• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.6
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• pp.982-996
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• 2010

This study classified the body shapes of Korean obese women and investigated the differences of each body shape, using 2004 Size Korea data. For selecting the obesity sample, 7 obesity judgment indices were chosen through previous clothing-related studies. A total of 636 females defined as "obese" by 5 out of 7 indices were selected as subjects and 54 body measurements and obesity judgment indices were used in this study. Firstly, mean, standard deviation, minimum, and maximum values of each measurement and item were obtained from the descriptive analysis of 53 measurements. According to the descriptive analysis, all measurements and obesity judgment indices of the subjects demonstrated a serious obesity level shown by BMI 27.11, R$\ddot{o}$hrer index 1.76, Vervaeck index 104.77, Relative weight 133.00, WHR 0.90, and waist circumference 86.71cm. In addition, the measurements and indices showed considerable differences between minimum and maximum values. Significant differences were identified in all measurements and items at a significant level, p=.001. Each distribution of body types according to age, stature, bust, and waist circumference groups was provided in this study. Secondly, factor analyses were conducted using 38 measurement items to extract the body characteristics of obese women. Factor 1 was "circumference measurements & obesity judgment indices," Factor 2 was "heights & arm-related lengths," and Factor 3 was "size and ratio of waist circumference & hip circumference." Factor 4 was "lengths in upper body," Factor 5 was "back width in upper body," Factor 6 was "side neck point to bust & bust circumference," Factor 7 was "length in lower body & arm circumferences" and Factor 8 was "neck base circumference & front width in upper body." These 8 factors explained 76.54% of the total variance. Finally, 5 body types were selected in the cluster analysis. Type 1 (with big back widths & arm circumferences) was 15.5% of the entire subjects, Type 2 (the shortest and fattest, with big upper body) was 18.8%, Type 3 (with big breast) was 27.8%, Type 4 (the tallest and longest in arm lengths, with the smallest arm circumferences and lengths in torso) was 22.5%, and Type 5 (with big hips compared to waist circumferences, smaller height and upper body) was 15.5%. Fundamental differences were identified in all measurements and items at the significant level of p=.001. In addition, each distribution of body type according to age, height, bust, and waist circumference groups was provided in this study.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.377-389
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• 2019

This study conducted social network big data analysis to investigate consumer's perception of Japanese sporting goods related to Japanese boycott and to extract problems and variables by recognition. Social network big data analysis was conducted in two areas, "Japanese boycott" and "Japanese sporting goods". Months of data were collected and investigated. If you specify the research method, you will identify the issues of the times - keyword setting using social network analysis - clustering using CONCOR analysis using TEXTOM and Ucinet 6 programs - variable selection through expert meetings - questionnaire preparation and answering - and validity of questionnaire Reliability Verification - It consists of hypothesis verification using the structural model equation. Based on the results of using the big data of social networks, four variables of relevant characteristics, nationality, attitude, and consumption behavior were extracted. A total of 30 questions and 292 questionnaires were used for final hypothesis verification. As a result of the analysis, first, the boycott-related characteristics showed a positive relationship with nationality. Specifically, all of the characteristics related to boycotts (necessary boycott, sense of boycott, and perceived boycott benefits were positively related to nationality. In addition, nationality was found to have a positive relationship with consumption behavior.

• Knowledge Management Research
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• v.21 no.3
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• pp.215-228
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• 2020

This study introduces the case of a company that newly jumped into the competitive cosmetics market with a brand concept developed through big data analysis. Skin Reverse Lab, which possesses anti-aging material technology, launched a new brand in the skincare cosmetics market. Using a big data analysis program called Luminoso, SNS data was analyzed in four areas, which were consumer attitudes toward overall cosmetics, skincare products, competitors, and consumers' experiences of product use. The age groups and competitors were analyzed through the emotional analysis technique including context, which is the strength of Luminoso, and insights on consumers were derived through the related word analysis and word cloud techniques. Based on the analysis results, Logically Skin have won various awards in famous magazines and apps, and have been recognized as products that meet global trend standards. Besides, it has entered six countries including the United States and Hong Kong. The Logically Skin case is a case in which a new company entered the market with a new brand by deriving consumer insights only from external data, and it is significant as a case of applying AI-based sentiment analysis.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.48 no.3
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• pp.346-353
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• 2015

Glucocorticoids (GCs) are steroid hormones regulated through responses to stress to maintain diverse metabolic and homeostatic functions. GCs act on the glucocorticoid receptor (GR), a member of the nuclear receptor family. This study identified and characterized the GR gene from the big-belly seahorse Hippocampus abdominalis designating it HaGR. The open reading frame of the HaGR cDNA was 2,346 bp in length, encoding a 782-amino-acid polypeptide with a theoretical isoelectric point of 6.26 and predicted molecular mass of 86.8 kDa. Nuclear receptors share a common structural organization, comprising an N-terminal transactivation domain, DNA-binding domain, and C-terminal ligand-binding domain. The tissue-specific mRNA expression profile of HaGR was analyzed in healthy seahorses using a qPCR technique. HaGR mRNA was expressed ubiquitously in all of the tissues examined, with the highest expression levels in kidney, intestine, stomach, and gill tissues. The mRNA expression in response to immune challenge with lipopolysaccharide (LPS), polyinosinic:polycytidylic acid (poly I:C), Edwardsiella tarda, and Streptococcus iniae revealed that it is inducible in response to pathogen infection. These results suggest that HaGR is involved in the immune response of the big-belly seahorse.

