Wood moisture content prediction using feature selection techniques and a kernel method

Author:

Daassi-Gnaba Hela,Oussar Yacine,Merlan Maria,Ditchi Thierry,Géron Emmanuel,Holé Stéphane

Funder

French National Research Agency (ANR)

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference55 articles.

1. Analysis of options to move beyond 20% greenhouse gas emission reductions and assessing the risk of carbon leakage, Communication from the commission to the European Parliament, the council, the European economic and social Committee and the committee of regions, Brussels, 2010.

2. Scientific production of renewable energies worldwide;Manzano-Agugliaro;Renew. Sustain. Energy Rev.,2013

3. Moisture content determination in solid biofuels by dielectric and NIR reflection methods;Jensen;Biomass Bioenergy,2006

4. H. Hartmann, T. Böhm, Rapid moisture content determination of wood chips – results from comparative trials, in: Proceedings of the 1st World Conference on Biomass for Energy and Industry, Sevilla, James & James Ltd., London, 2001, pp. 571–574.

5. European Committee for Standardization, CENT/TC 335 EN 14774-1:2009 Determination of moisture content.

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