By Topic

A Hopfield neural network for flow field computation based on particle image velocimetry/particle tracking velocimetry image sequences

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Knaak, M. ; Inst. of Electron. & Lighting Technol., Tech. Univ. Berlin, Germany ; Rothlubbers, C. ; Orglmeister, R.

A new application of a Hopfield network for the detection of particle pairs in particle image velocimetry/particle tracking velocimetry (PIV/PTV) is described. PIV/PTV are the most advanced techniques for the examination of flow fields. Our aims are to apply these techniques to fluid mechanics and the investigation of hydraulic turbomachinery and artificial heart valves. To obtain correct particle correspondences in subsequent images, a specific cost function is defined and then mapped onto a two-dimensional Hopfield network. First investigations show better performance than conventional techniques for PTV/PIV. In comparison to conventional nearest neighbor techniques, the number of correct particle pairs detected significantly increases, whereas the number of mismatches decreases

Published in:

Neural Networks,1997., International Conference on  (Volume:1 )

Date of Conference:

9-12 Jun 1997