• The Journal of Society for e-Business Studies
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• v.1 no.2
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• pp.133-160
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• 1996

It is accomplished on the practical usage for STEP in enterprise. But they has feeble grasp of STEP, therfore it is hard to apply. That is due to take a negative policy with the lack of advertising and understanding, most of studies are going on a studentlike attitude rather than the practical usage or implementation, so they don't know exactly what is STEP, and how can they apply to their own enterprise. This paper aimed to present more detail understanding of STEP usage and practical usage of STEP data in enterprise. We describe EXPRESS that is description method of STEP, four ways of implementation method, and introduce to using ST-Developer and ST-Oracle by STEP Tools Inc. for more detail understand of STEP. Thereafter we propose practical usage of STEP with CAD systems and PDM systems on the knowledge of previous study, and propose total system implementation image on STEP data in enterprise, finally discuss conclusion and luther study issues.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.35-39
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• 2007

Recently Usage statistics are widely available from online content providers. However. the statistics are not yet available in a consistent data container and the administrative cost of individual provider-by-provider downloads is high. The Standardized Usage Statistics Harvesting Initiative (SUSHI) is developing an automated request and response protocol for moving Project COUNTER (Counting Online Usage of Networked Electronic Resources) Code of Practice usage statistics from providers to library electronic repositories. SUSHI will help libraries make better decisions by reducing the administrative overhead of using Project COUNTER statistics. Publishers in the recording and exchange of usage statistics for electronic resources, initially journals and databases. By following COUNTER's Code of Practice, vendors can provide library customers with Excel or CSV (comma delimited) files of usage data using COUNTER's standardized formats and data elements. The result is a consistent, credible, and compatible set of usage data from multiple content providers. On this study, We propose the acquisition model of usage data based on SUSHI for KESLI that is overseas electronic journal consortium in korea.

• 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.

• The Journal of Bigdata
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• v.1 no.1
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• pp.33-39
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• 2016

Recently, predicting personality with the help of smartphone usage becomes very interesting and attention grabbing topic in the field of research. At present there are some approaches towards detecting a user's personality which uses the smartphones usage data, such as call detail records (CDRs), the usage of short message services (SMSs) and the usage of social networking services application. In this paper, we focus on the assessing the correlation between MBTI based user personality and the smartphone usage data. We used $Na{\ddot{i}}ve$ Bayes and SVM classifier for classifying user personalities by extracting some features from smartphone usage data. From analysis it is observed that, among all extracted features facebook usage log working as the best feature for classification of introverts and extraverts; and SVM classifier works well as compared to $Na{\ddot{i}}ve$ Bayes.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.95-112
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• 2018

Purpose: To find out the appropriate probability distribution of credit card usage behavior by considering the relationship among income, expenditure and credit card usage amount. Such relationship is enabled by Korea's especially high penetration of credit card. Method: Goodness-of-fit test and effect size statistic W were used to identify the distribution of income and credit card usage amount. A simulation model is introduced to generate the credit card transactions on individual user level. Result: The three data sets for testing had either passed the chi-square test or showed low W values, meaning they follow the exponential distribution. And the exponential distribution turned out to fit the data sets well. The r values were very high. Conclusion: The credit card usage behavior, denoted as the counts of users by usage amount band, follows the exponential distribution. This distribution is easy to manipulate, has a variety of applications and generates important business implications.

• Journal of Korean Society for Quality Management
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• v.35 no.1
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• pp.136-141
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• 2007

For many products failures depend on age and usage and, in this case, failures are random points in a two-dimensional plane with the two axes representing age and usage. Models play an important role in decision-making. In this research, an accelerate failure test (AFT) model is proposed to deal with the two-dimensional data. The parameters are proposed to be estimated through maximum likelihood estimators.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of Distribution Science
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• v.20 no.9
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• pp.127-139
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• 2022

Purpose: As sharing economy is becoming increasingly relevant to people's lives, we want to understand why people participate in the sharing economy. We propose and validate three factors that are likely to influence consumers' choice of participating in the sharing economy at distribution market. Also, we found antecedents that affect these variables. These antecedents include appointment and return convenient, extended operating time, variety-seeking need, usage irregularity, other's clean usage, and feeling of membership between users. Research design, data, and methodology: This research collected 341 questionnaire data from participants in China. These participants were asked about the usage of DiDi, the most popular shareware in China. The data analysis and hypothesis testing were conducted using SPSS and Amos. Results: Usage convenience, usefulness of short-term usage, and trust in other users were found to have a positive impact on consumers' intention to participate in the sharing economy. In addition, we found that all the antecedents affect these variables positively. Conclusions: This research provides new driving factors for consumer participation in the sharing economy. Moreover, these findings will help managers develop marketing strategies for inducing the consumers to participate in the sharing economy.

• Korean Journal of Clinical Laboratory Science
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• v.48 no.4
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• pp.348-354
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• 2016

Data stored in hospital information systems has a great potential to improve adequacy assessment and quality management. Moreover, an establishment of a data warehouse has been known to improve quality management and to offer help to clinicians. This study constructed a data mart that can be used to analyze antibiotic usage as a part of systematic and effective data analysis of infection control information. Metadata was designed by using the XML DTD method after selecting components and evaluation measures for infection control. OLAP-a multidimensional analysis tool-for antibiotic usage analysis was developed by building a data mart through modeling. Experimental data were obtained from data on antibiotic usage at a university hospital in Cheonan area for one month in July of 1997. The major components of infection control metadata were antibiotic resistance information, antibiotic usage information, infection information, laboratory test information, patient information, and infection related costs. Among them, a data mart was constructed by designing a database to apply antibiotic usage information to a star schema. In addition, OLAP was demonstrated by calculating the statistics of antibiotic usage for one month. This study reports the development of a data mart on antibiotic usage for infection control through the implementation of XML and OLAP techniques. Building a conceptual, structured data mart would allow for a rapid delivery and diverse analysis of infection control information.