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dc.contributor.authorÇetinkaya, Ali
dc.contributor.authorÖztürk, Onur
dc.contributor.authorOkatan, Ali
dc.date.accessioned2019-04-30T09:12:54Z
dc.date.available2019-04-30T09:12:54Z
dc.date.issued2019-03-29
dc.identifier.issn2149-0104
dc.identifier.issn2149-5262
dc.identifier.urihttp://hdl.handle.net/11363/1145
dc.description.abstractAbstract- Repetitive tasks in the manufacturing industry is becoming more and more commonplace. The ability to write down a number set and operate the robot using that number set could increase the productivity in the manufacturing industry. For this purpose, our team came up with a robotic application which uses MNIST data set provided by Tensor flow to employ deep learning to identify handwritten digits. The system is equipped with a robotic arm, where an electromagnet is placed on top of the robotic arm. The movement of the robotic arm is triggered via the recognition of handwritten digits using the MNIST data set. The real time image is captured via an external webcam. This robot was designed as a prototype to reduce repetitive tasks conducted by humans. Keywords MNIST Handwritten Digit Recognition, Deep Learning, Embedded System Robotic Arm Controlen_US
dc.language.isoengen_US
dc.publisherİstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Pressen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.titleControlling A Robotic Arm Using Handwritten Digit Recognition Softwareen_US
dc.typearticleen_US
dc.relation.journalInternational Journal of Engineering Technologiesen_US
dc.contributor.departmentİstanbul Gelişim Üniversitesien_US
dc.identifier.volume5en_US
dc.identifier.issue1en_US
dc.identifier.startpage15en_US
dc.identifier.endpage23en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Yayınıen_US


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