关于齐次Cantor集的一个注记

设c是[0,1]上Hausdorff测度为正有限的齐次Cantor集类,本文证明了     

关于齐次Cantor集的一个注记

设Ｃ是［０,１］上Ｈａｕｓｄｏｒｆｆ测度为正有限的齐次Ｃａｎｔｏｒ集类,本文证明了,这里ｓ是Ｅ的Ｈａｕｓｄｏｒｆｆ维数,Ｈｓ（Ｅ）是Ｅ的ｓ维Ｈａｕｓｄｏｒｆｆ测度,Ｈｓ（Ｅ）的定义见引言,     

m分非均匀Cantor集的Hausdorff测度

证明了m分非均匀Cantor集的E的H ausdorff测度HS(E)=1.     

含参变量Cantor集的Hausdorff测度

本文研究了菱形为基本集所构成的的广义Cantor集的Hausdorff测度问题.利用菱形几何结构的相关证明方法,获得了此类广义Cantor集的Hausdorff测度准确值,推广了曾超益和许绍元等人的已有结果.     

三分Cantor集自乘积的Hausdorff测度的估计

本文证明了三分Cantor集C自乘积集C×C的Hausdorff测度,满足1≤H~((log_3)~4)(C×C)≤1.502879.     

齐次Cantor集的Hausdorff测度

本文完全确定了一类齐次Cantor集的Haudsorff测度.     

Cantor集随机重排的填充测度

获得了Cantor集随机重排后所得的随机集的填充测度,还得到了一般随机集的填充维数及某些“正则”序列所产生的随机集的Hausdorff测度及填充测度。     

直线Cantor集的Hausdorff中心测度

设K是由直线上迭代函数系统{φ1,φ2,…,φm}生成的吸引子,其中φi(z)=ρix+bi,i=1,2,…,m.称K为直线Cantor集.在压缩参数满足一定条件时,本文得到了K的Hausdorff中心测度精确值的计算公式.     

m分Cantor尘的Hausdorff测度

为得到一类相似分形的Hausdorff测度准确值．给出了m分Cantor尘的几何结构,利用几何度量关系对m分Cantor尘的Hausdorff测度准确值进行研究．证明了m分Cantor尘的Hausdorff测度准确为H^s（E）=1/（m-1）^s[（m-2k＋1）^2＋（m-1）^2]^s/2,其中s=logm4,m≥4,1≤k≤m．结果表明它是Cantor尘和Sierpinski地毯的Hausdorff测度的准确值的推广,4分Cantor尘和4分Sierpinski地毯的Hausdorff测度的准确值是其特例．     

Cantor集随机重排的Hausdorff测度

获得了Cantor集随机重排后所得的随机集的Hausdorff测度。     

Sum of Cantor sets: Self-similarity and measure

In this note it is shown that the sum of two homogeneous Cantor sets is often a uniformly contracting self-similar set and it is given a sufficient condition for such a set to be of Lebesgue measure zero (in fact, of Hausdorff dimension less than one and positive Hausdorff measure at this dimension).

     

扩散 Cantor 集的最小Hausdorff 测度

In this paper, we construct a scattered Cantor set having the value 1/2 of log2/log3- dimensional Hausdorff measure. Combining a theorem of Lee and Baek, we can see the value 21 is the minimal Hausdorff measure of the scattered Cantor sets, and our result solves a conjecture of Lee and Baek.     

Relation between spectral classes of a self-similar Cantor set

A self-similar Cantor set is completely decomposed as a class of the lower (upper) distribution sets. We give a relationship between the distribution sets in the distribution class and the subsets in a spectral class generated by the lower (upper) local dimensions of a self-similar measure. In particular, we show that each subset of a spectral class is exactly a distribution set having full measure of a self-similar measure related to the distribution set using the strong law of large numbers. This gives essential information of its Hausdorff and packing dimensions. In fact, the spectral class by the lower (upper) local dimensions of every self-similar measure, except for a singular one, is characterized by the lower or upper distribution class. Finally, we compare our results with those of other authors.     

The packing measure of the Cartesian product of the middle third Cantor set with itself

For the packing measure of the Cartesian product of the middle third Cantor set with itself, the exact value

     

对称Cantor集自乘积集的Hausdorff中心测度

设Cλ是由迭代函数系统(IFS){f1,f2}生成的对称Cantor集,其中f1(x)=λx, f2(x)=1-λ+λx,0<λ<1/2,x∈[0,1]．在压缩比λ满足一定条件时,本文得到了Cλ与其自身的笛卡尔乘积Cλ×Cλ的Hausdorff中心测度的计算公式．     

On multifractal of Cantor dust

We consider quasi-self-similar measures with respect to all real numbers on a Cantor dust. We define a local index function on the real numbers for each quasi-self-similar measure at each point in a Cantor dust, The value of the local index function at the real number zero for all the quasi-self-similar measures at each point is the weak local dimension of the point. We also define transformed measures of a quasi-self-similar measure which are closely related to the local index function. We compute the local dimensions of transformed measures of a quasi-self-similar measure to find the multifractal spectrum of the quasi-self-similar measure, Furthermore we give an essential example for the theorem of local dimension of transformed measure. In fact, our result is an ultimate generalization of that of a self- similar measure on a self-similar Cantor set. Furthermore the results also explain the recent results about weak local dimensions on a Cantor dust.