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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6163977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT rusguillermo logicalinferenceframeworkforexperimentaldesignofmechanicalcharacterizationprocedures AT melchorjuan logicalinferenceframeworkforexperimentaldesignofmechanicalcharacterizationprocedures |