• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.797-803
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• 2001

This paper obtained Bayes prediction density for the spatial linear model with non-informative prior. It showed the results that predictive inferences is completely unaffected by departures from the normality assumption in the direction of the elliptical family and the structure of prediction density is unchanged by more than one additional future observations.

• Geomechanics and Engineering
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• v.23 no.5
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• pp.493-502
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• 2020

The prediction of soil response under repetitive mechanical loadings remains challenging in geotechnical engineering applications. Modeling the cyclic soil response requires a robust model validation with an experimental dataset. This study proposes a unique method adopting linearity of model constant with the number of cycles. The model allows the prediction of the terminal density of sediments when subjected to repetitive changes in pore-fluid pressure based on the two-surface plasticity. Model simulations are analyzed in combination with an experimental dataset of sandy sediments when subjected to repetitive changes in pore fluid pressure under constant deviatoric stress conditions. The results show that the modified plastic moduli in the two-surface plasticity model appear to be critical for determining the terminal density. The methodology introduced in this study is expected to contribute to the prediction of the terminal density and the evolution of shear strain at given repetitive loading conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1883-1890
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• 1999

Wireless communication in an urban area, the accurate prediction of wave propagation characteristics are very important to determine communication service areas, select optimal base-stations, and design cells, etc. The CCIR model is a propagation prediction model using a shadowing by the buildings in an urban area. This model represent the shadowing rate by the means of the effect of shadowing between base-station and mobile unit in a shaped linear plane. But, This one occurred a lot of prediction error because it did not consider that density area by the buildings and terrain configurations by the hill and mountain on Line-Of-Sight. In this thesis, an improved propagation prediction model is proposed to reduce prediction error. We presents a new equation, which is using the SAS. This equation is associated with the shadow height by the buildings that considers the topology and the number of blocks that can affect the building shadow in the Line-Of-Sight. We measure the received electrical field level of base-station that high density area, medium density area, and low density area, and then compare and analysis the result to prediction of CCIR model and proposed model. The result compared with the measurement, the proposed model has the improvement of 9.71dB in a high density area, 9.66dB in a medium density area, and 4.02dB in a low density area better than the CCIR model. The result compared with the measurement, the proposed model has the improvement of 9.71dB in a high density area, 9.66dB in a medium density area, and 4.02dB in a low density area better than the CCIR model.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.273-278
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• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

• Structural Engineering and Mechanics
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• v.45 no.2
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• pp.201-209
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• 2013

A new dynamic reliability analysis of structure under repeated random loads is proposed in this paper. The proposed method is developed based on the idea that the probability density of several times random loads can be derived from the probability density of single-time random load. The reliability prediction models of structure based on time responses under several times random loads with and without strength degradation are obtained by using the stress-strength interference theory and probability density evolution method. The resulting differential equations in the prediction models can be solved by using the forward finite difference method. Then, the probability density functions of strength redundancy of the structures can be obtained. Finally, the structural dynamic reliability can be calculated using integral method. The efficiency of the proposed method is demonstrated numerically through a speed reducer. The results have shown that the proposed method is practicable, feasible and gives reasonably accurate prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4492-4507
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• 2021

In camera sensor networks (CSNs), in order to better identify the point, full-view problem requires capture any facing direction of target (point or intruder), and its coverage prediction and sensor density issues are more complicated. At present, a lot of research supposes that a large number of homogeneous camera sensors are randomly distributed in a bounded square monitoring region to obtain full-view rate which is close to 1. In this paper, we deduce the sensor density prediction model in heterogeneous deployed CSNs with arbitrary full-view rate. Aiming to reduce the influence of boundary effect, we introduce the concepts of expanded monitoring region and maximum detection area. Besides, in order to verify the performance of the proposed sensor density model, we carried out different scenarios in simulation experiments to verify the theoretical results. The simulation results indicate that the proposed model can effectively predict the sensor density with arbitrary full-view rate.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.1912-1921
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• 2020

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.21 no.2
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• pp.225-234
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• 2023

In the design of HLW repositories, it is important to confirm the performance and safety of buffer materials at high temperatures. Most existing models for predicting hydraulic conductivity of bentonite buffer materials have been derived using the results of tests conducted below 100℃. However, they cannot be applied to temperatures above 100℃. This study suggests a prediction model for the hydraulic conductivity of bentonite buffer materials, valid at temperatures between 100℃ and 125℃, based on different test results and values reported in literature. Among several factors, dry density and temperature were the most relevant to hydraulic conductivity and were used as important independent variables for the prediction model. The effect of temperature, which positively correlates with hydraulic conductivity, was greater than that of dry density, which negatively correlates with hydraulic conductivity. Finally, to enhance the prediction accuracy, a new parameter reflecting the effect of dry density and temperature was proposed and included in the final prediction model. Compared to the existing model, the predicted result of the final suggested model was closer to the measured values.

• Korean Journal of Metals and Materials
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• v.46 no.9
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• pp.554-562
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• 2008

The distribution and prediction interval for the misorientation angle of grain boundary at which $Cr_2N$ was precipitated during heating at $900^{\circ}C$ for $10^4$ sec were newly estimated, and followed by the estimation of mathematical and median rank methods. The probability density function of the misorientation angle can be estimated by a statistical analysis. And then the ($1-{\alpha}$)100% prediction interval of misorientation angle obtained by the estimated probability density function. If the estimated probability density function was symmetric then a prediction interval for the misorientation angle could be derived by the estimated probability density function. In the case of non-symmetric probability density function, the prediction interval could be obtained from the cumulative distribution function of the estimated probability density function. In this paper, 95, 99 and 99.73% prediction interval obtained by probability density function method and cumulative distribution function method and compared with the former results by median rank regression or mathematical method.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.307-309
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• 2000

This paper presents development of program for mid-tong term load forecasting in region and district respectively. In this program, at first, the region is classified by KEPCO branch which can be analyzed in light of curl·elation between load characteristics and economic indicator and then, prediction for load density in each region was performed by scenario of economic, population and city plan. Secondly, prediction for load density in each district is performed by methodology which is based on land use method. Finally efficiency for prediction work in each KEPCO branch could be identified by applying the developed program to the Seoul city in real.