• Journal of the Korean Home Economics Association
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• v.39 no.12
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• pp.177-188
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• 2001

The purpose of this study is to investigate the fitness according to drafting method of the block patterns for women in Korea. The major findings of this study are as follows: 1. According to each sensory test of the frontal view, back view, side view and silhouette Block Pattern I is the best of them in summation of the sensory tests score. Block, Pattern D and I have good shape too. However the best block Pattern D shows good score in evaluation of overall fitness and silhouette. 2. Most of block patterns, which show high scores in sensory tests, are the Compromise Method taking merits of the Proportional Method and Short Measure Method among the Pattern Drafting Methods. Box-shape patterns show low score in the sensory tests. 3. Regarding to the number of measurement, the patterns of the Compromise Method using 6-8 measurements seem to be optimal. In degree of fitness, loose fit type basic patterns are better than other patterns from a viewpoint of the total satisfaction.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• The korean journal of orthodontics
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• v.27 no.2
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• pp.245-258
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• 1997

Cephalometric radiographs, frontal photographs and profile silhouette phogographs of 68 young adult female who were model or were recommended to have esthetic face were used in this study. 7 Students in department of Art of Kyungpook national university and 15 orthodontists estimated profile slides which were made of 3 Profile silhouettes in parallel with FH plane. Profile silhouettes were made of soft tissue profile line of cephalometric radiograph. Only orthodontists estimated frontal photographs. Students and orthodontists score 9 in excellent case, score 7 in good case, score 5 in average case, score 3 in poor case. Correlation analysis between orthodontists' esthetic concept and Artists' esthetic concept, between frontal view esthetics and profile view esthetics which estimated by orthotontists, between profile view esthetics and profile measurements which consisted of measurements of 38 female who were scored above 5 mean score in profile silhouette by orthodontists were done. And the finding in this study indicated the following 1. Correlation between orthodontists' esthetic concept and Artists' esthetic concept in profile silhouette was significant (r=0.67,P=0.0001). 2. Correlation between frontal view esthetics and profile view esthetics which estimated by orthodontist was significant (r=0.26,P=0.0381). 3. Measurements which had significant correlation between profile measurements and profile view esthetics wer Na-Pog, to N', BNV to Pog', BNV/B' -Pog', Ls-Li-Pog', Li-B'-Pog' Z angle(P<0.05). 4 Mean and standard deviation of profile measurements of 38 female were obtained.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.5 s.164
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• pp.729-741
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• 2007

In order to examine the characteristics of brassiere products for the 1924 generation brands on the market and grasp problems, we selected three 'comparative brassieres', each one from among the 1924 underwear brands with the highest recognition and sales profit, and then designed a 'sample brassiere' pattern(75A) with a similar shape to the comparative brassieres. We set up the "New Cup Grading Rule" with a view of reflecting the wearing effect that was varied according to cup sizes, graded the sizes of 75AA and 75B with this method, and made the sample brassieres in three sizes. We conducted the wearing evaluation and body measurements of 9 subjects after analyzing the patterns and characteristics of the sample brassieres and three comparative brassieres. As a result of the wearing evaluation, the sample and comparative brassiere 2, the dimensions and shapes were appropriate for the 1924 generation consumers and expressed an overall natural silhouette, showed satisfactory results in the entire evaluation questions. On the other hand, the comparative brassiere 1 and 3 that tended toward making a big change in the physical characteristics got unsatisfactory evaluations in the dimensions of the cups, clothing pressure, and bust silhouette. As a result of observing the variation in body dimensions by body measurements when nude and when wearing each brassiere and then summing it up with the score of the wearing evaluation, it was proven that too much change in body shape can create a negative image by upsetting the balance of the whole silhouette. Therefore, it is desirable to develop brassiere products with proper dimensions and clothing pressure that can make a physical change that harmonizes the overall bust silhouette and the position and shape of the breasts.

• Journal of Platform Technology
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• v.11 no.3
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• pp.12-23
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• 2023

In the 2020s, the K-Pop market has been dominated by girl groups over boy groups and the fourth generation over the third generation. This paper presents methods and results on lyric clustering to investigate whether the generation of girl groups has started to change. We collected meta-information data for 1469 songs of 47 groups released from 2013 to 2022 and classified them into lyric information and non-lyric meta-information and quantified them respectively. The lyrics information was preprocessed by applying word-translation frequency vectorization based on previous studies and then selecting only the top vector values. Non-lyric meta-information was preprocessed and applied with One-Hot Encoding to reduce the bias of using only lyric information and show better clustering results. The clustering performance on the preprocessed data is 129%, 45% higher for Spherical K-Means' Silhouette Score and Calinski-Harabasz Score, respectively, compared to Hierarchical Clustering. This paper is expected to contribute to the study of Korean popular song development and girl group lyrics analysis and clustering.

