• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.193-204
• /
• 2022

Smart card data is representative mobility data and can be used for policy development by analyzing public transportation usage behavior. This paper deals with the problem of classifying metro stations using metro usage patterns as one of these studies. Since the previous papers dealing with clustering of metro stations only considered traffic among usage behaviors, this paper proposes clustering considering traffic time as one of the complementary methods. Passengers at each station were classified into passengers arriving at work time, arriving at quitting time, leaving at work time, and leaving at quitting time, and then the estimated shape parameter was defined as the characteristic value of the station by modeling each transit time to Weibull distribution. And the characteristic vectors were clustered using the K-means clustering technique. As a result of the experiment, it was observed that station clustering considering pass time is not only similar to the clustering results of previous studies, but also enables more granular clustering.

• The Journal of the Korea Contents Association
• /
• v.20 no.11
• /
• pp.644-655
• /
• 2020

The study examined differences between perceptions of AI speakers and intention to continuous use of AI speakers according to usage function. We divided usage patterns into single- and multi-function orientations based on the usage by different functions of audio content (music, news, and search), and analyzed the differences between perceptions of using AI speakers and the intention to continuous use. 335 men and women who had experience using AI speakers participated in an online survey. Results are as follows. First, men used AI speakers mainly for acquiring news, and the extent to which 20s and 40s acquire news was different. Second, perceptions of usefulness and ease of use were found to be higher in the multi-functional group(music-news-search). Last, regarding the intention to continuous use of AI speakers, the multi-functional group was highest, and users focusing on music listening were relatively higher than users for other functions. The findings of the study are expected to be used as foundational data for expanding the use of AI speakers and developing strategies for service provision in each AI speaker brand.

• Journal of The Korean Association of Information Education
• /
• v.17 no.1
• /
• pp.63-71
• /
• 2013

The purpose of this study was to investigate patterns of IT usage in gifted elementary students. There were 67 Computer Science gifted students and 38 Math/Science gifted students, a total of 105 students, who attended a Convergence Computer Science Camp for 3 days. They were given 20 questions on IT usage. The results showed that these gifted students started to use the computer from ages 7 to 9 (51.9%) and consider their level of usage as average (50.0%). They also expressed a desire to learn more to enhance learning. There were some differences between the Computer Science gifted students and Math/Science gifted students. The Computer Science gifted students spent more time at the computer, considered themselves as more capable in using the computer, and thought that the computer aided in learning more, Another difference is that Computer Science gifted students utilized the computer more for education and learning purposes(56.9%), whereas Math/Science gifted students used it for recreation purposes (40.5%). Furthermore, regarding areas of further interest, most Computer Science gifted students wanted to learn more about computer programming whereas Math/Science gifted students were more interested in learning presentation methods (26.3%). In conclusion, there was a difference between Computer Science gifted students and Math/Science gifted students in self-confidence, areas of utilization and computer related areas.

• The Journal of the Korea Contents Association
• /
• v.18 no.3
• /
• pp.70-80
• /
• 2018

Due to the recent development of social media and mobile devices, graph data have been using in various fields. In addition, caching techniques for reducing I/O costs in the process of large capacity graph data have been studied. In this paper, we propose a multi-layer caching scheme considering the connectivity of the graph, which is the characteristics of the graph topology, and the history of the past subgraph usage. The proposed scheme divides a cache into Used Data Cache and Prefetched Cache. The Used Data Cache maintains data by weights according to the frequently used sub-graph patterns. The Prefetched Cache maintains the neighbor data of the recently used data that are not used. In order to extract the graph patterns, their past history information is used. Since the frequently used sub-graphs have high probabilities to be reused, they are cached. It uses a strategy to replace new data with less likely data to be used if the memory is full. Through the performance evaluation, we prove that the proposed caching scheme is superior to the existing cache management scheme.

• The KIPS Transactions:PartB
• /
• v.13B no.2 s.105
• /
• pp.127-132
• /
• 2006

K-NN (k-Nearest Neighbors), which is a well-known instance-based learning algorithm, simply stores entire training patterns in memory, and uses a distance function to classify a test pattern. K-NN is proven to show satisfactory performance, but it is notorious formemory usage and lengthy computation. Various studies have been found in the literature in order to minimize memory usage and computation time, and NGE (Nested Generalized Exemplar) theory is one of them. In this paper, we propose RPA (Recursive Partition Averaging) and IRPA (Incremental RPA) which is an incremental version of RPA. RPA partitions the entire pattern space recursively, and generates representatives from each partition. Also, due to the fact that RPA is prone to produce excessive number of partitions as the number of features in a pattern increases, we present IRPA which reduces the number of representative patterns by processing the training set in an incremental manner. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• International Journal of Internet, Broadcasting and Communication
• /
• v.9 no.2
• /
• pp.1-10
• /
• 2017

The high costs of electric vehicle supply equipment (EVSE) and installation are currently a stumbling block to the proliferation of electric vehicles (EVs). The cost-effective solutions are needed to support the expansion of charging infrastructure. In this paper, we develope EV charging system based on the big data analysis of the power consumption patterns. The developed EV charging system is consisted of the smart EV outlet, gateways, powergates, the big data management system, and mobile applications. The smart EV outlet is designed to low costs of equipment and installation by replacing the existing 220V outlet. We can connect the smart EV outlet to household appliances. Z-wave technology is used in the smart EV outlet to provide the EV power usage to users using Apps. The smart EV outlet provides 220V EV charging and therefore, we can restore vehicle driving range during overnight and work hours.

• Journal of Korean Society of Water and Wastewater
• /
• v.22 no.1
• /
• pp.23-29
• /
• 2008

Throughout the analysis of field data from water distribution system, valid parameters were determined that can be included in the water service and design plan. This study investigates water consumption patterns to understand the variation of water-demand structures utilizing the pattern analysis of domestic purpose water. Water use data were collected by a public water resources management firm in Korea, Kwater, for 140 houses monitored during three years. Flow meters were installed at the faucet for drinking water, the shower booth, the laundry machine, bathroom sink, toilet, and garden faucet. Data was filtered using multiple physically meaningful criteria to improve analysis credibility. Mann Kendall and Spearman's Rho tests were used to carry out the analysis. Distinct factors of water consumption patterns can be determined for both increasing and decreasing trends of water use. Throughout the data analysis, the characterization of terms was classified and analyzed by the condition of the location of water-demand. Analysis of this data provide a physical basis for the parameter configuration of a reasonable design for a domestic water demand prediction model.

• Journal of Broadcast Engineering
• /
• v.18 no.1
• /
• pp.98-105
• /
• 2013

Patterns for generating and consuming contents are various in these days from conventional broadcasting contents to UCC. There are many researches on developing recommendation engines based on user's profile for providing desired contents. In this paper we propose a contents recommendation system using not only user's profile but other's profiles in closed user group of the social network based on patterns for user's consuming contents. The proposed recommendation agent update user's profile using usage history and other's profiles related to the user in the closed user group.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.5
• /
• pp.978-985
• /
• 2003

The implementation of abnormal behavior detection IDS is more difficult than the implementation of misuse behavior detection IDS because usage patterns are various. Therefore, most of commercial IDS is misuse behavior detection IDS. However, misuse behavior detection IDS cannot detect system intrusion in case of modified intrusion patterns occurs. In this paper, we apply data mining so as to detect intrusion with only audit data related in intrusion among many audit data. The agent in the distributed IDS can collect log data as well as monitoring target system. False positive should be minimized in order to make detection accuracy high, that is, core of intrusion detection system. So We apply data mining algorithm for prediction of modified intrusion pattern in the level of audit data learning.

• Journal of the Korean Society for Library and Information Science
• /
• v.50 no.3
• /
• pp.133-153
• /
• 2016

The objective of this study was to promote the use of collaborative digital reference service by identifying factors that affect user satisfaction and developing improvement measures based on the findings. Data were collected via a questionnaire administered to the users of the "Ask a Librarian" service and a survey to analyze the frequency and patterns of usage of the service. The survey analyzed the associations among subjects' demographic characteristics, information seeking patterns, factors that influence user recognition, service satisfaction, and follow-up intentions via responses to the questionnaire. Rapidity answers in factors of service satisfaction is found that the high impact of positive (+). According to the result of statistical analysis, the priority of service improvement strategies of digital reference service were suggested.