Detail publikace

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

RICHTER, J. ŠŤASTNÝ, J. JEDELSKÝ, J.

Originální název

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

Český název

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

Anglický název

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

Typ

článek ve sborníku

Jazyk

en

Originální abstrakt

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 further image processing functions that take into account the distance from the jet source.

Český abstrakt

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 further image processing functions that take into account the distance from the jet source.

Anglický abstrakt

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 further image processing functions that take into account the distance from the jet source.

Rok RIV

2013

Vydáno

26.06.2013

Místo

Brno

ISBN

978-80-214-4755-4

Kniha

MENDEL 2013

Strany od

221

Strany do

226

Strany počet

6

BibTex


@inproceedings{BUT101457,
  author="Jan {Richter} and Jiří {Šťastný} and Jan {Jedelský}",
  title="Estimations of Shape and Direction of an Air Jet Using Neural Networks.",
  annote="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 further image processing functions that take into account the distance from the jet source.",
  booktitle="MENDEL 2013",
  chapter="101457",
  howpublished="print",
  year="2013",
  month="june",
  pages="221--226",
  type="conference paper"
}