A Deep Learning-based Surrogate for the XRF Approximation of Elemental Composition within Archaeological Artefacts before Restoration-Reference-Cited by-同舟云学术

A Deep Learning-based Surrogate for the XRF Approximation of Elemental Composition within Archaeological Artefacts before Restoration

Published:2021 Issue: Volume:192 Page:2002-2011
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Stoean Ruxandra,Ionescu Leonard,Stoean Catalin,Boicea Marinela,Atencia Miguel,Joya Gonzalo

Publisher

Elsevier BV

Subject

General Engineering

Reference16 articles.

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2. Portable xrf on prehistoric bronze artefacts: Limitations and use for the detection of bronze age metal workshops;Nørgaard;Open Archaeology,2017

3. A. Brunetti, J. Fabian, C. W. L. Torre, N. Schiavon, A combined xrf/monte carlo simulation study of multilayered peruvian metal artifacts from the tomb of the priestess of chornancap, Applied Physics A 122.

4. A. Rakotondrajoa, M. Radtke, Machine learning based quantification of synchrotron radiation-induced x-ray fluorescence measurements – a case study, Machine Learning: Science and Technology 2.

5. An experimental design for the classification of archaeological ceramic data from cyprus, and the tracing of inter-class relationships;Charalambous;Journal of Archaeological Science: Reports,2016

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