Aohui Zhao

ORCID: 0009-0008-0363-6289
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About
Contact & Profiles
Research Areas
  • Non-Invasive Vital Sign Monitoring
  • Heart Rate Variability and Autonomic Control
  • HVDC Systems and Fault Protection
  • Particle accelerators and beam dynamics
  • Electromagnetic Launch and Propulsion Technology
  • Optical Imaging and Spectroscopy Techniques
  • Hemodynamic Monitoring and Therapy
  • Plasma Diagnostics and Applications
  • High-Voltage Power Transmission Systems
  • ECG Monitoring and Analysis
  • Phonocardiography and Auscultation Techniques
  • Power Systems Fault Detection
  • EEG and Brain-Computer Interfaces

Chongqing University of Technology
2018-2024

Princeton Plasma Physics Laboratory
2016

The Facility for Laboratory Reconnection Experiments (FLARE) is an intermediate laboratory experiment currently under construction at Princeton University by a consortium of five universities and two Department Energy (DoE) national laboratories, located the Plasma Physics (PPPL). goal FLARE to provide experimental accesses new regimes magnetic reconnection process related phenomena directly relevant heliophysics, astrophysics, fusion plasmas. device comprises vacuum chamber 9 coils sets...

10.1109/ipmhvc.2016.8012868 article EN 2016-07-01

Dry-type Air-core Reactor is widely used in power system. It an important reactive compensation equipment In recent years, the dry air reactor has suffered a number of failures operation. The failure occurred most frequently dry-type air-core and we cannot detect inter-turn short circuit faults neither single wire winding nor multiple windings. research protection almost blank at present. This paper presents method collecting unbalanced current between bus-bars to faults. numerical...

10.1088/1755-1315/188/1/012036 article EN IOP Conference Series Earth and Environmental Science 2018-10-30

Cuffless continuous blood pressure (BP) monitoring is essential for personalized health management. Although existing cuffless BP estimation applies advanced machine learning techniques and integrates PPG signals, it deficient in feature extraction fusion. In addition, inefficient to train the model separately different tasks. this study, an multi-domain local-global parallel multi-task network (MDLG-MTLNet) introduced. The MDLG-MTLNet was designed with three key aspects: first, temporal...

10.2139/ssrn.4705292 preprint EN 2024-01-01
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