• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.773-779
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• 2015

We study a statistical analysis about the fifth wave data of the Korea National Health and Nutrition Examination Survey based on linear regression models with measurement errors. The data is obtained from a national population-based complex survey. To demonstrate the availability of measurement error models, two results between the general linear regression model and measurement error model are compared based on the model selection criteria which are Akaike information criterion and Bayesian information criterion. For our study, we use the simulation extrapolation algorithm for measurement error model and the jackknife method for the estimation of standard errors.

• 대한안전경영과학회지
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• 제5권4호
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• pp.229-243
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• 2003

This paper suggests the process of performance measurement system development for B2B e-Marketplace using BSC (Balanced Scorecard). As the first step, main features and factors affecting performance of e-Business company and especially of B2B e-Marketplaces were derived based on the literatures. For the 2nd step, the nonnative performance measurement model for B2B e-Marketplace was suggested. Especially, the research range that the model covers is restricted to the neutral and systematic sourcing B2B e-Marketplace. And, the performance measurement model was based on BSC, for the BSC is very powerful and sound tool among all the available performance measurement systems. Also, the model was based on the AHP (Analytic Hierarchy Process) to make the model more structured, and to draw weights of performance measures from experts

• Journal of Electrical Engineering and Technology
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• 제4권2호
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• pp.149-158
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• 2009

It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.

• Journal of Electrical Engineering and Technology
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• 제7권6호
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• pp.845-851
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• 2012

Power system loads have a significant impact on a system. Although it is difficult to precisely describe loads in a mathematical model, accurately modeling them is important for a system analysis. The traditional load modeling method is based on the load components of a bus. Recently, the load modeling method based on measurements from a system has been introduced and developed by researchers. The two major components of a load modeling problem are determining the mathematical model for the target system and estimating the parameters of the determined model. We use the composite load model, which has both static and dynamic load characteristics. The ZIP model and the induction motor model are used for the static and dynamic load models, respectively. In this work, we propose the measurement-based parameter estimation method for the composite load model. The test system and related measurements are obtained using transient security assessment tool(TSAT) simulation program and PSS/E. The parameter estimation is then verified using these measurements. Cases are tested and verified using the sample system and its related measurements.

• Communications for Statistical Applications and Methods
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• 제22권4호
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• 정보처리학회논문지D
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• 제16D권3호
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• pp.353-360
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• 2009

대부분의 정보시스템은 컴포넌트 기반으로 아웃소싱에 의해 개발되고 있으며, 개발된 소프트웨어들은 통합되어 유지보수 되고 있다. 그러나 유지보수의 생산성과 효율성 제고를 위한 기반 연구인 비용 측정지표 및 비용 추정 모델에 관한 연구가 부족하다. 본 연구는 컴포넌트 기반의 소프트웨어에 대한 유지보수의 비용에 대한 측정지표를 제안하고, 제안한 측정지표의 의한 컴포넌트 소프트웨어의 유지보수 비용 추정 모델을 연구한다. 컴포넌트 기반의 유지보수 비용 측정지표를 추정하기 위하여 기존에 제시된 지표들을 비교 정리하고, 정리된 측정지표가 컴포넌트 소프트웨어 유지보수 유형에 따라 어떻게 적용되는지를 분류하고 유형별 비용 측정지표를 제안한다. 또, 제안한 측정지표를 이용한 컴포넌트 소프트웨어 유지보수 유형에 따른 비용 추정 모델을 제안한다. 제안한 추정 모델의 사례연구를 실시하여 유효성을 검증한다.

• 한국정밀공학회지
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• 제17권2호
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• pp.114-120
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• 2000

Fossil power plant availability is significantly affected by gradual degradations of equipment as operation of the plant continues. It is quite important to determine whether or not to replace some equipment and when to replace the equipment. Performance calculation and analysis can provide the information. Robustness in the performance calculation can be increased by using verification ＆ validation of measured input data. We suggest new algorithm in which estimation relation for validated measurement can be obtained using correlation between measurements. Input estimation model is obtained using design data and acceptance measurement data of domestic 16 fossil power plant. The model consists of finding mostly correlated state variable in plant state and mapping relation based on the model and current state of power plant.

• Smart Structures and Systems
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• 제24권6호
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• pp.709-721
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• 2019

This paper investigates the framework of vision-based dense displacement and strain measurement of miter gates with the approach for the quantitative evaluation of the expected performance. The proposed framework consists of the following steps: (i) Estimation of 3D displacement and strain from images before and after deformation (water-fill event), (ii) evaluation of the expected performance of the measurement, and (iii) selection of measurement setting with the highest expected accuracy. The framework first estimates the full-field optical flow between the images before and after water-fill event, and project the flow to the finite element (FE) model to estimate the 3D displacement and strain. Then, the expected displacement/strain estimation accuracy is evaluated at each node/element of the FE model. Finally, methods and measurement settings with the highest expected accuracy are selected to achieve the best results from the field measurement. A physics-based graphics model (PBGM) of miter gates of the Greenup Lock and Dam with the updated texturing step is used to simulate the vision-based measurements in a photo-realistic environment and evaluate the expected performance of different measurement plans (camera properties, camera placement, post-processing algorithms). The framework investigated in this paper can be used to analyze and optimize the performance of the measurement with different camera placement and post-processing steps prior to the field test.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1645-1651
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• 2016

A lot of data, particularly in the medical field, contain variables that have a measurement error such as blood pressure and body mass index. On the other hand, recently smoothing methods are often used to solve a complex scientific problem. In this paper, we study a Bayesian curve-fitting under functional measurement error model. Especially, we extend our previous model by incorporating covariates free of measurement error. In this paper, we consider penalized splines for non-linear pattern. We employ a hierarchical Bayesian framework based on Markov Chain Monte Carlo methodology for fitting the model and estimating parameters. For application we use the data from the fifth wave (2012) of the Korea National Health and Nutrition Examination Survey data, a national population-based data. To examine the convergence of MCMC sampling, potential scale reduction factors are used and we also confirm a model selection criteria to check the performance.

• 한국정밀공학회지
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• 제16권10호
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• pp.172-181
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• 1999

The objective of this research is to develop a measurement error model for sculptured surfaces in On-Machine Measurement (OMM) process based on a closed-loop configuration. The geometric error model of each axis of a vertical CNC Machining center is derived using a 4${\times}$4 homogeneous transformation matrix. The ideal locations of a touch-type probe for the scupltured surface measurement are calculated from the parametric surface representation and X-, Y- directional geometric errors of the machine. Also, the actual coordinates of the probe are calculated by considering the pre-travel variation of a probe and Z-directional geometric errors. Then, the step-by-step measurement error analysis method is suggested based on a closed-loop configuration of the machining center including workpiece and probe errors. The simulation study shows the simplicity and effectiveness of the proposed error modeling strategy.