Estimations of Shape and Direction of an Air Jet Using Neural Networks.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F13%3APU105017" target="_blank" >RIV/00216305:26210/13:PU105017 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Estimations of Shape and Direction of an Air Jet Using Neural Networks.

Popis výsledku v původním jazyce

Analysis of airflow properties is an important step during validation of functionality of air distribution systems in a closed environment such as vents in car cabins. Optical visualization methods, based on imaging of the airflow visualization using smoke or fog, are often applied in such cases. The aim of this work is in an automation of processing of such images captured during visualization. It can be accomplished, besides special mathematical methods, using neural networks. We have employed a multilayer perceptron network for a detection of fog-containing areas in airflow images. Network learning was used and documented here for a recognition of the fog presence in individual pixels of the image based on colour intensities of the pixel neighbourhood. The fog detection was used for estimation of the jet shape. Hopfield network, which allows to relate the jet with one of the four basic flow directions, was applied consequently. The information about jet direction is important for fu

Název v anglickém jazyce

Estimations of Shape and Direction of an Air Jet Using Neural Networks.

Popis výsledku anglicky

Analysis of airflow properties is an important step during validation of functionality of air distribution systems in a closed environment such as vents in car cabins. Optical visualization methods, based on imaging of the airflow visualization using smoke or fog, are often applied in such cases. The aim of this work is in an automation of processing of such images captured during visualization. It can be accomplished, besides special mathematical methods, using neural networks. We have employed a multilayer perceptron network for a detection of fog-containing areas in airflow images. Network learning was used and documented here for a recognition of the fog presence in individual pixels of the image based on colour intensities of the pixel neighbourhood. The fog detection was used for estimation of the jet shape. Hopfield network, which allows to relate the jet with one of the four basic flow directions, was applied consequently. The information about jet direction is important for fu

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/TE01020020" target="_blank" >TE01020020: Centrum kompetence automobilového průmyslu Josefa Božka</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

MENDEL 2013

ISBN

978-80-214-4755-4

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

221-226

Název nakladatele

Neuveden

Místo vydání

Brno

Místo konání akce

Brno University of Technology

Datum konání akce

26. 6. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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