Moisture content recognition for wood chips in pile using supervised classification

[1]  Hela Daassi-Gnaba,et al.  Wood moisture content prediction using feature selection techniques and a kernel method , 2017, Neurocomputing.

[2]  Hela Daassi-Gnaba,et al.  External vs. Internal SVM-RFE: The SVM-RFE Method Revisited and Applied to Emotion Recognition , 2015 .

[3]  Consolación Gil,et al.  Scientific production of renewable energies worldwide: An overview , 2013 .

[4]  A. Inoue,et al.  MOISTURE CONTENT PREDICTION OF WOOD DRYING PROCESS USING SVM-BASED MODEL , 2012 .

[5]  Hela Daassi-Gnaba,et al.  Enhanced Emotion Recognition by Feature Selection to Animate a Talking Head , 2012, ESANN.

[6]  Bruce Denby,et al.  Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers , 2011, EURASIP J. Wirel. Commun. Netw..

[7]  Roberto Di Pietro,et al.  "Who Counterfeited My Viagra?" Probabilistic Item Removal Detection via RFID Tag Cooperation , 2011, EURASIP J. Wirel. Commun. Netw..

[8]  Adnan Al Anbuky,et al.  Overview and comparison of microwave noncontact wood measurement techniques , 2010, Journal of Wood Science.

[9]  Henrik Andersson,et al.  Wood defect classification based on image analysis and support vector machines , 2010, Wood Science and Technology.

[10]  Laurence R. Schimleck,et al.  Kernel regression methods for the prediction of wood properties of Pinus taeda using near infrared spectroscopy , 2010, Wood Science and Technology.

[11]  Jun Cao,et al.  Soft sensor modeling of moisture content in drying process based on LSSVM , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[12]  Cao Jun,et al.  Comparison on prediction wood moisture content using ARIMA and improved neural networks , 2009, 2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[13]  Raida Jirjis,et al.  Comparison of different methods for the determination of moisture content in biomass. , 2006 .

[14]  Hans Hartmann,et al.  Moisture content determination in solid biofuels by dielectric , 2006 .

[15]  G. Schajer,et al.  Measurement of wood grain angle, moisture content and density using microwaves , 2006, Holz als Roh- und Werkstoff.

[16]  Yury Shramkov,et al.  Modeling the dielectric properties of wood , 2006, Wood Science and Technology.

[17]  Moisture Content Measurements on Sawdust with Radio Frequency Spectroscopy , 2005 .

[18]  Yury Shramkov,et al.  Measuring the dielectric properties of wood at microwave frequencies , 2005, Wood Science and Technology.

[19]  Erik Dahlquist,et al.  Methods for determination of moisture content in woodchips for power plants—a review , 2004 .

[20]  G. Wahba,et al.  Multicategory Support Vector Machines , Theory , and Application to the Classification of Microarray Data and Satellite Radiance Data , 2004 .

[21]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[22]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[23]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[24]  T. Böhm,et al.  RAPID MOISTURE CONTENT DETERMINATION OF WOOD CHIPS – RESULTS FROM COMPARATIVE TRIALS , 2002 .

[25]  Klaus Kupfer,et al.  4. Internationale Konferenz „Electromagnetic Wave Interaction with Water and Moist Substances” – ein Rückblick (4th International Conference “Electromagnetic Wave Interaction with Water and Moist Substances” – a Review) , 2002 .

[26]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[27]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[28]  W L James,et al.  A microwave method for measuring moisture content, density, and grain angle of wood , 1985 .