• Journal of the Korean Geotechnical Society
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• v.31 no.10
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• pp.5-16
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• 2015

Considering the conversion of the Korea Construction Standards to Limit State Design (LSD), we analyzed the resistance bias factor for pullout resistance, as a part of the development of the Load and Resistance Factor Design (LRFD) for soil nailing; very few studies have been conducted on soil nailing. In order to reflect the local characteristics of soil nailing, such as the design and construction level, we collected statistics on pullout tests conducted on slopes and excavation construction sites around the country. In this study a database was built based on the geotechnical properties, soil nailing specifications, and pullout test results. The resistance bias factors are calculated to determine the resistance factor of the pullout resistance for gravity and pressurized grouting method, which are the most commonly used methods in Korea; moreover, we have relatively sufficient data on these methods. We found the resistance bias factors to be 1.144 and 1.325, which are relatively conservative values for predicting the actual ultimate pullout resistance. It showed that our designs are safer than those found in a research case in the United States (NCHRP Report); however, there was an uncertainty, $COV_R$, of 0.27-0.43 in the pullout resistance, which is relatively high. In addition, the pressurized grouting method has a greater margin of safety than the gravity grouting method, and the actual ultimate pullout resistance determined using the pressurized grouting method has low uncertainty.

• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.6C
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• pp.505-511
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• 2008

The global navigation satellite system (GNSS) is using a direct sequence/spread spectrum (DS/SS) modulation. In order to recover the information data, the DS/SS system first performs a two-step synchronization process: acquisition and tracking. The acquisition process adjusts the phase difference between the received and locally generated acquisition sequences within ${\pm}T_c/2$ or less, where $T_c$ is the chip period. The tracking process performs fine synchronization. In this paper, we focus on the tracking issue. The single delta delay locked loop($\Delta$-DLL) is the optimal tracking scheme for a GNSS in the absence of multipath signals, where $\Delta$ means the spacing between the early and late correlation time offset. In the multipath environments, however, the $\Delta$-DLL suffers from huge estimation bias(denoted by $\beta$) caused by distorted correlation values. Although some modified schemes such as a $\Delta$-DLL with a narrow $\Delta$ and a double delta DLL (${\Delta}^{(2)}$-DLL) were proposed to reduce the estimation bias, they cannot remove the estimation bias completely and need more accurate acquisition process. This paper proposes a novel tracking scheme that can dramatically reduce the estimation bias, using the maximum slope change among the correlation outputs.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.349-359
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• 2022

This research is a descriptive study that aimed to identify the health knowledge related stroke, optimistic bias, and social support of middle-aged adults and the effect these had on their health-promoting lifestyle This study was conducted by collecting 220 adults aged between 40 to 60 years from C City D city and K city. Data were analyzed using descriptive analysis, ANOVA, t-test, Pearson's correlation coefficients and multiple regression with the SPSS 23.0 program. The average health-promoting lifestyle was 44.27 points. The health-promoting lifestyle of the study participants showed a positive correlation between the optimistic bias(r=.18, p=.001) and social support(r= .61, p<.000). According to the results of multiple regression analysis, perception of Necessity for Stroke Education(β=.12, p=.010), optimistic bias(β=.18, p=.040), and social support(β=.48, p<.000) were shown to be significant factors that affected the health-promoting lifestyle of the participants. These variables explained 38.5%. Therefore, an health education program to improve the health-promoting lifestyle related to stroke in adults should be considered as a way to enhance social support and reduce optimistic bias.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.271-282
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• 2006

The aim of this study is to propose a tree-structured clustering for mixed data. We suggest a scaling method to reduce the variable selection bias among categorical variables. In numerical examples such as credit data, German credit data, we note several differences between tree-structured clustering and K-means clustering.

