• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.733-745
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• 2017

Local composite quantile regression is a useful non-parametric regression method widely used for its high efficiency. Data smoothing methods using kernel are typically used in the estimation process with performances that rely largely on the smoothing parameter rather than the kernel. However, $L_2$-norm is generally used as criterion to estimate the performance of the regression function. In addition, many studies have been conducted on the selection of smoothing parameters that minimize mean square error (MSE) or mean integrated square error (MISE). In this paper, we explored the optimality of selecting smoothing parameters that determine the performance of non-parametric regression models using local linear composite quantile regression. As evaluation criteria for the choice of smoothing parameter, we used mean absolute error (MAE) and mean integrated absolute error (MIAE), which have not been researched extensively due to mathematical difficulties. We proved the uniqueness of the optimal smoothing parameter based on MAE and MIAE. Furthermore, we compared the optimal smoothing parameter based on the proposed criteria (MAE and MIAE) with existing criteria (MSE and MISE). In this process, the properties of the proposed method were investigated through simulation studies in various situations.

• Korean Journal of Food Science and Technology
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• v.40 no.5
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• pp.510-515
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• 2008

Microwave energy was applied to the extraction of functional catechins from grape seed. The solvent, absolute ethanol, reached the boiling point when exposed for less than 3 min microwave treatment at 100 W. The effects of independent variables in microwave-assisted extraction (MAE), including microwave power (0-160W, $X_1$), ethanol concentration (0-100%, $X_2$) and extraction time (1-5 min, $X_3$), were investigated on each response variable ($Y_n$), and the contents of catechin and its derivatives were determined via response surface methodology, thereby allowing us to predict their optimal extraction conditions. The predicted maximal values of (+)-catechin, procyanidin $B_2$, (-)-epicatechin, and (-)-epicatechin gallate were 137.99, 72.78, 222.38, and 9.59 mg%, respectively, under different MAE conditions. The predicted extraction conditions for maximum catechin responses were as follows: 104.10 W of microwave power, 45.35% of EtOH, and 4.89 min of extraction time for (+)-catechin (137.99 mg%), 133.16 W, 46.16% and 4.49 min for procyanidin $B_2$ (72.78 mg%), 136.00 W, 41.37% and 4.39 min for (-)-epicatechin (222.38 mg%), 143.20 W, 37.51% and 1.88 min for (-)-epicatechin gallate (9.59 mg%), respectively. The contents of (+)-catechin, procyanidin 1B2 and (-)-epicatechin in MAE were similarly influenced by three independent variables, whereas (-)-epicatechin gallate was influenced less profoundly by ethanol concentration and extraction time.

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.222-233
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• 2010

This paper aims at predicting the BDI from Jan. to Dec. 2010 using such econometric techniues of the univariate time series as stochastic ARIMA-type models and Hodrick-Prescott filtering technique. The multivariate cause-effect econometric model is not employed for not assuring a higher degree of forecasting accuracy than the univariate variable model. Such a cause-effect econometric model also fails in adjusting itself for the post-sample. This article introduces the two ARIMA models and five Intervention-ARIMA models. The monthly data cover the period January 2000 through December 2009. The out-of-sample forecasting performance is compared between the ARIMA-type models and the random walk model. Forecasting performance is measured by three summary statistics: root mean squared error (RMSE), mean absolute error (MAE) and mean error (ME). The RMSE and MAE indicate that the ARIMA-type models outperform the random walk model And the mean errors for all models are small in magnitude relative to the MAE's, indicating that all models don't have a tendency of overpredicting or underpredicting systematically in forecasting. The pessimistic ex-ante forecasts are expected to be 2,820 at the end of 2010 compared with the optimistic forecasts of 4,230.