• The Research Journal of the Costume Culture
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• v.18 no.3
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• pp.465-475
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• 2010

For this study, 18 kinds of cut and sew or seamless knitted test garment were made. Samples differed from each other by skirt angles($90^{\circ}$, $180^{\circ}$), gauges(7G, 12G, 15G), and grain directions(bias direction, wale direction, and radial direction). After measuring the mechanical properties of various gauges on the seamless knitting machine, I compared shape of the knitted flare skirt through subjective evaluation on appearances. The data analysis methods used in this study were descriptive statistics, one-way ANOVA, Duncan-test, correlation analysis, and regression analysis. The subjective evaluation on appearances of knitted flare skirts showed the following: In case of $90^{\circ}$ skirt, the seamless skirt showed a much higher score in every gauge expect that of the cut and sew 12G, and silhouette of 15G wale direction. In case of the $180^{\circ}$ skirt, the seamless type showed a much higher score in every item over the cut and sew expect the silhouette part of 7G wale direction.

• The korean journal of orthodontics
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• v.51 no.6
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• pp.419-427
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• 2021

Objective: The purpose of this study was to evaluate the esthetic preference for various Korean silhouette profiles. Methods: The Korean average male and female profiles were modified by changing the lower facial height and anteroposterior lip position to produce nine types of profiles. In order to test intrarater reliability, the average profile was copied once more to be included for evaluation. A questionnaire containing 10 profiles for each sex, each of which had to be rated for preference on a numerical rating scale from 0 to 10, was administered to 30 adult orthodontic patients, 30 dental students, 30 orthodontists, and 30 dentists excluding orthodontists. The data were statistically analyzed using the intraclass correlation coefficient (ICC), independent t-test, and one-way ANOVA. Results: The ICC of overall intrarater reliability was 0.629. For several profiles, significantly higher scores were given to male profiles than to female profiles (p < 0.05). However, no significant differences were found in the scores for all profiles among the four rater groups. Among the short profiles, a significantly higher score was given to the retruded profile, and among the vertically average and long profiles, a significantly higher score was given to the horizontally average profile (p < 0.001). Among all the profiles, significantly lower scores were given to the protruded profile (p < 0.001). Conclusions: This study revealed good overall intrarater reliability, with several types of male profiles being esthetically preferred over female profiles. Moreover, while retruded and horizontally average profiles were generally preferred, protruded profiles were not.

• The Journal of Bigdata
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• v.6 no.2
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• pp.29-38
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• 2021

Deviation of route in aviation safety management is a dangerous factor that can lead to serious accidents. In this study, the anomaly score is calculated by classifying the tracks through clustering and calculating the distance from the cluster center. The study was conducted by extracting tracks within 100 km of the airport from the ADS-B track data received for one year. The wake was vectorized using linear interpolation. Latitude, longitude, and altitude 3D coordinates were used. Through PCA, the dimension was reduced to an axis representing more than 90% of the overall data distribution, and k-means clustering, hierarchical clustering, and PAM techniques were applied. The number of clusters was selected using the silhouette measure, and an abnormality score was calculated by calculating the distance from the cluster center. In this study, we compare the number of clusters for each cluster technique, and evaluate the clustering result through the silhouette measure.

• Journal of the Semiconductor & Display Technology
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• v.23 no.3
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• pp.21-28
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• 2024

Unsupervised learning is a type of machine learning, and unlike supervised learning or reinforcement learning, a target value for input value is not given. Clustering is mainly used for such unsupervised learning. One of the representative methods of such clustering is K-means clustering. Since K-means clustering is a method of determining the number of clusters and continuing to find the central point of the data allocated to the cluster, there is a problem that the clustered group may not be the optimal cluster. In this study, particle swarm optimization algorithm, which determines the motion vector by adding various variables as well as the center point, is applied to K-means clustering. The improved K-means clustering makes it possible to move toward better outcome values even when the center of cluster no longer change. In the conventional clustering method, the center of the cluster moves to the center of the data belonging to the cluster, and clustering ends when the cluster does not change, so other characteristics other than the center value are excluded. Unlike the conventional clustering method, the improved clustering method uses a central value, an average value, and a random value as variables, and a particle swarm optimization algorithm that modifies the vector for each iteration is applied. As a result, improved clustering method derived a better result value than the existing clustering method in the group's fitness index, silhouette score.

• Journal of the Korean Society of Costume
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• v.60 no.9
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• pp.106-119
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• 2010

The purpose of this study is to examine American students' perceptions of contemporary fashion design that incorporates Korean traditional costume. The findings, which are based on a survey of American students' aesthetic response to the fashion designs, are as follows. First, the impressions of American students about the fashion designs were affected by two major factors, Tradition and Trend. The Tradition factor was related to the impressions traditional, formal, elegant, classic, romantic, gorgeous, and natural, but was correlated negatively to the impressions dynamic, modern, and casual. The Trend factor was related to the impressions chic, trendy, and clear but not dandy. Designs with elongated shape had a positive score for the Tradition factor and designs with curvy line had a positive score for the Trend factor. Second, American students gave visual priority to the aspects of shape such as garment type and silhouette when they evaluated the designs. Color was less important than the aspect of shape in their fashion image perception. Therefore, they categorized the designs by similarity of garment types, and then sub-categorized them by color. The meaning of Korean traditional motifs or details was not significant to American students. Third, American students showed the tendency that the more they evaluated the designs to be gorgeous or trendy, the more they liked the designs. Furthermore, they liked the designs which have a positive score for the Trend factor.