eBiltegia - Repositorio digital de Mondragon Unibertsitatea
El Repositorio digital de Mondragon Unibertsitatea da acceso abierto al texto completo de los documentos generados en la universidad resultado de su actividad académica, investigadora e institucional, cumpliendo con los compromisos de las políticas de Acceso Abierto de la institución:
Su objetivo es promover la ciencia abierta, incrementar la visibilidad e impacto institucional, garantizar la preservación segura de los contenidos digitales, y facilitar su interoperabilidad mediante estándares técnicos.Podrás encontrar tesis doctorales, trabajos de fin de grado y master, material didáctico, publicaciones de la universidad, documentos de trabajo, preprints, postprints, artículos, actas de congresos, sets de datos, documentos institucionales, etc.
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Steady-state temperature calculation tool for multilayer PCBs
(IEEE, 2025)The adoption of GaN-based devices in power converters offers significant improvements in efficiency and power density, but also intensifies thermal challenges by concentrating heat in smaller volumes. High current and ... -
Lagunarteko hizkera langai ikastolan: bi ikasturteko esku-hartzearen inplementazioa eta emaitzak
(Euskaltzaindia, 2025-10-30)Ikerketa hau Euskaltzaindiaren Mintzola egitasmoak, Ikastolen Elkarteak eta Mondragon Unibertsitateak elkarlanean gauzatu dute. 2022-23 eta 2023-24 ikasturteetan, Lehen Hezkuntzako laugarren eta bosgarren mailako ikasleekin, ... -
Battery Heterogeneity Challenge: From Single to Multiple Cell System Modelling
(2025)Significant efforts have been dedicated to optimising the performance of battery systems by improving energy management and battery sizing strategies. Recent advancements have shifted their focus on model-based optimisations ... -
Bridging the Gap between ECMs and PBMs: Electrode-level Extended ECM
(2025)Accurate and efficient Li-ion battery models are essential for control, diagnostics, and system-level integration. While physics-based models (PBMs) offer detailed electrochemical insight, they are often too complex for ... -
Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation
(IEEE, 2025)Federated Learning (FL) is an emerging machine learning paradigm that enables multiple parties to collaboratively train models without sharing raw data, ensuring data privacy. In Vertical FL (VFL), where each party holds ...
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