Xiaodong Feng | Robotics | Best Researcher Award

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Prof. Xiaodong Feng | Robotics | Best Researcher Award

Prof. Xiaodong Feng at Shaoxing University, China

Feng Xiaodong, born in June 1987, is an Associate Professor at the School of Civil Engineering, Shaoxing University of Arts and Sciences. Recognized as one of the first leading talents in Zhejiang Province’s 5246 Talent Project and a young talent under Shaoxing’s Special Branch Plan, he specializes in structural engineering. With an international academic footprint and a strong background in intelligent structural systems, Dr. Feng has led numerous national-level projects and published extensively in high-impact journals. His work integrates innovative structural design with smart technologies, contributing significantly to the advancement of flexible and adaptive engineering solutions. 🌉📚🤖

Publication Profile

Scopus

Academic Background

Feng Xiaodong completed his undergraduate studies in Civil Engineering at Central South University (2006–2010), followed by a master’s in Solid Mechanics (2010–2012) and a Ph.D. in Structural Engineering (2012–2016), under Professor Guo Shaohua. He then pursued postdoctoral research at Zhejiang University (2018–2020) with Professor Luo Yaozhi. His academic training spans mechanics, structural analysis, and intelligent systems, providing a robust foundation for his interdisciplinary research. 📘🎓🔬

Professional Experience

Dr. Feng began his academic career at Shaoxing University of Arts and Sciences in 2016 as a Lecturer, advancing to Associate Professor and Laboratory Director in 2021. He also served as a visiting scholar at Kyoto University, Japan (2022–2023). Throughout his career, he has led key research initiatives, mentored students, and collaborated with both academic and industrial partners on advanced structural systems. His experience bridges practical engineering applications and cutting-edge research. 🏢👨‍🏫🌍

Awards and Honors

Feng Xiaodong has received multiple prestigious awards, including two First Prizes and two Second Prizes from the China Steel Structure Association for technological innovation and scientific progress in large-span structures. He also received honors from the Invention and Entrepreneurship Award and Zhejiang Province. These accolades recognize his contributions to the development, design, and digital construction of complex spatial structures, as well as intelligent construction technologies. His pioneering work in structural mechanics and smart infrastructure has earned both national and regional acclaim. 🏆🏗️

Research Focus

Dr. Feng’s research revolves around flexible, movable, and intelligent structures, integrating AI and machine learning for structural design and health monitoring. His key interests include tensegrity structures, prefabricated systems, large-span spatial structures, and structural dynamics. He also focuses on collaborative structural-material design and building industrialization. His interdisciplinary approach combines theoretical innovation with practical applications, aimed at advancing the construction industry’s automation and intelligence. 🤖🧠🏗️📊

Publication Top Notes

📄 Vibration control and robustness analysis of tensegrity structures via fuzzy dynamic sliding mode control method
🗓️ Year: 2024 | 📚 Journal: Structures | 📊 Cited by: 3

📄 Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection
🗓️ Year: 2024 | 📚 Journal: Applied Intelligence | 📊 Cited by: 1

📄 Structural-topic aware deep neural networks for information cascade prediction
🗓️ Year: 2024 | 📚 Journal: PeerJ Computer Science | 📊 Cited by: 1

Conclusion

Dr. Feng Xiaodong, an Associate Professor at Shaoxing University of Arts and Sciences, is a nationally recognized expert in Structural Engineering, specializing in intelligent structures, AI-integrated design, and prefabricated construction technologies. With over 20 peer-reviewed publications in leading journals such as Soft Robotics, Structures, and Structural Control & Health Monitoring, he demonstrates consistent research excellence. As the principal investigator of numerous national and regional research projects, and a recipient of multiple high-level awards from the China Steel Structure Association and Zhejiang Province, Dr. Feng has shown both academic leadership and practical innovation. His international exposure as a Visiting Scholar at Kyoto University and his role in training future engineers across disciplines further underscore his qualifications. Dr. Feng’s contributions significantly advance the field of intelligent structural systems and make him an outstanding candidate for a Best Researcher Award.

 

 

Dr. zhendong zhao | robotics | Best Scholar Award

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Dr. zhendong zhao | robotics | Best Scholar Award

Ph.D. graduate student, Hebei University of Technology, China

Zhendong Zhao is a passionate Ph.D. graduate student at the School of Artificial Intelligence and Data Science, Hebei University of Technology, China . With a strong foundation in control science and engineering, Zhendong is deeply involved in cutting-edge research in embodied intelligence and robotics powered by large language models (LLMs). His dedication to integrating AI with robotics has already led to the development of novel frameworks that enhance collaborative and autonomous robotic systems 🤖.

Publication Profile

ORCID

🎓 Education Background:

Zhendong Zhao is currently pursuing his Ph.D. in Control Science and Engineering at Hebei University of Technology. His academic path has been rooted in artificial intelligence, with a strong emphasis on integrating LLMs into real-world robotic applications. He has been trained in advanced AI theories, machine learning, and robotic systems that lay the groundwork for his research endeavors 📚.

