• Journal of the Korean Society of Costume
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• v.57 no.4 s.113
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• pp.9-17
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• 2007

Somatotype is human body shape and physique type which can be classified not only by the size, but also by the shape or posture of the body. Postural variations in the alignment of the back, shoulder, and neck can have an adverse effect on the fit of garments designed to hang from the shoulders. There have been some previous studies about the lateral upper torso by analyzing photographic measurements. In this study, 3D body scan images were used to classify the side somatotype of upper lateral method even though they are major data in the classification of upper torso. This study focused on following objective.; 1) To apply new and developing technology into the apparel industry analyzing 3D body scan images. 2) To classify upper laterla torso using the data through the new improver technology, 3D body scanner. 3) To propose basic materials for well fitted garments for each type of figure. The test subjects for this study were two hundreds nine female aged 19 years and up who were recruited in Cornell university body scan research team. Seventeen Variables(12 angles, 5 lengths) out of 3D body scan data were measured based on these landmarks and applied to analyze. The result of factor analysis indicated that 6 factors were extracted through factor analysis and orthogonal rotation by the method of Varimax and those factors comprise 62.5% of total variance. And the somatotype of upper body is classified into 3 types of figures according to cluster analysis; Bent forward posture, Straight posture, Swayback posture. Future study could be addressed about the somatotype of body by the age group based on the large database with wide variety of age.

• Imaging Science in Dentistry
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• v.54 no.2
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• pp.147-157
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• 2024

Purpose: The aim of this study was to explore the correlations of cone-beam computed tomographic findings with the apnea-hypopnea index in patients with obstructive sleep apnea. Materials and Methods: Forty patients with obstructive sleep apnea were selected from the ear-nose-throat (ENT) outpatient clinic, Faculty of Medicine, Mansoura University. Cone-beam computed tomography was performed for each patient at the end of both inspiration and expiration. Polysomnography was carried out, and the apnea-hypopnea index was obtained. Linear measurements, including cross-sectional area and the SNA and SNB angles, were obtained. Four oral and maxillofacial radiologists categorized pharyngeal and retropalatal airway morphology and calculated the airway length and volume. Continuous data were tested for normality using the Kolmogorov-Smirnov test and reported as the mean and standard deviation or as the median and range. Categorical data were presented as numbers and percentages, and the significance level was set at P<0.05. Results: The minimal value of the cross-sectional area, SNB angle, and airway morphology at the end of inspiration demonstrated a statistically significant association (P<0.05) with the apnea-hypopnea index, with excellent agreement. No statistically significant difference was found in the airway volume, other linear measurements, or retropalatal airway morphology. Conclusion: Cone-beam computed tomographic measurements in obstructive sleep apnea patients may be used as a supplement to a novel radiographic classification corresponding to the established clinical apnea-hypopnea index classification.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.6
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• pp.579-589
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• 2004

The radar signals are generally very sensitive to relative orientations between radar and target. Thus, the performance of a target recognition system significantly deteriorates as the region of aspect angles becomes broader. To address this difficulty, in this paper, we propose a method based on the multi-aspect information in order to improve the classification capability ever for a wide angular region. First, range profiles are used to extract feature vectors based on the central moments and principal component analysis(PCA). Then, a classifier with the use of multi-aspect information is applied to them, yielding an additional improvement of target recognition capability. There are two different strategies among the classifiers that can fuse the information from multi-aspect radar signals: independent methodology and dependent methodology. In this study, the performances of the two strategies are compared within the frame work of target recognition. The radar cross section(RCS) data of six aircraft models measured at compact range of Pohang University of Science and Technology are used to demonstrate and compare the performances of the two strategies.

• Journal of the Korean association of regional geographers
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• v.19 no.4
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• pp.737-746
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• 2013

This study was carried out to suggest plan of earth surface erosion by typifying landforms in Gwangeung Forest. Elements of landform were classifyed as hierachical system by scale. Scale for classification set a decision as four categories. We could classify landforms which level zero is 4 levels of elements, level one is 6, level two is twelve, level three is twenty seven. However, micro landforms of valley bottom which is hard to mapping made a categorization as upper valley, middle valley, artificial channel valley. Plan for soil erosion suggested yarding corridor, landform management for surroundings of slope and bridge using rock and gravel, road construction for forest management stable bedrock rather than soil layer, repose angles and piling up rocks for channel walls, and setting up buffer zone when forest thinning be carried out. The result of this research will be expected to provide information for forest management of mountainous areas by landform types.

• Korean Journal of Applied Biomechanics
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• v.16 no.4
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• pp.95-103
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• 2006

The purpose of this study was to review the relevant literature about coaching and thereupon, survey the coaching methods used for golf lesson to reinterpret them and thereby, describe in view of kinetics the swing errors committed frequently by amateur golfers and suggest more scientific golf coaching methods. For this purpose, kinetic elements were divided into accuracy and power ones and therewith, the variables affecting such elements were identified. For this study, a total of 60 amateur golfer were sampled, and their swing forms were photographed with two high-speed digital cameras, and the resultant images were analyzed to determine the errors of each form kinetically, which would be analyzed again with the program V1-5000. The kinetic elements could be identified as accuracy, power and accuracy & power. Thus, setup and trajectory were classified into accuracy elements, while differences of inter-joint angles, cocking and delayed hitting. Lastly, timing and axial movement were classified into accuracy & power elements. Three errors were identified in association with setup. The errors related with trajectory elements accounted for most (6) of the 20 errors. Three errors were determined for inter-joint angle differences, and one error was associated with cocking and delayed hitting. Lastly, one error was classified into timing error, while five errors were associated with axial movement. Finally, as a result of arranging the errors into a cross table, it was found that the errors were associated with each other between take-back and back-swing, take-back and follow-through, back-swing and back-swing top, and between back-swing and down-swing. Namely, an error would lead to other error repeatedly. So, it is more effective to identify all the errors for every form and correct them comprehensively rather than single out the errors and correct them one by one.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.117-125
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• 2015

