• Journal of The Korean Association of Information Education
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• v.20 no.1
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• pp.1-12
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• 2016

The purpose of this study is to investigate and analyze the elementary teachers' use of smart-phone applications (apps) in teaching-learning activities. The range of study includes the current usage patterns of apps in teaching-learning activities, elementary school teachers' understanding about apps usage in their classroom and providing the guideline about how to use apps for each subject in the classroom. We surveyed 100 elementary school teachers who are interested in smart education in Seoul. These teachers have an experience of working in a smart research school or have a computer-related master's degree. We expect that the result of the study will helpful for the elementary school teachers to design teaching materials using apps.

• Korean Journal of Human Ecology
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• v.20 no.2
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• pp.311-325
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• 2011

The purpose of this study was to analyze patterns of time usage of the retired elderly so as to improve their quality of life. The subjects of this study were 225 elderly people in Jeonnam Province. The statistics used for data analysis were frequency, percentage, mean, standard deviation, t-test, and multiple regression. The major findings were as follows : (1) The retired elderly spend more time in physiological activities and leisure, and there was no day difference in time use. (2) On weekdays, the variables affecting labor time were age, former job, health state, and education. On the weekends, education, health state, and former job had significant effects on the amount of time spent on labor. (3) The variables affecting participation and volunteer time were: monthly living expenses, age, spouse, former job, and house, on weekdays, and on the weekends, significant factors were spouse, age, and former job. (4) The variables affecting leisure time were education, age, monthly living expenses, religion, and economic state, on the weekdays, and on weekends, the significant factors were education, economic state, house, religion, and former job.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.233-240
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• 2017

Fingerprint authentication identifies individuals based on user specific information. It is widely used as it is convenient, secure and has no risk of leakage, loss, or forgotten. However, the latent fingerprints remaining on the smart device's surface are vulnerable to smudge attacks. We analyze the usage patterns of individuals using smart device and propose methods to reconstruct damaged fingerprint images using fingerprint smudges. We examine the feasibility of smudge attacks with frequent usage situations by reconstructing fingerprint smudges collected from touch screens. Finally, we empirically verify the vulnerability of fingerprint authentication systems by showing high attack rates.

• Journal of the Korean Dietetic Association
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• v.11 no.4
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• pp.440-448
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• 2005

This study explores some basic issues behind adults' seeking and using patterns of alternative medicine as well as health food (health food/medicine). In order to do this, 791 adult participants in Seoul and Busan were interviewed face-to-face. The results of the survey showed that 1) interpersonal influence was the most influential factor in relation to the adoption of health food/medicine(46.9% of the participants reported on the influences), 2) keeping healthy was the main motivation for the usage of health food/medicine(34.5% of the participants), 3) mass media was the important information source for health food/medicine, 4) with regard to trustworthiness of information sources, experts were believed to be the most trustworthy while information from acquaintances were thought less, and finally, 5) pharmacies and health food stores were main suppliers of health food/medicine. This study suggests some marketing strategies for health food/medicine. For example, it can be suggested that interpersonal communication among other information channels should be focused and might be increased trust by using professionals.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.245-254
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• 2012

In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

• International Journal of Contents
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• v.15 no.1
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• pp.39-51
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• 2019

Spontaneous speech provides rich information defining the linguistic characteristics of individuals. As such, computational analysis of speech would enhance the efficiency involved in evaluating patients' speech. This study aims to provide a method to differentiate the persons with and without aphasia based on language usage. Ten aphasic patients and their counterpart normal controls participated, and they were all tasked to describe a set of given words. Their utterances were linguistically processed and compared to each other. Computational analyses from PCA (Principle Component Analysis) to machine learning were conducted to select the relevant linguistic features, and consequently to classify the two groups based on the features selected. It was found that functional words, not content words, were the main differentiator of the two groups. The most viable discriminators were demonstratives, function words, sentence final endings, and postpositions. The machine learning classification model was found to be quite accurate (90%), and to impressively be stable. This study is noteworthy as it is the first attempt that uses computational analysis to characterize the word usage patterns in Korean aphasic patients, thereby discriminating from the normal group.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.1
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• pp.127-138
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• 2019

This study was to evaluate the walking speed of elderly people by using the travel route big data collected by travel diary and smart phone application. We analyzed the change of walking behavior in the residential street environments of the elderly whether they had a company or not. We interpreted the meaning based on previous studies. In addition, the characteristics of elderly people's use of the residential street environment were analyzed by comparing the change in spatial speed according to the companion. The result reveals that the usage patterns of the residential street environments change depending on whether they were accompanied or not. First, the elderly tend to do more social activities while walking alone than when they were accompanied. When they were accompanied the social activities occur in empty lot near the residential area. However, the social activities of the elderly occur in open space such as neighborhood park or playground while walking alone. Finally, This study has strength that it empirically analyzes the elderly's walking behavior and usage paths in small outdoor spaces, including residential streets.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.723-732
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• 2022

The success of enterprise information system(EIS) is crucial to align with corporate strategies and eventually attain corporate goals. Since one of the factors to information system success is system use, managerial efforts to measure the level of EIS utilization is vital. In this paper, the EIS utilization level is analyzed using system access log data. In particular, process sequence patterns and clustering of similar functions are identified in more detail based on a process mining method, in addition to basic access log statistics. The result of this research can be used to improve existing information system design by finding real IS usage sequences and function clusters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.