arXiv subjects

Li Bin

Publications and source records attributed to Li Bin.

3 recordsLinked to original sources

arXiv2026

Asteroseismology study of a new faint ZZ Ceti J053009.62+594557.0 discovered in WFST

In this work, we present a detailed asteroseismological analysis of WFST J053009.62+594557.0, a newly discovered faint pulsating white dwarf by the Wide Field Survey Telescope (WFST) with a Gaia G magnitude of 19.13. Analysis of two nights of high-precision WFST g band photometry reveals three significant pulsation frequencies with high signal-to-noise ratios. Follow-up P200/DBSP spectroscopy classifies the object as a DA white dwarf with Teff=11,609 $\pm$ 605 K and M = 0.63$\pm$ 0.22 $M_{\odot}$. To probe its internal structure, we construct asteroseismological models with the White Dwarf Evolution Code (WDEC). After exploring sufficient matching models, best-fitting solutions yield Teff=11,850$\pm$ 10 K and M = 0.600 $\pm$ 0.005 $M_{\odot}$, consistent with independent constraints from Gaia color-magnitude diagram, Gaia XP spectrum, P200 spectral fitting, SED fitting, and Gaia parallax. It has shown that the asteroseismological distance agrees with the Gaia parallax to 1.45\%.

astro-ph.SR↗

arXiv2023

SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities

The rapid advance in artificial intelligence technology has facilitated the prosperity of digital humanities research. Against such backdrop, research methods need to be transformed in the intelligent processing of ancient texts, which is a crucial component of digital humanities research, so as to adapt to new development trends in the wave of AIGC. In this study, we propose a GPT model called SikuGPT based on the corpus of Siku Quanshu. The model's performance in tasks such as intralingual translation and text classification exceeds that of other GPT-type models aimed at processing ancient texts. SikuGPT's ability to process traditional Chinese ancient texts can help promote the organization of ancient information and knowledge services, as well as the international dissemination of Chinese ancient culture.

cs.CL↗

arXiv2021

Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars

Subspace methods are essential to high-resolution environment sensing in the emerging unmanned systems, if further combined with the millimeter-wave (mm-Wave) massive multi-input multi-output (MIMO) technique. The estimation of signal/noise subspace, as one critical step, is yet computationally complex and presents a particular challenge when developing high-resolution yet low-complexity automotive radars. In this work, we develop a fast randomized-MUSIC (R-MUSIC) algorithm, which exploits the random matrix sketching to estimate the signal subspace via approximated computation. Our new approach substantially reduces the time complexity in acquiring a high-quality signal subspace. Moreover, the accuracy of R-MUSIC suffers no degradation unlike others low-complexity counterparts, i.e. the high-resolution angle of arrival (AoA) estimation is attained. Numerical simulations are provided to validate the performance of our R-MUSIC method. As shown, it resolves the long-standing contradiction in complexity and accuracy of MIMO radar signal processing, which hence have great potentials in real-time super-resolution automotive sensing.

eess.SP↗