• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.87-95
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• 2014

In the regression model, Kang and Huh (2006) studied the estimation of the discontinuous variance function using the Nadaraya-Watson estimator with the squared residuals. The local linear estimator of the log-variance function, which may have the whole real number, was proposed by Huh (2013) based on the kernel weighted local-likelihood of the ${\chi}^2$-distribution. Chen et al. (2009) estimated the continuous variance function using the local linear fit with the log-squared residuals. In this paper, the estimator of the discontinuous log-variance function itself or its derivative using Chen et al. (2009)'s estimator. Numerical works investigate the performances of the estimators with simulated examples.

• Mass Spectrometry Letters
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• v.13 no.1
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• pp.11-19
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• 2022

Although translational research is referred to clinical chemistry measures, correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm have not been carefully considered in bioanalytical assays yet. The objective of this study was to identify steroidogenic roles in preeclampsia and verify accuracy of quantitative results by comparing two different linear regression models with weighting factor of 1 and 1/x². A liquid chromatography-mass spectrometry (LC-MS)-based adrenal steroid assay was conducted to reveal metabolic signatures of preeclampsia in both serum and placenta samples obtained 15 preeclamptic patients and 17 age-matched control pregnant women (33.9 ± 4.2 vs. 32.8 ± 5.6 yr, respectively) at 34~36 gestational weeks. Percent biases in the unweighted model (w_i = 1) were inversely proportional to concentrations (-739.4 ~ 852.9%) while those of weighted regression (w_i = 1/x²) were < 18% for all variables. The optimized LC-MS combined with the weighted linear regression resulted in significantly increased maternal serum levels of pregnenolone, 21-deoxycortisol, and tetrahydrocortisone (P < 0.05 for all) in preeclampsia. Serum metabolic ratio of (tetrahydrocortisol + allo-tetrahydrocortisol) / tetrahydrocortisone indicating 11β-hydroxysteroid dehydrogenase type 2 was decreased (P < 0.005) in patients. In placenta, local concentrations of androstenedione were changed while its metabolic ratio to 17α-hydroxyprogesterone responsible for 17,20-lyase activity was significantly decreased in patients (P = 0.002). The current bioanalytical LC-MS assay with corrected weighting factor of 1/x² may provide reliable and accurate quantitative outcomes, suggesting altered steroidogenesis in preeclampsia patients at late gestational weeks in the third trimester.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

• Korean Journal of Health Education and Promotion
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• v.32 no.2
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• pp.27-37
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• 2015

Objectives: As the increasing of the length of the lifespan, more recent policy interest are concerned with how many years of life are lived without functional disability or activity limitation. We investigated the relationship between deprivation and activity limitation at the 251 local authority level. Methods: The data were derived from the 2010 Census 10% sample data. Crude and age-standardized activity limitation rates by gender at the ages of 50 or over and deprivation index were calculated. Mapping and multiple linear regression analysis were applied to explore relationship between area activity limitation and area deprivation. Results: There were considerable differences in activity limitation rate across the 251 local authorities. Age-standardized activity limitation rate in both male and female were strongly associated with the level of area deprivation. Especially, low social class, male unemployment, or non-apartment residents at the local level were strong positive association with local authorities' age-standardized activity limitation. Conclusion: More policy attention is needed for tackling regional inequality in activity limitation among older adults.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.18-26
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• 1997

Grinding burn formed on the ground surface is related to the maximum temperature of workpiece surface and wheel tempertaure in the grinding process. The thermal characteristics of workpiece and grinding conditions on the surface tempertaure of the oxidation growing layer after get out of contact with the grinding wheel. The assumption used in grinding power signatures leads to the local temperature distribution between grinding wheel and workpiece, i.e., a single curve determines temperatures anywhere within the grinding wheel at anytime. This information is useful in the study of the grinding burn penetration into the wheel and thus provides an presentation of grinding trouble monitoring for the burning. On the basis of grinding power signatures in the wheel, thermally optimum grinding conditions are defined and controlled. To cope with grinding burn, the use of grinding power signatures is an effective monitoring systems when occurring the grinding process. In this paper, the identified parameters suggested in this study which are derived from the grinding power signatures are presented, and prediction model by grinding power utilized a linear regression algorithm is applied.

