• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Design & Manufacturing
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• v.15 no.4
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• pp.14-23
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• 2021

In this study, we have analyzed the access status and the data usage trend of the public Wi-Fi on the bus, which has not been carried out in the previous studies. The analysis period of this study is 5 months from Nov. 2020 to Mar. 2021. When we compared the access status of Seoul metropolitan and the non-metropolitan region against each region's deployment status ratio, the access ratio of the metropolitan region was higher than the non-metropolitan region, of which the gap was 4.53%. The access for each region showed the growing trend, which was 43.5% on average. The data usage also showed the growing trend, 2.7% on average. Weekly data usage showed the growing trend irrespective of weekdays or weekends. The data usage of the weekdays was 695GB higher than weekends. The data usage during commuting hours including school (7:00~9:00 a.m. and 4:00~6:00 p.m.) was higher than 3,000GB. We can conclude that bus public Wi-Fi was used more actively in non-metropolitan region than Seoul metropolitan region by the office workers and students. The secure access also showed the growing trend. And the secure data usage also showed the growing trend.

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.127-139
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• 2014

This study presents a logging method to trace the usage patterns of existing smartphone apps. The actual smartphone app itself, not a specially developed similar app with usage logging, would be used best for the experiment of observing the usage patterns. For this purpose, we used a method of injecting logging codes into existing smartphone app. Using this method, we conducted an experiment to trace usage patterns of a commercial IPTV app, and found that the method is very useful for acquiring detail usage log without influencing participants.

• Fashion & Textile Research Journal
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• v.10 no.3
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• pp.307-315
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• 2008

The purpose of this study was to examine the influence of purchase behavior of Cosmeceuticals according to appearance concern. The subjects of this study were 709 women aged for 20 to 50 living in Daegu. The questionnaires were conveniently sampled from December 20 to 31, 2007. The SPSS 12.0 package was use for data analysis which included Frequency analysis, Factor analysis, Cronbach's a and Analysis Structural Equation Modeling by use of Amos 5.0. The results of this study were as follows; Appearance concern had influence on usage behavior of cosmeceuticals and usage satisfaction of cosmeceuticals, but appearance concern had not influence on usage preference of cosmeceuticals. Usage behavior of cosmeceuticals had influence on usage satisfaction of cosmeceuticals and usage preference of cosmeceuticals. And usage satisfaction of cosmeceuticals had influence on usage preference of cosmeceuticals.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.77-85
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• 2020

The purpose of this study is to explore the changing trajectory of using educational media through longitudinal data analysis. We categorize the feature of using educational media as usage for learning, usage for information, and usage for the game. We explore the longitudinal changing patterns of usage for learning, usage for information, and usage for the game by LGM(Longitudinal Growth Modeling). We also find the gender difference between these longitudinal changing trajectories. We used 3,499 samples of KYPS middle school second-grade panel data. We found these results: (a) Both usage for learning and information are statically significant variability in initial level and rate of change. Both of the changing trajectories have increased. (b) Girls have a higher rate of the change both in the usage of learning and information than boys over time. (c) There is a statistically significant individual variability in initial levels and rate of change in the usage of the game over time. (d) Boys have a higher rate of initial value than girls in the usage of games, but there is no significant difference in the rate of changing trajectories.

• Journal of People, Plants, and Environment
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• v.24 no.2
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• pp.229-244
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• 2021

Background and objective: The purpose of this study was to comprehensively analyze the user behavior in order to cope appropriately with the increasing demand for mountain usage of those in their 20s and 30s and to allocate resources efficiently. Methods: To analyze the behavior of mountain hiking users, an exploratory data analysis (EDA) was conducted on the data which had been collected in the app Tranggle. The main target are users in their 20s and 30s who visited the mountains in the metropolitan area in 2019-2020. Among them, we have selected data on the top 13 mountains based on the frequency of visits. After data pre-processing, mountain usage patterns were analyzed through statistical analysis and visualization. Results: Compared to 2019, the number of users in 2020 increased 1.36 times. The utilization rate of the well-established hiking trails has also increased. The usage of mountain on weekends (Saturday > Sunday) was still the highest, and the difference in the usage between the days of the week decreased. Outside of work hours, early morning usage has increased and night-time usage has decreased. There was no significant change in usages depending on activity type, level (experience point) and exercise properties. Conclusion: Since the COVID-19 outbreak, the usage of mountains has been changing towards low user density and short-distance trip. in the post-COVID-19 era, the function and role of forests in daily life are expected to increase. To cope with this, further research needs to be carried out with consideration of the wider demographic and social characteristics.

