• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2008.10a
• /
• pp.444-447
• /
• 2008

This study is intended to develop a improved version of Cross Impact Analysis Model based on Scaling Theory. In developing the model, we applied the scale transformation technique and regression technique to existing CIA model. Improved CIA model is composed of two sub-models: 'model for impact value measurement,' and 'model for impact value conversion'. We applied a technique which measures data by ordinal scale and then transforms them into interval scale and ratio scale data to CIA model. The accuracy of forecasting and the usability of CIA application have been improved.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2009.05a
• /
• pp.70-73
• /
• 2009

The aim of this research is to obtain a numerical material model for an amorphous glassy polymer, polycarbonate (PC), which can be used in finite element analysis (FEA) of the micro thermal imprint process near the glass transition temperature. An understanding of the deformation behavior of the PC specimens was acquired by performing tensile stress relaxation tests. The viscoelastic material model based on generalized Maxwell model was introduced for the material near Tg to establish the FE model based on the commercial FEA code ABAQUS/Standard with a suitable set of parameters obtained for this material model from the test data. Further validation of the model and parameters was performed by comparing the analysis of FE model results to the experimental data.

• Health Policy and Management
• /
• v.16 no.2
• /
• pp.21-48
• /
• 2006

As prior studies indicate that chronic diseases are mainly attributed to health behavior, preventive health care rather than treatment for illness needs to improve health status. Since chronic conditions require long-term therapy, health care expenditures to treat chronic diseases have been substantial burden at national level. In this point of view, this study suggests that the health promotion program should be based on Knowledge Based System Using Data Mining Technique, we developed a predictive model for preventive healthcare management on diabetes mellitus. Generally, in the outbreak of diabetes mellitus there is a difference in lifestyle and the risk factors according to gender. So we developed a predictive model in accordance with gender difference and applied the Logistic Regression Model based on Data Mining process. The result of the study were as follow. The lift of the last predictive model was an average 2.23 times(male model : 2.13, female model 2.33) more improved than in the random model in upper 10% group. The health risk factors of diabetes mellitus are gender, age, a place of residence, blood pressure, glucose, smoking, drinking, exercise rate. On the basis of these factors, we suggest the program of the health promotion.

• New & Renewable Energy
• /
• v.20 no.1
• /
• pp.95-101
• /
• 2024

Since 1990, the Renewable Big Data Research Lab at the Korea Institute of Energy Technology has been observing solar radiation at 16 sites across South Korea. Serving as the National Reference Standard Data Center for Renewable Energy since 2012, it produces essential data for the sector. By 2020, it standardized meteorological year data from 22 sites. Despite user demand for data from approximately 260 sites, equivalent to South Korea's municipalities, this need exceeds the capability of measurement-based data. In response, our team developed a method to derive solar radiation data from satellite images, covering South Korea in 400,000 grids of 500 m × 500 m each. Utilizing satellite-derived data and ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), we produced standard meteorological year data for 1,000 sites. Our research also focused on data measurement traceability and uncertainty estimation, ensuring the reliability of our model data and the traceability of existing measurement-based data.

• The Journal of the Acoustical Society of Korea
• /
• v.28 no.3
• /
• pp.268-272
• /
• 2009

In this paper, we propose a Gaussian Mixture Model (GMM) based incremental robust adaptation with a forgetting factor for the speaker verification. Speaker recognition system uses a speaker model adaptation method with small amounts of data in order to obtain a good performance. However, a conventional adaptation method has vulnerable to the outlier from the irregular utterance variations and the presence noise, which results in inaccurate speaker model. As time goes by, a rate in which new data are adapted to a model is reduced. The proposed algorithm uses an incremental robust adaptation in order to reduce effect of outlier and use forgetting factor in order to maintain adaptive rate of new data on GMM based speaker model. The incremental robust adaptation uses a method which registers small amount of data in a speaker recognition model and adapts a model to new data to be tested. Experimental results from the data set gathered over seven months show that the proposed algorithm is robust against outliers and maintains adaptive rate of new data.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.4
• /
• pp.789-797
• /
• 2003

In this paper, we consider two components system which have bivariate weibull model with bivariate random censored data. We proposed large sample test for independence based on maximum likelihood estimator and relative frequency estimator, respectively. Also we derive asymptotic properties for the large sample tests and present a numerical study.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.3
• /
• pp.571-578
• /
• 2003

In this paper, we consider two components system which have bivariate weibull model with bivariate type I censored data. We proposed maximum likelihood estimator and relative frequency estimator for the reliability of series system. Also, we construct approximate confidence intervals for the reliability based on the two proposed estimators. And we present a numerical study.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.05a
• /
• pp.121-126
• /
• 2003

In this paper, we consider two-components system which the lifetimes follow bivariate pareto model with censored data. We develop large sample tests for testing independence between two-components. Also we present simulated study which is the test based on asymptotic normal distribution in testing independence.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.2
• /
• pp.331-337
• /
• 2004

In the analysis of massive volume data, a neural network model is a useful tool. To implement the Neural network model, it is important to select initial value. Since the initial values are generally used as random value in the neural network, the convergent performance and the prediction rate of model are not stable. To overcome the drawback a possible method use samples randomly selected from the whole data set. That is, coefficients estimated by logistic regression based on the samples are the initial values.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1992.10a
• /
• pp.6-6
• /
• 1992

This work deals with nonlinear least squares method for estimating unknown universial constants C in a computer simulation code real experimental data(or database) and computer simulation data. The best linear unbiased predictor based on a spatial statistical model is fitted from the computer simulation data. Then nonlinear least squares estimation method is applied to the real data using the fitted prediction model(or simulation predictor) as if it were the true simulation model. An application to the computational nuclear fusion device is presented.