Zobrazeno 1 - 10
of 305
pro vyhledávání: '"Xiao Zhitao"'
Publikováno v:
AUTEX Research Journal, Vol 19, Iss 1, Pp 8-16 (2019)
In this paper, a new method based on phase congruency is proposed to measure pitch lengths and surface braiding angles of two-dimensional biaxial braided composite preforms. Lab space transform and BM3D (block-matching and 3D filter) are used first t
Externí odkaz:
https://doaj.org/article/7119a9d3ec684096a3fd500e24df4a8a
Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usually simply assume that a graph and its augmented graph as a positive
Externí odkaz:
http://arxiv.org/abs/2403.02719
Publikováno v:
In Computational Materials Science June 2024 242
Autor:
Liu, Yanbei, Shan, Wanjin, Wang, Xiao, Xiao, Zhitao, Geng, Lei, Zhang, Fang, Du, Dongdong, Pang, Yanwei
Publikováno v:
In Pattern Recognition January 2024 145
Publikováno v:
BioMedical Engineering OnLine, Vol 11, Iss 1, p 31 (2012)
Abstract Background For the treatment of low back pain, the following three scenarios of posterior lumbar interbody fusion (PLIF) were usually used, i.e., PLIF procedure with autogenous iliac bone (PAIB model), PLIF with cages made of PEEK (PCP model
Externí odkaz:
https://doaj.org/article/63823097cda4412b9b6c1eb4ac419cba
We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node representation by describing its neighborhood as a perceptual whole in a hol
Externí odkaz:
http://arxiv.org/abs/1911.11430
This paper considers uplink massive multiple-input multiple-output (MIMO) systems with lowresolution analog-to-digital converters (ADCs) over Rician fading channels. Maximum-ratio-combining (MRC) and zero-forcing (ZF) receivers are considered under t
Externí odkaz:
http://arxiv.org/abs/1906.09841
Akademický článek
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Publikováno v:
J. Tong, R. Hu, J. Xi, Z. Xiao, Q. Guo, and Y. Yu, "Linear shrinkage estimation of covariance matrices using low-complexity cross-validation," Signal Processing, vol.148, pp. 223-233, July 2018
Shrinkage can effectively improve the condition number and accuracy of covariance matrix estimation, especially for low-sample-support applications with the number of training samples smaller than the dimensionality. This paper investigates parameter
Externí odkaz:
http://arxiv.org/abs/1810.08360
Publikováno v:
In Information Sciences November 2022 616:204-216