𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Gauges for the self-similar sets

✍ Scribed by Sheng-You Wen; Zhi-Xiong Wen; Zhi-Ying Wen


Publisher
John Wiley and Sons
Year
2008
Tongue
English
Weight
152 KB
Volume
281
Category
Article
ISSN
0025-584X

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

For a self‐similar set E with the open set condition we completely determine the class of its Hausdorff gauges and the class of its prepacking gauges. Moreover, its Hausdorff measures and its packing premeasures with respect to the corresponding gauges are estimated. Without the open set condition we prove that a doubling gauge function is a packing gauge of E if and only if it is a prepacking gauge of E. Also, we give some extensions and applications of these results. Here a gauge function is called a Hausdorff, a prepacking, and a packing gauge of a set, if with respect to the function the set has positive and finite Hausdorff measure, packing premeasure, and packing measure, respectively. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)


📜 SIMILAR VOLUMES


Self-similar structure on intersection o
✍ Wenxia Li; Yuanyuan Yao; Yunxiu Zhang 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 229 KB

## Abstract For a homogeneous symmetric Cantor set __C__, we consider all real numbers __t__such that the intersection __C__∩(__C__ + __t__)is a self‐similar set and investigate the form of the corresponding iterated function systems. © 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim

Dimensions for random self–conformal set
✍ Yan–Yan Liu; Jun Wu 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 177 KB

## Abstract A set is called regular if its Hausdorff dimension and upper box–counting dimension coincide. In this paper, we prove that the random self–conformal set is regular almost surely. Also we determine the dimensions for a class of random self–conformal sets.

Learning-based similarity measurement fo
✍ Athena Tocatlidou 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 407 KB 👁 2 views

The work described in this paper proposes a method for the measurement of similarity, viewed from the decision maker's perspective. At first, an algorithm is presented that generalizes a discrete fuzzy set F, representing a model, given another discrete fuzzy set G representing new evidence. The alg