Next Article in Journal
Gas Leak Location Detection Based on Data Fusion with Time Difference of Arrival and Energy Decay Using an Ultrasonic Sensor Array
Next Article in Special Issue
Air-Coupled Excitation of a Slow A0 Mode Wave in Thin Plastic Films by an Ultrasonic PMN-32%PT Array
Previous Article in Journal
Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement
Previous Article in Special Issue
Novel Configurations of Ultrahigh Frequency (≤600 MHz) Analog Frontend for High Resolution Ultrasound Measurement
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(9), 2984; https://doi.org/10.3390/s18092984

Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures

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.
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)
Full-Text   |   PDF [468 KB, uploaded 17 September 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Rus, G.; Melchor, J. Logical Inference Framework for Experimental Design of Mechanical Characterization Procedures. Sensors 2018, 18, 2984.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top