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Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures

Optimizing an experimental design is a complex task when a model is required for indirect reconstruction of physical parameters from the sensor readings. In this work, a formulation is proposed to unify the probabilistic reconstruction of mechanical parameters and an optimization problem. An informa...

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Autores principales: Rus, Guillermo, Melchor, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163977/
https://www.ncbi.nlm.nih.gov/pubmed/30205422
http://dx.doi.org/10.3390/s18092984
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author Rus, Guillermo
Melchor, Juan
author_facet Rus, Guillermo
Melchor, Juan
author_sort Rus, Guillermo
collection PubMed
description Optimizing an experimental design is a complex task when a model is required for indirect reconstruction of physical parameters from the sensor readings. In this work, a formulation is proposed to unify the probabilistic reconstruction of mechanical parameters and an optimization problem. An information-theoretic framework combined with a new metric of information density is formulated providing several comparative advantages: (i) a straightforward way to extend the formulation to incorporate additional concurrent models, as well as new unknowns such as experimental design parameters in a probabilistic way; (ii) the model causality required by Bayes’ theorem is overridden, allowing generalization of contingent models; and (iii) a simpler formulation that avoids the characteristic complex denominator of Bayes’ theorem when reconstructing model parameters. The first step allows the solving of multiple-model reconstructions. Further extensions could be easily extracted, such as robust model reconstruction, or adding alternative dimensions to the problem to accommodate future needs.
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spelling pubmed-61639772018-10-10 Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures Rus, Guillermo Melchor, Juan Sensors (Basel) Article Optimizing an experimental design is a complex task when a model is required for indirect reconstruction of physical parameters from the sensor readings. In this work, a formulation is proposed to unify the probabilistic reconstruction of mechanical parameters and an optimization problem. An information-theoretic framework combined with a new metric of information density is formulated providing several comparative advantages: (i) a straightforward way to extend the formulation to incorporate additional concurrent models, as well as new unknowns such as experimental design parameters in a probabilistic way; (ii) the model causality required by Bayes’ theorem is overridden, allowing generalization of contingent models; and (iii) a simpler formulation that avoids the characteristic complex denominator of Bayes’ theorem when reconstructing model parameters. The first step allows the solving of multiple-model reconstructions. Further extensions could be easily extracted, such as robust model reconstruction, or adding alternative dimensions to the problem to accommodate future needs. MDPI 2018-09-07 /pmc/articles/PMC6163977/ /pubmed/30205422 http://dx.doi.org/10.3390/s18092984 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rus, Guillermo
Melchor, Juan
Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
title Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
title_full Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
title_fullStr Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
title_full_unstemmed Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
title_short Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures
title_sort logical inference framework for experimental design of mechanical characterization procedures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163977/
https://www.ncbi.nlm.nih.gov/pubmed/30205422
http://dx.doi.org/10.3390/s18092984
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