• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.31-38
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• 2003

A simple method is proposed to detect the number of change points and test the location and size of multiple change points with jump discontinuities in an otherwise smooth regression model. The proposed estimators are based on a local linear regression fit by the comparison of left and right one-side kernel smoother. Our proposed methodology is explained and applied to real data and simulated data.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.33-52
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• 2023

This study observes the difference in the actual regional ripple effect of the decrease in admission resources due to the decrease in school age population, which has been weak in empirical studies, and how much the decrease in competition rate by department nationwide provides a significant shock to the decrease in enrollment rate in the population unit. An empirical quantitative analysis was attempted. As a result of applying the panel-tobit regression model, a clear gap was confirmed in the decrease in the registration rate due to the decrease in the competition rate both nationally and in the provinces, and in particular, a highly significant relationship was derived with the decrease in the recruitment rate. In particular, the sensitivity of the risk of unrecruitment due to a decrease in competition rate was the highest in the Jeolla region (0.499), followed by the Gangwon region (0.475) and the Gyeongsang region (0.471), and the metropolitan region (0.158) was confirmed to be the most stable. This suggests that the gap in insufficient funding has gradually widened by region over the past 10 years, and that the shock wave becomes more pronounced in the provinces farther away from the metropolitan area. Based on this study, if we deviate from the standardized application of university development policies for the metropolitan area and regional universities, and present a customized higher education strategy for each region, it will be an opportunity to prevent local extinction due to a decrease in the school-age population and achieve coexistence with higher education institutions and regions at the same time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4060-4079
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• 2020

Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.