jizhou Cao | Data-Driven Decision Making | Best Scholar Award

Mr. jizhou Cao | Data-Driven Decision Making | Best Scholar Award

Student | Xinjiang University | China

Mr. Jizhou Cao is a dedicated academic and researcher currently serving at Xinjiang University. With a background in civil engineering and machine learning, he has significantly contributed to the understanding of reinforced concrete (RC) column shear behaviour, integrating advanced machine learning techniques into structural engineering. His work has explored the initial failure process in RC columns and prediction methods for shear capacity, demonstrating a unique synergy between civil engineering and machine learning. Mr. Cao’s research has been published in well-respected journals, furthering the application of machine learning to solve real-world engineering problems.

Profile

Scopus

Education

Mr. Cao earned his master’s degree from Hainan University, where he gained a solid foundation in civil engineering. He continued his academic journey by pursuing further studies at Xinjiang University, which has fostered his research interests in the intersection of civil engineering and machine learning. His educational path reflects a blend of practical expertise and theoretical understanding, particularly in the realm of structural analysis and innovative technologies such as machine learning.

Experience

With years of academic and research experience, Mr. Cao has engaged in multiple projects that apply cutting-edge technologies to civil engineering problems. His work has focused on developing predictive models for the shear capacity of RC columns and understanding the failure processes in concrete structures using machine learning techniques. He has also been involved in consultancy projects, contributing his expertise to real-world applications. His professional journey highlights his commitment to advancing both the scientific understanding and practical application of structural engineering.

Research Interest

Mr. Cao’s primary research interests lie in the integration of machine learning with civil engineering, particularly in structural analysis and the failure mechanisms of reinforced concrete structures. His research aims to bridge the gap between computational techniques and practical engineering solutions, with a special focus on the prediction of shear failure in RC columns. His work seeks to improve the accuracy of structural safety evaluations and enhance the resilience of concrete structures under various loading conditions.

Award

Mr. Cao has been recognized for his contributions to the field of civil engineering and machine learning. His research has garnered attention from leading academic institutions, with multiple nominations for prestigious awards such as the Young Scientist Award and the Excellence in Innovation Award. These accolades reflect his impactful contributions to advancing engineering practices, particularly in the realm of structural safety and the application of machine learning.

Publications

Mr. Cao has authored several influential articles, contributing to the academic discourse on machine learning applications in civil engineering. Some of his key publications include:

“Exploring the initial state of the shear failure process in RC columns based on machine learning,” Journal of Structural Engineering, 2024.

“Prediction of shear capacity of RC columns and discussion on shear contribution via the explainable machine learning,” Structural Safety Journal, 2023. These works have been cited by numerous researchers, highlighting the significance of his research in the field.

His publications have addressed critical aspects of structural engineering and have demonstrated the potential of machine learning to revolutionize the field.

Conclusion

Mr. Jizhou Cao’s work stands as a testament to the potential of machine learning in reshaping civil engineering practices. His academic background, coupled with a strong research focus on shear failure prediction in RC columns, underscores his commitment to advancing both theoretical and applied knowledge in structural engineering. As he continues to explore innovative solutions through machine learning, Mr. Cao is poised to make lasting contributions to the safety and efficiency of civil infrastructure, enhancing the way engineers approach complex structural challenges. His dedication to research and innovation makes him a valuable asset to both academia and the engineering community.

Xinxin Zhang | Data Mining | Best Researcher Award

Dr. Xinxin Zhang | Data Mining | Best Researcher Award

School of Architecture and Art Design | Hebei University of Technology | China

Dr. Zhang Xinxin is a lecturer at the School of Architecture and Art Design at Hebei University of Technology, China. She holds a Ph.D. from East China University of Science and Technology (2020) and is a leading academic in the fields of Kansei engineering and industrial design theory. With a passion for innovative methodologies, she has significantly contributed to the academic discourse in her field, consistently producing high-quality research and publications.

Profile

Orcid

Education

Dr. Zhang earned her doctorate in 2020 from East China University of Science and Technology, one of China’s top research universities. During her academic journey, she developed expertise in integrating technical knowledge with design methodologies, shaping her into a thought leader in industrial design and Kansei engineering. Her education laid a solid foundation for her current academic and research achievements.

Experience

Currently, Dr. Zhang serves as a lecturer at Hebei University of Technology, where she combines teaching, research, and mentoring. With a background enriched by her doctoral studies, she has been instrumental in educating future designers and engineers. Her teaching focuses on industrial design principles and methods, blending practical and theoretical approaches. Her professional contributions extend to active participation in academic conferences and collaboration with interdisciplinary teams.

Research Interests

Dr. Zhang specializes in Kansei engineering and industrial design theory and methods. Kansei engineering, a discipline that explores the emotional and psychological impact of products on users, forms the cornerstone of her research. Her innovative approaches aim to bridge the gap between user needs and design functionality, advancing both academic and practical applications in these areas.

Awards

Dr. Zhang has been recognized for her exceptional contributions to industrial design research. Notable awards include acknowledgments from prestigious design and engineering organizations in China, celebrating her work in advancing the field. Her nomination for national and international academic awards underscores her status as an emerging leader in her discipline.

Publications

Dr. Zhang has published extensively in her research areas, with key contributions including:

Recognizing materials in cultural relic images using computer vision and attention mechanism

    • Authors: Huining Pei, Chuyi Zhang, Xinxin Zhang, Xinyu Liu, Yujie Ma
    • Publication Year: 2024

Designing the color of electric motorcycle products emotionally based on the dynamic field theory and deep learning

    • Authors: Man Ding, Haocheng Qin, Xinxin Zhang, Liwen Ma
    • Publication Year: 2024

Research on chaos of product color image system driven by brand image

    • Authors: Xinxin Zhang, Yueying Li, Huining Pei, Man Ding
    • Publication Year: 2023

Target Mining and Recognition of Product Form Innovation Design Based on Image Word Similarity Model

    • Authors: Qinwei Zhang, Zhifeng Liu, Xinxin Zhang, Chunyang Mu, Shuo Lv, Miaochao Chen
    • Publication Year: 2022

On the Prediction of Product Aesthetic Evaluation Based on Hesitant‐Fuzzy Cognition and Neural Network

    • Authors: Xinying Wu, Minggang Yang, Zishun Su, Xinxin Zhang, Ning (Chris) Chen
    • Publication Year: 2022

Research on Product Primitives Recognition in a Computer-Aided Brand Product Development System

    • Authors: Wenjin Wang, Jianning Su, Xinxin Zhang, Kai Qiu, Shutao Zhang
    • Publication Year: 2021

Conclusion

Dr. Zhang Xinxin’s dedication to research, teaching, and innovation in Kansei engineering and industrial design has established her as a promising academic and researcher. Her work, recognized by numerous publications and citations, reflects a commitment to advancing user-centric design principles. With a strong academic background and significant contributions to her field, Dr. Zhang continues to inspire the next generation of designers and researchers.