| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Furat, Orkun
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 20233D analysis of equally X-ray attenuating mineralogical phases utilizing a correlative tomographic workflow across multiple length scalescitations
- 2021Efficient fitting of 3D tessellations to curved polycrystalline grain boundaries
- 2021Efficient Fitting of 3D Tessellations to Curved Polycrystalline Grain Boundariescitations
- 2021Quantitative assessment of microstructural changes of hydrated cement blends due to leaching and carbonation, based on statistical analysis of image datacitations
- 2020Multiscale Tomographic Analysis for Micron-Sized Particulate Samples
- 2020Multiscale Tomographic Analysis for Micron-Sized Particulate Samplescitations
- 2019Mineralogical and microstructural response of hydrated cement blends to leachingcitations
- 2019Stochastic modeling of multidimensional particle properties using parametric copulascitations
- 2019Statistical 3D analysis and modeling of complex particle systems based on tomographic image datacitations
- 2018On microstructure-property relationships derived by virtual materials testing with an emphasis on effective conductivitycitations
Places of action
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article
Stochastic modeling of multidimensional particle properties using parametric copulas
Abstract
<jats:title>Abstract</jats:title><jats:p>In this paper, prediction models are proposed which allow the mineralogical characterization of particle systems observed by X-ray micro tomography (XMT). The models are calibrated using 2D image data obtained by a combination of scanning electron microscopy and energy dispersive X-ray spectroscopy in a planar cross-section of the XMT data. To reliably distinguish between different minerals the models are based on multidimensional distributions of certain particle characteristics describing, for example, their size, shape, and texture. These multidimensional distributions are modeled using parametric Archimedean copulas which are able to describe the correlation structure of complex multidimensional distributions with only a few parameters. Furthermore, dimension reduction of the multidimensional vectors of particle characteristics is utilized to make non-parametric approaches such as the computation of distributions via kernel density estimation viable. With the help of such distributions the proposed prediction models are able to distinguish between different types of particles among the entire XMT image.</jats:p>