• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.5
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• pp.788-803
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• 2021

This study aims to obtain basic data to develop clothes for dementia patients by classifying types of dementia patients. Data was collected from those dementia patients who entered a nursing home. This study analyzed a total of 221 sheets. Furthermore, descriptive statistics, cross-tabulation, and K-means clustering were performed for statistical processing using Minitab 14. As a result, dementia patients were classified into four types: inactive-dependent, active-problematic behavior, activity-autonomy, and inactive-offensive. Inactive-dependent type was a group with the most severe disability in cognitive and daily activity functions; however, they lacked behavioral and psychological symptoms and problematic behavior with clothes. Active-problematic behavior type showed the most behavioral and psychological problems and problematic behavior with clothes. Activity-autonomy type was a group without any problematic behaviors. Moreover, the inactive-offensive type had very good cognitive function toward humans. The study imply that it is necessary to provide clothes with proper functions based on the types of patients rather than providing them uniform clothes because clinical and clothes behaviors differ significantly depending on the types of dementia patients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.486-487
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• 2019

Smartphones have various sensors built in. In particular, acceleration sensors are used to analyze human behavior because they can detect movement of objects. Previous studies have analyzed the behavior of people by analyzing the magnitude of acceleration sensor values. In this study, we proposed a method of detecting the motion by applying the K-average of the acceleration sensor value built in the smartphone. We proposed a method of recognizing walking and running, which is basic human behavior, by applying K-average of acceleration sensor value of smartphone.

• Fashion & Textile Research Journal
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• v.8 no.4
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• pp.427-432
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• 2006

The purpose of this study was 1) to segment the market of inbound Japanese tourists based on the importance of tour activity that tourists perceived and 2) to examine the behavior of each segmentation purchasing cultural fashion items in Korea. Data were collected using a self-administered questionnaire survey in Seoul. Clustering analysis, Chisquare, and ANOVA test were used to conduct the data analysis on 288 out of 400 questionnaires. The inbound Japanese tourists market was segmented into 3 groups; culture oriented group, shopping oriented group, and multi-activity group. Three groups were significantly different in terms of age, income, purchase amount, purchase criteria, and degree of shopping satisfaction. Marketing strategies for segmented markets were discussed.

• Journal of Preventive Medicine and Public Health
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• v.31 no.1 s.60
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• pp.72-81
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• 1998

The purpose of this study was to investigate the relation between health behavior patterns and demographic, socio-economic characteristics, health status, health information in Korea. The quantification method through canonical correlation analysis was conducted to the data from Korea National Health Survey in 1995, which consisted of 5,805 persons. The health lifestyle patterns were quantified as good diet lifestyle, passive lifestyle to the negative direction and drinker lifestyle, smoker lifestyle, hedonic lifestyle and fitness lifestyle to the positive direction. The covariate were related to health lifestyle patterns in the order of sex, age, marital status, occupation, health information, economic status, level of physical labour and health status. Characteristics of male, age below 50, married, blue colored worker, no health information, low in economic status, heavy level of physical labour, and poor in health status were positively related to drinker lifestyle, smoker lifestyle, hedonic lifestyle, fitness lifestyle sequentially.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.523-531
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• 2014

Design effect is often used in designing and planning sample surveys and/or in evaluating the efficiency of complex design features of the surveys. In this study, we applied Gabler et al. (2006)'s design effect model to 2013 Consumer behavior survey for food that was carried out by stratified two-stage sampling. Usability and adequacy of the design model to a real survey data are discussed and evaluated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.1
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• pp.63-74
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• 2022

Purpose: The purpose of this study is to present an evacuation performance analysis model that can derive vulnerable evacuation spaces with considering the movement behavior as per the elderly groups in the event of a fire in a geriatric hospital. Methods: The evacuation characteristics of geriatric hospital users were investigated through the review of precedent studies. First, the occupant conditions and the evacuation scenario were set to analyze a study target hospital. Then, the evacuation simulation was carried out considering the group types and the density of each group. Finally, an evacuation performance analysis model according to the group type was presented based on the simulation results. Results: The results of this study are as follows: (1) The evacuation performance according to the group type is to be clarified through the suggested study model. (2) It is necessary to secure a ramp or an emergency elevator to distribute the evacuation personnel at the design stage because congestion occurs due to collisions between evacuees on the stairs and delays the evacuation time. (3) It is necessary to consider the evacuation stairs and openings of sufficient size by analyzing the frequency of congestion occurrence and the escape routes of occupants in advance to identify the space where the evacuation flow overlaps. Implications: It is expected that the study result is to be used as primary data for studies that consider the elderly and clustering evacuation behavior in the event of a fire in a geriatric hospital.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.469-477
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• 2009

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance, Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software, In this paper, power estimation model is developed using statistical analysis, The proposed model analyzes processor behavior Quantitatively using the data of performance monitoring events and power consumption collected by executing various benchmark programs, And then representative hardware events on power consumption are selected using hierarchical clustering, The power prediction model is established by regression analysis in which the selected events are independent variables and power is a response variable, The proposed model is applied to a PXA320 mobile processor based on Intel XScale architecture and shows average estimation error within 4% of the actual measured power consumption of the processor.

• Journal of Korean Library and Information Science Society
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• v.40 no.4
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• pp.139-158
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• 2009

This paper used the network analysis method to analyse a variety of attributes of searcher's search behaviors which was appeared on search access log data. The results of this research are as follows. First, the structure of network represented depending on the similarity of the query that user had inputed. Second, we can find out the particular searchers who occupied in the central position in the network. Third, it showed that some query were shared with ego-searcher and alter searchers. Fourth, the total number of searchers can be divided into some sub-groups through the clustering analysis. The study reveals a new recommendation algorithm of associated searchers and search query through the social network analysis, and it will be capable of utilization.