Lijuan Wang received her BEng degree in Computer Science and Technology from Qiqihar University, Heilongjiang, China in 2010 and her PhD degree in Measurement and Automation from North China Electric Power University (NCEPU), Beijing, China in 2014. She subsequently obtained a second PhD degree in Electronic Engineering from the University of Kent, Canterbury, UK in 2017. After that she worked at the University of Teesside as a Lecturer in Instrumentation and Control Engineering. Since June 2018 she has been a Lecturer in Electronic Engineering at the University of Kent.
She was awarded the Best Student Poster in 2014 I2MTC (International Instrumentation and Measurement Technology Conference), the Best Presentation Award in 2015 ISMTMF (International Symposium on Measurement Techniques for Multiphase Flows), the Prize for Excellent PhD Thesis by NCEPU, the 2015 IEEE Graduate Fellowship by the IEEE Instrumentation and Measurement Society and the 2019 J. Barry Oakes Advancement Award in recognition of her contributions to the development of soft computing models for multiphase flow measurement.
Her main areas of expertise include multiphase flow measurement, condition monitoring of mechanical systems, computational modelling (FEM, CFD), electrostatic sensing, hyperspectral imaging and machine learning.
Research interests
Flow measurement and instrumentation
Condition monitoring of mechanical systems
UAV-based remote sensing
Quality assessment of recycled materials
Medical image processing
Teaching
Current Teaching Commitments
EL025 Engineering Principles (practical sessions)
EL311 First Year Engineering Applications Project (project supervision)
EL562 Engineering Group Project (computer interfacing and project supervision)
EL673 Digital Systems Design (formal testability)
EL600 Final Year Project (project supervision)
Previous Teaching Commitments
Fundamentals of Mathematics
Introduction to Electronics
Supervision
PhD research topics
Condition Monitoring of Wind Turbines through Multispectral Imaging
Vibration Measurement of Rotor-bearing Systems Using Electrostatic Sensors
Gas-solid Flow Measurement in Fluidized Beds through Multi-modal Sensing and Deep Learning
Molten Salt Metering in Solar Power Stations through Multi-modal Sensing and Data Modelling
CO2 Flow Metering under CCS Conditions Using Coriolis Mass Flowmeters and Soft Computing Techniques
Professional
Professional Qualifications
Member of IEEE
Fellow of Higher Education Academy
Prizes and Awards
2019 J. Barry Oaks Advancement Award “For contribution to the development of soft computing models for multiphase flow measurement”, IEEE Instrumentation and Measurement Society
2015 IEEE Graduate Fellowship Award, IEEE Instrumentation and Measurement Society.
2015 Excellent PhD Thesis, North China Electric Power University
2015 Best Presentation Award, International Symposium on Measurement Techniques For Multiphase Flows, Sapporo, Japan.
2014 Best Student Poster Award (1st Place), IEEE International Instrumentation and Measurement Technology Conference, Montevideo, Uruguay.
2014 Student Travel Award, IEEE International Instrumentation and Measurement Technology Conference, Montevideo, Uruguay.
Publications
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