Guoqiang Li | Engineering | Innovative Research Award

Published on by China Scientist

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

Nan Nan | Engineering | Best Researcher Award

Published on by China Scientist

Dr. Nan Nan | Engineering | Best Researcher Award

Nan Nan is a lecturer at the University of Science and Technology Liaoning, specializing in Mineral Processing Engineering. With a PhD from Northeastern University, China, their research focuses on flotation reagents, process mineralogy, and the comprehensive utilization of mineral resources. Nan Nan has published several SCI papers and received funding from the Liaoning Higher Education Research Fund. They have also led industry projects, including process optimization for various mineral processing plants, and won the 2nd Prize of the Science and Technology Progress Award from the China Gold Association.

Dr. Nan Nan | University of Science and Technology Liaoning | China

Profile

SCOPUS  ID

ORCID ID

🎓 Education

  • Nan Nan earned a PhD in Mineral Processing Engineering from Northeastern University, China, where they gained in-depth knowledge and expertise in mineral processing technologies.

💼 Experience

  • Currently serving as a lecturer at the University of Science and Technology Liaoning, Nan Nan has gained substantial industry experience by leading and participating in multiple enterprise-commissioned projects. Notably, these include energy efficiency evaluations and process examination projects for several mineral processing plants and mines across China.

 🏆 Honors and Awards

  • Nan Nan has been recognized with the 2nd Prize of the Science and Technology Progress Award from the China Gold Association. Additionally, Nan Nan has received funding support from the Liaoning Higher Education Research Fund.

🛠️ Skills and Certifications

  • Nan Nan possesses strong expertise in mineral processing engineering, flotation performance, process mineralogy, and the development of flotation reagents. With experience in optimizing grinding processes and flotation collectors, Nan Nan has contributed to enhancing the efficiency and cost-effectiveness of mineral processing operations.

🔬 Research Focus

  • Nan Nan’s research primarily focuses on mineral processing engineering, flotation reagent research and development, process mineralogy, and the comprehensive utilization of mineral resources. The work aims to advance flotation technology, process optimization, and to reduce operational costs, particularly in harsh mining conditions such as those in Northeast China.

Conclusion

  • Nan Nan is an ideal candidate for the Research for Best Researcher Award due to their exceptional academic background, groundbreaking contributions to industry practices, and continuous pursuit of research innovation. Their combination of industry collaborations, academic publications, and awarded research makes them a deserving nominee for this prestigious recognition.

📄Publications

  • Molecular modeling of interactions between N-(Carboxymethyl)-N-tetradecylglycine and fluorapatite
    Authors: Nan, N., Zhu, Y., Han, Y., Liu, J.
    Journal: Minerals, 2019, 9(5), 278
  • Flotation performance and mechanism of α-Bromolauric acid on separation of hematite and fluorapatite
    Authors: Nan, N., Zhu, Y., Han, Y.
    Journal: Minerals Engineering, 2019, 132, pp. 162–168
  • Froth flotation giant surfactants
    Authors: Li, Z., Fu, Y., Li, Z., Zhu, Y., Li, Y.
    Journal: Polymer, 2019, 162, pp. 58–62