• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.40-47
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• 2011

Image analysis algorithm for the quality evaluation of ginseng seedling was investigated. The images of ginseng seedling were acquired with a color CCD camera and processed with the image analysis methods, such as binary conversion, labeling, and thinning. The processed images were used to calculate the length and weight of ginseng seedlings. The length and weight of the samples could be predicted with standard errors of 0.343 mm, and 0.0214 g respectively, $R^2$ values of 0.8738 and 0.9835 respectively. For the evaluation of the three quality grades of Gab, Eul, and abnormal ginseng seedlings, features from the processed images were extracted. The features combined with the ratio of the lengths and areas of the ginseng seedlings efficiently differentiate the abnormal shapes from the normal ones of the samples. The grade levels were evaluated with an efficient pattern recognition method of support vector machine analysis. The quality grade of ginseng seedling could be evaluated with an accuracy of 95% and 97% for training and validation, respectively. The result indicates that color image analysis with support vector machine algorithm has good potential to be used for the development of an automatic sorting system for ginseng seedling.

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.5
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• pp.521-530
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• 2017

Improving the microstructure of NiO/YSZ is one of several approaches used to enhance the electrical and mechanical properties of an anode support in Solid Oxide Fuel Cells (SOFCs). The aim of the work reported in this paper was to predict the relationship between these microstructural changes and the resulting properties. To this end, modification of the anode microstructure was carried out using different sizes of Poly (Methyl Methacrylate) (PMMA) beads as a pore former. The electrical conductivity and mechanical strength of these samples were measured using four-probe DC, and three-point bend-test methods, respectively. Thermal etching followed by high resolution SEM imaging was performed for sintered samples to distinguish between the three phases (NiO, YSZ, and pores). Recently developed image analysis techniques were modified and used to calculate the porosity and the contiguity of different phases of the anode support. Image analysis results were verified by comparison with the porosity values determined from mercury porosimetry measurements. Contiguity of the three phases was then compared with data from electrical and mechanical measurements. A linear relationship was obtained between the contiguity data determined from image analysis, and the electrical and mechanical properties found experimentally. Based upon these relationships we can predict the electrical and mechanical properties of SOFC support from the SEM images.

• Fashion & Textile Research Journal
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• v.6 no.6
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• pp.715-722
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• 2004

The social culture critics say that the current situation of the boom in pretty men happened because stereotyped notions of sex roles have changed. However, no scientific study has been done as yet to support this theory. Therefore, this study analyzed the present situation and examined it from many different points of view. I asked 600 men to fill out the questionnaire: 300 from Daegu and the other 300 from Seoul. I analyzed four 461 of them. The inner/outer image of Korean males in their 20s was analyzed into seven factors, a positive image, a progressive image, an affirmative image, a sensible image, an exemplary image, a conscious image, and an active image. The demographic result based on the inner/outer image factors showed a significant difference in ages for a sensible image, a conscious image, and an active image.

• Journal of the Korean Society of School Health
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• v.31 no.3
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• pp.203-213
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• 2018

Purpose: This study provides information for the development of educational programs for improving awareness of support to the elderly among adolescents. Methods: The 350 students of high schools located in Seoul were selected as subjects and surveyed using structured questionnaires during 11th~15th of April in 2014. The 327 subjects were used for analysis and the response rate was 93.4%. SPSS/WIN18.0 program was performed for t-test, ANOVA and regression analysis. Results: First, adolescents with parents aged over 50 compared to those with parents younger than 40 showed high level of awareness of support to the elderly. The high level of cognitive factors including image and perception towards the elderly (t=3.07, p=.01) were significant factors of awareness of support to the elderly. In experiential factors, adolescents with extensive influence of media and experiences with cohabitation with grandparents, and experiences with voluntary service activities were statistically significant with the high level of awareness of support to the elderly. Second, in regression analysis, adolescents' economic support awareness was higher with the stronger influence in the elderly-related media (${\beta}=.221$, p=.032). The emotional support awareness was higher among the eldest sons (${\beta}=.220$, p=.017) and perception (${\beta}=.352$, p=.001) to elderly (${\beta}=.221$, p=.032). Physical support awareness was higher when the positive image for the elderly (${\beta}=.223$, p=.016) and the high perception towards the elderly (${\beta}=.293$, p=.007). Conclusion: The awareness of support to the elderly should be constantly strengthened to the adolescents. Despite the rapid increase of the elderly, studies are not sufficient. Further research will be necessary.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.525-528
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• 2009

