• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.3
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• pp.339-346
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• 2018

A geological repository system consists of a disposal canister with packed spent fuel, buffer material, backfill material, and intact rock. The buffer is indispensable to assure the disposal safety of high-level radioactive waste. Since the heat generated from spent nuclear fuel in a disposal canister is released to the surrounding buffer materials, the thermal properties of the buffer material are very important in determining the entire disposal safety. Especially, since thermal expansion can cause thermal stress to the intact rock mass in the near-field, it is very important to evaluate thermal expansion characteristics of bentonite buffer materials. Therefore, this paper presents a thermal expansion coefficient prediction model of the Gyeongju bentonite buffer materials which is a Ca-bentonite produced in South Korea. The linear thermal expansion coefficient was measured considering heating rate, dry density and temperature variation using dilatometer equipment. Thermal expansion coefficient values of the Gyeongju bentonite buffer materials were $4.0{\sim}6.0{\times}10^{-6}/^{\circ}C$. Based on the experimental results, a non-linear regression model to predict the thermal expansion coefficient was suggested and fitted according to the dry density.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.363-374
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• 2012

This paper studies the problem of tracking a re-entry vehicle (RV) in order to predict its impact point on the ground. Re-entry target dynamics combined with super-high speed has a complex non-linearity due to ballistic coefficient variations. However, it is difficult to construct a database for the ballistic coefficient of a unknown vehicle for a wide range of variations, thus the reliability of target tracking performance cannot be guaranteed if accurate ballistic coefficient estimation is not achieved. Various techniques for ballistic coefficient estimation have been previously proposed, but limitations exist for the estimation of non-linear parts accurately without obtaining prior information. In this paper we propose the ballistic coefficient ${\beta}$ model-based interacting multiple model-extended Kalman filter (${\beta}$-IMM-EKF) for precise tracking of an RV. To evaluate the performance, other ballistic coefficient model based filters, which are gamma augmented filter, gamma bootstrapped filter were compared and assessed with the proposed ${\beta}$-IMM-EKF for precise tracking of an RV.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.1
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• pp.61-71
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• 2016

This study performed the physical and mechanical experiment on the samples of costal marine clays individually collected in western and southern regions to identify the characteristics of western and southern costal marine clay. Based on the experiment result, the characteristics of costal marine clay is identified undrained shear strength. Based on the experiment result on the physical and mechanical characteristics of costal marine clays, the regression is presented that can analyze the mechanical characteristics of undrained shear strength in costal marine clay of Korea, region of Korea and western-southern region. The correlation of uniaxial compressive strength and undrained shear strength was suitable for use of western-southern region correlation equation. The test result of Jeonnam Yeosu area compares with prediction results of previous researchers formula and western-southern region formula. Prediction results appear highest reliability on the 0.827 of coefficient of determination in the prediction results of the western-southern region formula.

• Smart Media Journal
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• v.11 no.5
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• pp.38-47
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• 2022

Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R² value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R² values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.12-20
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• 1999

In this paper, we compared speaker verification performance of the speech data collected in clean environment and in channel environment. For the improvement of the performance of speaker verification gathered in channel, we have studied on the efficient feature parameters in channel environment and on the preprocessing. Speech DB for experiment is consisted of Korean doublet of numbers, considering the text-prompted system. Speech features including LPCC(Linear Predictive Cepstral Coefficient), MFCC(Mel Frequency Cepstral Coefficient), PLP(Perceptually Linear Prediction), LSP(Line Spectrum Pair) are analyzed. Also, the preprocessing of filtering to remove channel noise is studied. To remove or compensate for the channel effect from the extracted features, cepstral weighting, CMS(Cepstral Mean Subtraction), RASTA(RelAtive SpecTrAl) are applied. Also by presenting the speech recognition performance on each features and the processing, we compared speech recognition performance and speaker verification performance. For the evaluation of the applied speech features and processing methods, HTK(HMM Tool Kit) 2.0 is used. Giving different threshold according to male or female speaker, we compare EER(Equal Error Rate) on the clean speech data and channel data. Our simulation results show that, removing low band and high band channel noise by applying band pass filter(150～3800Hz) in preprocessing procedure, and extracting MFCC from the filtered speech, the best speaker verification performance was achieved from the view point of EER measurement.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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• pp.978-985
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• 2021

