• Journal of agriculture & life science
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• v.44 no.4
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• pp.1-7
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• 2010

Data was collected to develop equation for predicting stemp taper for Carpinus laxiflora in Jeju Experimental Forests. The Models tested for choosing the best-fit equations were Max & Burkhart's model, Kozak's model, and Lee's model. Performance of the equations in predicting stem diameter at a specific point along a stem was evaluated with fit and validation statistics and distribution of residuals on predicted values. In result, all the three models gave slightly better values of fitting statistics. In plotting residuals against predicted diameter, Max & Burkhart's model showed underestimation in predicting small diameter and Lee's Model did the same in predicting small diameter. Based on the above analysis of the three models in predicting stem taper, Kozak's model was chosen for the best-fit stem taper equations, and its parameters were given for C. laxiflora. Kozak's model was used to develop a stem volume table of outside bark for C. laxiflora.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• Korea Trade Review
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• v.47 no.4
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• pp.215-231
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• 2022

Analysis and forecasting of the Baltic Capsize Index (BCI) is important for managing an entity's losses and risks from the uncertainty and volatility of the fast-changing maritime transport market in the future. This study conducted volatility transition analysis through the GARCH model, using BCI which is highly related to steel raw materials. As for the data, 2,385 monthly data were used from March 1999 to March 2021. In this study, after basic statistical analysis, unit root and cointegration test, the GARCH, EGARCH, and DCC-GARCH models were used for volatility transition analysis. As the results of GARCH and EGARCH model, we confirmed that all variables had no autocorrelation between the standardized residuals for error terms and the square of residuals, that the variability of all variables at this time was likely to persist in the future, and that the variability of the time-series error term impact according to Iron ore trade (IoT). In addition, through the EGARCH model, the magnitude convenience of all variables except the Iron ore price (IOP) and Capesize bulk fleet (BCF) variables was greater than the positive value (+). As a result of analyzing the DCC-GARCH (1,1) model, partial linear combinations were confirmed over the entire period. Estimating the effect of variability transition on BCF and C5 with statistically significant linear combinations with BCI confirmed that the impact of BCF on BCI was greater than the impact of BCI itself.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.39-48
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• 2024

In this paper, we introduce an extended GARCH model designed to address asymmetric leverage effects. The variance in the standard GARCH model is composed of past conditional variances and past squared residuals. However, it is not possible to model asymmetric leverage effects with squared residuals alone, so in this paper, we propose a new extended GARCH model to explain the leverage effects using the Yeo-Johnson transformation which adjusts transformation parameter to make asymmetric data more normal or symmetric. We utilize the reverse properties of Yeo-Johnson transformation to model asymmetric volatility. We investigate the characteristics of the proposed model and parameter estimation. We also explore how to derive forecasts and forecast intervals in the proposed model. We compare it with standard GARCH and other extended GARCH models that model asymmetric leverage effects through empirical data analysis.

• Journal of IKEEE
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• v.28 no.2
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• pp.168-173
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• 2024

Speaker recognition refers to a technology that analyzes voice frequencies that are different for each individual and compares them with pre-stored voices to determine the identity of the person. Deep learning-based speaker recognition is being applied to many fields, and pet robots are one of them. However, the hardware performance of pet robots is very limited in terms of the large memory space and calculations of deep learning technology. This is an important problem that pet robots must solve in real-time interaction with users. Lightening deep learning models has become an important way to solve the above problems, and a lot of research is being done recently. In this paper, we describe the results of research on lightweight speaker recognition for pet robots by constructing a voice data set for pet robots, which is a specific command type, and comparing the results of models using residuals. In the conclusion, we present the results of the proposed method and Future research plans are described.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.301-311
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• 2024

In South Korea, following the 2016 Gyeongju and 2017 Pohang earthquakes, the need for earthquake disaster prevention has been increasing. Reliable techniques for probabilistic seismic hazard analysis and ground motion models are required for quantifying earthquake damage. Recently, there has been growing demand for deep underground facilities, necessitating accurate quantification techniques for earthquake damage in deep underground. In this study, ground motion models within rock were proposed using ground motion data measured at borehole seismic stations. A regression analysis, a type of empirical technique, was applied to 17 periods selected in a range from 0.01 to 10 s of spectral accelerations to develop the ground motion models. Residual analysis was performed to evaluate and improve the prediction performance of the ground motion model, with correction factors added to the model equation. When applying the proposed model, the group means of residuals approached zero, and the standard deviation of total residuals, similar to existing models proposed in other countries, confirmed the reliability of the proposed model.