• Korean Journal of Computational Design and Engineering
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• v.18 no.1
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• pp.36-48
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• 2013

In general, the product is used under several circumstances including environmental and usage conditions. According to the circumstances, the product has various performance degradation processes. In order to optimize the lifecycle of product usage, it is important to observe the degradation process and make suitable decisions on product operations. However, there are not much research works in evaluating the degree of product degradation based on product usage data. Recently, due to emerging ICT (Information and Communication Technology) technologies, it becomes possible to get the product usage data. Based on the gathered data, it is possible to analyze the degree of product degradation. The analysis of product usage data can improve product use and product design with advanced decisions. To this end, this study addresses one approach based on FMEA/FMECA method, called PDMCA (Performance, Degradation Modes and Criticality Analysis) for evaluating product degradation status and making suitable decisions.

• Human Ecology Research
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• v.56 no.6
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• pp.493-506
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• 2018

This study analyzes factors that determine the usage of digital information by senior consumers in the mobile environment. Senior consumers are alienated from digital information in South Korea; therefore, there have been increasing attempts to resolve this digital divide and reduce the digital information usage gap between young adults and senior consumers. The study used panel data from the National Information Society Agency (2017); there were 1,463 participants, aged 50-79 years. SPSS 19.0 was used to conduct the statistical analyses for frequency, factor analysis, Cronbach' ${\alpha}$ analysis, descriptive analysis and hierarchical multiple-regression analysis. The results are as follows. First, negative attitude toward information society negatively influenced the mobile digital information usage in diversity context. Second, average monthly income, degree of digital device usage motivation, positive attitude toward digital device usage, digital literacy of PC & Mobile, and family support positively influenced the mobile digital information usage in diversity context. Third, negative attitudes toward information society negatively influenced the mobile digital information usage in quality context. Forth, average monthly income, degree of digital device usage motivation, positive attitude toward digital device usage, digital literacy of PC & mobile, and family support positively influenced the mobile digital information usage in quality context. The study results can help further understand mobile usage behaviors among senior citizens and the implications on their quality of life in the digital information era.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.41-57
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• 2023

This paper aims to analyse the factors influencing the implementation of smart factories and their performance after implementation, using the grounded theory analysis method based on interview data. The research subjects were 21 companies that were selected by the Smart Manufacturing Innovation Promotion Group under the SME Technology Information Promotion Agency in 2020-2021 as the best case smart factory implementation companies, and introduced the intermediate stage 1 or above. A total of 87 concepts were generated as a result of the analysis. We were able to classify them into 16 detailed categories, and finally derived six broad categories. These six categories are "motivation for adoption", "adoption context", "adoption level", "technology adoption", "usage effect" and "management effect". As a result of the overall structure analysis, it was found that the adoption level of smart factory is determined by the adoption motivation, the IT technology experience affects the adoption level, the adoption level determines the usage and usage satisfaction, internal and external training affects the usage and usage satisfaction, and the performance or results obtained by the usage and usage are reduced defect rate, improved delivery rate and improved productivity. This study was able to derive detailed variables of environmental factors and technical characteristics that affect the adoption of smart factories, and explore the effects on the usage effects and management effects according to the level of adoption. Through this study, it is possible to suggest the direction of adoption according to the characteristics of SMEs that want to adopt smart factories.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.831-840
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• 2001

In this study, We introduce the process of web usage mining, which has lately attracted considerable attention with the fast diffusion of world wide web, and explain the web log data, which Is the main subject of web usage mining. Also, we illustrate some real examples of analysis for web log data and look into practical application of web usage mining for eCRM.