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Proceedings Paper

Comparison of artificial neural network and multilinear regression analysis models in estimation of pulp flow speed from low coherence Doppler flowmetry measurement data
Author(s): Manne Hannula; Erkki Alarousu; Tuukka Prykäri; Risto Myllylä
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Paper Abstract

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 performances of Artificial Neural Network (ANN) and Multilinear Regression (MLR) methods in prediction of pulp flow speed from the LCDF measurement data were compared. In the study the pulp flow speed was estimated distinctly from consecutive frequency bands of the LCDF data with both methods. The smallest estimation error in flow speed with the ANN method was 20% and with the MLR method 30%, depending on the selected frequency band. The results indicate the relationship between characteristics of the LCDF measurement and pulp flow speed includes remarkable number of nonlinear components. The result is in line with theoretical calculations about the Doppler shifts occurrence in the LCDF data.

Paper Details

Date Published: 25 April 2007
PDF: 5 pages
Proc. SPIE 6606, Advanced Laser Technologies 2006, 660616 (25 April 2007); doi: 10.1117/12.729497
Show Author Affiliations
Manne Hannula, Univ. of Oulu (Finland)
Erkki Alarousu, Univ. of Oulu (Finland)
Tuukka Prykäri, Univ. of Oulu (Finland)
Risto Myllylä, Univ. of Oulu (Finland)

Published in SPIE Proceedings Vol. 6606:
Advanced Laser Technologies 2006
Dan C. Dumitras; Maria Dinescu; Vitally I. Konov, Editor(s)

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