Neural network analysis of pulp flow speed in low coherence Doppler flowmetry measurement

Manne Hannula*, Erkki Alarousu, Tuukka Prykäri, Risto Myllylä

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Low Coherence Doppler Flowmetry (LCDF) measurement produces a signal, which frequency domain characteristics are in connection to the speed of the flow. In this study a LCDF measurement data of pulp flow in a capillary was analyzed with a simple Artificial Neural Network (ANN) method to estimate the flow speed. The accuracy of the method proved to be good, validation of the method resulted in absolute error of 14 +- 11 percentage units (mean+-std) in flow speed estimation. The results of the study can be utilized in development of industrial pulp flow speed measurement instruments.

Original languageEnglish (US)
Title of host publicationAdvanced Laser Technologies 2006
StatePublished - 2007
Externally publishedYes
EventAdvanced Laser Technologies 2006 - Brasov, Romania
Duration: Sep 8 2006Sep 12 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherAdvanced Laser Technologies 2006


  • Intelligent systems
  • LDF
  • Paper machines

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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