impact identification

Impact identification using nonlinear dimensionality reduction and supervised learning

October 3, 2019
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Smart Materials and Structures Vol. 28, No. 11, pp. 115005, 2019 V. Meruane, C. Espinoza, E. Lopez Droguett, A. Ortiz-Bernardin Abstract Real-time monitoring systems that can automatically locate and identify impacts as they occur have become increasingly attractive for ensuring safety and preventing catastrophic accidents in aerospace structures. In most cases, a set of piezoelectric

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Accepted Paper: Impact location and quantification on an aluminum sandwich panel using principal component analysis and linear approximation with maximum entropy

March 30, 2017
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Paper Accepted for Publication in Entropy V. Meruane, P. Véliz, E. López Droguett, A. Ortiz-Bernardin, “Impact location and quantification on an aluminum sandwich panel using principal component analysis and linear approximation with maximum entropy.” ABSTRACT To avoid structural failures it is of critical importance to detect, locate and quantify impact damage as soon as it

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Impact location and quantification on an aluminum sandwich panel using principal component analysis and linear approximation with maximum entropy

March 30, 2017
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Entropy Vol. 19, No. 4, pp. 137, 2017 V. Meruane, P. Véliz, E. López Droguett, A. Ortiz-Bernardin Abstract To avoid structural failures it is of critical importance to detect, locate and quantify impact damage as soon as it occurs. This can be achieved by impact identification methodologies, which continuously monitor the structure, detecting, locating, and quantifying

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Accepted Paper: A novel impact identification algorithm based on a linear approximation with maximum entropy

August 8, 2016
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Paper Accepted for Publication in Smart Materials and Structures N. Sanchez, V. Meruane, A. Ortiz-Bernardin, “A novel impact identification algorithm based on a linear approximation with maximum entropy.” ABSTRACT This article presents a novel impact identification algorithm that uses a linear approximation handled by a statistical inference model based on the maximum-entropy principle, termed linear

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A novel impact identification algorithm based on a linear approximation with maximum entropy

August 8, 2016
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Smart Materials and Structures Vol. 25, No. 9, pp. 095050, 2016 N. Sanchez, V. Meruane, A. Ortiz-Bernardin Abstract This article presents a novel impact identification algorithm that uses a linear approximation handled by a statistical inference model based on the maximum-entropy principle, termed linear approximation with maximum entropy (LME). Unlike other regression algorithms as Artificial

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