000099789 001__ 99789
000099789 005__ 20230519145409.0
000099789 0247_ $$2doi$$a10.1109/ACCESS.2021.3052864
000099789 0248_ $$2sideral$$a123347
000099789 037__ $$aART-2021-123347
000099789 041__ $$aeng
000099789 100__ $$0(orcid)0000-0003-0379-4347$$aVilla, J.
000099789 245__ $$aVessel Recognition in Induction Heating Appliances - A Deep-Learning Approach
000099789 260__ $$c2021
000099789 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099789 5203_ $$aThe selection of a vessel by an induction-hob user has a significant impact on the performance of the appliance. Due to the induction heating physical phenomena, there exist many factors that modify the equivalent impedance of induction hobs and, consequently, the operational conditions of the inverter. In particular, the type of vessel, which is a sole decision of the user, strongly affects these parameters. Besides, the ferromagnetic properties of the different materials the vessels are made with, vary differently with the excitation level, and given that most of the domestic induction hobs are based on an ac-bus voltage arrangement, the excitation level continuously varies. The algorithm proposed in this work takes advantage of this fact to identify the equivalent impedance of the load and recognize the pot. This is accomplished through a phase-sensitive detector that was already proposed in the literature and the application of deep learning. Different convolutional neural networks are tested on an augmented experimental-based dataset and the proposed algorithm is implemented in an experimental prototype with a system-on-chip. The proposed implementation is presented as an effective and accurate method to characterize and discriminate between different pots that could enable further functionalities in new generations of induction hobs.
000099789 536__ $$9info:eu-repo/grantAgreement/ES/DGA/FSE$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2019-103939RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PTQ-17-09045$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/RTC-2017-5965-6
000099789 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099789 590__ $$a3.476$$b2021
000099789 592__ $$a0.927$$b2021
000099789 594__ $$a6.7$$b2021
000099789 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b79 / 164 = 0.482$$c2021$$dQ2$$eT2
000099789 591__ $$aTELECOMMUNICATIONS$$b43 / 93 = 0.462$$c2021$$dQ2$$eT2
000099789 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b105 / 277 = 0.379$$c2021$$dQ2$$eT2
000099789 593__ $$aComputer Science (miscellaneous)$$c2021$$dQ1
000099789 593__ $$aEngineering (miscellaneous)$$c2021$$dQ1
000099789 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099789 700__ $$0(orcid)0000-0002-0795-8743$$aNavarro, D.$$uUniversidad de Zaragoza
000099789 700__ $$0(orcid)0000-0001-5832-1163$$aDominguez, A.
000099789 700__ $$0(orcid)0000-0002-8007-5613$$aArtigas, J.I.$$uUniversidad de Zaragoza
000099789 700__ $$0(orcid)0000-0003-4633-4551$$aBarragan, L.A.$$uUniversidad de Zaragoza
000099789 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000099789 773__ $$g9 (2021), 16053-16061$$pIEEE Access$$tIEEE Access$$x2169-3536
000099789 8564_ $$s1710229$$uhttps://zaguan.unizar.es/record/99789/files/texto_completo.pdf$$yVersión publicada
000099789 8564_ $$s2722693$$uhttps://zaguan.unizar.es/record/99789/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099789 909CO $$ooai:zaguan.unizar.es:99789$$particulos$$pdriver
000099789 951__ $$a2023-05-18-13:53:46
000099789 980__ $$aARTICLE