®®®® SIIA Público

Título del libro: Iecon 2015 - 41st Annual Conference Of The Ieee Industrial Electronics Society
Título del capítulo: Outlier mining of a vision sensing databasefor SVM regression improvement

Autores UNAM:
FABIAN NATANAEL MURRIETA RICO;
Autores externos:

Idioma:
Inglés
Año de publicación:
2015
Palabras clave:

Artificial intelligence; Data handling; Error correction; Industrial electronics; Measurement errors; Multivariant analysis; Pattern recognition; Regression analysis; Robots; Statistics; Structural health monitoring; Support vector machines; Mahalanobis distances; Measurement system; Multivariate outlier analysis; Nonlinear behavior; outliers; Spatial measurements; Support vector machine regressions; Vision sensing; Spatial variables measurement


Resumen:

A 3D spatial measurement system has been enhanced by computational intelligence. The measurement system is based in opto-electronic scanning instrumentation for industrial task, robot navigation, medical scanning, and structural health monitoring applications. This paper presents new research performed in the data processing of a vision sensing database. Multivariate outlier analysis has been implemented by Mahalanobis distance in order to improve Support Vector Machine (SVM) regression algorithm results. Measurement error regression data has been used for spatial 3D measurements error correction. Demonstrating that optical systems for measurements that present non-linear behavior could be positive impacted by computational intelligence. © 2015 IEEE.


Entidades citadas de la UNAM: