M
Moisés Silva
Researcher at Federal University of Pará
Publications - 26
Citations - 523
Moisés Silva is an academic researcher from Federal University of Pará. The author has contributed to research in topics: Structural health monitoring & Kernel principal component analysis. The author has an hindex of 9, co-authored 26 publications receiving 322 citations. Previous affiliations of Moisés Silva include Los Alamos National Laboratory.
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Journal ArticleDOI
Machine learning algorithms for damage detection: Kernel-based approaches
TL;DR: Four kernel-based algorithms for damage detection under varying operational and environmental conditions, namely based on one-class support vector machine, support vector data description, kernel principal components analysis and greedy kernel principal component analysis are presented.
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A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges
Moisés Silva,Adam Santos,Eloi Figueiredo,Reginaldo Santos,Claudomiro Sales,João C. W. A. Costa +5 more
TL;DR: A novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability is proposed.
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Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges
Adam Santos,Eloi Figueiredo,Moisés Silva,Reginaldo Santos,Claudomiro Sales,João C. W. A. Costa +5 more
TL;DR: In this paper, a hybrid approach based on a standard genetic algorithm (GA) was proposed to improve the stability of the EM algorithm on the searching of the optimal number of clusters and their parameters, strengthening the damage classification performance.
Journal ArticleDOI
Deep principal component analysis: An enhanced approach for structural damage identification:
Moisés Silva,Adam Santos,Adam Santos,Reginaldo Santos,Eloi Figueiredo,Claudomiro Sales,João C. W. A. Costa +6 more
TL;DR: The experimental results demonstrate that capturing the most slight nonlinear variations in the data can lead to improved data normalization and, consequently, better damage detection and quantification performances.
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A global expectation-maximization based on memetic swarm optimization for structural damage detection:
Adam Santos,Moisés Silva,Reginaldo Santos,Eloi Figueiredo,Claudomiro Sales,João C. W. A. Costa +5 more
TL;DR: A memetic algorithm based on particle swarm optimization (PSO) to improve the stability and reliability of the EM algorithm, a global EM (GEM-PSO), in searching for the optimal number of components (or data clusters) and their parameters, which enhances the damage classification performance.