• The Journal of the Acoustical Society of Korea
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• v.25 no.4E
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• pp.154-158
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• 2006

In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.

• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.80-91
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• 2022

The purpose of this study is to contribute to strengthening the response of local governments to the emergence of new infectious diseases by identifying the urban characteristics affecting their spread. To this end, the urban characteristics influencing the spread of infectious diseases were identified from previous studies. Moreover, the variations in the impact of urban characteristics that affected the number of confirmed COVID-19 patients was spatially analyzed using geographically weighted regression (GWR). The analysis indicated that the explanatory power of the GWR was approximately 12.4% higher than that of the ordinary least squares method. Moreover, the explanatory power of the model in the northern regions, such as Seoul, Gyeonggi, and Gangwon, was particularly high, indicating that the urban characteristics affecting the spread of COVID-19 vary by region. The results of this study can be used as a basis for suggesting the formulation of customized policies reflecting the characteristics of each local government rather than a uniform spread reduction policy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.611-620
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• 2018

With the revitalization of alley market area becoming a major goal of the urban regeneration project, an understanding on customer characteristics that affect the sales of alley market areas is needed. As spatial heterogeneity appears to exist in alley market areas, the use of GWR (Geographically Weighted Regression) is required as an alternative to OLS (Ordinary Least Squares) regression. This study analyzes effects of customer characteristics on sales of 1007 alley market areas in Seoul. Comparing R squared and AICc, results show that GWR is better than OLS regression. According to OLS regression, the ratio of female, the ratio of 40's and 50's, the number of employees, the opening rate of establishment, the density of building and the size of alley market area have positive effects on sales, while the ratio of 20's and 30's, the distance of bus stop and that of subway station have negative effects. As a result of comparing local regression coefficients of geographically weighted regression analysis, the ratio of female customers has the greatest effect on the northwestern region, followed by the southwestern region, the central region and the northeastern region. The ratio of 20's and 30's and that of 40's and 50's effect on the southeastern and northeastern regions, and then the southwestern region. It is expected that this study will help to identify marketing target for each alley market area.

• 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 Academy of Nursing
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• v.53 no.4
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• pp.426-438
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• 2023

Purpose: Walking practice is a representative indicator of the level of physical activity of local residents. Although the world health organization addressed reduction in prevalence of insufficient physical activity as a global target, the rate of walking practice in Korea has not improved and there are large regional disparities. Therefore, this study aimed to explore the spatial variations of walking practice and its associated factors in Korea. Methods: A secondary analysis was conducted using Community Health Outcome and Health Determinants Database 1.3 from Korea Centers for Disease Control and Prevention. A total of 229 districts was included in the analysis. We compared the ordinary least squares (OLS) and the geographically weighted regression (GWR) to explore the associated factors of walking practice. MGWR 2.2.1 software was used to explore the spatial distribution of walking practice and modeling the GWR. Results: Walking practice had spatial variations across the country. The results showed that the GWR model had better accommodation of spatial autocorrelation than the OLS model. The GWR results indicated that different predictors of walking practice across regions of Korea. Conclusion: The findings of this study may provide insight to nursing researchers, health professionals, and policy makers in planning health programs to promote walking practices in their respective communities.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1171-1180
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• 2010

It is well known that the asymptotic convergence rates of nonparametric regression estimator gets worse as the dimension of covariates gets larger. One possible way to overcome this problem is reducing the dimension of covariates by using single index models. Two coefficient estimation methods in single index models are introduced. One is semiparametric least square estimation method, which tries to find approximate solution by using iterative computation. The other one is weighted average derivative estimation method, which is non-iterative method. Both of these methods offer the parametric convergence rate to normal distribution. However, practical comparison of these two methods has not been done yet. In this article, we compare these methods by examining the variances of estimators in various models.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.251-266
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• 2016

Intelligent interpolation methods such as dasymetric mapping are considered to be the best way to disaggregate zone-based population data by observing and utilizing the internal variation within each source zone. This research reviews the advantages and problems of the dasymetric mapping method, and presents a geographically weighted regression (GWR) based method to take into consideration the spatial heterogeneity of population density - land cover relationship. The locally adaptive intelligent interpolation method is able to make use of readily available ancillary information in the public domain without the need for additional data processing. In the case study, we use the preclassified National Land Cover Dataset 2011 to test the performance of the proposed method (i.e. the GWR-based multi-class dasymetric method) compared to four other popular population estimation methods (i.e. areal weighting interpolation, pycnophylactic interpolation, binary dasymetric method, and globally fitted ordinary least squares (OLS) based multi-class dasymetric method). The GWR-based multi-class dasymetric method outperforms all other methods. It is attributed to the fact that spatial heterogeneity is accounted for in the process of determining density parameters for land cover classes.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.223-235
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• 2011

