• Health Policy and Management
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• v.6 no.1
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• pp.29-58
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• 1996

This study was conducted to identify the factors that affect the output of health conters. An analystical model employed in this study was developed by modifying 'input-output model' and 'organizational behavior model'. Data were collected form two source; the 1995 report of thealth center which was submitted to the Ministry of Health and Welfare and a mail survey questionary of officers at health center, including 66 directors and 1,768 staffs of the health centers in southern region. The major findings are as follows: That analysis has identified the factors associated with dependent variables: medical services provided by the health center and health program performance(HPP). The number of primary medical facilities was negatively associated with health center performance while the number of staffs, job satisfaction, and professional background of health center directors were positively associated. These independent variables accounted for 40.1% of the variance of dependent variables. The variance of HPP was significantly explained by the number of health subcenter and primary health post, priority level of public health program by hief executive officers(CEOs) and legislator. A significant relationship was found between leadership types of health center directors and the performance of maternal and child health program. Considering these results, the authors suggested that the role in medical care service of health center in the should be rearranged at local level because medical care service of the health center is competing with primary medical facilities in the same region. It is also suggested that educational efforts be made to improve leadership of the health center directors and concern with public health program by the CEOs and legislators of local governments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2541-2551
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• 1996

Human visual systems are sensitive to noise on the flat intensity area. But it becomes less sensitive on the edge area. Recently, many types of spatially adaptive image restoration methods have been proposed, which employ the above mentioned huan visual characteristics. The present paper presents an adaptive image restoration method, which increases sharpness of the edge region, and smooths noise on the flat intensity area. For edge detection, the proposed method uses the visibility function based on the local variance on each pixel. And it adaptively changes the regularization parameter. More specifically, the image to be restored is divided into a number of steps from the flat area to the edge regio, and then restored by using the finite impulse response constrained least squares filter.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1004-1019
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• 2021

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image's high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.

• Culinary science and hospitality research
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• v.21 no.3
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• pp.29-39
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• 2015

This study examined the factors that affect the relationship among local specialties food quality, value perception and customer behavioral intention. A total of 280 questionnaires were distributed to consumers, of which 268 were deemed suitable for analysis after the removal of 12 unusable responses. In order to perform statistical analyses required for the study, SPSS 18.0 Statistical Program was employed for frequency analysis, factor analysis, and reliability analysis. The results of the exploratory factor analysis showed that three factors regarding local food specialties quality were extracted from all measurements with a KMO of 0.827 and a total cumulative variance of 65.638%. With regard to value perception, six factors were extracted with a total cumulative variance of 59.855% and a KMO score of 0.782. One factor for behavioral intention was extracted that accounted for a total cumulative variance of 64.427% and a KMO score of 0.757. All factors were significant to 0.000 and the correlation between variables was significant. Thus, based on the results, the main research hypothesis that identifies the relationships between value perception and behavioral intention was partially adopted.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.325-332
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• 2022

Determination of VLBI IVP (Very Long Baseline Interferometry Invariant Point) position with high accuracy is required to compute local tie vectors between the space geodetic techniques. In general, reflective targets are attached on VLBI antenna and slant distances, horizontal and vertical angles are measured from the pillars. Then, adjustment computation is performed by using the mathematical model which connects measurements and unknown parameters. This indicates that the accuracy of the estimated solutions is affected by the accuracy of the measurements. One of issues in local tie surveying, however, is that the reflective targets are not in favorable condition, that is, the reflective sheet target cannot be perfectly aligned to the instrument perpendicularly. Deviation from the line of sight of an instrument may cause different type of measurement errors. This inherent limitation may lead to incorrect stochastic modeling for the measurements in adjustment computation procedures. In this study, error characteristics by measurement types and pillars are analyzed, respectively. The analysis on the studentized residuals is performed after adjustment computation. The normality of the residuals is tested and then equal variance test between the measurement types are performed. The results show that there are differences in variance according to the measurement types. Differences in variance between distances and angle measurements are observed when F-test is performed for the measurements from each pillar. Therefore, more detailed stochastic modeling is required for optimal solutions, especially in local tie survey.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.133-142
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• 2012

This paper presents a salient region detection algorithm for auto-focusing based on the characteristics of a human's visual attention. To describe the saliency at the local, regional, and global levels, this paper proposes a set of novel features including multi-scale local contrast, variance, center-surround entropy, and closeness to the center. Those features are then prioritized to produce a saliency map. The major advantage of the proposed approach is twofold; i) robustness to changes in focus and ii) low computational complexity. The experimental results showed that the proposed method outperforms the existing low-level feature-based methods in the sense of both robustness and accuracy for auto-focusing.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.743-747
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• 2010

This paper presents an iterative adaptive hybrid image restoration algorithm for fast convergence. The local variance, mean, and maximum value are used to constrain the solution space. These parameters are computed at each iteration step using partially restored image at each iteration, and they are used to impose the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed and better performance than typical regularized constrained least squares (RCLS) approach.

• The Research Journal of the Costume Culture
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• v.14 no.4
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• pp.684-697
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• 2006

The purpose of this study was to exam the effect of fashion social responsibility of fashion corporate brand on brand attachment and equity. A total of 217 female college students in Seoul and its suburb responded for this study. For data analysis, descriptive statistics, factor analysis, and multiple regression were used for this study. As the result, first, corporate social responsibility was classified into five factors such as social service, public local facility, economic responsibility, consumer protection and environmental protection factors. Second, brand attachment was classified into four factors such as love, interest, perception and trust factors. Third, brand equity was classified into four factors such as loyalty, quality-image, marketing and recognition factors. Generally, fashion social responsibility factors was correlated with higher scores on brand attachment and brand equity. Finally, the results revealed that corporate social responsibility accounted for 12% of the explained variance brand attachment, also brand attachment accounted for 32% of the explained variance brand equity, while Corporate social responsibility accounted for 14% of the explained variance brand equity. Based on these results, fashion brand marketing strategies would be suggested.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.85-93
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• 1992

In this paper, we propose a parameter-free smoothing method for speckle images, i.e., an adaptive least squares image smoothing technique implemented in a multistep environment. The pertinent smoothing window size at a given pixel is determined by the discontinuity measure which is defined by the ratio of the local variance and mean squares of intensity values of pixels over the smoothing window centered there. The mode of the discontinuity measure at each step is estimated to replace the noise variance parameter that is required in the adaptive smoothing. Computer simulation shows that the proposed multistep technique can smooth homogeneous regions satisfactorily while preserving fine details near boundaries.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.179-181
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• 2009

In this paper, we propose an adaptive noise detection and modified Gaussian removal algorithm using local statistics for impulse noise. In order to determine constraints for noise detection, the local mean, variance, and maximum values are used. In addition, a modified Gaussian filter that integrates the tuning parameter to remove the detected noises. Experimental results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection.