两条相互独立的非对称Cauchy过程轨道的乘积集的分形性质

设Ｘ１,Ｘ２是Ｒ＾ｄ上两条相互独立的非对称Ｃａｕｃｈｙ过程,我们求出了,两条轨道的乘积集的确切Ｈａｕｓｄｏｒｆｆ测度函数ψ（ｈ）＝ｈ＾２／ｌｏｇ＾ｈ,同时ψ（ｈ）,同时ψ（ｈ）也是图集的乘积集的确切Ｈａｕｓｄｏｒｆｆ测度函数,另外,我们还求出了乘积集的Ｈａｕｓｄｏｒｆｆ维数和Ｐａｃｋｉｎｇ维数均为２,从而证明了乘 集仍然是分形集。     

多型随机递归集关于统计自相似测度的Multifractal分解

本文构造了一类多型随机递归集Ｋ,并利用 Ｆａｌｃｏｎｅｒ的方法［１］获得了Ｋ的重分形分解集Ｋａ（ａ＞０）的Ｈａｕｓｄｏｒｆｆ维数和Ｐａｃｋｉｎｇ维数．     

具有有限记忆的随机分形的重分形分解

Dryakhlov和Tempelman对具有有限记忆的随机分形集的Hausdorff维数进行了研究,本文对具有有限记忆的随机分形集K(ω)的重分形分解集Kα(ω)进行研究,得到了在一定条件下,这种随机分形集重分形分解集Kα(ω)的Hausdorff维数表达式.     

关于修改的盒维数的一个结果

首先证明了修改的下盒维数的乘积公式,继而给出了R上一类随机分形集的修改的盒维数的维数性质。     

SG(2,2)上布朗运动的维数性质

本文研究分形集合ＳＧ（２,２）上布朗运动的维数性质,得到了ＳＧ（２,２）上布朗运动的样本图以及象集的Ｈａｕｓｄｏｒｆｆ维数与盒维数。     

随机自仿射集的Hausdorff维数估计

定义了一类广泛的随机自仿射集,得到了此类集合的Ｈａｕｓｄｏｒｆｆ维数估计．此前的随机自相似（包括Ｇｒａｆ,Ｍａｕｌｄｉｎ与Ｆａｌｃｏｎｅｒ等定义的随机自相似情形）和Ｆａｌｃｏｎｅｒ定义的（严格）自仿射以及作者定义的μ 统计自仿射情形均成为该文结果的特例．     

概率空间上的分形性质

设（Ω，Ｆ，μ）为一概率空间，｛ｘｎ，ｎ≥１｝是定义在（Ω，Ｆ，μ）上的随机过程，Ｅ为β的任意子集，ｄｉｍμ（Ｅ）和Ｄｉｍμ（Ｅ）分别为Ｅ的Ｈａｕｓｄｏｒｆｆ和Ｐａｃｋｏｎｇ维数，若ｄｉｍμ（Ｅ）＝Ｄｉｍμ（Ｅ），则称Ｅ是正则集。     

概率空间（Ω,f,μ）中关于μ的packing维数与经典的实直线上的packing维...

本文将概率空间（Ω，ｆ，μ）中ｐａｃｋｉｎｇ维数的定义与经典的实直线上的ｐａｃｋｉｎｇ维数的定义相联系，证明了在Ｌｅｂｅｓｇｕｅ情形，对所有的Ａ∈ｆ，关于μ的ｐａｃｋｉｎｇ维数Ｄｉｍμ（Ａ）与被Ｔａｙｌｏｒ和Ｔｒｉｃｏｔ所定义的ｐａｃｋｉｎｇ维数Ｄｉｍ（Ａ）是一致的。Ｂｉｌｌｉｎｇｓｌｅｙ的结果与我们的结果相结合，表明在Ｌｅｂｅｓｇｕｅ情形，关于μ的分形与被Ｔａｙｌｏｒ所定义的分形是一致的。     

一类随机Moran集的分形维数

本文研究了随机压缩向量满足一定条件下的随机Moran集的分形维数.利用计算上盒维数的上界和分形维数之间的性质,得到Moran集各种分形维数. 并在一般情形下,给出随机Moran集的上盒维数的上界.     

一类准自相似集的研究

本文引入并研究了准自相似集，利用动力系统技巧讨论了其Ｈａｕｓｄｏｒｆｆ维数的上、下界，得到了一类严格准自相似集的Ｈａｕｓｄｏｒｆｆ维数公式并确定了一类由共形映射族所确定的准自相似集的Ｈａｕｓｄｏｒｆｆ维数．     

Multifractal analysis of the occupation measures of a kind of stochastic processes

Let {X(t), 0t1} be a stochastic process whose range is a random Cantor-like set depending on an -sequence (0<<1) and μ is the occupation measure of X(t). In this paper we examine the multifractal structure of μ and obtain the fractal dimensions of the sets of points of where the local dimension of μ is different from . It is interesting to notice that the final results of this paper are identical to those for the occupation measure of a stable subordinator with index , yet the stochastic process under consideration in this work is not even a Markov process.     

