• Journal of the East Asian Society of Dietary Life
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• v.25 no.2
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• pp.223-233
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• 2015

The purpose of this study was to categorize dietary lifestyles and analyze intake behavior regarding beverages according to lifestyles of 276 university students in Yeungnam region. Frequency analysis, factor analysis, cluster analysis, reliability analysis, one-way analysis of variance, and ${\chi}^2$ tests were conducted using SPSS V.21.0. In the factor analysis, dietary lifestyles were categorized into one of five factors: health-seeking type, popularity-seeking type, safety-seeking type, economy-seeking type and convenience-seeking type. According to cluster analysis, respondents were divided into four groups: convenience-seeking group, wellbeing-seeking group, popularity-seeking group, and indifference group. Chi-square tests showed that there were significant differences in gender, grade, dwelling status, and monthly allowance according to the four dietary lifestyle groups. The level of dietary habits and percentage of correct answers related to beverages of the wellbeing-seeking group were highest among the four groups. The frequency of beverage intake was significantly different for carbonated drinks, milk and dairy products, and sport drinks according to dietary lifestyle groups. In addition, for beverage preference, there were significant differences in carbonated drinks, sport drinks, tea and functional drinks according to dietary lifestyle groups.

• The Research Journal of the Costume Culture
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• v.22 no.6
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• pp.899-910
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• 2014

The purpose of this study was to analyze high school students' school uniform wearing attitude by group according to clothing attitude targeting Gyeongnam area. This study aims to provide preliminary data in the field of school uniform and marketing that clothing propensity by groups is considered. This study conducted a survey targeting 762 high school students in Gyeongnam. For statistical analysis, SPSS for Window 14.0 was used for frequency analysis, factor analysis, reliability analysis, multiple sponse analysis, cluster analysis, ANOVA analysis and Duncan's ex-post analysis method. As a result of cluster analysis on the clothing attitude, students were divided into 4 segmentation of fashion seeking group, fashion indifference group, conformity group and modesty group. As a result of verification on the difference in perception toward wearing school uniform by groups which were classified according to the propensity of clothing attitude, activity, stability, and practicality were all varied according to the propensity of clothing attitude. 4 groups were significant differences in the degree of consent to wearing school uniform, price of school uniforms, tendency to prefer famous brand when purchasing school uniform, experience of transforming school uniform, opinion about school uniform modification and reason for school uniform modification. While low graders were many in 'modesty group', upper graders were many in 'fashion seeking group', which means that more segmentalized satisfaction of clothing by group may be raised if such a fact is considered when planning clothing for high school students segmentalized by age.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.6
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• pp.1025-1038
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• 2018

This study identifies the socio-psychological factors on athleisure wear purchase behavior and focuses on the effects of participation motives in sports leisure activity. This research compares athleisure wear purchase behavior among the three groups segmented by participation motives for sports and leisure activity. An online survey on male and female consumers between the ages of 20 and 50 provided 319 responses that were analyzed using SPSS 20.0. The factor analysis and reliability analysis, cluster analysis, variance analysis, Duncan test, and Chi square test were conducted. The results were: 1) From a result of the factor analysis on consumers' sports leisure participation motives clearly showed factorial structures including social interaction, physical health, and pleasant feeling. 2) Cluster analysis indicated three groups of participation motives in sports and leisure activity such as passive motivating type, social interaction type, and wellness type. 3) Consumer groups showed significant differences in purchase reasons, preferred brands, product items, and pursued performance attributes for purchasing athleisure wear products.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.195-209
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• 2021

