Identifying the Structure of Soil Massifs by Numerical Quasiconformal Mapping Methods

2021 ◽  
Author(s):  
A. Ya. Bomba ◽  
M. V. Boichura
Keyword(s):  
Quasiconformal Mapping

On quasiconformal mapping of triply connected domains

2000 ◽  
Vol 40 (3) ◽  
pp. 281-297
Author(s):  
Z. Samsonia ◽  
M. Liparteliani
Keyword(s):  
Quasiconformal Mapping

scholarly journals Polycyclic quasiconformal mapping class subgroups

2011 ◽  
Vol 251 (2) ◽  
pp. 361-374
Author(s):  
Katsuhiko Matsuzaki
Keyword(s):  
Quasiconformal Mapping ◽  
Mapping Class

scholarly journals Application of quasiconformal mapping methods to the investigation of the explosive processes impact on the environment

2019 ◽  
pp. 17-18
Author(s):  
Kateryna Mykolaiivna Malash ◽  
Andrii Yaroslavovych Bomba
Keyword(s):  
Numerical Methods ◽  
Mathematical Models ◽  
Quasiconformal Mapping ◽  
Quasiconformal Mappings ◽  
Practical Application

The mathematical models used to study explosive processes are given. A class of problems investigating the influence of explosive processes on the environment by the quasiconformal mappings numerical methods are outlined and their practical application are described

Quasiconformal mapping

1999 ◽  
pp. 1-16
Author(s):  
Frederick Gardiner ◽  
Nikola Lakic
Keyword(s):  
Quasiconformal Mapping

scholarly journals On a Theorem of Schwarz Type for Quasiconformal Mappings in Space

1967 ◽  
Vol 29 ◽  
pp. 19-30
Author(s):  
Kazuo Ikoma
Keyword(s):  
Conformal Mapping ◽  
Quasiconformal Mapping ◽  
Quasiconformal Mappings ◽  
Space Domain ◽  
Moebius Transformation ◽  
Modulus Condition

A space ring R is defined as a domain whose complement in the Moebius space consists of two components. The modulus of R can be defined in variously different but essentially equivalent ways (see e.g. Gehring [3] and Krivov [5]), which is denoted by mod R. Following Gehring [2], we refer to a homeomorphism y(x) of a space domain D as a k-quasiconformal mapping, if the modulus conditionis satisfied for all bounded rings R with their closure , where y(R) denotes the image of R by y = y(x). Then, it is evident that the inverse of a k-quasi-conformal mapping is itself k-quasiconformal and that a k1-quasiconformal mapping followed by a k2-quasiconformal one is k1k2-quasiconformal. It is also well known that the restriction of a Moebius transformation to a space domain is equivalent to a 1-quasiconformal mapping of its domain.

scholarly journals Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Liu ◽  
Yulong Qiao
Keyword(s):  
Machine Learning ◽  
Quasiconformal Mapping ◽  
Kernel Function ◽  
Hyperspectral Image ◽  
Data Classification ◽  
Data Retrieval ◽  
Hyperspectral Data ◽  
The Internet ◽  
Discriminative Ability ◽  
Kernel Machine

Intelligent internet data mining is an important application of AIoT (Artificial Intelligence of Things), and it is necessary to construct large training samples with the data from the internet, including images, videos, and other information. Among them, a hyperspectral database is also necessary for image processing and machine learning. The internet environment provides abundant hyperspectral data resources, but the hyperspectral data have no class labels and no so high value for applications. So, it is important to label the class information for these hyperspectral data through machine learning-based classification. In this paper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for internet-based hyperspectral data retrieval. The contributions include three points: the quasiconformal mapping-based multiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective for hyperspectral image classification and retrieval.

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