Kolmogorov numbers of Riemann–Liouville operators over small sets and applications to Gaussian processes

We investigate compactness properties of the Riemann–Liouville operator R_α of fractional integration when regarded as operator from L₂[0,1] into C(K), the space of continuous functions over a compact subset K in [0,1]. Of special interest are small sets K, i.e. those possessing Lebesgue measure zero (e.g. fractal sets). We prove upper estimates for the Kolmogorov numbers of R_α against certain entropy numbers of K. Under some regularity assumption about the entropy of K these estimates turn out to be two-sided. By standard methods the results are also valid for the (dyadic) entropy numbers of R_α. Finally, we apply these estimates for the investigation of the small ball behavior of certain Gaussian stochastic processes, as e.g. fractional Brownian motion or Riemann–Liouville processes, indexed by small (fractal) sets.     

On the Geometric Densities of Random Closed Sets

Abstract

In many applications it is of great importance to handle evolution equations about random closed sets of different (even though integer) Hausdorff dimensions, including local information about initial conditions and growth parameters. Following a standard approach in geometric measure theory such sets may be described in terms of suitable measures. For a random closed set of lower dimension with respect to the environment space, the relevant measures induced by its realizations are singular with respect to the Lebesgue measure, and so their usual Radon–Nikodym derivatives are zero almost everywhere. In this paper we suggest to cope with these difficulties by introducing random generalized densities (distributions) á la Dirac–Schwarz, for both the deterministic case and the stochastic case. In this last one we analyze mean generalized densities, and relate them to densities of the expected values of the relevant measures. Many models of interest in material science and in biomedicine are based on time dependent random closed sets, as the ones describing the evolution of (possibly space and time inhomogeneous) growth processes; in such a situation, the Delta formalism provides a natural framework for deriving evolution equations for mean densities at all (integer) Hausdorff dimensions, in terms of the local relevant kinetic parameters of birth and growth. In this context connections with the concepts of hazard function, and spherical contact distribution function are offered.     

The role of Billingsley dimensions in computing fractal dimensions on Cantor-like spaces

We consider a Cantor-like set as a geometric projection of a Bernoulli process. P. Billingsley (1960) and C. Dai and S.J. Taylor (1994) introduced dimension-like indices in the probability space of a stochastic process. Under suitable regularity conditions we find closed formulae linking the Hausdorff, box and packing metric dimensions of the subsets of the Cantor-like set, to the corresponding Billingsley dimensions associated with a suitable Gibbs measure. In particular, these formulae imply that computing dimensions in a number of well-known fractal spaces boils down to computing dimensions in the unit interval endowed with a suitable metric. We use these results to generalize density theorems in Cantor-like spaces. We also give some examples to illustrate the application of our results.

     

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.     

G-fuzzy topologies on algebraic structures

Considering complete Boolean algebras as sets of truth values the structure of a fuzzy topological group, fuzzy topological ring, etc., is specified. The probabilistic completion of ordinary topological algebraic structures shows the applicability of these concepts to the theory of stochastic processes, e.g., a new definition of the stochastic integral is presented in Section 5.     

Construction and Dimension Analysis for a Class of Fractal Functions

In this paper, we construct a class of nowhere differentiable continuous functions by means of the Cantor series expression of real numbers. The constructed functions include some known nondifferentiable functions, such as Bush type functions. These functions are fractal functions since their graphs are in general fractal sets. Under certain conditions, we investigate the fractal dimensions of the graphs of these functions, compute the precise values of Box and Packing dimensions, and evaluate the Hausdorff dimension. Meanwhile, the Holder continuity of such functions is also discussed.     

在Pbkc（c[0,1]）与Pbkc（Lp[0,1]）取值的集值随机变量（1）

对拟连续测度空间(G,β,u)的一致有界等度连续函数族,通过包含关系,取凸包和闭包,构造了在Pbkc(c[0,1])与Pbkc(Lp[0,1])取值的集值随机变量及连续的集值映射,深化了集值随机过程理论研究.     

Generalizations of the centroid with an application in stochastic geometry

The centroid of a subset of with positive volume is a well‐known characteristic. An interesting task is to generalize its definition to at least some sets of zero volume. In the presented paper we propose two possible ways how to do that. The first is based on the Hausdorff measure of an appropriate dimension. The second is given by the limit of centroids of ε‐neighbourhoods of the particular set when ε goes to 0. For both generalizations we discuss their existence and basic properties. Then we focus on sufficient conditions of existence of the second generalization and on conditions when both generalizations coincide. It turns out that they can be formulated with the help of the Minkowski content, rectifiability, and self‐similarity. Since the centroid is often used in stochastic geometry as a centre function for certain particle processes, we present properties that are needed for both generalizations to be valid centre functions. Finally, we also show their continuity on compact convex m‐sets with respect to the Hausdorff metric topology.