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

Manne Hannula; Erkki Alarousu; Tuukka Prykäri; Risto Myllylä

doi:10.1117/12.730208

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 proceeding › Conference contribution › peer-review

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 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 language	English (US)
Title of host publication	Advanced Laser Technologies 2006
DOIs	https://doi.org/10.1117/12.730208
State	Published - 2007
Externally published	Yes
Event	Advanced Laser Technologies 2006 - Brasov, Romania Duration: Sep 8 2006 → Sep 12 2006

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6606
ISSN (Print)	0277-786X

Other

Other	Advanced Laser Technologies 2006
Country/Territory	Romania
City	Brasov
Period	09/8/06 → 09/12/06

Keywords

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

Access to Document

10.1117/12.730208

Cite this

@inproceedings{42cfdca65f2f4380a9c1c74342e993cb,

title = "Neural network analysis of pulp flow speed in low coherence Doppler flowmetry measurement",

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 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.",

keywords = "Intelligent systems, LDF, Paper machines",

author = "Manne Hannula and Erkki Alarousu and Tuukka Pryk{\"a}ri and Risto Myllyl{\"a}",

year = "2007",

doi = "10.1117/12.730208",

language = "English (US)",

isbn = "0819467448",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Advanced Laser Technologies 2006",

note = "Advanced Laser Technologies 2006 ; Conference date: 08-09-2006 Through 12-09-2006",

}

Hannula, M, Alarousu, E, Prykäri, T & Myllylä, R 2007, Neural network analysis of pulp flow speed in low coherence Doppler flowmetry measurement. in Advanced Laser Technologies 2006., 66061L, Proceedings of SPIE - The International Society for Optical Engineering, vol. 6606, Advanced Laser Technologies 2006, Brasov, Romania, 09/8/06. https://doi.org/10.1117/12.730208

Neural network analysis of pulp flow speed in low coherence Doppler flowmetry measurement. / Hannula, Manne; Alarousu, Erkki; Prykäri, Tuukka et al.
Advanced Laser Technologies 2006. 2007. 66061L (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6606).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Neural network analysis of pulp flow speed in low coherence Doppler flowmetry measurement

AU - Hannula, Manne

AU - Alarousu, Erkki

AU - Prykäri, Tuukka

AU - Myllylä, Risto

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

KW - Intelligent systems

KW - LDF

KW - Paper machines

UR - http://www.scopus.com/inward/record.url?scp=35148827386&partnerID=8YFLogxK

U2 - 10.1117/12.730208

DO - 10.1117/12.730208

M3 - Conference contribution

AN - SCOPUS:35148827386

SN - 0819467448

SN - 9780819467447

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Advanced Laser Technologies 2006

T2 - Advanced Laser Technologies 2006

Y2 - 8 September 2006 through 12 September 2006

ER -

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

Abstract

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Other

Keywords

ASJC Scopus subject areas

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