• Korean Journal of Soil Science and Fertilizer
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• v.17 no.1
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• pp.18-23
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• 1984

Spatial variability of 96 laboratory-measured infiltration rates on the Hwadong SiCL was studied using geostatistical concepts. The measurement was made at the nodes of the regular grid consisting of 12 rows and 8 columns. Sample spacing within rows and columns was 3 and 2m, respectively. Kriging was a means of spacial prediction that can be used for the infiltration rate. It was optimal in the sense that it provided estimates at unrecorded places without bias and with minimum and known variance. An attempt has been made with original data to verily the validity of all assumptions (Stationarity, Variogram models, etc.) by Jack-knifing procedure and frequency distribution. Variogram models were not different from other models, such as linear in calculation of both kriged values and variances in justification of its choice for simplicity. Correlation coefficient for a one-to-one relationship between measured and kriged values was found to be 0.308, which was not significantly different at 1% significance level.

• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.175-194
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• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.

• Journal of Korean Academy of Nursing
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• v.52 no.5
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• pp.511-521
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• 2022

Purpose: This study aimed to examine the moderating effect of organizational justice on the relationship between self-efficacy and nursing performance among clinical nurses. Methods: In January 2021, a cross-sectional survey was conducted with 224 clinical nurses recruited from a university-affiliated hospital in Suwon, South Korea. Participants completed online-based, self-report structured questionnaires. Collected data were analyzed using multiple regression and a simple model of PROCESS macro with a 95% bias-corrected bootstrap confidence interval. Results: Self-efficacy and organizational justice were found to be significant predictors of nursing performance. These two predictors explained the additional 34.8% variance of nursing performance in the hierarchical regression model, after adjusting the other covariates. In addition, organizational justice moderated the relationship between self-efficacy and nursing performance among the clinical nurses. In particular, at low self-efficacy level, participants with high organizational justice had higher nursing performance compared to those with low organizational justice. Conclusion: Enhancing organizational justice can be used as an organizational strategy for improving the organizational culture in terms of distribution, procedure, and interaction. Ultimately, these efforts will contribute to the improvement of nursing performance through a synergistic effect on organizational justice beyond nurses' individual competency and self-efficacy.

• Publications of The Korean Astronomical Society
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• v.23 no.1
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• pp.1-12
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• 2008

Understanding of the basic characteristics of an astronomical instrument is a prerequisite to obtaining reliable data from the instrument. We have analyzed more than 1,000 calibration images from the Fairchild 486 CCD (hereafter the Maidanak 4k CCD system) attached to the AZT-22 1.5m telescope at Maidanak Astronomical Observatory in Uzbekistan. The Maidanak 4k CCD system supports three readout modes through 1, 2, or 4 amplifiers. In most cases observers use 4-amplifier readout mode to save time. We have tested the stability and seasonal variation of zero levels and confirm that two quadrants of the images (Amp 1 & 2) show no appreciable seasonal variation. but the other two quadrants (Amp 3 & Amp 4) show an evident seasonal variation in the bias level. The Cryo Tiger, the cooling system used at the Maidanak 4k CCD system, maintains the CCD temperature at -108'E, and effectively suppresses the dark electrons. The mean value versus the variance plot of the flat images does not show the expected relation for an ideal Poisson noise distribution and this is attributed to the large variation in quantum efficiency between different pixels. In addition, we confirm that there is no appreciable difference in gain between readout amplifiers, but there is a large variation in quantum efficiency across CCD chip especially in U. Due to the finite length of shutter opening and closing time, the effective exposure time varies across the science images. We introduce two parameters to quantify the effect of this uneven illumination and present a method to remove these effects. We also present a method to remove the interference patterns appearing in the images obtained with longer wavelength filters and investigate the spatial variation of the point spread function.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.635-651
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• 2012

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.11
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• pp.21-27
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• 2002

When decision-directed channel estimation is used for QAM-OFDM systems, the optimal shape of the two-dimensional filter depends on the amplitudes of the modulated symbols as well as the channel characteristics such as delay spread, Doppler frequency, and signal-to-noise ratio. While most conventional channel estimation methods did not consider the amplitudes of the modulated symbols because of the large computational complexity, we propose a simple channel estimation method for multi-level-amplitude-modulated systems. The proposed method can effectively reduce the noise variance of the estimates with small-sized filtering and there is a possibility of reducing the implementation cost and producing better results by avoiding the bias due to large filter sizes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.267-277
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• 2022

Due to the increasing proportion of cloud and remote working environments, various information security incidents are occurring. Insider threats have emerged as a major issue, with cases in which corporate insiders attempting to leak confidential data by accessing it remotely. In response, insider threat detection approaches based on machine learning have been developed. However, existing machine learning methods used to detect insider threats do not take biases and variances into account, which leads to limited performance. In this paper, boosting-type ensemble learning algorithms are applied to verify the performance of malicious insider detection, conduct a close analysis, and even consider the imbalance in datasets to determine the final result. Through experiments, we show that using ensemble learning achieves similar or higher accuracy to other existing malicious insider detection approaches while considering bias-variance tradeoff. The experimental results show that ensemble learning using bagging and boosting methods reached an accuracy of over 98%, which improves malicious insider detection performance by 5.62% compared to the average accuracy of single learning models used.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.141-147
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• 2024

Progressive photon mapping is a widely adopted rendering technique that conducts a kernel-density estimation on photons progressively generated from lights. Its hyperparameter, which controls the reduction rate of the density estimation, highly affects the quality of its rendering image due to the bias-variance tradeoff of pixel estimates in photon-mapped results. We can minimize the errors of rendered pixel estimates in progressive photon mapping by estimating the optimal parameters based on gradient-based optimization techniques. To this end, we derived the gradients of pixel estimates with respect to the parameters when performing progressive photon mapping and compared our estimated gradients with finite differences to verify estimated gradients. The gradient estimated in this paper can be applied in an online learning algorithm that simultaneously performs progressive photon mapping and parameter optimization in future work.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.