• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

• Proceedings of the Korean Environmental Health Society Conference
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• 2005.11a
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• pp.118-120
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• 2005

본 연구는 송전선 주변 거주 초등학교 학생들을 대상으로 시간 활동 행태에 따른 여러 미세환경, 즉 주거환경, 학교환경, 학원, PC방, 교통에서의 자기장 노출수준을 다양한 매트릭스를 이용하여 비교 ${\cdot}$ 분석함으로써 연구대상자들의 활동한 미세환경 인자를 평가하여 시간별행동양식에 따른 24 시간 개인노출수준을 시간가중 평균모델을 이용하여 예측하고 비교 ${\cdot}$ 평가하였다. 본 연구는 154kV 고압 송전선로가 초등학교 옥상을 통과하는 B 초등학교 125명을 최종 선정하여 2003년 7월부터 2004년 2월 말까지 실시하였으며, 시간가중평균모델을 바탕으로 하여 자기장 노출량 평가 모델을 개발하였다. 전체 연구대상자들의 미세환경 내 평균 자기장값으로 계산한 시간가중 평균모델 (TWA model II)을 이용하여 예측된 자기장 개인노출수준을 보면 실측된 개인노출수준과 약간의 상관성을 보였다(Pearson r = 0.34 ${\sim}$ 0.35). Spot 측정한 값과 24 시간 stationary 측정값을 이용한 TWA Model II-2로 예측한 결과 실측값간의 상관성이 0.65 ${\sim}$ 0.85 로 산출되어 TWA 모델 중 가장 실측값을 잘 설명하였다.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.395-403
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• 2002

In the third phase of the response surface methods, the first-order model is assumed and the curvature of the response surface is checked with a fractional factorial design augmented by centre runs. We further assume that a true model is a quadratic polynomial. To choose an optimal design, Box and Draper(1959) suggested the use of an average mean squared error (AMSE), an average of MSE of y(x) over the region of interest R. The AMSE can be partitioned into the average prediction variance (APV) and average squared bias (ASB). Since AMSE is a function of design moments, region moments and a standardized vector of parameters, it is not possible to select the design that minimizes AMSE. As a practical alternative, Box and Draper(1959) proposed minimum bias design which minimize ASB and showed that factorial design points are shrunk toward the origin for a minimum bias design. In this paper we propose a robust AMSE design which maximizes the minimum efficiency of the design with respect to a standardized vector of parameters.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.111-118
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• 2023

For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.259-271
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• 2019

A statistical downscaling method was adopted in order to establish the high-resolution wave prediction system in the East Sea coastal area. This system used forecast data from the Global Wave Watch (GWW) model, and the East Sea and Busan Coastal Wave Watch (CWW) model operated by the Korea Meteorological Administration (KMA). We used the CWW forecast data until three days and the GWW forecast data from three to seven days to implement the statistical downscaling method (inverse distance weight interpolation and conditional merge). The two-dimensional and station wave heights as well as sea surface wind speed from the high-resolution coastal prediction system were verified with statistical analysis, using an initial analysis field and oceanic observation with buoys carried out by the KMA and the Korea Hydrographic and Oceanographic Agency (KHOA). Similar to the predictive performance of the GWW and the CWW data, the system has a high predictive performance at the initial stages that decreased gradually with forecast time. As a result, during the entire prediction period, the correlation coefficient and root mean square error of the predicted wave heights improved from 0.46 and 0.34 m to 0.6 and 0.28 m before and after applying the statistical downscaling method.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.161-164
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• 2004

You 와 Rao (2002)는 소지역 추정시 유사 최량선형 비편향 예측에서 설계 가중 값을 사용하는 방법을 발전시켰다. 특히 소지역 평균들을 추정하기 위하여 유사-최량선형 비편향 예측 추정량을 제안하였다. 우리는 소지역 추정에서 실용적으로 이용되는 몇 가지 추가적인 성질을 연구하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.563-568
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• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• Journal of the Korean Geotechnical Society
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• v.38 no.12
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• pp.103-112
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• 2022

The existing subsidence prediction method based on the measurement data were confirmed in this study through literature research. It was confirmed that the hyperbolic method and the Asaoka method showed high accuracy, while the other prediction methods showed significantly low accuracy. Based on the analysis results, the limitations of the existing prediction equations were derived, and the improvement method of the settlement prediction equations was suggested. In this study, a weighted nonlinear regression analysis method that gives higher weight to the later data was proposed to improve the existing hyperbolic method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.373-374
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• 2016

Residual Differnetial pulse-code Modulation (RDPCM) 기법은 비디오의 압축을 위한 시간 및 공간 예측 후 남은 잔여신호를 인접 화소를 이용하여 추가적인 중복정보를 제거하는 기법을 의미한다. 본 논문에서는 우선 잔차 신호의 예측을 위하여 인접 화소 사이 선형 가중 합으로 예측 모델을 세우고, 각 가중치를 $L_1$ 정규화를 포함하는 비용함수를 통해 추정함으로써 보다 효율적인 부호화 성능을 제공하는 알고리즘을 제안한다.