Converting Utility Meters from Analogue to Smart based on Deep Learning Models
| dc.contributor.author | Humberto J.Cabeza Barreto | |
| dc.contributor.author | Ilker Kurtulan | |
| dc.contributor.author | Suleyman Inci | |
| dc.contributor.author | Mert Nakıp | |
| dc.contributor.author | Cüneyt Güzeliş | |
| dc.contributor.author | Barreto, Humberto J Cabeza | |
| dc.contributor.author | Kurtulan, Ilker | |
| dc.contributor.author | Guzelis, Cuneyt | |
| dc.contributor.author | Inci, Suleyman | |
| dc.contributor.author | Nakip, Mert | |
| dc.date.accessioned | 2025-10-06T17:50:50Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | In this paper we proposed a system that automatically interprets the data of the utility meters by analyzing the photo of an analogue meter. In addition it sends the meter data to the consumers and the providers. We based the system on Convolutional Neural Networks (CNN) where we compared the You Only Look Once (YOLO) and a LeNet as CNN models. We collected the data for the training of each CNN model from the demonstration set of the project. Our results show that the YOLO model is reliable and fast. The model has a 99% accuracy for the gas meter and 98% accuracy for the water meter. © 2020 Elsevier B.V. All rights reserved. | |
| dc.identifier.doi | 10.1109/ASYU50717.2020.9259849 | |
| dc.identifier.isbn | 9781728191362 | |
| dc.identifier.scopus | 2-s2.0-85097959802 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097959802&doi=10.1109%2FASYU50717.2020.9259849&partnerID=40&md5=11679a0cbdfec8d3d69e9d4b3d49c88b | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/9150 | |
| dc.identifier.uri | https://doi.org/10.1109/ASYU50717.2020.9259849 | |
| dc.language.iso | English | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2020 Innovations in Intelligent Systems and Applications Conference ASYU 2020 | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Analog Meters, Convolutional Neural Networks, Image Segmentation, Lenet, Machine Learning, Yolo, Convolutional Neural Networks, Intelligent Systems, Cnn Models, Learning Models, Deep Learning | |
| dc.subject | Convolutional neural networks, Intelligent systems, CNN models, Learning models, Deep learning | |
| dc.subject | YOLO | |
| dc.subject | Analog Meters | |
| dc.subject | Convolutional Neural Networks | |
| dc.subject | Machine Learning | |
| dc.subject | Image Segmentation | |
| dc.subject | Lenet | |
| dc.title | Converting Utility Meters from Analogue to Smart based on Deep Learning Models | |
| dc.type | Conference Object | |
| dspace.entity.type | Publication | |
| gdc.author.scopusid | 57220963189 | |
| gdc.author.scopusid | 57212473263 | |
| gdc.author.scopusid | 57220955001 | |
| gdc.author.scopusid | 55937768800 | |
| gdc.author.scopusid | 57220954600 | |
| gdc.bip.impulseclass | C5 | |
| gdc.bip.influenceclass | C5 | |
| gdc.bip.popularityclass | C4 | |
| gdc.coar.type | text::conference output | |
| gdc.collaboration.industrial | false | |
| gdc.description.department | ||
| gdc.description.departmenttemp | [Barreto H.J.C.] Yaşar University, Department of Electrical-Electronics Engineering, Izmir, Turkey; [Kurtulan I.] Yaşar University, Department of Electrical-Electronics Engineering, Izmir, Turkey; [Inci S.] Yaşar University, Department of Electrical-Electronics Engineering, Izmir, Turkey; [Nakip M.] Yaşar University, Department of Electrical-Electronics Engineering, Izmir, Turkey; [Guzelis C.] Yaşar University, Department of Electrical-Electronics Engineering, Izmir, Turkey | |
| gdc.description.endpage | 4 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| gdc.description.startpage | 1 | |
| gdc.identifier.openalex | W3107884798 | |
| gdc.index.type | Scopus | |
| gdc.oaire.diamondjournal | false | |
| gdc.oaire.impulse | 2.0 | |
| gdc.oaire.influence | 2.7882865E-9 | |
| gdc.oaire.isgreen | false | |
| gdc.oaire.popularity | 3.5854575E-9 | |
| gdc.oaire.publicfunded | false | |
| gdc.oaire.sciencefields | 0202 electrical engineering, electronic engineering, information engineering | |
| gdc.oaire.sciencefields | 02 engineering and technology | |
| gdc.openalex.collaboration | National | |
| gdc.openalex.fwci | 1.3868 | |
| gdc.openalex.normalizedpercentile | 0.79 | |
| gdc.opencitations.count | 3 | |
| gdc.plumx.crossrefcites | 1 | |
| gdc.plumx.mendeley | 14 | |
| gdc.plumx.scopuscites | 4 | |
| gdc.scopus.citedcount | 4 | |
| gdc.virtual.author | Nakip, Mert | |
| gdc.virtual.author | Güzeliş, Cüneyt | |
| person.identifier.scopus-author-id | Barreto- Humberto J.Cabeza (57220954600), Kurtulan- Ilker (57220963189), Inci- Suleyman (57220955001), Nakıp- Mert (57212473263), Güzeliş- Cüneyt (55937768800) | |
| relation.isAuthorOfPublication | 670a1489-4737-49fd-8315-a24932013d60 | |
| relation.isAuthorOfPublication | 10f564e3-6c1c-4354-9ce3-b5ac01e39680 | |
| relation.isAuthorOfPublication.latestForDiscovery | 670a1489-4737-49fd-8315-a24932013d60 | |
| relation.isOrgUnitOfPublication | ac5ddece-c76d-476d-ab30-e4d3029dee37 | |
| relation.isOrgUnitOfPublication.latestForDiscovery | ac5ddece-c76d-476d-ab30-e4d3029dee37 |