Guoqiang Li | Engineering | Innovative Research Award

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Dr. Guoqiang Li | Engineering | Innovative Research Award 

Dr. Guoqiang Li is a Lecturer and Master’s Supervisor at the School of Marine Engineering. He received his Ph.D. in Mechanical Engineering from Huazhong University of Science and Technology, following a Bachelor’s degree from Dalian Maritime University. His research focuses on the reliability analysis, anomaly detection, and intelligent fault diagnosis of offshore electromechanical equipment. He has led several national and provincial research projects and has expertise in industrial big data, AI algorithms, and smart operation platforms. Dr. Li is also a recipient of multiple science and teaching awards and has authored officially published textbooks.

Dr. Guoqiang Li | Jimei University | China

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Education

  • Dr. Guoqiang Li holds a Bachelor’s degree from Dalian Maritime University and earned both his Master’s and Doctoral degrees in Mechanical Engineering from Huazhong University of Science and Technology. His advanced training laid a strong foundation in engineering principles, particularly in the context of mechanical reliability and intelligent systems applied to offshore environments.

Experience

  • Dr. Li is currently serving as a Lecturer and Master’s Supervisor at the School of Marine Engineering. Since joining academia, he has been involved in teaching, mentoring graduate students, and spearheading innovative research. His contributions extend beyond his university role, having participated in national and provincial-level research initiatives and collaborated with major institutions such as Wuhan University of Technology. He has played both principal and collaborative roles in a variety of R&D projects focusing on marine power systems and intelligent control technologies.

Awards and Recognition

  • Dr. Li has received multiple accolades throughout his academic and research journey. These include prestigious Science and Technology Awards, recognition for Teaching Achievements, and the authorship of officially published textbooks. These honors underscore his excellence in both academic instruction and scientific innovation.

Skills and Certifications

  • His core competencies lie in reliability analysis, intelligent fault diagnosis, and predictive maintenance of offshore electromechanical systems. He is proficient in applying industrial big data analytics, artificial intelligence algorithms, and edge-cloud collaborative computing. Dr. Li is also skilled in the development of intelligent operation platforms and industrial internet systems that support real-time monitoring, diagnostics, and equipment self-regulation.

Research Focus

  • Dr. Li’s research centers on the intelligent monitoring and fault management of offshore equipment. He is especially interested in anomaly detection, condition assessment, and data-driven fault prediction. His work integrates cutting-edge technologies such as generative AI, deep reinforcement learning, and multi-source data fusion to enhance the autonomy and intelligence of marine mechanical systems. His goal is to develop systems capable of zero-sample learning, predictive maintenance, and self-healing control in complex maritime environments.

Conclusion

  • Dr. Guoqiang Li is a forward-thinking researcher and educator whose work lies at the intersection of artificial intelligence and marine engineering. With a firm academic grounding and an expanding portfolio of impactful projects, he continues to contribute to the advancement of intelligent fault diagnostics and system automation in offshore industries. His research and innovations are well-positioned to address the growing demand for smart, reliable, and efficient marine technologies.

Publications

  • Zero-sample fault diagnosis of rolling bearings via fault spectrum knowledge and autonomous contrastive learning
    Authors: Guoqiang Li, Meirong Wei, Defeng Wu, Yiwei Cheng, Jun Wu
    Journal: Expert Systems with Applications

  • Wavelet knowledge-driven transformer for intelligent machinery fault detection with zero-fault samples
    Authors: Guoqiang Li, Meirong Wei, Haidong Shao, Pengfei Liang, Chaoqun Duan
    Journal: IEEE Sensors Journal

  • Zero-fault sample wavelet knowledge-driven industrial robot fault detection
    Authors: Guoqiang Li, Meirong Wei, Defeng Wu, et al.
    Journal: Journal of Instrumentation

  • Deep reinforcement learning-based online domain adaptation method for fault diagnosis of rotating machinery
    Authors: Guoqiang Li, Jun Wu, Chao Deng, Xuebing Xu, Xinyu Shao
    Journal: IEEE/ASME Transactions on Mechatronics

