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Article

Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures

by 1,2,3 and 1,2,3,*
1
Department of Structural Mechanics, University of Granada, 18071 Granada, Spain
2
Biosanitary Research Institute, 18016 Granada, Spain
3
MNat Scientific Unit of Excellence, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2984; https://doi.org/10.3390/s18092984
Received: 18 July 2018 / Revised: 28 August 2018 / Accepted: 1 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Ultrasonic Sensors 2018)
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. View Full-Text
Keywords: inverse problem; inference Bayesian updating; model-class selection; stochastic inverse problem; probability logic; experimental design inverse problem; inference Bayesian updating; model-class selection; stochastic inverse problem; probability logic; experimental design
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MDPI and ACS Style

Rus, G.; Melchor, J. Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures. Sensors 2018, 18, 2984. https://doi.org/10.3390/s18092984

AMA Style

Rus G, Melchor J. Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures. Sensors. 2018; 18(9):2984. https://doi.org/10.3390/s18092984

Chicago/Turabian Style

Rus, Guillermo, and Juan Melchor. 2018. "Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures" Sensors 18, no. 9: 2984. https://doi.org/10.3390/s18092984

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