💼 Professional Experience:

As a doctoral researcher at Hebei University of Technology, Zhendong Zhao has contributed to developing innovative robotic frameworks. His research focuses on leveraging large language models to enhance robot collaboration, especially in healthcare contexts like nursing robots. Despite his early career stage, Zhendong has demonstrated remarkable potential through independent research and publications in top-tier journals 🧪.

🏅 Awards and Honors:

While no formal awards have been mentioned yet, Zhendong Zhao’s impactful research and publication record already highlight his contributions to AI and robotics. His work has earned attention through prestigious platforms such as IEEE Robotics and Automation Letters and Bioengineering, positioning him as a rising researcher in the field 🏆.

🔬 Research Focus:

Zhendong’s research centers around embodied intelligence and LLM-based dual-arm robotics. He introduced a dual-agent framework named DABICO for task planning in nursing robots. This system builds a dual-agent (left-arm and right-arm) structure that emphasizes inter-agent communication and validation mechanisms. His research pushes the frontier of how LLMs can coordinate physical tasks across robotic limbs in real-time, especially for bimanual coordination tasks in caregiving robots 🦾.

✅ Conclusion:

Zhendong Zhao stands out as a forward-thinking Ph.D. researcher who merges AI theory with practical robotic implementations. With multiple publications in high-impact journals and a focused vision for advancing embodied intelligence, Zhendong is poised to become a significant contributor to the future of intelligent robotics.

📝 Top Publications :

The Multi-Agentization of a Dual-Arm Nursing Robot Based on Large Language Models
📅 Published: 2025
📚 Journal: Bioengineering
🔗 DOI: 10.3390/bioengineering12050448
🔁 Cited by: Crossref-indexed (specific citation count pending)

A Dual-Agent Collaboration Framework Based on LLMs for Nursing Robots to Perform Bimanual Coordination Tasks
📅 Published: 2025
📚 Journal: IEEE Robotics and Automation Letters
🔗 DOI: 10.1109/LRA.2025.3533476
🔁 Cited by: Crossref-indexed (specific citation count pending)

Heung-Shik Lee | Robotics | Best Researcher Award

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Prof. Heung-Shik Lee | Robotics | Best Researcher Award

Professor, Joongbu University, South Korea

Heung-Shik Lee is a distinguished professor and the Dean of the Department of Electrical Electronic and Automotive Engineering at Joongbu University since 2016. 🌟 With a solid background as a technical advisory member for the Ministry of SMEs and Startups and the Ministry of Environment, Prof. Lee has made significant contributions to the field. Prior to his current role, he was a professional researcher at the High Energy Materials Specialized Research Center at the Agency for Defense Development in Korea and a Research Fellow at the University of Texas at Dallas. 🌐

Profile

Strengths for the Award:

  1. Innovative Research Focus: Prof. Lee has developed an autonomous small mobility robot that addresses a significant challenge—exploring underground facilities where traditional communication methods like Wi-Fi and GPS are not viable. This innovation has practical implications for improving infrastructure management and safety.
  2. Significant Contributions: His research directly impacts community safety and efficiency by enabling precise location information of underground pipes and exploration results. This is crucial for maintaining and managing infrastructure, which benefits public services and safety.
  3. Professional Expertise: Prof. Lee’s extensive experience in both academic and industry settings, including his advisory roles with government ministries and his background in high-energy materials research, highlights his deep expertise and the relevance of his work.
  4. Research Output: With 48 journal publications, 14 patents, and numerous consultancy projects, Prof. Lee demonstrates a high level of productivity and impact in his field. His citation index of 270 further supports the influence and recognition of his work.
  5. Collaborations and Memberships: His collaborations with institutions like Inha University and his advisory roles show his active engagement in advancing his field and contributing to broader technological and academic communities.

Areas for Improvement:

  1. Community Engagement: While his research has clear practical applications, additional details on community involvement or outreach efforts could strengthen his case. Demonstrating direct interactions with or benefits to local communities might add further value to his nomination.
  2. Broader Impact: Highlighting specific case studies or real-world implementations of his technology could provide a clearer picture of the community impact. Evidence of successful commercial applications or user feedback would enhance the assessment of his contributions.
  3. Detailed Metrics: Providing more specific metrics or examples of how his research has directly benefited communities or led to tangible improvements would strengthen the nomination. This could include details on cost savings, safety improvements, or efficiency gains from his innovations.

Education

Prof. Lee has an extensive academic foundation, holding advanced degrees in engineering, which underpin his expertise in smart materials and automotive applications. 📚

Experience

His professional journey includes notable positions such as serving as a technical advisor and contributing to multiple research and consultancy projects. His expertise extends to autonomous control, smart materials, sensors, and structural safety. 🚀

Research Interests

Prof. Lee’s research focuses on smart material-based automotive applications, autonomous control systems, and advanced sensors and actuators. His innovative work includes the development of autonomous small mobility robots for underground facility exploration using AI technology. 🤖

Awards

Prof. Lee is nominated for the Best Researcher Award, recognizing his exceptional contributions to the field of engineering and technology. 🏆

Publications

Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces

Analysis of Used Cars Price Determinant Factors Using IPA | IPA

Multiple IMU sensor fusion using resolution refinement method to reduce quantization error