According to the 2019-2020 social media usage survey conducted by the Seoul e-commerce center, 5 out of 10 consumers have experienced shopping through social media. The cost of traditional advertising media has been reduced and advertising spending on social media has risen by 74%, indicating that social media is becoming a more important marketing element. While the number of users of social media has increased and corporate marketing activities have increased accordingly, research has been conducted in various aspects of marketing such as user motivation for social media, satisfaction, and purchase intention. There was no subdivided study on the differences in the social media usage frequency of consumers in actual purchasing behavior. This study attempted to identify differences in consumer characteristics by cluster in the agrifood purchase situation by grouping them by type according to the frequency of use of social media for consumers who purchase agri-food online. Product involvement, product need, and online purchase channel Consumer characteristics such as demographic distribution, perceived risk, and eating and lifestyle in each cluster were checked for the three agrifood purchase situations including choice, and types for each cluster were presented. To this end, questionnaire data on the frequency of social media use and online agrifood purchase behavior were collected from 245 consumers, and the validity of the measurement variables was secured through factor analysis and reliability analysis. As a result of cluster analysis according to the frequency of social media use, it was divided into three clusters. The first cluster was a group that mainly used open social media, and the second cluster was a group that used both open and closed social media and online shopping malls; The third cluster was a group with low online media usage overall, and the characteristics of each cluster appeared. Through regression analysis, the effect on product involvement, product need, and purchase channel selection when purchasing agri-food online through each of the three clusters was confirmed through regression analysis. As a result of the regression analysis, the characteristic of cluster 1 in the situation of purchasing agri-food online is a male in his 30s living in a rural area who has no reluctance to purchase agri-food on social media or online shopping malls. The characteristics of cluster 2 are mainly consumers who are interested in purchasing health food, and the consumer characteristics are represented. In the case of cluster 3, when purchasing products online, they purchase after considering quality and price a lot, and the consumer characteristics are represented as people who are more confident in purchasing offline than online. Through this study, it is judged that by identifying the differences in consumer characteristics that appear in the agri-food purchase situation according to the frequency of social media use, it can be helpful in strategic judgments in marketing practice on social media customer targeting and customer segmentation.

• Korean Journal of Human Ecology
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• v.19 no.1
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• pp.103-117
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• 2010

The purpose of this study was broken down into four sections. First, this study identified both single workers' lifestyles and their motives for going to restaurants. The study classified the consumers and then confirmed the attributes of selection that the consumers considered important when eating out at family restaurants. Second, the study analyzed the consumers' lifestyles, motives for going to restaurants (user motive), and the attributes of selection for the restaurant. Third, the study analyzed how the attributes of selection toward the family restaurant affect the consumer's satisfaction and the consumer's intention of revisiting the restaurant. Fourth, the study analyzed how consumer satisfaction affects the intention to revisit the family restaurant. After the execution of the aforementioned four steps, the study was successful in meeting its purpose: to provide information to enhance the satisfaction of the consumer and to provide foundation material for consumer policy. In regards to the analytical method, this study performed a Frequency Analysis, Factor Analysis, Reliability Analysis, Cluster Analysis, ANOVA Analysis, and Multiple Regression Analysis. The analytical results from the study are as follows. Lifestyle was categorized into 4 factors and 4 clusters. User motive was grouped into 3 factors and 4 clusters. The attributes of selection for family restaurants were categorized into 4 factors. Based on the lifestyle cluster, the attributes of selection for family restaurants showed a statistically significant difference. In addition, based on the cluster of user motive, the attributes of selection for family restaurants showed a statistically significant difference. Lastly, the attributes of selection for family restaurants were found to affect both the intention of revisiting the restaurant and consumer satisfaction after the consumers used the restaurants.

• Journal of Veterinary Clinics
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• v.28 no.4
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• pp.422-426
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• 2011

