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dc.contributor.author신승준-
dc.date.accessioned2018-09-11T07:49:06Z-
dc.date.available2018-09-11T07:49:06Z-
dc.date.issued2016-08-
dc.identifier.citation한국멀티미디어학회논문지, v. 19, no.8, page. 1516-1529en_US
dc.identifier.issn1229-7771-
dc.identifier.issn2384-0102-
dc.identifier.urihttp://koreascience.or.kr/article/ArticleFullRecord.jsp?cn=MTMDCW_2016_v19n8_1516-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/75110-
dc.description.abstractWhile global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.en_US
dc.description.sponsorshipThis research was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2016R1C1B1008820). This research was also supported by the Pukyong National University Research Fund (No. C-D-2015-1267)en_US
dc.language.isoko_KRen_US
dc.publisher한국멀티디어학회en_US
dc.subjectSmart Factoren_US
dc.subjectBig Dataen_US
dc.subjectData Analyticsen_US
dc.subjectMachine Learningen_US
dc.subjectManufacturing Execution Systemen_US
dc.subjectEnergy Predictionen_US
dc.title스마트공장을 위한 빅데이터 애널리틱스 플랫폼 아키텍쳐 개발en_US
dc.title.alternativeDeveloping a Big Data Analytics Platform Architecture for Smart Factoryen_US
dc.typeArticleen_US
dc.identifier.doi10.9717/kmms.2016.19.8.1516-
dc.relation.journal한국멀티미디어학회지-
dc.contributor.googleauthor신승준-
dc.contributor.googleauthor우정엽-
dc.contributor.googleauthor서원철-
dc.contributor.googleauthorShin, Seung-Jun-
dc.contributor.googleauthorWoo, Jungyub-
dc.contributor.googleauthorSeo, Wonchul-
dc.relation.code2012101439-
dc.sector.campusS-
dc.sector.daehakDIVISION OF INDUSTRIAL INFORMATION STUDIES[S]-
dc.sector.departmentDIVISION OF INDUSTRIAL INFORMATION STUDIES-
dc.identifier.pidsjshin-
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