Dr. Weiwei Qian | Transfer learning | Best Researcher Award

Dr. Weiwei Qian, School of Artiffcial Intelligence, Nanjing University of Information Science and Technology, China

Dr. Weiwei Qian is an Associate Professor at Nanjing University of Information Science and Technology 🎓. His research focuses on equipment intelligent diagnosis and life prediction, particularly in the field of rotating machinery health monitoring under complex environments ⚙️. He has led numerous projects and published extensively in prestigious journals such as IEEE Transactions on Industrial Informatics and Pattern Recognition 📝. Dr. Qian’s innovative work includes the development of deep learning models for robust fault diagnosis, contributing significantly to the stable operation and maintenance of machinery in energy and power sectors 🔍.

Publication Profile:

Orcid

Experience:

Dr. Weiwei Qian leads research initiatives aimed at monitoring the health conditions of rotating machinery in complex energy and power environments 🔄. His focus is on developing precise, stable, and rapid intelligent systems for equipment health recognition, along with life prediction algorithms. This research is crucial for ensuring the stable and reliable operation of machinery, playing a vital role in intelligent operation and maintenance strategies ⚙️. Currently, Dr. Qian oversees several projects, including the Jiangsu Youth Fund and University General Fund, along with four horizontal projects. He also contributes to intelligent wind speed forecasting for the “smart weather and intelligent algorithm” wind farm project within his team 🌬️.

Research Focus:

Dr. Weiwei Qian’s research primarily focuses on intelligent fault diagnosis of machinery, especially bearings, under varying working conditions and data scarcity challenges 🛠️. His work spans across prestigious journals such as IEEE Transactions on Instrumentation and Measurement, Engineering Applications of Artificial Intelligence, and Applied Sciences. Dr. Qian’s expertise lies in developing advanced algorithms and models, including deep sparse topology networks and transfer learning methods, to enhance fault diagnosis accuracy and reliability. Through his contributions, he significantly advances the field of machinery health monitoring and plays a crucial role in ensuring the efficiency and reliability of industrial equipment in diverse operational environments ⚙️.

Publication Top Notes:

“Intelligent Fault Diagnosis of Bearings With Both Working Condition Variation and Target Data Scarcity” 🛠️ Published in IEEE Transactions on Instrumentation and Measurement, 2023 📅
“Deep Sparse Topology Network for Robust Bearing Fault Diagnosis by Maximizing Prior Knowledge Functions” 🔄 Published in IEEE Transactions on Industrial Informatics, 2022 📅
“Adaptive nearest neighbor reconstruction with deep contractive sparse filtering for fault diagnosis of roller bearings” 🔄 Published in Engineering Applications of Artificial Intelligence, 2022 📅
“Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery” 🔄 Published in Applied Sciences, 2021 📅
“Deep transfer network for rotating machine fault analysis” 🔄 Published in Pattern Recognition, 2019 📅
“A novel convolutional transfer feature discrimination network for unbalanced fault diagnosis under variable rotational speeds” 🔄 Published in Measurement Science and Technology, 2019 📅
“A novel transfer learning method for robust fault diagnosis of rotating machines under variable working conditions” 🔄 Published in Measurement, 2019 📅
“Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines” 🔄 Published in Neurocomputing, 2019 📅
“Construction of a batch-normalized autoencoder network and its application in mechanical intelligent fault diagnosis” 🔄 Published in Measurement Science and Technology, 2019 📅
“A New Transfer Learning Method and its Application on Rotating Machine Fault Diagnosis Under Variant Working Conditions” 🔄 Published in IEEE Access, 2018 📅

Dr. Fangxin Fang | Machine learning | Best Researcher Award

Dr. Fangxin Fang, Imperial College London, United Kingdom

Dr. Fangxin Fang 🌐 is a pioneering researcher in numerical simulations of nonlinear fluid flows 🌊. With a PhD from James Cook University 🎓, Australia, and extensive experience at Imperial College London 🏛️, he integrates advanced techniques like Machine Learning and Data Assimilation for accurate predictions. Dr. Fang’s innovative work has led to software development 🖥️, numerous publications 📚, and successful grant applications totaling over £10M 💰. A leader in project management and collaboration, he fosters interdisciplinary research 🤝. His contributions extend to teaching 🎓, mentoring, and impactful engagement with industry and academia worldwide 🌍.

Publication Profile:

Scopus

Google scholar

Education:

🎓 Dr. Fangxin Fang’s educational journey reflects a commitment to academic excellence across diverse continents. Beginning with a Bachelor’s in Engineering from Hehai University, China, they laid the groundwork for their scholarly pursuits. This was followed by a Master of Science degree from the North China Institute of Water Conservancy and Hydropower, China, where they deepened their expertise in a specialized field. Undeterred by geographical boundaries, they pursued doctoral studies at James Cook University, Australia, earning a PhD. Each academic milestone represents their dedication to learning and growth, culminating in a comprehensive understanding of their field. 🌐

Employment:

🌟 Dr. Fangxin Fang’s professional journey spans continents, showcasing their invaluable contributions to the global scientific community. They commenced as an Engineering Assistant at the China Institute of Water Resources and Hydropower Research, Beijing, China, before transitioning to academia as a Lecturer at the North China Institute of Water Conservancy and Hydropower, Beijing. Following their passion, they pursued doctoral studies at James Cook University, Australia, setting the stage for a distinguished career. Over the years, they’ve held pivotal roles, from Research Assistant at the University of Auckland, New Zealand, to Postdoctoral Researcher in Toulouse, France. Currently, they serve as a Senior Research Fellow at Imperial College, London, UK, solidifying their legacy in Earth Science and Engineering. 🌍

Research Focus:

Dr. Fangxin Fang’s 🌊 research focus lies at the intersection of computational fluid dynamics and advanced data-driven methods. With a keen emphasis on numerical simulations, his work delves into understanding complex fluid dynamics in various domains such as oceanography, atmospheric science, and urban environments. Utilizing techniques like reduced order modeling, machine learning, and deep convolutional networks, Dr. Fang explores nonlinear spatio-temporal fluid flows. His contributions pave the way for enhanced predictions of phenomena like ocean currents, air pollution dispersion, and flood dynamics. Dr. Fang’s innovative research transcends traditional boundaries, offering insights crucial for environmental sustainability and disaster management 🌍.