Big geo data surface approximation using radial basis functions: A comparative study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932155" target="_blank" >RIV/49777513:23520/17:43932155 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.cageo.2017.08.007" target="_blank" >http://dx.doi.org/10.1016/j.cageo.2017.08.007</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cageo.2017.08.007" target="_blank" >10.1016/j.cageo.2017.08.007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Big geo data surface approximation using radial basis functions: A comparative study

Popis výsledku v původním jazyce

Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.

Název v anglickém jazyce

Big geo data surface approximation using radial basis functions: A comparative study

Popis výsledku anglicky

Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless metody pro vizualizaci velkých časově-prostorových vektorových dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computers &amp; Geosciences

ISSN

0098-3004

e-ISSN

—

Svazek periodika

109

Číslo periodika v rámci svazku

December 2017

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

51-58

Kód UT WoS článku

000415663200006

EID výsledku v databázi Scopus

2-s2.0-85027709533