• Journal of the Korean Society of Food Culture
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• v.21 no.4
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• pp.375-380
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• 2006

The purpose of this study was to research eating-out information search methods according to college student's lifestyle and their influences on overall satisfaction at fast-food restaurants in eastern province of Kangwondo. Lifestyle was divided into 7 factors and 6 clusters. According to the results, information search methods through Newspaper, magazine and word of mouth were used the most preferably by Cluster 3, 'Brand preference intention'. And TV advertising was used the most preferably by Cluster 4, 'Convenience intention', and the advertisement through internet was used the most preferably by Cluster 5, 'Health ${\cdot}$ effort intention'. However, Information searches through TV advertising and word of mouth had negative influence on the overall satisfaction. But method through internet had positive influences on the overall satisfaction. Eventually, it's proved that information search methods had significant differences according to student's lifestyle. And some information search methods influenced their overall satisfaction. Therefore, food-sonics corporations need to try reducing negative images of various advertisements and activating positive aspects of specialized promotion instruments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.109-123
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• 2024

The purpose of this study is to identify strategic insights for cyber universities to secure a competitive advantage based on market analysis grounded in customer needs and motivations. As a research method, we surveyed and analyzed college students using conjoint analysis, identified the importance of cyber university components, estimated the utility of each detailed level, and identified the configuration of cyber universities most preferred by potential customers. In the study results, the importance of attributes that appeared by analyzing all respondents was in the order of 'expected ourcoms after graduation', 'department characteristic', 'cyber university name', and 'learning management style'. Cluster analysis was performed, divided into two groups, and conjoint analysis was performed. For Cluster 1, the importance values of the components were 'expected outcomes after graduation,' 'learning management style,' 'cyber university name,' and 'department characteristics,' in that order. For Cluster 2, the importance values were 'expected outcomes after graduation,' 'department characteristics,' 'cyber university name,' and 'learning management style,' in that order. As an application of the research, As an application of the study, it is suggested that analyzing the preferences of potential customers in the entire group is not accurate; therefore, segmenting the groups for analysis and strategy formulation can be useful.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.3
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• pp.163-173
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• 2024

This study aims to establish an online shopping mall marketing strategy based on big data analysis methods. The customer cluster analysis method was utilized to analyze customer purchase patterns and segment them into customer groups with similar characteristics. Data was collected from orders placed over one year in 2023 at 'Jeonbuk Saengsaeng Market', the official online shopping mall for agricultural, fish, and livestock products of Jeonbuk Special Self-Governing Province. K-means clustering was conducted by creating variables such as 'TotalPrice' and 'ElapsedDays' for analysis. The study identified four customer groups, and their main characteristics. Furthermore, regions corresponding to customer groups were analyzed using pivot tables. This facilitated the proposal of a marketing strategy tailored to each group's characteristics and the establishment of an efficient online shopping mall marketing strategy. This study is significant as it departs from the traditional reliance on the intuition of the person in charge to operate a shopping mall, instead establishing a shopping mall marketing strategy through objective and scientific big data analysis. The implementation of the marketing strategy outlined in this study is expected to enhance customer satisfaction and boost sales.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.9
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• pp.1-9
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• 2011

In this paper, we propose an efficient method to create clusters of geographically distributed base stations which cooperatively transmit signals in cellular mobile communication systems. The proposed method utilizes a sequential selection approach to choose candidate base stations which can provide maximum weighted sum-rate gain when they participate in the cooperative transmission with the existing cluster. In particular, the proposed method limits the maximum number of base stations in a cluster by considering the system operational and implementation complexities. Moreover, the combinations of clusters dynamically change along with variations of channel environments. Through computer simulations, performance of the proposed method is verified by comparing with the non-cooperative transmission method and the static clustering method. Numerical result shows that the proposed sequential selection based clustering method is especially advantageous for the performance improvement of lower percentile users in terms of average throughput, and thus the proposed method can effectively improve the fairness among users.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• Journal of the Korean Graphic Arts Communication Society
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• v.15 no.1
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• pp.41-55
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• 1997

We examine various digital halftoning technique and their application to printings. Three such technique are the error diffusion, cluster dither and disperse dither method. This paper describes a new tone correction halftone method to compensate dot gain. Input digital level are transformed by tone correction characteristics. The function of tone correction are solved from the printer response characteristic. The experimental results show that the proposed method is useful and valid.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.543-553
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• 2002

Principal component analysis is applied to reduce p-dimensions into q-dimensions ( $q {\leq} p$). Any partition of a collection of data points with p and q variables generated by the application of six hierarchical clustering methods is re-classified by discriminant analysis. From the application of discriminant analysis through each hierarchical clustering method, correct classification ratios are obtained. The results illustrate which method is more reasonable in exploratory data analysis.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.12-14
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• 1997

The analysis of eigenvalue and eigenvector is a crucial procedure for many electromagnetic computation problems. However, eigenpair computation is timing-consuming task. Thus, its parallelization is required for designing large-scale and precision three-dimensional electromagnetic machines. In this paper, the Davidson method is parallelized on a cluster of workstations. Performance of the parallelization scheme is reported. This scheme is applied to a ridged waveguide design problem.

• Journal of the Korean Institute of Navigation
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• v.24 no.1
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• pp.47-56
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• 2000

As a shipboard dependency for the safety and reliability becomes very important, the need for solid systems providing non-stop workload has been increased. This system is heartbeat that transmits shipboard state, audit and control information to the land. So, this paper describes a ship's LAN configuration method for the safety and reliability. In order to achieve these requirements, network, server and disk fault tolerance techniques are surveyed, and dual network configuration model, cluster server configuration method are presented and tested based on the survey.

• Journal of Nutrition and Health
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• v.56 no.2
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• pp.203-216
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• 2023

Purpose: This study examines the weight control behavior depending on university students' motives of using social media. Methods: The participants were 447 university students in the Jeonbuk area. Collected data were analyzed using factor analysis, cluster analysis, analysis of variance, and χ² tests with SPSS v. 26.0. Considering the motives of using social media, we investigated the usage of social media, dietary behavior related to social media, and weight control behavior. Results: Using the K-clustering method, the motives to use social media were categorized into three clusters: cluster 1 was the interest-centered group, cluster 2 was the multipurpose information-seeking group, and cluster 3 was the relationship-centered group. Among the various social media sites, YouTube (86.8%), Instagram (76.1%), and Facebook (61.1%) were the most visited by the subjects. The dietary behavior related to social media in cluster 2 was significantly higher than clusters 1 and 3 (p < 0.001). Clusters 1 and 2 showed a significantly higher dissatisfaction with one's weight (p < 0.05) and consequent interest in weight control than cluster 3 (p < 0.001). Cluster 2 used weight control-related information from social media significantly more than other clusters (p < 0.05). Weight control experiences in cluster 1 and 2 were significantly higher than in cluster 3 (p < 0.001). Conclusion: Differences in dietary behavior related to social media and weight control behavior were observed between cluster types of motivation to use social media. Based on the usage motives of university students and their behaviors, we propose that educational programs should be conducted for weight control using social media.