Moisture content recognition for wood chips in pile using supervised classification

Moisture content recognition for wood chips in pile using supervised classification Wood Sci Technol (2018) 52:1195–1211 https://doi.org/10.1007/s00226-018-1023-0 ORIGINAL Moisture content recognition for wood chips in pile using supervised classification 1 1 1 1 Hela Daassi‑Gnaba  · Yacine Oussar  · Maria Merlan  · Thierry Ditchi  · 1 1 Emmanuel Géron  · Stéphane Holé Received: 3 June 2016 / Published online: 29 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Wood chips moisture content (MC) is a key parameter for controlling the biofuel product qualities and properties. Since no knowledge-based model is available to recognize MC, machine learning methods are promising techniques to design black-box models for MC prediction or recognition. As wood permittiv- ity strongly changes in the presence of water, an electromagnetic module is used to probe the reflectivity of wood chip piles. In the present paper, the recognition of three wood chip piles of different MC categories is performed using support vector machines (SVMs). SVM-recursive feature elimination is implemented to rank and select reflection coefficients to design optimized linear SVM classifiers that attribute MC class of wood chips in a pile. Experiments show that the proposed approach is effective and requires a limited computational power. The global classification per - formance exceeds 95%. Introduction Wood is the main source of biomass used for http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wood Science and Technology Springer Journals

Moisture content recognition for wood chips in pile using supervised classification

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Life Sciences; Wood Science & Technology; Ceramics, Glass, Composites, Natural Materials; Operating Procedures, Materials Treatment
ISSN
0043-7719
eISSN
1432-5225
D.O.I.
10.1007/s00226-018-1023-0
Publisher site
See Article on Publisher Site

Abstract

Wood Sci Technol (2018) 52:1195–1211 https://doi.org/10.1007/s00226-018-1023-0 ORIGINAL Moisture content recognition for wood chips in pile using supervised classification 1 1 1 1 Hela Daassi‑Gnaba  · Yacine Oussar  · Maria Merlan  · Thierry Ditchi  · 1 1 Emmanuel Géron  · Stéphane Holé Received: 3 June 2016 / Published online: 29 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Wood chips moisture content (MC) is a key parameter for controlling the biofuel product qualities and properties. Since no knowledge-based model is available to recognize MC, machine learning methods are promising techniques to design black-box models for MC prediction or recognition. As wood permittiv- ity strongly changes in the presence of water, an electromagnetic module is used to probe the reflectivity of wood chip piles. In the present paper, the recognition of three wood chip piles of different MC categories is performed using support vector machines (SVMs). SVM-recursive feature elimination is implemented to rank and select reflection coefficients to design optimized linear SVM classifiers that attribute MC class of wood chips in a pile. Experiments show that the proposed approach is effective and requires a limited computational power. The global classification per - formance exceeds 95%. Introduction Wood is the main source of biomass used for

Journal

Wood Science and TechnologySpringer Journals

Published: May 29, 2018

References

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