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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorArruti Romero, Asier
dc.contributor.authorAgote San Sebastian, Anartz
dc.contributor.authorAlberdi Esuain, Borja
dc.contributor.authorAizpuru, Iosu
dc.contributor.otherChen, Minjie
dc.date.accessioned2024-11-14T14:40:40Z
dc.date.available2024-11-14T14:40:40Z
dc.date.issued2024
dc.identifier.issn2644-1314en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178265en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6777
dc.description.abstractThis paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.en
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2024 IEEEen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectopen source softwareen
dc.subjectData-driven methodsen
dc.subjectMachine learningen
dc.subjectArtificial Intelligenceen
dc.subjectPower magneticsen
dc.subjectPower ferritesen
dc.subjectODS 7 Energía asequible y no contaminantees
dc.subjectODS 9 Industria, innovación e infraestructuraes
dc.subjectODS 12 Producción y consumo responsableses
dc.titleMagNet challenge for data-driven power magnetics modelingen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceIEEE Open Journal of Power Electronicsen
local.contributor.groupSistemas electrónicos de potencia aplicados al control de la energía eléctricaes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1109/OJPEL.2024.3469916en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
dc.unesco.tesaurohttp://vocabularies.unesco.org/thesaurus/concept3052en
dc.unesco.clasificacionhttp://skos.um.es/unesco6/3304en


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International