For a national survey in which wide geographic region or an entire country is targeted, multi-stage sampling approach is widely used to overcome the problem of simple random sampling, to consider both herd- and animallevel factors associated with disease occurrence, and to adjust clustering effect of disease in the population in the calculation of sample size. The aim of this study was to establish sample size for estimating bovine tuberculosis (TB) in Korea using stratified two-stage sampling design. The sample size was determined by taking into account the possible clustering of TB-infected animals on individual herds to increase the reliability of survey results. In this study, the country was stratified into nine provinces (administrative unit) and herd, the primary sampling unit, was considered as a cluster. For all analyses, design effect of 2, between-cluster prevalence of 50% to yield maximum sample size, and mean herd size of 65 were assumed due to lack of information available. Using a two-stage sampling scheme, the number of cattle sampled per herd was 65 cattle, regardless of confidence level, prevalence, and mean herd size examined. Number of clusters to be sampled at a 95% level of confidence was estimated to be 296, 74, 33, 19, 12, and 9 for desired precision of 0.01, 0.02, 0.03, 0.04, 0.05, and 0.06, respectively. Therefore, the total sample size with a 95% confidence level was 172,872, 43,218, 19,224, 10,818, 6,930, and 4,806 for desired precision ranging from 0.01 to 0.06. The sample size was increased with desired precision and design effect. In a situation where the number of cattle sampled per herd is fixed ranging from 5 to 40 with a 5-head interval, total sample size with a 95% confidence level was estimated to be 6,480, 10,080, 13,770, 17,280, 20.925, 24,570, 28,350, and 31,680, respectively. The percent increase in total sample size resulting from the use of intra-cluster correlation coefficient of 0.3 was 22.2, 32.1, 36.3, 39.6, 41.9, 42.9, 42,2, and 44.3%, respectively in comparison to the use of coefficient of 0.2.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1462-1470
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• 2013

This paper proposes an SWAD-KNH(Sybil & Wormhole Attack Detection using Key, Neighbor list and Hop count) technique which consists of an SWAD(Sybil & Wormhole Attack Detection) module detecting an Worm attack and a KGDC(Key Generation and Distribution based on Cluster) module generating and an sense node key and a Group key by the cluster and distributing them. The KGDC module generates a group key and an sense node key by using an ECDH algorithm, a hash function, and a key-chain technique and distributes them safely. An SWAD module strengthens the detection of an Sybil attack by accomplishing 2-step key acknowledgement procedure and detects a Wormhole attack by using the number of the common neighbor nodes and hop counts of an source and destination node. As the result of the SWAD-KNH technique shows an Sybil attack detection rate is 91.2% and its average FPR 3.82%, a Wormhole attack detection rate is 90%, and its average FPR 4.64%, Sybil and wormhole attack detection rate and its reliability are improved.

• Journal of the Korean Applied Science and Technology
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• v.37 no.5
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• pp.1418-1429
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• 2020

This study was conducted on men and women in their teens and 50s living in Seoul, Incheon and Gyeonggi-do to find out the relevance between demographic characteristics of lifestyle clusters and the characteristics of beauty salon use. To analyze the data of a total of 522 people collected, statistical processing was performed with analysis of frequency, analysis of factor, analysis of reliability, analysis of cluster, analysis of variance and analysis of cross. As a result, lifestyle group types were classified as fashion and social focus types, family-oriented types, and family-free types. These types were highly related to age, final education, and marital status among demographic characteristics, and were also found to be highly relevant to the characteristics of beauty salon use, such as beauty salon location and frequently used beauty services. The results of this study are thought to be the basic data that can be used for beauty salon marketing.

• Journal of KIISE
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• v.42 no.11
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• pp.1430-1440
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• 2015

In contrast with battery-based wireless sensor networks (WSNs), solar-powered WSNs can operate for a longtime assuming that there is no hardware fault. Meanwhile, a mobile sink can save the energy consumption of WSN, but its ineffective movement may incur so much energy waste of not only itself but also an entire network. To solve this problem, many approaches, in which a mobile sink visits only on clustering-head nodes, have been proposed. But, the clustering scheme also has its own problems such as energy imbalance and data instability. In this study, therefore, a cluster-based energy-aware data-sharing scheme (CE-DSS) is proposed to effectively support a mobile sink in a solar-powered WSN. By utilizing the redundant energy efficiently, CE-DSS shares the gathered data among cluster-heads, while minimizing the unexpected black-out time. The simulation results show that CE-DSS increases the data reliability as well as conserves the energy of the mobile sink.