Zobrazeno 1 - 10
of 338
pro vyhledávání: '"Jichun Li"'
Autor:
Jichun Li
Publikováno v:
Electronic Research Archive, Vol 32, Iss 3, Pp 1901-1922 (2024)
In this paper, we presented a review on some recent progress achieved for simulating Maxwell's equations in perfectly matched layers and complex media such as metamaterials and graphene. We mainly focused on the stability analysis of the modeling equ
Externí odkaz:
https://doaj.org/article/29b6caade7444e4b83211264dc4ed9ec
Publikováno v:
Mathematical Biosciences and Engineering, Vol 21, Iss 3, Pp 4521-4553 (2024)
The vegetation pattern generated by aeolian sand movements is a typical type of vegetation patterns in arid and semi-arid areas. This paper presents a vegetation-sand model with nonlocal interaction characterized by an integral term with a kernel fun
Externí odkaz:
https://doaj.org/article/12069dfc2da94cd088392dafcd83a4be
Autor:
Wenhe Xie, Yuan Ren, Fengluan Jiang, Xin-Yu Huang, Bingjie Yu, Jianhong Liu, Jichun Li, Keyu Chen, Yidong Zou, Bingwen Hu, Yonghui Deng
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-9 (2023)
Abstract Organic-inorganic molecular assembly has led to numerous nano/mesostructured materials with fantastic properties, but it is dependent on and limited to the direct interaction between host organic structure-directing molecules and guest inorg
Externí odkaz:
https://doaj.org/article/d4075315cf8d4f0c802017c15b3ed0c3
Autor:
Weimin Tan, Yinyin Cao, Xiaojing Ma, Ganghui Ru, Jichun Li, Jing Zhang, Yan Gao, Jialun Yang, Guoying Huang, Bo Yan, Jian Li
Publikováno v:
Engineering, Vol 23, Iss , Pp 90-102 (2023)
Congenital heart disease (CHD) is the leading cause of infant death. An artificial intelligence (AI)-based CHD diagnosis network (CHDNet) is an echocardiogram video-based binary classification model that judges whether echocardiogram videos contain h
Externí odkaz:
https://doaj.org/article/1feac75bbd084921adc700504743eae6
Autor:
Min Li, Jing Chen, Bo He, Guoying He, Chen-Guang Zhao, Hua Yuan, Jun Xie, Guanghua Xu, Jichun Li
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
IntroductionProviding stimulation enhancements to existing hand rehabilitation training methods may help stroke survivors achieve better treatment outcomes. This paper presents a comparison study to explore the stimulation enhancement effects of the
Externí odkaz:
https://doaj.org/article/dd6f9f8d878d4f4e95b59f3be1849199
Publikováno v:
ACS Central Science, Vol 7, Iss 11, Pp 1885-1897 (2021)
Externí odkaz:
https://doaj.org/article/cfaac2d85a3b4a9f88bad53793eb9dcb
Publikováno v:
IEEE Access, Vol 7, Pp 19291-19302 (2019)
Complex-valued time-varying Sylvester equation (CVTVSE) has been successfully applied into mathematics and control domain. However, the computation load of solving CVTVSE will rise significantly with the increase of sampling rate, and it is a challen
Externí odkaz:
https://doaj.org/article/41a2bebb0e0f4e5ea92bfb29623f1fb8
Publikováno v:
IEEE Access, Vol 7, Pp 58945-58950 (2019)
In this paper, to accelerate the convergence speed of Zhang neural network (ZNN), two finite-time recurrent neural networks (FTRNNs) are presented via devising two novel design formulas. For verifying the advantages of the proposed FTRNN models, a so
Externí odkaz:
https://doaj.org/article/c10c8ff5a537480ca89708b301a42723
Publikováno v:
IEEE Access, Vol 7, Pp 132763-132774 (2019)
Manual preparation of fungal samples for Fourier Transform Infrared (FTIR) spectroscopy involves sample washing, homogenization, concentration and spotting, which requires time-consuming and repetitive operations, making it unsuitable for screening s
Externí odkaz:
https://doaj.org/article/6a9b37857f3f4b25b20bbb831fc9fd97
Autor:
Jichun Li
Publikováno v:
Results in Applied Mathematics, Vol 9, Iss , Pp 100136- (2021)
In this paper, we investigate a system of governing equations for modeling wave propagation in graphene. Compared to our previous work (Yang et al., 2020) , here we re-investigate the governing equations by eliminating two auxiliary unknowns from the
Externí odkaz:
https://doaj.org/article/d4d4dd0166574e24afbfca3e82368c72