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

Manne Hannula; Erkki Alarousu; Tuukka Prykari; Risto Myllyla

doi:10.1117/12.730208

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Conference proceeding

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

Manne Hannula, Erkki Alarousu, Tuukka Prykari and Risto Myllyla

ADVANCED LASER TECHNOLOGIES 2006, Vol.6606(1), pp.66061L-66061L-4

Proceedings of SPIE

01/01/2007

DOI: https://doi.org/10.1117/12.730208

Abstract

Optics

Physical Sciences

Science & Technology

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.

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Title: Neural network analysis of pulp flow speed in low coherence Doppler flowmetry measurement
Creators - without role: Manne Hannula - University of Oulu
Erkki Alarousu - University of Oulu
Tuukka Prykari - University of Oulu
Risto Myllyla - University of Oulu
Contributors - without role: D C Dumitras
M Dinescu
Konov
Publication Details: ADVANCED LASER TECHNOLOGIES 2006, Vol.6606(1), pp.66061L-66061L-4
Series: Proceedings of SPIE
Publisher: Spie-Int Soc Optical Engineering
Number of pages: 4
Identifiers: 9944279608331
Academic Unit: King Abdullah University of Science & Technology
Language: English
Resource Type: Conference proceeding