In the modern age, it is important for people to maintain a good sitting posture because they spend long hours sitting. Posture correction treatment requires a great deal of time and expenses with continuous observation by a specialist. Therefore, there is a need for a system with which users can judge and correct their postures on their own. In this study, we collected users' postures and judged whether they are normal or abnormal. To obtain a user's posture, we propose a four-joint motion capture system that uses inertial sensors. The system collects the subject's postures, and features are extracted from the collected data to build a database. The data in the DB are classified into normal and abnormal postures after posture learning using the K-means clustering algorithm. An experiment was performed to classify the posture from the joints' rotation angles and positions; the normal posture judgment reached a success rate of 99.79%. This result suggests that the features of the four joints can be used to judge and help correct a user's posture through application to a spinal disease prevention system in the future.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.539-554
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• 2018

Effective interaction between user and device is considered an important ability of IoT devices. For some applications, it is necessary to recognize human facial expressions in real time and make accurate judgments in order to respond to situations correctly. Therefore, many researches on facial image analysis have been preceded in order to construct a more accurate and faster recognition system. In this study, we constructed an automatic recognition system for facial expressions through two steps - a facial recognition step and a classification step. We compared various models with different sets of data with pixel information, landmark coordinates, Euclidean distances among landmark points, and arctangent angles. We found a fast and efficient prediction model with only 30 principal components of face landmark information. We applied several prediction models, that included linear discriminant analysis (LDA), random forests, support vector machine (SVM), and bagging; consequently, an SVM model gives the best result. The LDA model gives the second best prediction accuracy but it can fit and predict data faster than SVM and other methods. Finally, we compared our method to Microsoft Azure Emotion API and Convolution Neural Network (CNN). Our method gives a very competitive result.

• Journal of Oral Medicine and Pain
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• v.23 no.2
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• pp.143-155
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• 1998

This study was performed to investigate the factor that might affect mandibualr body rotation. For the study, 115 patients with temporomandibular disorders and 35 dental students without angy signs and symptoms of temporomandibular disorders were randomly selected as the patient group and the contreol group, respectively. Preferred chewing side, Angle' classification, lateral guidance pattern, and affected side were clinically recorded, and the amount of Mandibular body rotational torque movement was measured in wide opening and closure, in right and left excursion with vertical and lateral distance in frontal plane, right and left rotational angel in horizontal and in frontal plane. Masticatory muscle activity of anteriorocclusal contact pattern on maximal hard biting were also observed synchronously with BioEMG and T-Scan , respectively. The observed items were muscle activity of anterior temporalis and superficial masseter, and tooth contact status related to contact number, force, duration, and occlusal unbalance between right and left arch. The data collected were analyzed by SAS statistical program. The results of this study were as follows : 1. Mean value of vertical distance in frontal plane in wide opening and closure was more in control subjects than in patients, but there was no difference for rotational angle. In right excursion, rotational angles were greater in patient group than in control group. 2. Comparison among the subjects by preferred chewing side did not reveal any significant difference, but comparison among patients by affected side showed more rotational amount in bilaterally affected patients than in unilaterally affected patients. 3. Comparison among the subjects by Angle's classification or lateral guidance pattern revealed no difference. There was also no difference between preferred chewing side and contralateral side, and between affected side and contralateral side. 4. Positive correlation in madibular rotational torque movements were observed among vertical distance, total horizontal rotation angle, electromyographic activity of anterior temporalis, tooth contact number, and tooth contact force but total frontal rotation angle almost did not show any correlation with other variables except vertical distance.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.372-380
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• 2010

Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is one of the fastest growing field in the computer industry. Because of According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies. Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer buying patterns in automotive market with data mining using decision tree as a quinlan C4.5 and basic statics methods.

• Journal of Information Technology Services
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• v.23 no.1
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• pp.55-79
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• 2024

Korea, which is expected to enter a super-aged society in 2025, is facing the most worrisome crisis worldwide. Efforts are urgently required to examine problems and countermeasures from various angles and to improve the shortcomings. In this regard, from a new viewpoint, we intend to derive useful implications by applying the recent natural language processing techniques to online articles. More specifically, we derive three research questions: First, what topics are being reported in the online media and what is the public's response to them? Second, what is the relationship between these aging-related topics and individual happiness factors? Third, what are the strategic directions and implications for benchmarking discussed to solve the problem of population aging? To find answers to these, we collect Naver portal articles related to population aging and their classification categories, comments, and number of comments, including other numerical data. From the data, we firstly derive 33 topics with a semi-supervised BERTopic by reflecting article classification information that was not used in previous studies, conducting sentiment analysis of comments on them with a current open-source large language model. We also examine the relationship between the derived topics and personal happiness factors extended to Alderfer's ERG dimension, carrying out additional 3~4-gram keyword frequency analysis, trend analysis, text network analysis based on 3~4-gram keywords, etc. Through this multifaceted approach, we present diverse fresh insights from practical and theoretical perspectives.