https://oak.chosun.ac.kr/handle/2020.oak/18666 2025-08-21T17:03:27Z https://oak.chosun.ac.kr/handle/2020.oak/18688 Title: Detection of Face Direction by Using Inter-Frame Difference Author(s): Bongseog Jang; Sang-Hyun Bae Abstract: Applying image processing techniques to education, the face of the learner is photographed, and expression and movement are detected from video, and the system which estimates degree of concentration of the learner is developed. For one learner, the measuring system is designed in terms of estimating a degree of concentration from direction of line of learner's sight and condition of the eye. In case of multiple learners, it must need to measure each concentration level of all learners in the classroom. But it is inefficient because one camera per each learner is required. In this paper, position in the face region is estimated from video which photographs the learner in the class by the difference between frames within the motion direction. And the system which detects the face direction by the face part detection by template matching is proposed. From the result of the difference between frames in the first image of the video, frontal face detection by Viola-Jones method is performed. Also the direction of the motion which arose in the face region is estimated with the migration length and the face region is tracked. Then the face parts are detected to tracking. Finally, the direction of the face is estimated from the result of face tracking and face parts detection. 2015-12-31T15:00:00Z https://oak.chosun.ac.kr/handle/2020.oak/18683 Title: Comparative Molecular Field Analysis of CXCR-2 Inhibitors Author(s): Sathya. B Abstract: CXC chemokine receptor 2 (CXCR2) is a prominent chemokine receptor on neutrophils. The neutrophilic inflammation in the lung diseases is found to be largely regulated through CXCR2 receptor. Antagonist of CXCR2 may reduce the neutrophil chemotaxis and alter the inflammatory response. Hence, in the present study, ligand based Comparative molecular field analysis (CoMFA) was performed on a series of CXCR2 antagonist named pyrimidine-5-carbonitrile-6-alkyl derivatives. The optimum CoMFA model was obtained with statistically significant cross-validated coefficients ($q^2$ 수식 이미지) of 0.568 and conventional coefficients ($r^2$ 수식 이미지) of 0.975. The contour maps suggest the important structural modifications and this study can be used to guide the development of potent CXCR2 antagonist. 2015-12-31T15:00:00Z https://oak.chosun.ac.kr/handle/2020.oak/18687 Title: Generalizations of Polynomials in Chebyshev Form Author(s): Seon-Hong Kim Abstract: Arbitrary polynomial of degree n can be written in Chebyshev form. In this paper, we generalize this Chebyshev form and study its root distributions 2015-12-31T15:00:00Z https://oak.chosun.ac.kr/handle/2020.oak/18686 Title: Statistical Analysis on the Emotion Effects of Academic Achievement Author(s): Heung Kou; Young Chun Ko Abstract: The purpose of this study is to investigate the emotion effects on academic achievement for university students. The results are as follows. Resulting on the each emotions difference by the statistical variables, anxiety scores by gender showed a significant difference in the p<.01 level(F=7.685). The males anxiety(2.478, standard deviation: 0.180) had significantly lower scores than females(3.076, standard deviation: 0.168). But fear, anger, activity, and sociability scores were not significantly different respectively between male and female students. To see the emotions effect of academic achievement, the analysis method of the linear regression line was used. As the result, anxiety, fear, anger, activity, and sociability did not significantly influence academic achievement. And so unlike previous methods, the analysis method of the quadratic regression curve was used. As the result, anxiety, fear, anger, activity, and sociability showed did significantly influence academic achievement respectively within 5% of statistical significance level, to more than F=3.06. Therefore, the values on academic achievement of the each anxiety, fear, anger, activity, and sociability showed a quadratic regression curve. That is, [Academic achievement]=$-0.9685{\times}[Anxiety 수식 이미지] 수식 이미지^2+5.1342{\times}[Anxiety 수식 이미지] 수식 이미지+8.2679$ 수식 이미지,[Academic achievement]=$-1.0638{\times}[Fear 수식 이미지] 수식 이미지^2+5.5694{\times}[Fear 수식 이미지] 수식 이미지+7.5635$ 수식 이미지,[Academic achievement]=$-1.3497{\times}[Anger 수식 이미지] 수식 이미지^2+9.1284{\times}[Anger 수식 이미지] 수식 이미지+0.6720$ 수식 이미지,[Academic achievement]=$-1.0589{\times}[Activity 수식 이미지] 수식 이미지^2+7.4386{\times}[Activity 수식 이미지] 수식 이미지+1.8272$ 수식 이미지,[Academic achievement]=$-1.6830{\times}[Sociability 수식 이미지] 수식 이미지^2+11.2325{\times}[Sociability 수식 이미지] 수식 이미지-3.8258$ 수식 이미지. Therefore, we were able to determine the following conclusions. First, we were able to predict the degree of academic achievement by the each emotions scale. Second, when the each emotion scores of students was a moderate, the academic achievement was most excellent. So, in order for the students to become higher academic achievement, the maintenance of medium degree of the each emotions scores is required. 2015-12-31T15:00:00Z