Machine Learning Approaches for the Inversion of the Radiative Transfer Equation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009

AUTHORS

Esteban Garcia-Cuesta , Fernando de la Torre , Antonio J. de Castro

ABSTRACT

Estimation of the constituents of a gas (e.g. temperature, concentration) from high resolution spectroscopic measurements is a fundamental step to control and improve the efficiency of combustion processes governed by the Radiative Transfer Equation (RTE). Typically such estimation is performed using thermocouples; however, these sensors are intrusive and must undergo the harsh furnace environment. In this paper, we follow a machine learning approach to learn the relation between the spectroscopic measurements and gas constituents such as temperature, concentration and length. This is a challenging problem due to the non-linear behavior of the RTE and the high dimensional data obtained from sensor measurements. We perform a comparative study of linear and neural network regression models, using canonical correlation analysis (CCA), principal component analysis (PCA), reduced rank regression (RRR), and kernel canonical correlation (KCCA) to reduce the dimensionality. More... »

PAGES

319-331

References to SciGraph publications

  • 2006. Spectral High Resolution Feature Selection for Retrieval of Combustion Temperature Profiles in INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING – IDEAL 2006
  • Book

    TITLE

    Advances in Computational Algorithms and Data Analysis

    ISBN

    978-1-4020-8918-3
    978-1-4020-8919-0

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-1-4020-8919-0_22

    DOI

    http://dx.doi.org/10.1007/978-1-4020-8919-0_22

    DIMENSIONS

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