  • Convolutional neural network-based Bayesian Gaussian mixture for intelligent fault diagnosis of rotating machinery
    Authors: Guoqiang Li, Jun Wu, Chao Deng, Zuoyi Chen, Xinyu Shao
    Journal: IEEE Transactions on Instrumentation and Measurement

Mr. Yunfei Xia | Engineering | Best Researcher Award

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Mr. Yunfei Xia | Engineering| Best Researcher Award

Yunfei Xia is a postgraduate student at China People’s Police University, specializing in Safety Engineering. With a foundation in Fire Engineering, he has participated in two research projects and authored three academic papers, including one SCI journal article. His research focuses on fire risk assessment, particularly in battery pack production processes, where he developed a novel safety risk assessment model using the DEMATEL-ANP method. Yunfei’s work addresses critical gaps in fire safety, offering dynamic solutions for industrial risk management and safety optimization

 

Mr. Yunfei Xia | China People’s Police University | China

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🎓 Education

  • Mr. Yunfei Xia holds a strong academic foundation in fire and safety engineering. He completed his undergraduate studies in Fire Engineering and pursued postgraduate studies specializing in Safety Engineering at China People’s Police University. His academic journey reflects his commitment to advancing safety in critical engineering domains.

💼 Experience

  • Yunfei Xia has participated in two significant research projects, demonstrating his growing expertise in fire risk assessment. With a focus on applied research, he has authored three academic papers, including one SCI journal article and two conference papers. His work reflects a dedication to addressing real-world safety challenges, particularly in the context of fire and battery production processes.

🛠️ Skills and Certifications

  • Yunfei Xia is skilled in fire risk assessment and the application of advanced analytical methods such as the DEMATEL-ANP model. He excels at evaluating complex relationships among risk factors and developing dynamic, data-driven solutions for improving safety in engineering and industrial settings.

🔬 Research Focus

  • Mr. Xia’s research focuses on fire risk assessment, with a specific emphasis on the safety challenges associated with battery pack production processes. His innovative work addresses gaps in traditional risk assessment methodologies, offering dynamic evaluation techniques and insights for improving safety management in high-risk industries.

🔥 Contributions

  • Yunfei Xia has developed a novel safety risk assessment model for battery pack production using the DEMATEL-ANP method. This model analyzes the intricate relationships and impacts of risk factors, providing a more comprehensive and dynamic approach to safety management. His contributions offer valuable insights for mitigating fire risks and improving industrial safety standards.

Conclusion

  • Yunfei Xia is an outstanding candidate for the Research for Best Researcher Award. His innovative contributions to safety engineering, demonstrated academic achievements, and impactful research addressing global safety challenges make him a strong contender. Awarding Yunfei this recognition would celebrate his work and inspire further advancements in safety engineering and industrial risk management.

📄Publications

  • Research on Fuzzy Comprehensive Evaluation of Fire Safety Risk of Battery Pack Production Process Based on DEMATEL-ANP Method
    Authors: Yunfei Xia, Qingming Guo, Lei Lei, Jiong Wu, Xin Su, Jianxin Wu
    Journal: Fire

Xiaohua Xia | Mechanical Engineering | Best Researcher Award

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Assoc Prof Dr. Xiaohua Xia | Mechanical Engineering | Best Researcher Award

Leadership and Research Team Involvement

  • Dr. Xia serves as the Director of the Institute of Quality Control and leads the Engineering Equipment Intelligent Vision Scientific Research Team at Chang’an University. This leadership role underscores his active contribution to advancing research in intelligent vision technologies and engineering equipment quality control, a highly relevant area with potential societal impact.
Assoc Prof Dr. Xiaohua Xia , Chang’an University, China

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🌱EARLY ACADEMIC PURSUITS

  • Xiaohua Xia began his academic journey by earning a Bachelor of Science (BS) degree from China University of Petroleum in 2009. His commitment to advancing his knowledge led him to pursue a Ph.D. from Xi’an Jiaotong University, graduating in 2015 . These foundational years were crucial in building a solid knowledge base in engineering and set the stage for his future contributions in quality control and intelligent vision in engineering equipment.

