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1.
在修改的Ohta_Okamoto数字签名方案基础上,设计出了一个新的(t,n)门限签名方案。该方案具有可验证性和防止内部成员合谋攻击;而且验证者在验证群签名的有效性计算的复杂性与一般个人签名方案一样。该方案的安全性是基于大整数的因式分解,因此在计算上是安全的。 相似文献
2.
Influence of Structural Non-Stationarity of Surface Roughness on Morphological Characterization and Mechanical Deformation of Rock Joints 总被引:2,自引:2,他引:0
N. Fardin 《Rock Mechanics and Rock Engineering》2008,41(2):267-297
Summary Structural non-stationarity of surface roughness affects accurate morphological characterization as well as mechanical behaviour
of rock joints at the laboratory scale using samples with a size below the stationarity threshold. In this paper, the effect
of structural non-stationarity of surface roughness is investigated by studying the scale dependence of surface roughness
and mechanical behaviour of rock joints. The results show that the structural non-stationarity mainly affects the accurate
characterization of the surface roughness of the fracture samples. It also controls the amount and location of the contact
areas during shear tests, which in turn affects the mechanical properties and asperity degradation of the samples. It is concluded
that for accurate determination of the morphological and mechanical properties of rock joints at laboratory and field scales,
samples with size equal to or larger than the stationarity threshold are required.
Author’s address: Nader Fardin, Rock Mechanics Group, Department of Mining Engineering, Faculty of Engineering, University
of Tehran, P.O. Box: 11365/4563, Tehran, Iran 相似文献
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Chang-Jo F. Chung 《Mathematical Geology》1993,25(7):851-865
Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances,
,
,
, and
forY andZ: (ii) defining new variables by
and
and lettingA=C+D andB=C–D, and obtaining MLE for variances,
and
forA andB; (iii) estimating the correlation coefficient YZ by
and the covariance
YZ
by
. The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada. 相似文献
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一种基于InSAR相干系数的SAR阴影提取方法 总被引:1,自引:0,他引:1
给出了一种新的阴影提取方法,根据阴影区域在InSAR相干系数图上的特性,利用Sigma滤波和最优阈值分割算法实现阴影的自动提取。实验结果表明,本方法是一种高效的阴影提取方法。 相似文献