• The Journal of Korean Medicine
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• v.38 no.4
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• pp.104-109
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• 2017

Objectives: Epicardial fat is true visceral fat that is known to be associated with metabolic syndrome, high abdominal fat, insulin resistance, coronary artery diseases, low coronary flow reserve and subclinical atherosclerosis. Dampness-Phlegm pattern is one of the pattern diagnosis of traditional Korean medicine. Previous studies showed that Dampness-Phlegm pattern is associated with hypertension, dyslipidemia, metabolic syndrome. This study is intended to find association between Dampness-Phlegm pattern and epicardial fat thickness. Methods: This study was a community-based single center trial. Ischemic stroke patients within 30 days after their ictus were enrolled. Epicardial fat thickness was measured using transthoracic echocardiography. Other measured and obtained variables are medical history, weight, height, body mass index, fasting blood glucose, cholesterol, triglycerol, high density lipoprotein, lipid and low density lipoprotein. Results: Three hundred sixty six were enlisted, and one hundred forty were diagnosed with the Dampness-Phlegm pattern. Dampness-Phlegm pattern group had significantly thicker epicardial fat. Binary logistic regression also showed statistically significant result. Conclusions: This study showed close association between epicardial fat and Dampness-Phlegm pattern. This result suggests a clue to standardization of pattern identification.

• Fashion & Textile Research Journal
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• v.12 no.1
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• pp.1-9
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• 2010

A purpose of this study was to find out how the casual and formal style clothes of stripe pattern giving variety by pattern direction, pattern width, and contrast coloration have an effect on image of wearers. 432 stimuli were made and 2,800 testee evaluated them using semantic differential scale. As a result, five image dimensions were drawn as a factor of attractiveness, activeness, gracefulness, visibility, and tenderness. In consequence of analysing the image difference by stripe pattern and clothing style, the stripe pattern and clothing style affect image presentation as a significant clue. And besides, as a result of positioning stimuli by image, pattern direction, coloration, and tone combination were important clues that decide image. Consequently, clothing style, stripe pattern, and contrast coloration were made clear as an efficient parameter in image presentation of clothing wearers.

• Fashion & Textile Research Journal
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• v.12 no.5
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• pp.642-649
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• 2010

This study was designed to generate briefs pattern for women in their twenties using 3D parametric body model. 151 women in their 20's were random sampled and measured using Martine's anthropometry. And one subject was chosen as the representative subject for 3D scanning. Parametric model was generated of using CATIA P3, Unigraphics NX4.0, Rapidform 2006. And the 3D surface of parametric body model was flattened onto the 2D plane. 3 downscale ratios(0%, 10%, 15%) were applied to generated pattern to figure out what downscale ratio was suitable to make briefs with stretch fabric. 4 kinds of experimental briefs were made with stretch fabrics(0%, 10%, 15% downscale) and worn on the dressform. Subjective evaluation on the appearance was done and the data was analyzed by ANOVA with post-hoc test. Briefs pattern was generated through the process of flattening the parametric surface and arranging the patches to make briefs pattern by dart manipulation. The different ration of outline and area between 3D surface and 2D pattern were 0.22% and 0.09% respectively. It showed that a parametric model could provide a desirable pattern with minute size error. The results of subjective evaluation on the appearance of 4 experimental briefs showed that stretch briefs with 15% downscale ratio was evaluated most highly in most items. Findings imply that it is feasible to apply 3D parametric model to generate patterns for various items considering various fabric properties.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.5
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• pp.846-858
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• 2008

This study was designed to propose a method to draft bodice block pattern from 3D body scan data. Subjects were ten elderly women in their 60's, who wear basic size(B: 94cm, W: 82cm) garment. Scanning was done using 3D whole body scanner(WB4, Cyberware). Measurements for 3D data and cross section were attained using Auto CAD, by which a upper bodice pattern for elderly women was drawn on the basis of short measured method. The results are as following: As for most items, no significant differences were shown between measurements from Martin's anthropometry and those from 3D scan data, suggesting measurement from 3D scan data could be used to draft a pattern. The drafting equations acquired were as follows; width of pattern=B/2+5.5, width of waist=W/2+3.5cm, dart amount=8cm. Dart distributions were 23%(B.P.) : 20%(front armpit) : 17%(side seam) : 18%(back armpit) : 15%(back protruded point) : 7% (center back line). Through wearing test using 5-point Likert scale, resultant pattern was evaluated as appropriate for elderly women's pattern to get over 4 point. As a result, it might be said that 3D scanning application is effective for elderly women in that it doesn't take time so much as Martin's anthropometry and that their body shape vary compared with those of young women.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.112-127
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• 2002

A useful pattern is a pattern that contributes much to learning. For a classification problem those patterns near the class boundary surfaces carry more information to the classifier. For a regression problem the ones near the estimated surface carry more information. In both cases, the usefulness is defined only for those patterns either without error or with negligible error. Using only the useful patterns gives several benefits. First, computational complexity in memory and time for learning is decreased. Second, overfitting is avoided even when the learner is over-sized. Third, learning results in more stable learners. In this paper, we propose a pattern 'utility index' that measures the utility of an individual pattern. The utility index is based on the bias and variance of a pattern trained by a network ensemble. In classification, the pattern with a low bias and a high variance gets a high score. In regression, on the other hand, the one with a low bias and a low variance gets a high score. Based on the distribution of the utility index, the original training set is divided into a high-score group and a low-score group. Only the high-score group is then used for training. The proposed method is tested on synthetic and real-world benchmark datasets. The proposed approach gives a better or at least similar performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.296-300
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• 2000

In previous papers, it proved relevant to using together with EFCI and ER switch for effective ABR traffic managements. It also applied to EFCI and ER switch algorithm, that consider ABR traffic as poisson pattern. However, in recently network environment, it has been proved about traffic pattern, that is similar to self-similar pattern than poisson pattern. In this paper, we will compare previous poisson pattern with self-similar pattern under ATM network.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.44-51
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• 2008

Pattern matching and pattern searching in time series data have been active issues in a number of disciplines. This paper suggests a novel pattern matching technology which can be used in the field of stock market analysis as well as in forecasting stock market trend. First, we define conceptual patterns, and extract data forming each pattern from given time series, and then generate learning model using Hidden Markov Model. The results show that the context-based pattern matching makes the matching more accountable and the method would be effectively used in real world applications. This is because the pattern for new data sequence carries not only the matching itself but also a given context in which the data implies.