• East Asian Economic Review
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• v.23 no.4
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• pp.333-351
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• 2019

We use a data-mining bootstrap procedure to investigate the predictability test in the eight Asia-Pacific regional stock markets using in-sample and out-of-sample forecasting models. We address ourselves to the data-mining bias issues by using the data-mining bootstrap procedure proposed by Inoue and Kilian and applied to the US stock market data by Rapach and Wohar. The empirical findings show that stock returns are predictable not only in-sample but out-of-sample in Hong Kong, Malaysia, Singapore, and Korea with a few exceptions for some forecasting horizons. However, we find some significant disparity between in-sample and out-of-sample predictability in the Korean stock market. For Hong Kong, Malaysia, and Singapore, stock returns have predictable components both in-sample and out-of-sample. For the US, Australia, and Canada, we do not find any evidence of return predictability in-sample and out-of-sample with a few exceptions. For Japan, stock returns have a predictable component with price-earnings ratio as a forecasting variable for some out-of-sample forecasting horizons.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• Journal of the Korean Solar Energy Society
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• v.30 no.2
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• pp.16-21
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• 2010

Computer simulation of buildings and solar energy systems are being used increasingly in energy assessments and design. This paper discusses the possibility of using sunshine duration data instead of global hourly solar irradiation (GHSI) data for localities with abundant data on sunshine duration. For six locations in South Korea where global radiation is currently measured, the global radiation was calculated using Sunshine Duration Radiation Model (SDRM), compared and analyzed. Results of SDRM has been compared with the measured data on the coefficients of determination (R2), root-mean-square error (RMSE) and mean bias error (MBE). This study recommends the use of sunshine duration based irradiation models if measured solar radiation data is not available.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.88-96
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• 2013

Much attention has been needed in water resource management at the watershed due to drought and flooding issues caused by climate change in recent years. Increase in air temperature and changes in precipitation patterns due to climate change are affecting hydrologic cycles, such as evaporation and soil moisture. Thus, these phenomena result in increased runoff at the watershed. The Soil and Water Assessment Tool (SWAT) model has been used to evaluate rainfall-runoff at the watershed reflecting effects on hydrology of various weather data such as rainfall, temperature, humidity, solar radiation, wind speed. For bias-correction of RCP data, at least 30 year data are needed. However, for most gaging stations, only precipitation data have been recorded and very little stations have recorded other weather data. In addition, the RCP scenario does not provide all weather data for the SWAT model. In this study, two scenarios were made to evaluate whether it would be possible to estimate streamflow using measured precipitation and long-term average values of other weather data required for running the SWAT. With measured long-term weather data (scenario 1) and with long-term average values of weather data except precipitation (scenario 2), the estimate streamflow values were almost the same with NSE value of 0.99. Increase/decrease by ${\pm}2%$, ${\pm}4%$ in temperature and humidity data did not affect streamflow. Thus, the RCP precipitation data for Hongcheon watershed were bias-corrected with measured long-term precipitation data to evaluate effects of climate change on streamflow. The results revealed that estimated streamflow for 2055s was the greatest among data for 2025s, 2055s, and 2085s. However, estimated streamflow for 2085s decreased by 9%. In addition, streamflow for Spring would be expected to increase compared with current data and streamflow for Summer will be decreased with RCP data. The results obtained in this study indicate that the streamflow could be estimated with long-term precipitation data only and effects of climate change could be evaluated using precipitation data as shown in this study.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.474-477
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• 1997

In this paper, an efficient attitude determination algorithm based on the Kalman Filter which combines earth/sun sensor data with gyro data in a mutually compensating manner is presented. Quaternion is used as the attitude state to save computation time and to prevent the gimbal-lock situation associated with Euler angles. Gyro data allows the use of the kinematic equation instead of space vehicle's dynamic equation which is usually based on approximation of the actual dynamics and inaccurate torque information. The gyro data are used to propagate the attitude through kinematic equation and the earth/sun sensor data are used to update the attitude and estimate the gyro bias. Simulation results for the KOMPSAT attitude determination system are presented.