Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 신승준 | - |
dc.date.accessioned | 2018-09-11T07:49:06Z | - |
dc.date.available | 2018-09-11T07:49:06Z | - |
dc.date.issued | 2016-08 | - |
dc.identifier.citation | 한국멀티미디어학회논문지, v. 19, no.8, page. 1516-1529 | en_US |
dc.identifier.issn | 1229-7771 | - |
dc.identifier.issn | 2384-0102 | - |
dc.identifier.uri | http://koreascience.or.kr/article/ArticleFullRecord.jsp?cn=MTMDCW_2016_v19n8_1516 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/75110 | - |
dc.description.abstract | While 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.sponsorship | This 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.iso | ko_KR | en_US |
dc.publisher | 한국멀티디어학회 | en_US |
dc.subject | Smart Factor | en_US |
dc.subject | Big Data | en_US |
dc.subject | Data Analytics | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Manufacturing Execution System | en_US |
dc.subject | Energy Prediction | en_US |
dc.title | 스마트공장을 위한 빅데이터 애널리틱스 플랫폼 아키텍쳐 개발 | en_US |
dc.title.alternative | Developing a Big Data Analytics Platform Architecture for Smart Factory | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.9717/kmms.2016.19.8.1516 | - |
dc.relation.journal | 한국멀티미디어학회지 | - |
dc.contributor.googleauthor | 신승준 | - |
dc.contributor.googleauthor | 우정엽 | - |
dc.contributor.googleauthor | 서원철 | - |
dc.contributor.googleauthor | Shin, Seung-Jun | - |
dc.contributor.googleauthor | Woo, Jungyub | - |
dc.contributor.googleauthor | Seo, Wonchul | - |
dc.relation.code | 2012101439 | - |
dc.sector.campus | S | - |
dc.sector.daehak | DIVISION OF INDUSTRIAL INFORMATION STUDIES[S] | - |
dc.sector.department | DIVISION OF INDUSTRIAL INFORMATION STUDIES | - |
dc.identifier.pid | sjshin | - |
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