• Tuberculosis and Respiratory Diseases
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• v.61 no.3
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• pp.218-226
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• 2006

Background: The lung volume and diffusing capacity are influenced by ethnicity. However, there are no equations for predicting the normal lung volume in the adult Korean population, and there is only one equation for diffusing capacity. The aim of this study is to select the most suitable reference equation for the Korean population. Method: 30 men and 33 women at Hanyang University Guri Hospital, and 27 men and 34 women at Asan Medical Center in healthy nonsmoking adults were enrolled in this study. The subject's age, gender, height, weight, lung volume by plethysmography, and diffusing capacity by a single breathing method were obtained. The most suitable equation with the lowest sum of residuals between the observed and predicted values for lung volume and diffusing capacity was selected. Result: At Hanyang University Guri Hospital, the equations with the lowest sum of residuals in the total lung capacity were ECSC's equation in males (sum of residual: 0.04 L) and Crapo/Morris's equation (-1.04) in women. At the Asan Medical Center, the equations with the lowest sum of residuals in the total lung capacity were Goldman/Becklake's equation in males (sum of residual: -2.35) and the ECSC's equation -4.49) in women. The equations with the lowest sum of residuals in the Diffusing capacity were Roca's equation in males (sum of residual: -13.66 ml/min/mmHg) and Park's in women (25.08) in Hanyang University Guri hospital and Park's equation in all cases in the Asan Medical Center (male: -1.65, female: -6.46). Conclusions: Until a reference equstion can be made for healthy Koreans by sampling, ECSC's equation can be used for estimating the lung volume and Park's can be used for estimating the diffusing capacity.

• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.320-329
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• 2017

This study aims to evaluate whether the accuracy of LiDAR DEM is affected by the changes of the five input levels ('1','3','5','7' and '9') of median parameter ($F_{md}$), mean parameter ($F_{mn}$) of the Filtering Algorithm (FA) in the GroundFilter module and median parameter ($I_{md}$), mean parameter ($I_{mn}$) of the Interpolation Algorithm (IA) in the GridSurfaceCreate module of the FUSION in order to present the combination of parameter levels producing the most accurate LiDAR DEM. The accuracy is measured by the residuals calculated by difference between the field elevation values and their corresponding DEM elevation values. A multi-way ANOVA is used to statistically examine whether there are effects of parameter level changes on the means of the residuals. The Tukey HSD is conducted as a post-hoc test. The results of the multi- way ANOVA test show that the changes in the levels of $F_{md}$, $F_{mn}$, $I_{mn}$ have significant effects on the DEM accuracy with the significant interaction effect between $F_{md}$ and $F_{mn}$. Therefore, the level of $F_{md}$, $F_{mn}$, and the interaction between two variables are considered to be factors affecting the accuracy of LiDAR DEM as well as the level of $I_{mn}$. As the results of the Tukey HSD test on the combination levels of $F_{md}{\ast}F_{mn}$, the mean of residuals of the '$9{\ast}3$' combination provides the highest accuracy while the '$1{\ast}1$' combination provides the lowest one. Regarding $I_{mn}$ levels, the mean of residuals of the both '3' and '1' provides the highest accuracy. This study can contribute to improve the accuracy of the forest attributes as well as the topographic information extracted from the LiDAR data.

• Journal of the Korean Statistical Society
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• v.10
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• pp.105-121
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• 1981

The problem of selection of a subset containing the largest of several slope parameters of regression equations is considered. The proposed selection procedure is based on the weighted median estimators for regression parameters and the median of rescaled absolute residuals for scale parameters. Those estimators are compared with the classical least squares estimators by a simulation study. A Monte Carlo comparison is also made between the new procedure based on the weighted median estiamtors and the procedure based on the least squares estimators. The results show that the proposed procedure is quite robust with respect to the heaviness of distribution tails.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.283-306
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• 1993

An outlier detection diagnostic for the detection of k-consecutive atypical observations is considered. The proposed diagnostic is based on the innovational variance estimate utilizing both the interpolated and the predicted residuals. We adopt the interpolation method to construct the proposed diagnostic by replacing atypical observations. The perfomance of the proposed diagnositc is investigated by simulation. A real example is presented.