Although ordinary least squares (OLS) regression analysis can be used to examine the spatial relationships between environmental equity and urban quality of life, this global method may mask the local variations in the relationships between them. These geographical variations can not be captured without using local methods. In this context, this paper explores the spatially varying relationships between environmental equity and urban quality of life across the Atlanta metropolitan area by geographically weighted regression (GWR), a local method. Environmental equity and urban quality of life were quantified with an integrated approach of GIS and remote sensing. Results show that generally, there is a negatively significant relationship between them over the Atlanta metropolitan area. The results also suggest that the relationships between environmental equity and urban quality of life vary significantly over space and the GWR (local) model is a significant improvement on the OLS (global) model for the Atlanta metropolitan area.

• Journal of Radiation Protection and Research
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• v.45 no.4
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• pp.171-177
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• 2020

Background: This study aims to determine the effective atomic number (Zeff) from dual-energy image sets obtained using a conventional computed tomography (CT) simulator. The estimated Zeff can be used for deriving the stopping power and material decomposition of CT images, thereby improving dose calculations in radiation therapy. Materials and Methods: An electron-density phantom was scanned using Philips Brilliance CT Big Bore at 80 and 140 kVp. The estimated Zeff values were compared with those obtained using the calibration phantom by applying the Rutherford, Schneider, and Joshi methods. The fitting parameters were optimized using the nonlinear least squares regression algorithm. The fitting curve and mass attenuation data were obtained from the National Institute of Standards and Technology. The fitting parameters obtained from stopping power and material decomposition of CT images, were validated by estimating the residual errors between the reference and calculated Zeff values. Next, the calculation accuracy of Zeff was evaluated by comparing the calculated values with the reference Zeff values of insert plugs. The exposure levels of patients under additional CT scanning at 80, 120, and 140 kVp were evaluated by measuring the weighted CT dose index (CTDIw). Results and Discussion: The residual errors of the fitting parameters were lower than 2%. The best and worst Zeff values were obtained using the Schneider and Joshi methods, respectively. The maximum differences between the reference and calculated values were 11.3% (for lung during inhalation), 4.7% (for adipose tissue), and 9.8% (for lung during inhalation) when applying the Rutherford, Schneider, and Joshi methods, respectively. Under dual-energy scanning (80 and 140 kVp), the patient exposure level was approximately twice that in general single-energy scanning (120 kVp). Conclusion: Zeff was calculated from two image sets scanned by conventional single-energy CT simulator. The results obtained using three different methods were compared. The Zeff calculation based on single-energy exhibited appropriate feasibility.

• Animal Bioscience
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• v.34 no.9
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• pp.1451-1459
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• 2021

Objective: The objectives of the study were to evaluate reproductive performance and selection response through genetic trend of community-based breeding programs (CBBPs) of Bonga sheep. Methods: Reproduction traits data were collected between 2012 and 2018 from Bonga sheep CBBPs. Phenotypic performance was analyzed using the general linear model procedures of Statistical Analysis System. Genetic parameters were estimated by univariate animal model for age at first lambing (AFL) and repeatability models for lambing interval (LI), litter size (LS), and annual reproductive rate (ARR) traits using restricted maximum likelihood method of WOMBAT. For correlations bivariate animal model was used. Best model was chosen based on likelihood ratio test. The genetic trends were estimated by the weighted regression of the average breeding value of the animals on the year of birth/lambing. Results: The overall least squares mean±standard error of AFL, LI, LS, and ARR were 375±12.5, 284±9.9, 1.45±0.010, and 2.31±0.050, respectively. Direct heritability estimates for AFL, LI, LS, and ARR were 0.07±0.190, 0.06±0.120, 0.18±0.070, and 0.25±0.203, respectively. The low heritability for both AFL and LI showed that these traits respond little to selection programs but rather highly depend on animal management options. The annual genetic gains were -0.0281 days, -0.016 days, -0.0002 lambs and 0.0003 lambs for AFL, LI, LS, and ARR, respectively. Conclusion: Implications of the result to future improvement programs were improving management of animals, conservation of prolific flocks and out scaling the CBBP to get better results.