Jiaming Zhong - Artificial intelligence - Best Researcher Award

Wuyi university - China

AUTHOR PROFILE

SCOPUS

📚 SCIENTIFIC RESEARCH ACHIEVEMENTS

Jiaming Zhong has made significant contributions to the fields of video classification and tactile sensing. His groundbreaking papers include "Exploring Cross-video Matching for Few-shot Video Classification via Dual-Hierarchy Graph Neural Network Learning," published in Image and Vision Computing, and "Text-guided Graph Temporal Modeling for Few-Shot Video Classification," featured in Engineering Applications of Artificial Intelligence. These studies, published in top-tier journals, highlight Zhong's innovative approaches in utilizing graph neural networks and multimodal models for advanced video analysis and classification.

🛠️ PATENTS AND TECHNOLOGICAL INNOVATIONS

Zhong holds several patents that showcase his expertise in developing practical solutions for various technological challenges. His patents include methods for video anomaly classification, chip defect detection, and mobile robot obstacle avoidance. These patents reflect his commitment to translating theoretical research into tangible technological advancements that address real-world problems.

🔬 PROJECT EXPERIENCE: PEEL RECOGNITION

In a project focused on the precise identification of Chenpi years using a multimodal model, Zhong's work involved designing lightweight modules and fine-tuning models to achieve high recognition accuracy. His use of the CLIP multimodal model for feature extraction led to a remarkable 99% accuracy in recognizing Chenpi years with limited sample data. This project, detailed on GitHub, demonstrates his proficiency in applying advanced machine learning techniques to practical problems.

🎥 PROJECT EXPERIENCE: FEW-SHOT VIDEO CLASSIFICATION

Zhong's research in video behavior classification involved addressing challenges related to data scarcity and model capabilities. Collaborating with Macau University of Science and Technology and Wuyi University, he developed a dual-hierarchy graph neural network that significantly improved classification performance through cross-video frame matching. This innovative approach was published in Image and Vision Computing and showcased Zhong's ability to enhance model performance through sophisticated temporal modeling.

🔍 PROJECT EXPERIENCE: MULTIMODAL REPRESENTATION LEARNING

In a project focused on multimodal video behavior analysis, Zhong led efforts to develop a novel framework for self-supervised learning using multimodal data. This project, supported by a 500,000 RMB research grant, involved developing a text-guided feature optimization module and a query text token learning mechanism. His research aimed to leverage multimodal knowledge to improve the classification performance of few-shot video behaviors, with results published in top journals.

📈 IMPACTFUL RESEARCH AND PUBLICATIONS

Zhong's work has significantly impacted the fields of video classification and sensor technology. His papers in renowned journals and his patents contribute to advancing the understanding and application of these technologies. His research not only addresses current challenges but also paves the way for future innovations in these areas.

🏆 ACKNOWLEDGEMENTS AND RECOGNITION

Zhong's contributions to scientific research and technology have earned him recognition within the academic and professional communities. His innovative work in video classification and sensor technology continues to influence the field and inspire further research and development.

NOTABLE PUBLICATION

Ultra-sensitive and stable All-Fiber iontronic tactile sensors under high pressure for human movement monitoring and rehabilitation assessment
Authors: K. Ma, D. Su, B. Qin, Y. Xin, X. He
Year: 2024
Journal: Chemical Engineering Journal

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7
Authors: F. Deng, J. Chen, L. Fu, J. Li, N. Li
Year: 2024
Journal: Frontiers in Plant Science

Exploring cross-video matching for few-shot video classification via dual-hierarchy graph neural network learning
Authors: F. Deng, J. Zhong, N. Li, D. Wang, T.L. Lam
Year: 2023
Journal: Image and Vision Computing

EARLY ACADEMIC PURSUITS

Dr. Samir Khatir embarked on his academic journey by earning a PhD in Mechanical Engineering from Boumerdes University, Algeria, in collaboration with Centre Val de Loire, France, focusing on damage detection using optimization techniques. He later pursued a second PhD in Civil Engineering at Ghent University, Belgium, specializing in artificial intelligence for fast crack identification in steel plate structures. His academic pursuits reflect a strong foundation in engineering and a commitment to advancing knowledge in his field.

PROFESSIONAL ENDEAVORS

Dr. Samir Khatir has held various prestigious positions, including Technical Manager at Btecch in Brussels, Belgium, and part-time distinguished researcher at CEATS Centre, Ho Chi Minh City Open University, Vietnam. Additionally, he serves as an editor-in-chief and member of the editorial board for several scientific journals, contributing to the dissemination of knowledge in his field. His extensive experience spans research, academia, and industry, showcasing his versatility and expertise.

CONTRIBUTIONS AND RESEARCH FOCUS

Dr. Samir Khatir's research focuses on a wide range of topics, including characterization of metals and composite materials, design optimization, damage identification, static and dynamic tests, machine learning, and tribological analysis in metal contact. He has made significant contributions to projects addressing modal updating and structural health monitoring in metal bridges, fast crack identification using machine learning in steel plates, and impact identification in composite materials. His research underscores his dedication to advancing engineering solutions through innovative methodologies.

IMPACT AND INFLUENCE

Dr. Samir Khatir's work has had a profound impact on the field of engineering, particularly in the areas of structural health monitoring, damage identification, and optimization techniques. His collaborations with prestigious institutions and his role as a visiting researcher and research member highlight his influence and recognition within the global research community. His contributions have contributed to advancements in the understanding and application of artificial intelligence for predictive analysis in engineering structures.

ACADEMIC CITES

Dr. Samir Khatir's research has been widely cited and recognized in the academic community, with numerous publications and collaborations with renowned institutions. His work has been instrumental in shaping the discourse and driving innovation in engineering research, particularly in the application of machine learning techniques for fast prediction and damage identification in structural materials.

LEGACY AND FUTURE CONTRIBUTIONS

As Dr. Samir Khatir continues to excel in his career, his legacy in the field of engineering is poised to grow. His future contributions are expected to further enhance our understanding of structural behavior and advance predictive modeling techniques using artificial intelligence. Through his dedication to research and innovation, he will continue to shape the future of engineering and inspire the next generation of researchers and practitioners.