Recently, image classification has been an important task in various fields. Generally, the performance of image classification is not good without the adjustment of image features. Therefore, it is desired that the way of automatic feature extraction. In this paper, we propose an image classification method which adjusts image features automatically. We assume that texture features are useful in image classification tasks because natural images are composed of several types of texture. Thus, the classification accuracy rate is improved by using distribution of texture features. We obtain texture features by calculating image features from a current considering pixel and its neighborhood pixels. And we calculate image features from distribution of textures feature. Those image features are adjusted to image classification tasks using Genetic Algorithm. We apply proposed method to classifying images into "head" or "non-head" and "male" or "female".

• Journal of Korean Academy of Nursing
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• v.36 no.1
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• pp.37-44
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• 2006

Purpose: This research has been conducted in order to understand the major factors that affect self-esteem of adolescents. Methods: Data was collected by questionnaires from 1155 students at middle and high school in Seoul and Kyungkido, Korea. The Instrument tools utilized in this study were self-esteem, body-image, problematic behavior, depression, school adjustment, social support tool and thoroughly modified to verify validity and reliability. The collected data have been analyzed using SPSS 11.0 program. The variable of family harmony and counseling partner was treated as a dummy variable. Seven outliers which were bigger than 3 in absolute value were found, so after taking them off, Multiple Regression was used for further analysis. Result: The major factors that affect self-esteem of adolescents were depression, social support, body-image, problematic behavior, school adjustment, and family harmony, which explained $54.7\%$ of self-esteem. Conclusion: It has been confirmed that the regression equation model of this research may serve as a self-esteem prediction factors in adolescents.

• Asian Oncology Nursing
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• v.11 no.3
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• pp.221-228
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• 2011

Purpose: This study was aimed to identify the influencing factors on the quality of life among breast cancer survivors. Methods: The subjects were 159 female patients who visited out-patient department (OPD) after the mass removal surgery for breast cancer and had completed adjuvant treatments such as chemotherapy, radiation therapy at a university hospital and a general hospital. Data collection was conducted using the Ferrell QOL scale, the Mishel uncertainty scale, the Fitts & Osgoods body image scale revised by Jeon & Kim. the Rosenberg self-esteem scale, and the Kang family support scale. Results: The level of QOL in the participants was in the middle. There were a significant correlation between QOL, uncertainty, self-esteem, and family support. There were significant differences in QOL with the perceived health condition and the best support person. In a regression analysis, the most powerful predictor of QOL was body image (21.7%). Altogether uncertainty and perceived health condition explained 28.6% of the variance of QOL of the participants. Conclusion: Body image, uncertainty, and perceived health condition were important predictors of QOL. These results demonstrated the need for developing interventions to improve QOL of breast cancer survivors.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.871-886
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• 2011

This paper proposes a powerful SVM-ASM filter, the adaptive switching median(ASM) filter based on support vector machines(SVMs), to effectively reduce impulse noise in corrupted images while preserving image details and features. The proposed SVM-ASM filter is composed of two stages: SVM impulse detection and ASM filtering. SVM impulse detection determines whether the pixels are corrupted by noise or not according to an optimal discrimination function. ASM filtering implements the image filtering with a variable window size to effectively remove the noisy pixels determined by the SVM impulse detection. Experimental results show that the SVM-ASM filter performs significantly better than many other existing filters for denoising impulse noise even in highly corrupted images with regard to noise suppression and detail preservation. The SVM-ASM filter is also extremely robust with respect to various test images and various percentages of image noise.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.10
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• pp.1-6
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• 1997

This paper presents the theoretical derivaion relating and image correlationcoeffcient capable of assessing image quality, with phase-error variance in antenna aperture domain. We show that when the phase-error variance of a range bin selected as an adaptive beamformer is known, the quality of the reconstructed image is predictable and moreover, the resultant correlation coeffcient is obviously greater than the derive dlower boudn. To support the derivation, real data are used for image formation where the dominant scatterer algorithm (DSA) is applied for phase compensations.