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Multivariate outlier mining and regression feedback for 3D measurement improvement in opto-mechanical system

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Abstract

This paper presents a technical research of an opto-mechanical system for 3D measurement. The system scanning technology is based on a mechanical rotary mirror and the computational processing of an optoelectronic signal, with application in structural health monitoring. The system has been enhanced by multivariate outlier mining and regression feedback. Multivariate outlier analysis has been implemented to detection and removal of atypical values, in order to contribute to the accuracy improvement of artificial intelligence regression algorithms. The regression of error data has been used for the correction of the 3D measurements error. New research performed in the signal and data processing of the 3D measurement system demonstrated the effectiveness of signal processing strategies as a tool for a feedback loop in any measurement system.

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Authors and Affiliations

  1. Autonomous University of Baja California, Blvd. Benito Juarez S/N, Parcela 44, 21280, Mexicali, Baja California, Mexico

    Wendy Flores-Fuentes, Oleg Sergiyenko, Félix F. Gonzalez-Navarro, Moisés Rivas-López, Julio C. Rodríguez-Quiñonez, Daniel Hernández-Balbuena, Vera Tyrsa & Lars Lindner

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  1. Wendy Flores-Fuentes
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  2. Oleg Sergiyenko
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  3. Félix F. Gonzalez-Navarro
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  4. Moisés Rivas-López
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  5. Julio C. Rodríguez-Quiñonez
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  6. Daniel Hernández-Balbuena
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  7. Vera Tyrsa
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  8. Lars Lindner
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Correspondence to Wendy Flores-Fuentes.

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Flores-Fuentes, W., Sergiyenko, O., Gonzalez-Navarro, F.F. et al. Multivariate outlier mining and regression feedback for 3D measurement improvement in opto-mechanical system. Opt Quant Electron 48, 403 (2016). https://doi.org/10.1007/s11082-016-0680-1

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  • DOI: https://doi.org/10.1007/s11082-016-0680-1

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