 🌈🧠

  2. Bearing Fault Prediction Based on Mixed Domain Features and GWO‐SVM
    Journal of Electrical and Computer Engineering, 2024 ⚙️📊

  3. Two-Branch Feature Interaction Fusion Method Based on Generative Adversarial Network
    Electronics, 2023 🔄🧠

  4. A Kitchen Standard Dress Detection Method Based on the YOLOv5s Embedded Model
    Applied Sciences, 2023 👕📷

  5. HELOP: Multi-target tracking based on heuristic empirical learning algorithm and occlusion processing
    Displays, 2023 👁️🎯