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Deconvolution of X-ray Diffraction Profiles Using Genetic Algorithms and Differential Evolution

2015-06Capítulo de livro Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorSantos, Sidolina P.
dc.contributor.authorGomez-Pulido, Juan A.
dc.contributor.authorSanchez-Bajo, Florentino
dc.date.accessioned2025-10-27T14:21:32Z
dc.date.available2025-10-27T14:21:32Z
dc.date.issued2015-06
dc.descriptionPart of the book series: Lecture Notes in Computer Science (LNTCS,volume 9095).
dc.descriptionBooK part in: Advances in Computational Intelligence 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part II Conference proceedings, 2015
dc.descriptionConference city Palma de Mallorca
dc.descriptionConference date 10 June 2015 - 12 June 2015
dc.descriptionConference code 119669
dc.description.abstractSome optimization problems arise when X-ray diffraction profiles are used to determine the microcrystalline characteristics of materials, like the detection of diffraction peaks and the deconvolution process necessary to obtain the pure diffraction profile. After applying the genetic algorithms to solve satisfactorily the first problem, in this work we propose two evolutionary algorithms to solve the deconvolution problem. This optimization problem targets the objective of obtaining the profile that contains the microstructural characteristics of a material from the experimental data and instrumental effects. This is a complex problem, ill-conditioned, since not only there are many possible solutions, but also some of them lack physical sense. In order to avoid such circumstance, the regularization techniques are used, where the optimization of some of their parameters by means of intelligent computing permits to obtain the optimal solutions of the problem.eng
dc.identifier.citationSantos, S.P., Gomez-Pulido, J.A., Sanchez-Bajo, F. (2015). Deconvolution of X-ray Diffraction Profiles Using Genetic Algorithms and Differential Evolution. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_42
dc.identifier.doi10.1007/978-3-319-19222-2_42
dc.identifier.isbn9783319192215
dc.identifier.isbn9783319192222
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10400.8/14381
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-319-19222-2_42
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofAdvances in Computational Intelligence
dc.rights.uriN/A
dc.subjectDeconvolution
dc.subjectDifferential evolution
dc.subjectDiffraction profiles
dc.subjectGenetic algorithms
dc.subjectX-ray
dc.titleDeconvolution of X-ray Diffraction Profiles Using Genetic Algorithms and Differential Evolutioneng
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage514
oaire.citation.startPage503
oaire.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
oaire.citation.volume9095
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSantos
person.givenNameSidolina
person.identifier.orcid0009-0009-5676-7941
relation.isAuthorOfPublication4d56204e-7de0-4234-97f3-784a0dc99462
relation.isAuthorOfPublication.latestForDiscovery4d56204e-7de0-4234-97f3-784a0dc99462

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