• IE interfaces
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• v.24 no.4
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• pp.373-378
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• 2011

High level of accuracy in forecasting heat demand of each district is required for operating and managing the district heating efficiently. Heat demand has a close connection with the demands of the previous days and the temperature, general demand forecasting methods may be used forecast. However, there are some exceptional situations to apply general methods such as the exceptional low demand in weekends or vacation period. We introduce a new method to forecast the heat demand to overcome these situations, using the linearities between the demand and some other factors. Our method uses the temperature and the past 7 days' demands as the factors which determine the future demand. The model consists of daily and hourly models which are multiple linear regression models. Appling these two models to historical data, we confirmed that our method can forecast the heat demand correctly with reasonable errors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3594-3607
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• 2017

The traditional feature extraction methods such as principal component analysis (PCA) cannot obtain the local structure of the samples, and locally linear embedding (LLE) cannot obtain the global structure of the samples. However, a common drawback of existing PCA and LLE algorithm is that they cannot deal well with the sparse problem of the samples. Therefore, by integrating the globality of PCA and the locality of LLE with a sparse constraint, we developed an improved and unsupervised difference algorithm called Sparse Difference Embedding (SDE), for dimensionality reduction of high-dimensional data in small sample size problems. Significantly differing from the existing PCA and LLE algorithms, SDE seeks to find a set of perfect projections that can not only impact the locality of intraclass and maximize the globality of interclass, but can also simultaneously use the Lasso regression to obtain a sparse transformation matrix. This characteristic makes SDE more intuitive and more powerful than PCA and LLE. At last, the proposed algorithm was estimated through experiments using the Yale and AR face image databases and the USPS handwriting digital databases. The experimental results show that SDE outperforms PCA LLE and UDP attributed to its sparse discriminating characteristics, which also indicates that the SDE is an effective method for face recognition.

• Korean Journal of Agricultural Science
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• v.41 no.3
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• pp.169-175
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• 2014

Objective of this study was to investigate effects of pre-processing method and number of sampling leaves on stability of the reflectance measurement for Chinese cabbage and kale leaves. Chinese cabbage and kale were transplanted and cultivated in a plant factory. Leaf samples of the kale and cabbage were collected at 4 weeks after transplanting of the seedlings. Spectra data were collected with an UV/VIS/NIR spectrometer in the wavelength region from 190 to 1130 nm. All leaves (mature and young leaves) were measured on 9 and 12 points in the blade part in the upper area for kale and cabbage leaves, respectively. To reduce the spectral noise, the raw spectral data were preprocessed by different methods: i) moving average, ii) Savitzky-Golay filter, iii) local regression using weighted linear least squares and a $1^{st}$ degree polynomial model (lowess), iv) local regression using weighted linear least squares and a $2^{nd}$ degree polynomial model (loess), v) a robust version of 'lowess', vi) a robust version of 'loess', with 7, 11, 15 smoothing points. Effects of number of sampling leaves were investigated by reflectance difference (RD) and cross-correlation (CC) methods. Results indicated that the contribution of the spectral data collected at 4 sampling leaves were good for both of the crops for reflectance measurement that does not change stability of measurement much. Furthermore, moving average method with 11 smoothing points was believed to provide reliable pre-processed data for further analysis.

• Journal of the Korean earth science society
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• v.28 no.2
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• pp.214-226
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• 2007

Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.795-802
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• 2020

This study seeks to determine the effect of the application of accounting information system (AIS), internal control system, and human resource (HR) competency on the quality of local government financial statements (FS). This study uses agency theory and compliance theory. Agency theory is used to explain that there is a link between the society as the principal and the government as the agent. Compliance theory is a theory, which states that every agency is obliged to comply with regulations because the law-drafting authority has the right to dictate behavior. The population in this study were employees of the financial division of local government organizations in one of the districts in Central Java, Indonesia. The total samples in this study was 106 respondents. The data used are primary data taken from distributing questionnaires to respondents. The method of analysis used to test the hypothesis was multiple linear regression analysis. The results of hypothesis testing indicate that HR competency has an effect on the quality of FS; however, the application of AIS and internal control system has no effect on the quality of FS. The implication of this research underlines the importance of HR competency to improve the quality of local government FS.