• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.86-91
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• 2006

본 연구는 M사의 제조공정에서 얻어진 대 용량 데이터를 사용하여 실리콘 웨이퍼의 중요 품질특성 중 하나인 Warp 불량원인을 분석한 사례이다. 이론적으로는 많은 양의 데이터를 확보하고 있을 경우 검출력의 향상으로 공정의 미세한 변화를 보다 민감하게 탐지할 수 있을 것으로 생각된다. 그러나 현실적으로는 불필요한 정보 혹은 많은 잡음 요인들의 개입으로 인하여 공정에 대한 올바른 이해가 더 어려울 수도 있다. 본 연구는 공정에 대한 경험과 기술적인 지식을 활용하여 분석의 기본 방향을 설정하고 많은 양의 데이터를 체계적으로 분석한 후 분석 결과를 실질적인 측면에서 재검토하여 의미 있는 결과를 도출하는 순서로 진행되었다. 데이터 분석의 과정 및 결과는 공정의 자동화로 수많은 데이터가 실시간으로 기록되는 상황에서 잡음요인들로 인한 영향을 배제하고 핵심요인에 의한 영향을 파악하는데 참고할 수 있을 것으로 사료된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.94-103
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• 2022

As a large amount of data generated by sensors or devices at the manufacturing site is transmitted to the server or client, problems arise in network processing time delay and storage resource cost increase. To solve this problem, considering the manufacturing site, where real-time responsiveness and non-disruptive processes are essential, QRC (Quotient Remainder Compression) and BL_beta compression algorithms that enable real-time and lossless compression were applied to actual manufacturing site sensor data for the first time. As a result of the experiment, BL_beta had a higher compression rate than QRC. As a result of experimenting with the same data by slightly adjusting the data size of QRC, the compression rate of the QRC algorithm with the adjusted data size was 35.48% and 20.3% higher than the existing QRC and BL_beta compression algorithms.

• Digital Contents
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• no.6 s.49
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• pp.7-8
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• 1997

데이터를 단순히 수집하고 보관한다는 차원이 아니라 이를 가공, 활용한다는 측면을 강조한 것이 데이터 웨어하우스 개념이다. 한국IBM, 한국디지탈, 한국NCR, 한국유니시스 등 대형컴퓨터 제조업체들은 자사의 엔터프라이즈급 서버를 근간으로 한 데이터 웨어하우스 솔루션을 주력사업으로 내세우며, 현재 유통, 금융, 통신시장으로 파고들고 있다. 그러나 데이터 웨어하우스를 구축하기 위해서는 적지않은 비용이 들기 때문에 시장이 만개되기 위해서는 다소 시간이 걸릴 것으로 전망된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.158-161
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• 2023

스마트팩토리는 기존 제조산업과 ICT(Information & Communication Technology)가 융합된 지능형 공장이다. 이는 직접적인 제조공정 과정이 수행되는 OT(Operational Technology) 영역(0~3계층)과 전사업무 관리를 수행하는 IT(Information Technology) 영역(4~5계층)으로 구분되며, 각 영역과 계층이 연결되어 제조·물류·유통 과정의 자동화 및 지능화를 제공한다. 그러나 각 영역과 계층이 연결됨에 따라 보안위협 벡터가 증가하고 있으며, 다영역·다계층 환경인 스마트팩토리에 적합한 대응체계 연구를 위해 영역별 보안위협 관련 데이터를 통합하여 처리 및 관리하는 아키텍처 연구가 필요한 실정이다. 이에 따라 본 논문에서는 스마트팩토리 환경 내 IT 및 OT 영역 장치를 식별하고 보안위협 관련 데이터 통합 처리 및 관리를 위한 아키텍처를 제안한다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.67-76
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• 2017