• Perspectives in Nursing Science
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• v.6 no.1
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• pp.67-76
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• 2009

Recently, small and medium-sized hospitals which are located in rural areas have many difficulties in securing high quality nurses. That is because working environments for nurses in small and medium-sized hospitals in rural areas are poor compared with those of big hospitals in urban. As a result, the migration of nurses from small and medium-sized hospitals in rural areas to big hospitals in urban is continuously happening. In general, big hospitals provide nurses with high level of salary and fringe benefits. To prevent the migration of nurses, chief executive officers of small & medium hospitals in rural areas have been interested in improving nurses' working conditions including wages. Also, they have raised nurses' salary and improved working conditions. But, basically these individualized efforts have some limit. In connection with this, medical interest groups have produced various voices in terms of interpretation and solutions for these issues. However, from the future perspectives, it seems evident that two approaches for both manpower supply and demand plans of nurses are necessary. They should contain not only accurate estimation of the supply-demand of nursing manpower but also the improvement of working conditions and wages of nurses. Estimation of nursing manpower supply-demand depends on the standards and criteria being used. Supply and demand may be met or not in accordance with the points emphasized on the decision. In the articles, issues regarding nursing manpower, levels of salary, other working conditions and social support system for child care are discussed. According to Joe's report (2005), most health institutions did not meet the guidelines of nurse staffing in Medical Law. The wages of nurse vary on every hospital and there is a big difference in wages' range. The average starting salary for a nurse is 22 million won a year. In case of tertiary hospitals, it reaches up to 30 million won a year. Nurse as a profession should have a strong responsibility and should take care of the patients for 24 hours with three working shifts. Also, most of them are female who have the burden of child rearing. Therefore, it is suggested to increase the salary, to provide comfortable working conditions, and to have social support system for nurses with household affairs.

• Smart Media Journal
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• v.6 no.3
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• pp.83-88
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• 2017

The data analysis system using Big-data is worthy to be used in various fields such as politics, traffic, natural disaster, shopping, customer management, medical care, and weather information. Particularly, the analysis of the momentum of an individual using an acceleration signal collected from a wearable device has already been widely used. However, since the data used in such a system stores only the data necessary for measuring the individual activity, it does not provide various analysis results other than the exercise amount of the individual. In this paper, I propose a system that analyzes collective behavior pattern and activity based on the acceleration signal that can be collected from personal smartphones for 24 hours a day and stored in big data. I also propose a system that sends acceleration signals and receives analysis results using standard messaging to use on various smart devices.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.359-367
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• 2019

As a strategy for strengthening local competitiveness, efficient use of regional assets is becoming more and more important. The key to regional identity and competitiveness is local assets. The purpose of this study is to derive the priority region for development by evaluating local assets. The analysis methods used in this study are Geographic Information System analysis, Big Data Trend analysis, and Analytic Hierarchy Process analysis. To assess the potential of local assets, the preference of assets, historical value, cluster of resources, wide-area transport accessibility, and population density were set as analysis indicators and itemized weights were applied using AHP to reflect the importance of each item. As a result of analyzing Yeongju city in Gyeongsangbuk-do, eight major points such as Buseoksa Temple, Sosu Seowon, Huibangsa Temple, Punggi Hot Spring Resort, Punggi Station, National Center for Forest Therapy, Yeongju east region and Museom Village were derived.

• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.13-21
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• 2021

This study aims to analyze tourist inflow trends and consumption patterns using a geographic information system and big data analysis system. Songdo Central Park and Chinatown were selected among the major tourist destinations in Incheon, and floating population analysis and card sales analysis were conducted for one month in June 2017. The number of tourists visiting Songdo Central Park from metropolitan cities across the country was highest in the order of Incheon Metropolitan City, Gyeonggi-do, and Seoul Metropolitan City, and the proportion of foreign tourists was the highest in China. The number of card consumption used by Chinatown tourists was 12.4% higher for men than for women, and the amount of card consumption was also higher for men by 18%. This study has implications for proposing a strategic plan for tourism policy by analyzing the inflow trend and consumption pattern of tourists and deriving major issues in the establishment of tourism policy. Based on this study, it is expected that it can be helpful in improving the construction of tourism infrastructure in the future.