• Smart Structures and Systems
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• v.14 no.6
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• pp.1247-1267
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• 2014

Real-Time Hybrid Simulation (RTHS) is a novel approach conceived to evaluate dynamic responses of structures with parts of a structure physically tested and the remainder parts numerically modelled. In RTHS, delay estimation is often a precondition of compensation; nonetheless, system delay may vary during testing. Consequently, it is sometimes necessary to measure delay online. Along these lines, this paper proposes an online delay estimation method using least-squares algorithm based on a simplified physical system model, i.e., a pure delay multiplied by a gain reflecting amplitude errors of physical system control. Advantages and disadvantages of different delay estimation methods based on this simplified model are firstly discussed. Subsequently, it introduces the least-squares algorithm in order to render the estimator based on Taylor series more practical yet effective. As a result, relevant parameter choice results to be quite easy. Finally in order to verify performance of the proposed method, numerical simulations and RTHS with a buckling-restrained brace specimen are carried out. Relevant results show that the proposed technique is endowed with good convergence speed and accuracy, even when measurement noises and amplitude errors of actuator control are present.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1487-1498
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• 2016

In monitoring a process, in-control process parameters must be estimated from the Phase I data. When we design the control chart based on the estimated process parameters, the control limits are usually chosen to satisfy a specific in-control average run length (ARL). However, as the run length distribution is skewed when the process is either in-control or out-of-control, the median run length (MRL) can be used as alternative measure instead of the ARL. In this paper, we evaluate the performance of Shewhart $\bar{X}$ chart with estimated parameters in terms of the average of median run length (AMRL) and the standard deviation of MRL (SDMRL) metrics. In simualtion study, the grand sample mean is used as a process mean estimator, and several competing process standard deviation estimators are used to evaluate the in-control performance for various amounts of Phase I data.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.710-715
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• 2010

As wind has become one of the fastest growing renewable energy sources, the key issue of wind energy conversion systems is how to efficiently operate the wind turbines in a wide range of wind speeds. The wind speed has a huge impact on the dynamic response of wind turbine. For this purpose, many control algorithms are in need for a method to measure wind speed to increase performance. Unfortunately, no accurate measurement of the effective wind speed is online available from direct measurements, which means that it must be estimated in order to make such control methods applicable in practice. In this paper, a new method based on Kalman filter and artificial neural network is presented for the estimation of the effective wind speed. To verify the performance of the proposed scheme, some simulation studies are carried out.

• Korean Journal of Health Education and Promotion
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• v.30 no.1
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• pp.65-81
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• 2013

Objectives: The present study was carried out to develop and evaluate comprehensive health care program to prevent infectious disease and promote health in child-care centers by Doctor of Korean medicine. Methods: A nonequivalent control group pretest-posttest design study was conducted on 568 children and 85 child care teacher at 12 child care facilities for 12 weeks from July to October 2012. The program was consist of management, education, screening under concepts of traditional preventive medicine, Yangsaeng and Chimibyeong. Children's medical utilization due to infectious disease and attendance means functional status were measured by reports from parents. The Difference in difference(DID) estimator was applied data analysis, and added Zero-inflated negative binomial regression model. Also, attitudes on the infection of teacher was measured and analyzed through t-test. Results: After the intervention, the total medical utilization due to infectious disease decreased, but not significantly. Total absence, early leave and lateness decreased significantly. But, Attitude on the infection of child care teacher was not changed. The parent's satisfaction showed positive overall. Conclusions: The intervention program may be effective in preventing infectious disease and managing health in child-care center partially. To measure long-term effect, long-term study improved is requested.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.239-249
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• 2013

The role of software process is getting more important to make good quality softwares. One of the measures to improve the software process is Defect Removal Efficiency(DRE). DRE gives a measure of the development team ability to remove defects prior to release. It is calculated as a ratio of defects resolved to total number of defects found. Software Engineering Levels are usually decided by CMMI Model. The model is designed to help organizations improve their software product and service development, acquisition, and maintenance processes. The score of software engineering levels can be calculated by CMMI model. The levels are composed of the three groups(absent, average, and advanced). This study is to find if there is any difference among the three categories in term of the result of software engineering levels on DRE. We propose One way ANOVA to analyze influence of software engineering levels on DRE. Bootstrap method is also used to estimate the sampling distribution of the original sample because the data are not sampled randomly. The method is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample. The data were collected in 106 software development projects by the survey. The result of this study tells that there is some difference of DRE among the groups. The higher the software engineering level of a specific company becomes, the better its DRE gets, which means that the companies trying to improve software process can increase their good management performance.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.589-597
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• 2021