💼 PROFESSIONAL ENDEAVORS

  • Xiaohua Xia currently holds the position of Associate Professor and Doctoral Supervisor at Chang’an University . In his role, he leads advanced research and supports the development of emerging scholars. As the Director of the Institute of Quality Control and the Leader of the Engineering Equipment Intelligent Vision Scientific Research Team at Chang’an University , Xia’s influence extends across multiple fields. His professional journey underscores a dedication to quality assurance and the intelligent automation of engineering practices.

🔬CONTRIBUTIONS AND RESEARCH FOCUS 

  • Xiaohua Xia’s research revolves around engineering equipment, quality control, and intelligent vision systems . By merging machine vision and quality control methodologies, his work aims to enhance the accuracy and reliability of engineering equipment diagnostics and assessments. His pioneering research in intelligent vision for engineering equipment has the potential to significantly reduce operational errors and enhance the efficiency of maintenance protocols.

 📚ACADEMIC CITES 

  • Xiaohua Xia’s research has garnered substantial academic citations , indicating the wide-reaching impact and value of his work. His publications are referenced by scholars and professionals alike, contributing to a growing body of knowledge in quality control and intelligent systems. This scholarly recognition highlights his expertise and the practical application of his research in both academic and industrial contexts.

🌍IMPACT AND INFLUENCE

  • Xia’s work has had a notable influence on the engineering and academic communities . His contributions have driven improvements in quality control and intelligent diagnostics, fostering a greater understanding of how machine vision can impact traditional engineering practices. His role as a doctoral supervisor further amplifies his influence by nurturing the next generation of engineers and researchers.

🌟LEGACY AND FUTURE AND CONTRIBUTIONS

  • As Xiaohua Xia continues to advance in his career, his contributions to intelligent vision systems and quality control are poised to leave a lasting legacy in engineering. His future endeavors are likely to expand upon his existing work, potentially venturing into new fields within engineering and automation . Xia’s impact on academia and industry ensures that his work will benefit both current and future developments in engineering technology.

📄Publications

    • Functionalized molybdenum tailings improve flame retardancy and mechanical properties of DOPO@chitosan-vanillin Schiff-containing rigid polyurethane foam
      Authors : Feng, S.; Wang, Y.; Zhou, Y.; … Su, O.; Xia, X.
      Journal : Materials Today Communications , 2024, Volume 41, Article 110825
    • Image correction for perspective distortion of cylindrical surfaces at arbitrary poses |
      Authors : Duan, Z.; Xia, X.; He, P.; Hu, P.
      Journal : Guangxue Jingmi Gongcheng/Optics and Precision Engineering , 2024, Volume 32, Issue 16, Pages 2577–2589
    • Fingerprint-inspired biomimetic tactile sensors for the surface texture recognition
      Authors : Qin, L.; Hao, L.; Huang, X.; … Xia, X.; Dong, G.
      Journal : Sensors and Actuators A: Physical , 2024 , Volume 371, Article 115275
    • Underwater image enhancement synthesizing multi- scale information and attention mechanisms | Underwater image enhancement synthesizing multi-scale information and attention mechanisms
      Authors : Xia, X.; Zhong, Y.; Hu, P.; … Geng, J.; Zhang, L.
      Journal : Guangxue Jingmi Gongcheng/Optics and Precision Engineering , 2024, Volume 32, Issue 10, Pages 1582–1594
    • Influence of Imaging Parameters on Shape from Focus of Large- Depth Objects | Influence of Imaging Parameters on Shape from Focus of Large-Depth Objects
      Authors : Xia, X.; Cao, Y.; Xiang, H.; Yuan, S.; Ge, Z.
      Journal : Guangxue Xuebao/Acta Optica Sinica , 2024, Volume 44, Issue 8, Article 0815001