New Method for Material Classification from TGA Data by Nonparametric Regression

Salvador Naya; Ricardo Cao; Ramón Artiaga; Ana García

doi:10.4028/www.scientific.net/MSF.514-516.1452

Paper Titles

Modeling and Computation of Hydroxyapatite Nanostructures and Properties
p.1434

Problems of Heat Source Modeling in Iso–Exothermic Materials Used as Riser Sleeves in Foundry
p.1438

Evolutional Friction Law in the Numerical Simulation of the Deep Drawing of a Rail
p.1443

Optimisation of Cordierite Processing through Taguchi Method
p.1448

New Method for Material Classification from TGA Data by Nonparametric Regression
p.1452

The Design and Characteristics of a Novel Ultrasonic Motor
p.1457

On the Possibility to Observe a Compensation Effect around the Glass Transition Temperature
p.1462

Multilayer Plug Assisted Thermoforming-Concept and Practical Usefulness
p.1467

Strain and Stress Distribution in Vickers Indentation of Coated Materials
p.1472

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New Method for Material Classification from TGA Data by Nonparametric Regression

Abstract:

The application of nonparametric method of functional regression for the classification of different types of materials studied by thermo-gravimetric analysis is proposed in this work. The method is illustrated with two case studies for classifying different materials. Some simulation study shows the performance of the method when the correlation between groups parameters and the error variance change.