In industrial site in various fields it will be generated in combination with large amounts of data have a correlation. It is able to collect a variety of data in types of industry process, but they are unable to integrate each other's association between each process. For the data of the existing industry, the set values of the molding condition table are input by the operator as an arbitrary value When a problem occurs in the work process. In this paper, design the fusion and analysis processing model of data collected for each industrial type, Prediction Case(Automobile Connect), a through for corporate earnings improvement and process manufacturing industries such as master data through standard molding condition table and the production history file comparison collected during the manufacturing process and reduced failure rate with a new molding condition table digitized by arbitrary value for worker, a new pattern analysis and reinterpreted for various malfunction factors and exceptions, increased productivity, process improvement, the cost savings. It can be designed in a variety of data analysis and model validation. In addition, to secure manufacturing process of objectivity, consistency and optimization by standard set values analyzed and verified and may be optimized to support the industry type, fits optimization(standard setting) techniques through various pattern types.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.16-21
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• 2019

Small and medium-sized Korean companies are currently changing their industrial structure faster than in the past due to various environmental factors (such as securing competitiveness and developing excellent products). In particular, the importance of collecting and utilizing data produced in smart factory environments is increasing as diverse devices related to artificial intelligence are put into manufacturing sites. This paper proposes an artificial intelligence-based smart factory model to improve the process of products produced at the manufacturing site with the recent smart factory. The proposed model aims to ensure the increasingly competitive manufacturing environment and minimize production costs. The proposed model is managed by considering not only information on products produced at the site of smart factory based on artificial intelligence, but also labour force consumed in the production of products, working hours and operating plant machinery. In addition, data produced in the proposed model can be linked with similar companies and share information, enabling strategic cooperation between enterprises in manufacturing site operations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.95-110
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• 2018

With the widespread use of smartphones, various personal information of users is being recorded on a smartphone in real time. For the purpose of preventing the loss of important personal information of users, manufacturer provides a smartphone backup applications. Recently, not only backup programs for PC but also backup mobile apps for smart phones have been provided. From the point of view acquiring forensic data, it is important not to compromise the acquisition possibilities and the integrity of the original data. Especially, in the case of Android smartphones, various studies are being carried out to acquire the data without damaging the integrity of the original data. However, there are limitations to apply the existing research methods. In this paper, we describe the process of acquiring data using the backup mobile app provided by the manufacturer without compromising the integrity of the latest smartphone.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.9
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• pp.1063-1070
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• 2011

The manufacturing execution system (MES) for product production handles different production processes according to the product characteristics and different types of data according to the process being considered. For efficiently providing the data pertaining to production equipment to production systems such as the MES, data collection through the equipment interface is required for obtaining the production data pertaining to field equipment. In this paper, a method is proposed for collecting the production data through the equipment interface in order to collect the various types of production-equipment data from the field. The proposed method is applied to a real manufacturing system to verify its efficiency. A more powerful MES can be constructed with a data acquisition system that acquires the status data at the shop-floor level.

• Journal of the KSME
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• v.57 no.8
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• pp.49-53
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• 2017

이 글에서는 스마트 팩토리 구현을 위해 고려되어야 하는 다양한 속성들 중에서 '데이터'에 대해서 논하고자 한다. 특히, 제조업의 관점에서 데이터의 가치를 평가하는 방법과 도메인 지식의 중요성에 대해 설명하였고, 중소기업에서 데이터를 활용하기 위한 방안을 함께 고민해보았다.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.11
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• pp.427-438
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• 2016

Car blackboxes record the information and status of driving. Since blackboxes are commonly used in daily life, the usage of video data recorded from blackboxes is increasing for investigating. Investigators use a own analysis tool suitable for their blackbox provided by the manufacturer in order to check the data. But the tools are not enough to use in the digital forensic analysis because they are dependent on a specific model of blackbox and provides ungeneralized functions. Moreover, if the manufacturer is bankrupt, then their own tools can not be obtained also. Therefore, the way data are stored in the blackboxes which are now in the market are investigated and the features and limitations which have blackbox's own analysis tools are checked. And a comprehensive tool for the analysis of blackboxes is designed and implemented as in this paper.