In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.494-500
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• 2020

This paper proposes a new distance estimation technique for indoor localization in ultra wideband (UWB) systems. The proposed technique is based on recurrent neural network (RNN), one of the deep learning methods. The RNN is known to be useful to deal with time series data, and since UWB signals can be seen as a time series data, RNN is employed in this paper. Specifically, the transmitted UWB signal passes through IEEE802.15.4a indoor channel model, and from the received signal, the RNN regressor is trained to estimate the distance from the transmitter to the receiver. To verify the performance of the trained RNN regressor, new received UWB signals are used and the conventional threshold based technique is also compared. For the performance measure, root mean square error (RMSE) is assessed. According to the computer simulation results, the proposed distance estimator is always much better than the conventional technique in all signal-to-noise ratios and distances between the transmitter and the receiver.

• Journal of The Korean Astronomical Society
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• v.55 no.2
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• pp.37-57
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• 2022

Using I-band images of 35 nearby (z < 0.1) type 1 active galactic nuclei (AGNs) obtained with Hubble Space Telescope, selected from the 70-month Swift-BAT X-ray source catalog, we investigate the photometric properties of the host galaxies. With a careful treatment of the point-spread function (PSF) model and imaging decomposition, we robustly measure the I-band brightness and the effective radius of bulges in our sample. Along with black hole (BH) mass estimates from single-epoch spectroscopic data, we present the relation between BH mass and I-band bulge luminosity (M_BH-M_I,bul relation) of our sample AGNs. We find that our sample lies offset from the M_BH-M_I,bul relation of inactive galaxies by 0.4 dex, i.e., at a given bulge luminosity, the BH mass of our sample is systematically smaller than that of inactive galaxies. We also demonstrate that the zero point offset in the M_BH-M_I,bul relation with respect to inactive galaxies is correlated with the Eddington ratio. Based on the Kormendy relation, we find that the mean surface brightness of ellipticals and classical bulges in our sample is comparable to that of normal galaxies, revealing that bulge brightness is not enhanced in our sample. As a result, we conclude that the deviation in the M_BH-M_I,bul relation from inactive galaxies is possibly because the scaling factor in the virial BH mass estimator depends on the Eddington ratio.

• Journal of Korea Trade
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• v.26 no.7
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• pp.1-18
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• 2022

Purpose - Recent international studies have largely focused on measuring the welfare gains from increased trade varieties. To adequately capture the variety gains, it is of importance to estimate the elasticity of substitution between varieties of trade goods because it is one of the key parameters to determine the magnitude of the variety gains. Using the import data of South Korea, this paper shows that the elasticities vary substantially across the estimators, which affects the magnitude of the gains from trade. Design/methodology - Empirical studies working on the gains from trade variety have heavily depended on the estimation methods for the elasticity of substitution between trade varieties, developed by Feenstra (1994) and refined by Broda and Weinstein (2006). We estimate and compare the estimated elasticities for 8,945 HS 10 goods of South Korea, obtained from the three estimation methods: Feenstra's weighted least square (F-WLS), Feenstra's feasible generalized least square (F-FGLS), and Broda and Weinstein's feasible generalized least square (BW-FGLS). Findings - Using the estimated elasticities from the F-FGLS, considered as a suitable estimator, A typical Korean consumer saved 228 dollars per year by the greater access to new import varieties. This leads to gains from imported variety of 2.06% of GDP. In 2017, a typical Korean consumer would gain by 611 dollars, compared with 2000. China is the country with the largest contribution (28.4%), followed by Japan and USA. About 50% of all the welfare gains come from the imports from the three main trade partners. The Southern Asian countries are more important to the South Korean welfare gain than the Western European countries. Originality/value - Existing studies have chosen one of the methods without any criterion for the choice and then estimated the elasticities of substitution between varieties of trade goods. This paper focuses on the estimation specifications and methods as the cause of the disparity in estimated elasticities and welfare gains from trade variety. According to the Ramsey RESET and White tests, the F-FGLS estimates are relatively better compared to the F-WLS and BW-FGLS estimates. As another contribution, this paper provides the first measure of the welfare gains from trade variety for South Korea, using the estimated elasticities of substitution between trade varieties.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.