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Entropy-based Inhomogeneity Detection in Fiber Materials

Author

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  • Patricia Alonso Ruiz

    (University of Connecticut)

  • Evgeny Spodarev

    (Ulm University)

Abstract

We study a change-point problem for random fields based on a univariate detection of outliers via the 3σ-rule in order to recognize inhomogeneities in glass fiber reinforced polymers (GFRP). In particular, we focus on GFRP modeled by stochastic fiber processes with high fiber intensity and search for abrupt changes in the direction of the fibers. As a measure of change, the entropy of the directional distribution is locally estimated within a window that scans the region to be analyzed.

Suggested Citation

  • Patricia Alonso Ruiz & Evgeny Spodarev, 2018. "Entropy-based Inhomogeneity Detection in Fiber Materials," Methodology and Computing in Applied Probability, Springer, vol. 20(4), pages 1223-1239, December.
    • Handle: RePEc:spr:metcap:v:20:y:2018:i:4:d:10.1007_s11009-017-9603-2
    DOI: 10.1007/s11009-017-9603-2
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    References listed on IDEAS

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    1. Malte Kuhlmann & Claudia Redenbach, 2015. "Estimation of Fibre Length Distributions from Fibre Endpoints," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1010-1022, December.
    2. Lianjie Shu & Wei Jiang & Kwok‐Leung Tsui, 2012. "A standardized scan statistic for detecting spatial clusters with estimated parameters," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(6), pages 397-410, September.
    3. Jun Li & Xin Zhang & Daniel R. Jeske, 2013. "Nonparametric multivariate CUSUM control charts for location and scale changes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(1), pages 1-20, March.
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    Cited by:

    1. Alexander Bulinski & Denis Dimitrov, 2021. "Statistical Estimation of the Kullback–Leibler Divergence," Mathematics, MDPI, vol. 9(5), pages 1-36, March.

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