Determination of Air Jet Shape with Complex Methods Using Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F21%3A43919356" target="_blank" >RIV/62156489:43110/21:43919356 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26210/21:PU140639

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-61659-5_3" target="_blank" >https://doi.org/10.1007/978-3-030-61659-5_3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-61659-5_3" target="_blank" >10.1007/978-3-030-61659-5_3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Determination of Air Jet Shape with Complex Methods Using Neural Networks

Popis výsledku v původním jazyce

This article deals with the computer evaluation of airflow images. The airflow is visualized by continuous gas fibers, as for example smoke, fog or another visible additive. One of the most important properties of airflow is the shape of the stream. The principle of determining the shape of the stream is the detection of the additive. For 2D images with a heterogeneous background, it may be very difficult to distinguish the additive from the environment. This paper deals with the possibility of detecting an additive in airflow images with a heterogeneous back-ground. Artificial neural networks will be used for this purpose.

Název v anglickém jazyce

Determination of Air Jet Shape with Complex Methods Using Neural Networks

Popis výsledku anglicky

This article deals with the computer evaluation of airflow images. The airflow is visualized by continuous gas fibers, as for example smoke, fog or another visible additive. One of the most important properties of airflow is the shape of the stream. The principle of determining the shape of the stream is the detection of the additive. For 2D images with a heterogeneous background, it may be very difficult to distinguish the additive from the environment. This paper deals with the possibility of detecting an additive in airflow images with a heterogeneous back-ground. Artificial neural networks will be used for this purpose.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Recent Advances in Soft Computing and Cybernetics

ISBN

978-3-030-61658-8

Počet stran výsledku

16

Strana od-do

25-40

Počet stran knihy

320

Název nakladatele

Springer Switzerland

Místo vydání

Cham

Kód UT WoS kapitoly

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