H.Y. Li

ORCID: 0009-0003-0414-3172
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About
Contact & Profiles
Research Areas
  • Dark Matter and Cosmic Phenomena
  • Astrophysics and Cosmic Phenomena
  • Traffic Prediction and Management Techniques
  • Energy Load and Power Forecasting
  • Geophysics and Gravity Measurements
  • Particle physics theoretical and experimental studies
  • Atomic and Subatomic Physics Research
  • Gamma-ray bursts and supernovae
  • Particle Detector Development and Performance
  • Energy, Environment, and Transportation Policies

Sichuan University
2024

Purple Mountain Observatory
2021

Chinese Academy of Sciences
2021

University of Science and Technology of China
2021

Abstract The first Water Cherenkov detector of the LHAASO experiment (WCDA-1) has been operating since April 2019. data for year have analyzed to test its performance by observing Crab Nebula as a standard candle. WCDA-1 achieves sensitivity 65 mCU per year, with statistical threshold 5 . To accomplish this, 97.7% cosmic-ray background rejection rate around 1 TeV and 99.8% 6 an approximate photon acceptance 50% is achieved after applying algorithm separate gamma-induced showers. angular...

10.1088/1674-1137/ac041b article EN Chinese Physics C 2021-05-24

Abstract CDEX-50 is a next-generation project of the China Dark Matter Experiment (CDEX) that aims to search for dark matter using 50-kg germanium detector array. This paper comprises thorough summary experiment, including an investigation potential background sources and development model. Based on baseline model, projected sensitivity weakly interacting massive particle (WIMP) also presented. The expected level within energy region interest, set 2–2.5 keVee, ∼0.01 counts keVee -1 kg day ....

10.1088/1475-7516/2024/07/009 article EN Journal of Cosmology and Astroparticle Physics 2024-07-01

This paper introduces a novel hybrid deep learning-based approach for short-term electricity demand forecasting in dance sport activities. Traditional learning methods often overlook important spatial dependencies and key features like trend seasonal patterns. To address these limitations, we propose model that combines Transformer temporal feature extraction Graph Neural Networks extraction, enabling prediction based on spatial-temporal features. Additionally, employ the decomposition...

10.1109/access.2024.3424688 article EN cc-by-nc-nd IEEE Access 2024-01-01
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