Ruhai Zhang

ORCID: 0009-0003-3419-8369
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About
Contact & Profiles
Research Areas
  • Language and cultural evolution
  • Heat transfer and supercritical fluids
  • Smart Grid and Power Systems
  • Phase Equilibria and Thermodynamics
  • Advanced Wireless Communication Techniques
  • Microgrid Control and Optimization
  • HVDC Systems and Fault Protection
  • Mathematics Education and Teaching Techniques
  • Wireless Communication Networks Research
  • Thermodynamic and Exergetic Analyses of Power and Cooling Systems
  • Machine Fault Diagnosis Techniques
  • Neurobiology of Language and Bilingualism
  • Power System Reliability and Maintenance
  • Cognitive and developmental aspects of mathematical skills
  • Optimal Power Flow Distribution
  • Spatial Cognition and Navigation

East China Normal University
2020-2025

The unprecedented growth of distributed renewable generation is changing the distribution network from passive to active, resulting in issues like reverse power flow, voltage violations, malfunction protection relays, etc. To ensure reliable and flawless operation active networks, an electrical device enabling management necessary, a hybrid transformer offers promising solution. This study introduces novel topology multi-mode control strategy achieve coordinated regulation networks. proposed...

10.3390/pr12020265 article EN Processes 2024-01-25

Aim In this study, we examined gender differences in fraction learning and explored potential underlying mechanisms. Methods The mediating effects of spatial ability mathematical anxiety on were tested elementary school students. A total 165 sixth-grade students (83 girls) from public schools participated the study. All participants completed a series tasks, including test, two tasks (spatial working memory mental rotation tests), knowledge test (incorporating arithmetic, number line,...

10.3389/fpsyg.2024.1464501 article EN cc-by Frontiers in Psychology 2024-12-12

The current research aimed to investigate the role that prior knowledge played in what structures could be implicitly learnt and also nature of memory buffer required for learning such structures. It is already established people can learn detect an inversion symmetry (i.e. a cross-serial dependency) based on linguistic tone types. present study investigated ability Simple Recurrent Network (SRN) explain implicit recursive We found SRN over types more effectively when given two categories...

10.31234/osf.io/2gwfy preprint EN 2020-11-21
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