• Tuberculosis and Respiratory Diseases
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• v.72 no.4
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• pp.352-359
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• 2012

Background: The purpose of this study was to evaluate whether measuring the ratio of descending aortic enhancement (DAE) to main pulmonary artery enhancement (MPAE) on pulmonary computed tomography angiography (PCTA) can predict poor outcome in patients with acute massive or submassive pulmonary embolism (PE). Methods: We retrospectively, reviewed computed tomgraphy findings and charts of 37 patients with acute PE and right ventricular dysfunction. We divided the enrolled patients into 3 groups; group Ia (n=8), comprised of patients with major adverse event (MAE); group Ib (n=5), consisted of those with PE-related MAE; and group II (n=29), those without MAE. We analyzed the right ventricular diameter (RVD)/left ventricular diameter (LVD) and DAE/MPAE on PCTA. Results: For observer 1, RVD/LVD in group Ia ($1.9{\pm}0.36$ vs. $1.44{\pm}0.38$, p=0.009) and group Ib ($1.87{\pm}0.37$ vs. $1.44{\pm}0.38$, p=0.044) were significantly higher than that of group II. For observer 2, RVD/LVD in group Ia ($1.71{\pm}0.18$ vs. $1.41{\pm}0.47$, p=0.027) was significantly greater than that of group II, but RVD/LVD of group Ib was not ($1.68{\pm}0.2$ vs. $1.41{\pm}0.47$, p=0.093). For both observers, there was a significant difference of DAE/MPAE between group Ib and group II ($0.32{\pm}0.15$ vs. $0.64{\pm}0.24$, p=0.005; $0.34{\pm}0.16$ vs. $0.64{\pm}0.22$, p=0.004), but no significant difference of DAE/MPAE between group Ia and group II ($0.51{\pm}0.3$ vs. $0.64{\pm}0.24$, p=0.268; $0.53{\pm}0.29$ vs. $0.64{\pm}0.22$, p=0.302). Intra-class correlation coefficient (ICC) for the measurement of DAE/MPAE (ICC=0.97) was higher than that of RVD/LVD (ICC=0.74). Conclusion: DAE/MPAE measured on PCTA may predict PE-related poor outcomes in patients with massive or submassive PE with an excellent inter-observer agreement.

• Food Science and Preservation
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• v.12 no.1
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• pp.62-67
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• 2005

Microwave-assisted extraction (50 W, 2,450 MHz, MAE) with properties of selective heating and subsequent extraction for certain phytochemicals from natural materials was applied to pre-establish the extraction conditions for total yield total phenolics, and electron donating ability (EDA) from Brossica oleacea. The experiments with $50\%$ EtOH solvent showed that 20 mesh in particle size of cabbage flake $(moisture\;4.5\%)$ and 1:10 (g/mL) in the sample to solvent ratio for both raw $(moisture\;90.2\%)$ and flake cabbages were optimal for MAE efficiency. Under these conditions, total yield increased with extraction tim, which was highest for raw cabbage extract in $50\%\;EtOH$ solvent followed by $100\%\;EtOH$ and water. While that of flake cabbage extracts was highest in $50\%\;EtOH$ followed by water and $100\%\;EtOH$. The contents of total phenolics and EDA in extracts gradually increased after 3 min of MAE, which were highest when using $100\%\;EtOH$ solvent followed by $50\%\;EtOH$ and water in raw cabbage and $50\%\;EtOH$ followed by water and $100\%\;EtOH$ in flake cabbage, respectively.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1129-1140
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• 2013

Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.150-158
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• 2014

A bunch of asphalt roads have been damaged frequently in relation to the rapid climate change. To solve and prevent this type of problems, many nationalities in the world have performed various researches. In this regard, the objective of this study is to develop prediction model as to the number of potholes occurred in seoul. At the same time, we have utilized empirical and statistical approaches in order for us to identify factors which is affecting the actual occurrence. The predictive model was determinded by using BHS (Basic Harmony Search) algorithm. Prediction was based on the weather and traffic data as well as data occurrence data of porthole. To assess the influences which are PAR(Pitch Adjusting Rate) and HMCR(Harmony Memory Considering Rate), we determined suitability by changing the values. In the process of the determining a predictive model, the predictive model composed Training data (2011, 2012 and 2013yrs data). To determine the suitability of the model, we have utilized Testing Set (2009 and 2010 yrs data). The suitability of the basic prediction model has been from RMSE(Root Mean Squared Error), MAE(Mean Absolute Error) and Coefficient of determination.

