99789 20230519145409.0 doi 10.1109/ACCESS.2021.3052864 sideral 123347 ART-2021-123347 eng Villa, J. (orcid)0000-0003-0379-4347 Vessel Recognition in Induction Heating Appliances - A Deep-Learning Approach 2021 Access copy available to the general public Unrestricted The selection of a vessel by an induction-hob user has a significant impact on the performance of the appliance. Due to the induction heating physical phenomena, there exist many factors that modify the equivalent impedance of induction hobs and, consequently, the operational conditions of the inverter. In particular, the type of vessel, which is a sole decision of the user, strongly affects these parameters. Besides, the ferromagnetic properties of the different materials the vessels are made with, vary differently with the excitation level, and given that most of the domestic induction hobs are based on an ac-bus voltage arrangement, the excitation level continuously varies. The algorithm proposed in this work takes advantage of this fact to identify the equivalent impedance of the load and recognize the pot. This is accomplished through a phase-sensitive detector that was already proposed in the literature and the application of deep learning. Different convolutional neural networks are tested on an augmented experimental-based dataset and the proposed algorithm is implemented in an experimental prototype with a system-on-chip. The proposed implementation is presented as an effective and accurate method to characterize and discriminate between different pots that could enable further functionalities in new generations of induction hobs. info:eu-repo/grantAgreement/ES/DGA/FSE info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2019-103939RB-I00 info:eu-repo/grantAgreement/ES/MICINN-AEI/PTQ-17-09045 info:eu-repo/grantAgreement/ES/MICINN-AEI/RTC-2017-5965-6 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.476 2021 COMPUTER SCIENCE, INFORMATION SYSTEMS 79 / 164 = 0.482 2021 Q2 T2 TELECOMMUNICATIONS 43 / 93 = 0.462 2021 Q2 T2 ENGINEERING, ELECTRICAL & ELECTRONIC 105 / 277 = 0.379 2021 Q2 T2 0.927 2021 Computer Science (miscellaneous) 2021 Q1 Engineering (miscellaneous) 2021 Q1 6.7 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Navarro, D. Universidad de Zaragoza (orcid)0000-0002-0795-8743 Dominguez, A. (orcid)0000-0001-5832-1163 Artigas, J.I. Universidad de Zaragoza (orcid)0000-0002-8007-5613 Barragan, L.A. Universidad de Zaragoza (orcid)0000-0003-4633-4551 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 9 (2021), 16053-16061 IEEE Access IEEE Access 2169-3536 1710229 http://zaguan.unizar.es/record/99789/files/texto_completo.pdf Versión publicada 2722693 http://zaguan.unizar.es/record/99789/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:99789 articulos driver 2023-05-18-13:53:46 ARTICLE