This paper proposes an artificial neural network (ANN) method to effectively detect spurious velocity vectors in a velocity field measured by particle image velocimetry (PIV). The neural network is a recurrent network referred to as a cellular neural network (CNN). The method is compared with the local-median method to remove measurement outliers. Both artificially generated velocity fields containing known errors and actual experimental data were used to study the performance of these methods. The influences of the velocity gradient and the error percentage are discussed. The CNN model was shown to be more efficient for removal of erroneous vectors.
Experiments in Fluids – Springer Journals
Published: Jan 28, 2003
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera