Ao Ma | Artificial Intelligence | Best Researcher Award

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Mr. Ao Ma | Artificial Intelligence | Best Researcher Award

Hunan University of Technology and Business | China

Mr. Ao Ma is an emerging researcher specializing in artificial intelligence, software engineering, control systems, and hardware architecture, currently serving at the School of Intelligent Engineering and Intelligent Manufacturing, Hunan University of Technology and Business (HUTB), China. At HUTB, he has pursued research that bridges intelligent computing and practical applications, particularly in developing high-efficiency, sustainable, and adaptive intelligent systems. His scholarly contributions include the article “Design and Research of High-Energy-Efficiency Underwater Acoustic Target Recognition System”, published in Electronics and earlier as a preprint on Preprints.org, where he collaborated with an interdisciplinary team to advance underwater acoustic detection technologies. This work highlights his ability to integrate artificial intelligence algorithms with hardware optimization to improve recognition accuracy while reducing energy consumption, addressing both scientific and industrial challenges in marine engineering. Alongside his academic research, Mr. Ma has demonstrated strong leadership and community engagement, earning the prestigious Yuntang Scholarship, an alumni-funded award that honors outstanding students who exhibit academic excellence, innovative spirit, and societal contribution. His recognition as an Outstanding Member of the Communist Youth League of China further reflects his exemplary performance, moral character, and leadership within the university community. With a clear research vision and dedication to advancing intelligent manufacturing and sustainable innovation, Mr. Ma continues to expand his impact in both academic and professional domains, positioning himself as a promising scholar committed to the integration of cutting-edge artificial intelligence with practical engineering solutions for the benefit of society.

Profile: Orcod

Featured publication

Ma, A., Yang, W., Tan, P., Lei, Y., Zhu, L., Peng, B., & Ding, D. (2025). Design and research of high-energy-efficiency underwater acoustic target recognition system. Electronics, 14(19), 3770.

Jingcheng Tong | Deep Learning | Best Researcher Award

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Mr. Jingcheng Tong | Deep Learning | Best Researcher Award

Beijing Institute of Graphic Communication | China

Mr. Jingcheng Tong, a postgraduate student at the Beijing Institute of Graphic Communication, China, is an emerging researcher whose work focuses on advancing artificial intelligence applications in industrial manufacturing through deep learning and computer vision technologies. As a student member actively contributing to this growing field, he has developed innovative object detection algorithms tailored for steel material identification and quality assessment, bridging the gap between advanced AI methods and traditional manufacturing practices. His notable research, including the publication CBH-YOLO: A steel surface defect detection algorithm based on cross-stage mamba enhancement and hierarchical semantic graph fusion in the SCI-indexed journal Neurocomputing, highlights his ability to design effective solutions that significantly improve defect detection accuracy, enhance efficiency, and reduce manual inspection costs. Mr. Tong’s interdisciplinary approach not only advances industrial automation and smart manufacturing initiatives but also demonstrates how applied artificial intelligence can modernize conventional production systems and elevate product quality standards. In addition to his technical expertise, he exhibits strong academic commitment and a forward-looking vision, aiming to extend his research toward broader industrial applications of AI that can support sustainable, intelligent, and globally competitive manufacturing. Through his scholarly contributions, practical innovations, and dedication to excellence, Mr. Jingcheng Tong exemplifies the promise and potential of the next generation of researchers committed to shaping the future of intelligent manufacturing technologies.

Profile : Orcid

Featured Publication

Tong, J. (2025). CBH-YOLO: A steel surface defect detection algorithm based on cross-stage mamba enhancement and hierarchical semantic graph fusion. Neurocomputing. Advance online publication.