• Journal of The Korean Ophthalmological Society
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• v.59 no.12
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• pp.1173-1180
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• 2018

Purpose: We evaluated the postoperative accuracy of intraocular lens power prediction for patients undergoing phacotrabeculectomy and identified preoperative factors associated with refractive outcome in those with primary open-angle glaucoma (POAG). Methods: We retrospectively reviewed the medical records of 27 patients who underwent phacotrabeculectomy to treat POAG. We recorded all discrepancies between predicted and actual postoperative refractions. We compared the data to those of an age- and sex-matched control group that underwent uncomplicated cataract surgery during the same time period. Preoperative factors associated with the mean absolute error (MAE) were identified via multivariate regression analyses. Results: The mean refractive error of the 27 eyes that underwent phacotrabeculectomy was comparable to that of the 27 eyes treated via phacoemulsification (+0.02 vs. -0.01 D, p = 0.802). The phacotrabeculectomy group exhibited a significantly higher MAE (0.65 vs. 0.35 D, p = 0.035) and more postoperative astigmatism (-1.07 vs. -0.66 D, p = 0.020) than the phacoemulsification group. The preoperative anterior chamber depth (ACD) and the changes in the postoperative intraocular pressure (IOP) were significantly associated with a greater MAE after phacotrabeculectomy. Conclusions: POAG treatment via combined phacoemulsification/trabeculectomy was associated with greater error in terms of final refraction prediction, and more postoperative astigmatism. As both a shallow preoperative ACD and a greater postoperative change in IOP appear to increase the predictive error, these two factors should be considered when planning phacotrabeculectomy.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.246-246
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• 2021

본 연구는 강우 발생시 유량을 추정하는 것에 목적이 있다. 이를 위해 본 연구는 선행연구의 모형 개발방법론에서 벗어나 딥러닝 알고리즘 중 하나인 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 유량을 추정하였다. 합성곱 신경망은 일반적으로 분류 문제 (classification)을 해결하기 위한 목적으로 개발되었기 때문에 불특정 연속변수인 유량을 모의하기에는 적합하지 않다. 이를 위해 본 연구에서는 합성곱 신경망의 완전 연결층 (Fully connected layer)를 개선하여 연속변수를 모의할 수 있도록 개선하였다. 대부분 합성곱 신경망은 RGB (red, green, blue) 사진 (photograph)을 이용하여 해당 사진이 나타내는 것을 예측하는 목적으로 사용하지만, 본 연구의 경우 일반 RGB 사진을 이용하여 유출량을 예측하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이를 위해 본 연구에서는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는 수문학적 이미지는 입력자료로 활용했다. 합성곱 신경망의 구조는 Convolution Layer와 Pulling Layer가 5회 반복하는 구조로 설정하고, 이후 Flatten Layer, 2개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 다시 1개의 Dense Layer가 이어지는 구조로 설계하였다. 마지막 Dense Layer의 활성화 함수는 분류모형에 이용되는 softmax 또는 sigmoid 함수를 대신하여 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 이와 함께 각 층의 활성화 함수는 정규화 선형함수 (ReLu)를 이용하였으며, 모형의 학습 평가 및 검정을 판단하기 위해 MSE 및 MAE를 사용했다. 또한, 모형평가는 NSE와 RMSE를 이용하였다. 그 결과, 모형의 학습 평가에 대한 MSE는 11.629.8 m3/s에서 118.6 m3/s로, MAE는 25.4 m3/s에서 4.7 m3/s로 감소하였으며, 모형의 검정에 대한 MSE는 1,997.9 m3/s에서 527.9 m3/s로, MAE는 21.5 m3/s에서 9.4 m3/s로 감소한 것으로 나타났다. 또한, 모형평가를 위한 NSE는 0.7, RMSE는 27.0 m3/s로 나타나, 본 연구의 모형은 양호(moderate)한 것으로 판단하였다. 이에, 본 연구를 통해 제시된 방법론에 기반을 두어 CNN 모형 구조의 확장과 수문학적 이미지의 개선 또는 새로운 이미지 개발 등을 추진할 경우 모형의 예측 성능이 향상될 수 있는 여지가 있으며, 원격탐사 분야나, 위성 영상을 이용한 전 지구적 또는 광역 단위의 실시간 유량 모의 분야 등으로의 응용이 가능할 것으로 기대된다.

• Proceedings of the Korean Society of Life Science Conference
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• 2005.09a
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• pp.67-67
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• 2005