This study aims to propose a prediction model for the drape coefficient using artificial neural networks and to analyze the nonlinear relationship between the drape properties and physical properties of fabrics. The study validates the significance of each factor affecting the fabric drape through multiple linear regression analysis with a sample size of 573. The analysis constructs a model with an adjusted R² of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m_warp, m_weft), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c_warp, c_weft, c_bias), and force required for 1% tension in the warp direction (f_warp). Finally, an artificial neural network was created using seven selected factors. The performance was examined by increasing the number of hidden neurons, and the most suitable number of hidden neurons was found to be 8. The mean squared error was .052, and the correlation coefficient was .863, confirming a satisfactory model. The developed artificial neural network model can be used for engineering and high-quality clothing design. It is expected to provide essential data for clothing appearance, such as the fabric drape.

• Korean Journal of Artificial Intelligence
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• v.6 no.2
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• pp.17-22
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• 2018

According to the World Health Organization, the top 10 causes of death worldwide include heart disease. Heart diseases include coronary disease, which induces acute myocardial infarction. Ticagrelor drugs are being used to treat acute alliances, but it has become difficult to breathe due to the drugs. In a related study, Tobias predicted that uric acid causes acute respiratory distress independently of other factors, including BNP. And in the Ahmad study, serum uric acid numbers were related to the left ventricle depending on the level of uric acid. Experimental data are data used after 155 patients who received coronary intervention took ticagrelor. The research methods were leveraged by gradient decent algorithm and linear regression. In order to avoid overfitting in the experiment, training data and test data were separated into 70 and 30 percent respectively. The experimental results lacked the predictability of other attributes except DT in the correlation coefficient and crystal coefficient. However, all attributes related to dyspnea other than DT are determined to be related to causing relaxation of the heart in the left ventricle. Therefore, the attribute causing dyspnea is determined to be an attribute causing relaxation of the heart of the DT and left ventricle.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.701-712
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• 2005

The suppression for logistic regression models has been debated no longer than that for linear regression models since, among many other reasons, sum of squares for regression (SSR) or coefficient of determination ($R^2$) could be defined into various ways. Based on four kinds of $R^2$'s: two kinds are most preferred, and the other two are proposed by Liao & McGee (2003), four kinds of SSR's are derived so that the suppression for logistic models is explained. Many data fitted to logistic models are generated by Monte Carlo method. We explore when suppression happens, and compare with that for linear regression models.

• Journal of Korean Society of Steel Construction
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• v.9 no.3 s.32
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• pp.431-440
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• 1997

The paper describes a theoretical model for the prediction of bridge temperatures from meteorological data measured at bridge site and local meteorological center together with existing finite element heat transfer theory and solar radiation transfer theory to determine the time dependent temperature distribution of bridge. In this analytical model, the most adequate equation for the calculation of solar radiation on the bridge surface, which is dominant in day time is described based on the results of several experimental studies for the solar energy. The validity of this model is tested against field data obtained from long term experimental program on Sadang Viaduct in Seoul. Also, this paper describes the linear correlation between design variables and meteorological data to establish analytical criteria for the prediction of the average temperature, which are responsible for the longitudinal deformation of the bridges and of the vertical differential temperature profiles. which are responsible for the bending deformations from the long term experimental results.

• Korean Journal of Food Science and Technology
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• v.20 no.6
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• pp.827-833
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• 1988

The objectives of this investigation were to develop an improved analytical method and to review with respect to experimental parameters and thermo-physical properties influencing the freezing time prediction. The results indicate that the relationship between freezing time and product size is dependent on the surface heat transfer coefficient. As the magnitude of surface heat transfer coefficient decreases, the influence of product size on freezing time becomes more profound. But the freezing time does decrease slightly as the coefficients are increased to values greater than 150 $w/m^2^{\circ}C$. In addition, influence of thermo-physical properties on the freezing time prediction shown generally density, water content, specific heat and thermal conductivity, in order of % difference. Multiple linear regression equation for freezing time prediction were obtained with respect to 4 different food materials with varying thickness.