Dr. Hailing Fu is a Professor of Autonomous Sensing Technology at Beijing Institute of Technology since 2023. He gained his PhD at Imperial College London (ICL) in 2018 and then worked as a research associate in the Structural Integrity and Heath Monitoring Group at ICL, and a lecturer at Loughborough University since 2019. He is a member of IEEE and Industrial Electronic Society Technical Committee on MEMS and nanotechnology and serviced as the TPC member for PowerMEMS and Transducers conferences.
His research interests include distributed sensing, wireless sensors, piezoelectric transducers, nonlinear dynamics, rotational energy harvesters and self-powered sensing. Dr. Fu has acted as a member of the Advisory Editorial Board for Hybrid Advances (Elsevier) and Next Energy (Elsevier), and a Guest Editor for multiple top-tier journals including Smart Materials and Structures and Journal of Sensors. He has published more than 40 publications and filed 3 UK patents with Outstanding Paper Finalist Awards in PowerMEMS 2019 and IEEE MEMS 2016 and Best Poster in Energy Harvesting 2016.
He is actively recruiting Postdocs, PhD and Master Students in the field of Mechanical Engineering, EEE, Control and Automation. Please contact him (email@example.com) if interested in working in his group or collaboration.
et al., 2023, A seesaw-inspired bistable energy harvester with adjustable potential wells for self-powered internet of train monitoring, Applied Energy, Vol:337, ISSN:0306-2619
et al., 2023, A multi-stable ultra-low frequency energy harvester using a nonlinear pendulum and piezoelectric transduction for self-powered sensing, Mechanical Systems and Signal Processing, Vol:189, ISSN:0888-3270
Masabi SN, Fu H, Theodossiades S, 2022, A bistable rotary-translational energy harvester from ultra-low-frequency motions for self-powered wireless sensing, Journal of Physics D-applied Physics, Vol:56, ISSN:0022-3727
et al., 2022, Rotational nonlinear double-beam energy harvesting, Smart Materials and Structures, Vol:31, ISSN:0964-1726
et al., 2021, Point cloud-based elastic reverse time migration for ultrasonic imaging of components with vertical surfaces, Mechanical Systems and Signal Processing, Vol:163, ISSN:0888-3270