• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.53-59
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• 2003

제조업 경쟁력 확보의 일환으로 최근 들어 각 기업에서 정보 기술의 활용이 급격히 증가하고 있는데 이 중에서 특히 제품 설계 단계에서는 PDM 시스템이, 생산 단계에서는 ERP 시스템이 널리 사용되고 있다. 제품 수명 주기의 초기 단계인 설계 단계에서 생성되거나 사용되는 많은 종류의 데이터는 생산 단계에서도 똑같이 사용되기 때문에 PDM 시스템과 ERP 시스템 간의 원활한 데이터 흐름은 전체 생산성 향상에 필수적인 요소라 하겠다. 본 연구에서는 PDM/ERP 시스템 간의 원활한 데이터 연동 환경을 개발하기 위래 연동대상 데이터 분석을 통해 필요한 기능적 요구사항을 도출하고 이를 공정 계획 시스템을 기반 구조로 하는 시스템으로 구현하고 항공기생산 과정에 적용하였다. 이러한 개발을 통해 공정 계획 작업 기간이 단축되고 데이터 정확도가 향상되는 효과를 볼 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.194-196
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• 2021

This focuses on the Cultivation Plan Service at the preliminary production phase is critical in that it supports agricultural producers' decision by providing related information such as predicted crop production or expected profits for consulting or other agricultural information when they plan to cultivate. This paper describes the reference architecture of the farming sector will benefit immensely from the implementation of farming data in farming contents repository which will serve as the knowledge base for the Cultivation Plan Service at the pre-production stage based on Big data analysis.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.255-256
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• 2020

생산 현장에서 발생되는 다양한 형태의 데이터는 스마트한 제조관리를 가능하게 하는 원동력으로 이를 효율적으로 저장하고 처리, 분석하는 일련의 과정이 4차 산업혁명 기반의 제조혁신에 능동적으로 대응하기 위한 핵심요소로서, 이와 관련한 다양한 연구들이 활발히 이루어지고 있다. 특히, 제조데이터 분석이라는 영역은 단순하게 기존의 데이터를 통계적인 접근 수단으로만 보는 것이 아니라 다양한 산업별 업종 도메인의 특성에 기반하여 빅데이터 분석과 기계학습 등의 인공지능 모델로 발전하고 있다. 본 논문에서는 다양한 산업별 제조현장을 이해하는 도메인 경험 및 특성을 고려하여 데이터를 효과적으로 저장, 처리, 분석할 수 있는 클라우드 형태의 빅데이터 시스템을 제안한다.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.231-233
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• 2007

The e-Science for High Energy Physics is to study high energy physics anytime and anywhere even if we are not on-site of accelerator latboratories. The concepts are 1) data production, 2) data processing and 3) data publication. The data production is to do remote control and take shifts remotely. The data processing is to run jobs anytime, anywhere with grid farms. The data publication is to work together to publish papers using collaborative environment such as EVO (Enabling Virtual Organization) system. We apply this concept to high energy physics, espessically, CDF experiment and show the results.

• The Korean Journal of Archival Studies
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• no.70
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• pp.5-40
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• 2021

Administrative information dataset records produced by various systems designed for work are difficult to manage on a case-by-case basis, requiring separate procedures to identify and evaluate data-sets. Identified data set records are apprasal and transferred to the records management system or disposed of. In this process, sufficient records management elements must be reflected in the production system itself in order to adhere to the principles of record management. In this paper, the functional requirements of the production system to accurately identify and safely manage data-sets were derived and applied based on the case of the KR property management system. It is hoped that this research on functional requirements of production systems will be added to lead to the creation of standards for functional requirements of data set production systems.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.548-553
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• 2006

Activity-based productivity data can be used as an significant reference in many areas of project management such as performance evaluation and project planning. However, the existence of various factors influencing construction productivity makes it difficult to collect and analyze the productivity data. In the most of the domestic construction sites, there is no systematic method to collect and analyze the productivity data along with information on influencing factors; it is common to heavily rely on experience and intuition of field managers when dealing with construction productivity data. Therefore it is necessary to develop a management system for collecting and utilizing the productivity data as well as the factors influencing construction productivity. The main objective of this research is to define the construction productivity and its influencing factors at the activity level. In addition, methodologies on how to analyze the productivity data and to estimate productivity of future projects are proposed.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.3-12
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• 2019

Despite the importance and function of productivity information, in the Korean construction industry, the method of collecting and analyzing productivity data has not been organized. Also, in most cases, productivity management is reliant on the experience and intuitions of field managers, and productivity data are rarely being utilized in planning and management. Accordingly, this study intends to develop a prediction model for interior finishes of apartment using deep learning techniques, so as to provide a foundation for analyzing the productivity impacting factors and predicting productivity. The result of the study, productivity prediction model for interior finishes of apartment using deep learning techniques, can be a basic module of apartment project management system by applying deep learning to reliable productivity data and developing as data is accumulated in the future. It can also be used in project engineering processes such as estimating work, calculating work days for process planning, and calculating input labor based on productivity data from similar projects in the past. Further, when productivity diverging from predicted productivity is discovered during construction, it is expected that it will be possible to analyze the cause(s) thereof and implement prompt response and preventive measures.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.291-292
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• 2017

한국과학기술정보연구원 융합기술연구본부 재난대응HPC연구센터에서는 초고성능컴퓨팅 기반의 풍수해 예측 및 피해 정보 생산기술을 연구개발하여 재난 재해에 대한 국가현안 대응 의사결정지원 시스템을 구축 중에 있다. HPC 기반의 풍수해 예측 시스템과 빅데이터 분석 기반의 피해 예측 시스템에 대한 연구를 독자적으로 진행하는 가운데, 최근 여러 분야에 적용되고 있는 빅데이터 분석 기술을 HPC 기반의 풍수해 예측 시스템에 적목시켜 더 정확하고 신속한 풍수해 예측 정보 생산에 기여하고자 한다. 본 연구는 빅데이터 분석을 위한 학습 데이터 생산을 목적으로 HPC 기반 태풍 예측의 주요 기상 인자들을 조정하여 서로 다른 성능의 예측 모델을 구축하고, 각 모델 별 태풍 시뮬레이션의 성능을 진단하였다. 향후 빅데이터 분석을 통한 예측 성능의 검증을 위해 HPC 기반 풍수해 예측 및 검증 데이터를 최대한 생산하고자 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1082-1086
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• 2007

This research has a purpose of constructing the data monitoring system that makes two-tier work state in the brick production factory to unification by reusing cinder. Monitoring system automatically manages data by using data managing processes such as a state managing process, a location managing process, a badness managing process, a circumstances managing process. In this research, the data management monitoring system manufactures state information of each processes received from RFID and transmits them to data monitoring system. Analyzed data through this system reuses the cinder, so it can effectively manage the production process of the factory which produces bricks through processing automation, faulty-ratio minimization, real-time monitoring and loading managing.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.442-445
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• 2002

Construction delay claim occurs more often than any other claim. Delay claim impacts adversely the company's existence as well as the project's monetary problem. Because the calculation of the delay is not quantitative, owing not to have enough evidence, it is hard to solve evenly the delay claim. So, it is need to cumulate structurely the construction data or evidence during the construction. This study considers the established study about construction delay claim database and the problems and then presents the relation method between phase schedule and database. Finally, this study established the productivity claim database modeling for construction project through conceptual database modeling and logical database modeling based on information needed to make construction productivity database. So. the purpose of the study is to establish the datamodel for productivity management of construction project for calculating the delay days considering the lost productivity.