• Title/Summary/Keyword: data process

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Spatio-temporal models for generating a map of high resolution NO2 level

  • Yoon, Sanghoo;Kim, Mingyu
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.3
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    • pp.803-814
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    • 2016
  • Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

Development of Blade Surface Modeling System Using Point Data (점 데이터를 이용한 블레이드 곡면 모델링 시스템 개발)

  • Kim, Yeoung-Il
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.18 no.10
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    • pp.110-115
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    • 2019
  • Stationary and rotating blades can be found in a steam turbine generator and the airfoil shapes of these blades can be defined by point data from an aerodynamic design system. The main design process of blades is composed of two steps: first, the blade surface is modeled with the point data; and then, the section data is generated which contains composite curves with line segments and arcs for CAE of the blade. The surface is modeled by a curve-net defined by the point data, which may be extended to obtain the section data to model the blade. This paper presents methods for automating the above-mentioned steps, which have been implemented in the commercial CAD/CAM system, Unigraphics, with API functions written in C-language. Finally, the proposed methods have been applied to model the blade of a steam turbine generator.

Ontology-based Facility Maintenance Information Integration Model using IFC-based BIM data

  • Kim, Karam;Yu, Jungho
    • International conference on construction engineering and project management
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    • 2015.10a
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    • pp.280-283
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    • 2015
  • Many construction projects have used the building information modeling (BIM) extensively considering data interoperability throughout the projects' lifecycles. However, the current approach, which is to collect the data required to support facility maintenance system (FMS) has a significant shortcoming in that there are various individual pieces of information to represent the performance of the facility and the condition of each of the elements of the facility. Since a heterogeneous external database could be used to manage a construction project, all of the conditions related to the building cannot be included in an integrated BIM-based building model for data exchange. In this paper, we proposed an ontology-based facility maintenance information model to integrate multiple, related pieces of information on the construction project using industry foundation classesbased (IFC-based) BIM data. The proposed process will enable the engineers who are responsible for facility management to use a BIM-based model directly in the FMS-based work process without having to do additional data input. The proposed process can help ensure that the management of FMS information is more accurate and reliable.

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Design and Analysis of Metrics for Enhancing Productivity of Datawarehouse (데이터웨어하우스의 개발생산성 향상을 위한 측정지표의 설계 및 분석)

  • Park, Jong-Mo;Cho, Kyung-San
    • Journal of Internet Computing and Services
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    • v.8 no.5
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    • pp.151-160
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    • 2007
  • A datawarehouse which extracts and saves the massive analysis data is used for marketing and decision support of business. However, the datawarehouse has the problem of increasing the process time and cost as well as has a high risk of process errors because it integrates vast amount of data from distributed environments. Thus, we propose a metrics for measurement in the area of productivity, process quality and data quality. Also through the evaluation using the proposed metrics, we show that our proposal provides productivity enhancement and process improvement.

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Research for Modeling the Failure Data for a Repairable System with Non-monotonic Trend (복합 추세를 가지는 수리가능 시스템의 고장 데이터 모형화에 관한 연구)

  • Mun, Byeong-Min;Bae, Suk-Joo
    • Journal of Applied Reliability
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    • v.9 no.2
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    • pp.121-130
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    • 2009
  • The power law process model the Rate of occurrence of failures(ROCOF) with monotonic trend during the operating time. However, the power law process is inappropriate when a non-monotonic trend in the failure data is observed. In this paper we deals with the reliability modeling of the failure process of large and complex repairable system whose rate of occurrence of failures shows the non-monotonic trend. We suggest a sectional model and a change-point test based on the Schwarz information criterion(SIC) to describe the non-monotonic trend. Maximum likelihood is also suggested to estimate parameters of sectional model. The suggested methods are applied to field data from an repairable system.

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Effects of the in-process calibration from IR detector for thermal diffusivity measurement by laser flash method (레이저 섬광법에 의한 열확산계수 측정시 적외검출소자에서 실시간 온도보정이 미치는 영향)

  • 이원식;배신철
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.10 no.6
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    • pp.795-802
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    • 1998
  • For measuring the thermal diffusivity by laser flash method, raw data have to be calibrated using temperature data. We have developed in-process calibration method and polynomial calibration in which thermal diffusivity can be calibrated during measuring, This method is different from existing temperature pre-process calibration method and exponential calibration having various source of error. Using this new calibration method, measurement accuracy was improved about 1∼2% compare to the value by the existing method. We also studied more accurate fitting curve as in Figure 4 was shown the result of measuring output characteristics of IR radiometer with temperature. As illustrated in data, in-process calibration method and polynomial calibration equation is proper than pre-process calibration method and exponential calibration.

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A Study on Application of CPLM using Process Model of the Pre-design stage (건축 기획단계 프로세스 모델의 CPLM 적용에 관한 연구)

  • Park, Do-Young;Jun, Yeong-Jin;Moon, Sung-Kon;Kim, Ju-Hyung;Kim, Jae-Jun
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2009.11a
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    • pp.205-208
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    • 2009
  • The purpose of this study is to apply the process model of the pre-design stage to CPLM(Construction Project Life-cycle Management). Life-cycle consists of 4 stage; Pre-design, Design, Construction, Maintenance. Each stage has organic relations between front and rear stage. Therefore it is important to manage and use the information data of each stage. But these data are not carried to the next stage smoothly, especially at the pre-design stage. It is even vague to define the process of the pre-design stage. To carry and share the information well, this study defines Pre-design stage process and CPLM at first, VA-Cityplanner which is the development system of the pre-design process model is applied to CPLM for the smooth current of the data between participants.

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Algorithm Improvement Through AI-Based Casting Process Parameter Optimization (AI 기반의 주조 공정 파라미터 최적화를 통한 알고리즘 개선)

  • Hyun Sim;Seo-Young Choi;Hyun-Wook Kim
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.3
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    • pp.441-448
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    • 2023
  • The quality of the casting process generates the largest source of defects in the manufacturing process, so its management is a key factor in productivity and quality evaluation. Based on the results of factor analysis, correlation analysis, and regression analysis with process data, this study aims to optimize the machine learning model to reduce the defect rate and verify the data suitability for smart factories.