dc.rights.license | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.contributor.author | Arruti Romero, Asier | |
dc.contributor.author | Agote San Sebastian, Anartz | |
dc.contributor.author | Alberdi Esuain, Borja | |
dc.contributor.author | Aizpuru, Iosu | |
dc.contributor.other | Chen, Minjie | |
dc.date.accessioned | 2024-11-14T14:40:40Z | |
dc.date.available | 2024-11-14T14:40:40Z | |
dc.date.issued | 2024 | |
dc.identifier.issn | 2644-1314 | en |
dc.identifier.other | https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178265 | en |
dc.identifier.uri | https://hdl.handle.net/20.500.11984/6777 | |
dc.description.abstract | This 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.iso | eng | en |
dc.publisher | IEEE | en |
dc.rights | © 2024 IEEE | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | open source software | en |
dc.subject | Data-driven methods | en |
dc.subject | Machine learning | en |
dc.subject | Artificial Intelligence | en |
dc.subject | Power magnetics | en |
dc.subject | Power ferrites | en |
dc.subject | ODS 7 Energía asequible y no contaminante | es |
dc.subject | ODS 9 Industria, innovación e infraestructura | es |
dc.subject | ODS 12 Producción y consumo responsables | es |
dc.title | MagNet challenge for data-driven power magnetics modeling | en |
dcterms.accessRights | http://purl.org/coar/access_right/c_abf2 | en |
dcterms.source | IEEE Open Journal of Power Electronics | en |
local.contributor.group | Sistemas electrónicos de potencia aplicados al control de la energía eléctrica | es |
local.description.peerreviewed | true | en |
local.identifier.doi | https://doi.org/10.1109/OJPEL.2024.3469916 | en |
oaire.format.mimetype | application/pdf | en |
oaire.file | $DSPACE\assetstore | en |
oaire.resourceType | http://purl.org/coar/resource_type/c_6501 | en |
oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | en |
dc.unesco.tesauro | http://vocabularies.unesco.org/thesaurus/concept3052 | en |
dc.unesco.clasificacion | http://skos.um.es/unesco6/3304 | en |