Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/522
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDuysak, Hüseyin-
dc.contributor.authorÖzkaya, Umut-
dc.contributor.authorYiğit, Enes-
dc.date.accessioned2021-12-13T10:26:51Z-
dc.date.available2021-12-13T10:26:51Z-
dc.date.issued2021-
dc.identifier.issn0018-9456-
dc.identifier.issn1557-9662-
dc.identifier.urihttps://doi.org/10.1109/TIM.2021.3085939-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/522-
dc.description.abstractSince the grain is a crucial food source, the determination of the quantity of stored grain in silos is inevitable in terms of commercial and correct inventory planning. In this study, a convolutional neural network (CNN) is developed to determine grain quantity using the spectrograms of the radar backscattering data. The radar backscattering signals of different amounts of grain for different grain surface condition types are collected using a stepped frequency continuous-wave radar system. In the scaled model silo, a total of 5681 measurements are carried out for grain stacks with different surface patterns and different weights (0-20 kg). Then, the dataset is constituted by using the spectrograms of these radar measurements. Randomly selected 4261 data corresponding to 75% of the dataset are used for training and the remaining 1420 data are used for testing. The proposed method is compared with pretrained CNN. Accuracy of the methods is given with metric parameters for both classification and regression. The classification task results of the proposed method are obtained as 98.45% accuracy, 98.15% sensitivity, 99.07% specitivity, 98.77% precision, 98.45% F1-Score, and 97.62% Matthews correlation coefficient. The regression task results are calculated as 0.3228 mean absolute error, 0.5150 mean absolute percentage error (MAPE), 0.9649 mean squared error, and 0.9823 root-mean-squared error. The proposed method is also compared with previous studies in the literature (with 3.29 MAPE) and its superiority is demonstrated with metric parameters. The results point out that, if CNN is properly modeled and trained, the combination of CNN and proper signal processing can provide effective results in the quantity measurement applications of the grain stacks.en_US
dc.description.sponsorshipKaramanoglu Mehmetbey UniversityKaramanoglu Mehmetbey University [BAP 19-M-17, BAP 22-M-18]en_US
dc.description.sponsorshipThis work was supported by Karamanoglu Mehmetbey University through the Projects of Scientific Investigation Unit under Grant BAP 19-M-17 and Grant BAP 22-M-18. The Associate Editor coordinating the review process was Dr. Mohamad Forouzanfar.en_US
dc.language.isoenen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.relation.ispartofIEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENTen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Network (Cnn)en_US
dc.subjectDeep Learningen_US
dc.subjectGrain Quantity Measurementen_US
dc.subjectRadar-Level Measurementen_US
dc.subjectVisionen_US
dc.subjectModelen_US
dc.titleDetermination of the Amount of Grain in Silos With Deep Learning Methods Based on Radar Spectrogram Dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TIM.2021.3085939-
dc.identifier.scopus2-s2.0-85107379023en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridyigit, Enes/0000-0002-0960-5335-
dc.authorwosidDUYSAK, HUSEYIN/V-8093-2017-
dc.identifier.volume70en_US
dc.identifier.wosWOS:000665007000005en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57201982632-
dc.authorscopusid57191610477-
dc.authorscopusid16032674200-
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
Show simple item record



CORE Recommender

WEB OF SCIENCETM
Citations

3
checked on Mar 23, 2024

Page view(s)

102
checked on Mar 25, 2024

Download(s)

4
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.