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Prof Dr. Funa Zhou | Fault diagnosis | Best Researcher Award

Professor,Β School of Logistic Engineering, Shanghai Maritime University,Β China

Professor Zhou Funa, born in April 1978, is a distinguished academic figure and Doctoral Supervisor at the School of Logistics Engineering, Shanghai Maritime University, China. πŸŽ“ With expertise in logistics, his contributions to education and research have left an indelible mark. 🌐 Professor Zhou can be reached at zhoufn@shmtu.edu.cn or contacted at +86 15800531379 and 021 38282619. πŸ“š His commitment to shaping the next generation of professionals in the field is evident, making him a valuable asset to the academic community. 🌟 Professor Zhou’s dedication to the School of Logistics Engineering reflects in both his teaching and mentorship. 🚒

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Research performance

Professor Zhou Funa has exhibited exceptional leadership by chairing 12 projects, showcasing his commitment to advancing research and innovation in the field of logistics engineering. πŸš€ Among these, his current project, funded by the National Natural Science Foundation of China (NSFC), focuses on Modular Recursive Federated Learning for Fault Diagnosis and Prediction of Port Machines. His previous accomplishments include completing projects like Deep Feature Extraction Based Fault Diagnosis and Life Prediction, A Data-driven Approach for Multimodal Fault Diagnosis, and Data-driven Multimodal Fault Diagnosis and Predictive Maintenance. 🌐 Professor Zhou’s extensive project portfolio underscores his dedication to advancing knowledge and solving real-world challenges in logistics engineering. 🌟

Awards and honors

Professor Zhou Funa’s remarkable contributions have garnered several prestigious awards and recognitions, underscoring his excellence in the field of logistics engineering. πŸ† In 2023, he received the Second Prize of Natural Science from the Chinese Association of Automation, highlighting his cutting-edge research. Previous accolades include the Second Prize of Scientific and Technological Progress in Henan Province (2017) and multiple awards from the Henan Provincial Natural Science Academic Award, including a first prize in 2011 and two second prizes in 2013. 🌟 His diverse achievements encompass leadership roles, teaching excellence, and recognition as an outstanding Communist Party member, solidifying his status as a distinguished figure in academia. πŸ‘¨β€πŸ«

Academic and Adjunct

Professor Zhou Funa stands as a recognized authority in the field of logistics engineering, contributing expertise as an Expert for the National Natural Science Foundation of China and a Young Expert for the Chinese Society of Automation. 🌐 His commitment to advancing knowledge extends to serving as a meticulous Reviewer for prestigious journals like the Journal of Automation and Electronics Journal. As a dedicated professional, he actively participates in various committees, including the Technical Process Failure Diagnosis and Safety Committee and the Intelligent Automation Committee of the Chinese Society of Automation. πŸ… Professor Zhou’s multifaceted engagement, from national foundations to local societies, reflects his commitment to the advancement of automation and technological innovation. πŸ‘¨β€πŸ”§

Publications Top Notes

 

  1. Title: “Attention Gate Guided Multiscale Recursive Fusion Strategy for Deep Neural Network-based Fault Diagnosis”
    • Authors: Zhang, Z., Zhou, F., Karimi, H.R., … Wen, C., Wang, T.
    • Journal: Engineering Applications of Artificial Intelligence
    • Publication Year: 2023
    • Volume: 126
    • Cited By: 3 (2023)
  2. Title: “An Active Federated Method Driven by Inter-Client Informativeness Variability of Labeled Data”
    • Authors: Zhou, F., Wang, C., Hu, X., Wang, C., Wang, T.
    • Journal: Signal, Image and Video Processing
    • Publication Year: 2023
    • Volume: 17(8)
    • Pages: 3973–3982
    • Cited By: 1 (2023)
  3. Title: “Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism”
    • Authors: Liu, S., Zhou, F., Tang, S., … Wang, C., Wang, T.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(10)
    • Pages: 1470
    • Year:Β  2023
  4. Title: “A Personalized Federated Learning-based Fault Diagnosis Method for Data Suffering from Network Attacks”
    • Authors: Zhang, Z., Zhou, F., Zhang, C., … Hu, X., Wang, T.
    • Journal: Applied Intelligence
    • Publication Year: 2023
    • Volume: 53(19)
    • Pages: 22834–22849
    • Cited By: 3 (2023)
  5. Title: “A Multiscale Recursive Attention Gate Federation Method for Multiple Working Conditions Fault Diagnosis”
    • Authors: Zhang, Z., Zhou, F., Wang, C., … Hu, X., Wang, T.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(8)
    • Pages: 1165
    • Year:Β  2023
  6. Title: “Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree”
    • Authors: Zhou, F., Yang, Y., Wang, C., Hu, X.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(4)
    • Pages: 606
    • Cited By: 2 (2023)
  7. Title: “Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate”
    • Authors: Tang, S., Wang, C., Zhou, F., Hu, X., Wang, T.
    • Journal: Machines
    • Publication Year: 2023
    • Volume: 11(2)
    • Pages: 153
    • Cited By: 1 (2023)
  8. Title: “Trend Feature Consistency Guided Deep Learning Method for Minor Fault Diagnosis”
    • Authors: Jia, P., Wang, C., Zhou, F., Hu, X.
    • Journal: Entropy
    • Publication Year: 2023
    • Volume: 25(2)
    • Pages: 242
    • Cited By: 3 (2023)

 

 

Funa Zhou | Fault diagnosis | Best Researcher Award

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