• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.1
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• pp.149-169
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• 2013

This exploratory study analyzed the structure of collaborative network among small libraries. The survey was conducted with the small libraries in Bucheon region. In order to analyze the structural features of the network, this study used social network analysis methods. The results of the study indicate that public small libraries has high density networks with other small libraries and performed a central role in the whole network. Private small libraries were, however, isolated in the whole network indicating their weak collaboration with other small libraries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.1-6
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• 2019

This study visualized users reaction about movies based on keywords with high frequency. For this work, we collected data of movie reviews on . A total of six movies were selected, and we conducted the work of data gathering and preprocessing. Semantic network analysis was used to understand the relationship among keywords. Also, NetDraw, packaged with UCINET, was used for data visualization. In this study, we identified the differences in characteristics of review contents regarding each movie. The implication of this study is that we visualized movie reviews made by sentence as keywords and explored whether it is possible to construct the interface to check users' reaction at a glance. We suggest that further studies use more diverse movie reviews, and the number of reviews for each movie is used in similar quantities for research.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.229-236
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• 2017

An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

• Smart Media Journal
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• v.3 no.3
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• pp.26-35
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• 2014

The statistical graphics is a design field focusing on the user perception aspects for the correct information delivery and the effective understanding, with the use of the quantitative data through the information analysis, extraction, visualization process. The statistical graphics with the big data composition factor is termed as the statistical big data graphics. In the statistical graphics the visual factors are used to reduce the errors in the perception part and to successfully deliver the information. However, in the statistical big data graphics the visual factors of the enormous data are causing the cognitive discouragements. The purpose of this study is to extract the cognitive discouragement factors from the big data statistical graphics, categorizing the types of the statistical big data graphics as 'network type', 'segment type', and 'mixed type', based on their compositional shapes, and explored the characteristics according to them. Especially, based on the visual main factors in the statistical big data graphics, We extracted the cognitive discouragement factors that appear in the high visualization as the four categories: 'multi-dimensional cases', 'various color', 'information overlap', and 'legibility of the writing'.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.9
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• pp.830-835
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• 2016

With the relaxation of regulations on unmanned aerial vehicles (UAVs) in the USA, the development of related industries is expected. Hence, it is anticipated that the number the UAVs will reach approximately 600,000 in the USA in 2017. However, automated flights of commercial UAVs are restricted owing to concerns about accidents. To deal with the possibility of collisions, several studies on collision prevention and the routing of UAVs have been conducted. However, these studies do not deal with various situations dynamically or provide efficient solutions. Therefore, we propose a centralized routing method for the UAV that uses vehicular networks. In the proposed scheme, vehicular networks regard UAVs as data packets to be routed. Accordingly, the proposed method reduces UAV processing power required for route searches. In addition, the routing efficiency for UAV flight paths can be improved since congestion can be minimized by using a vehicular network.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.75-82
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• 2023

The main purpose of this study is to analyze the metacognition of digital library search behavior of college students and to provide a fundamental data for the designing a user-centered online information retrieval system to find more optimal search results. In order to achieve the purpose of this study, metacognition was classified into the five main categories, including schema-training, planning, monitoring, evaluation, and transfer, and a total of twenty subcategories were included. A total of 112 students participated in the online questionnaire. The collected data were analyzed using SPSS version 26, and it was found that there was a significant correlation between metacognition of college students and their digital library searching behavior. In particular, the digital library search experience was found to be the strongest factor to be considered as the most important variable in digital library design based on the aspect of user metacognition.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.105-114
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• 2006

In ubiquitous environment where various kinds of computers are embedded in persons, objects and environment and they are interconnected and can be used in my place as necessary, different types of data need to be exchanged between heterogeneous machines through home network. In the environment, the efficient processing, transmission and monitoring of image data are essential technologies. We need to make research not only on traditional image processing such as spatial and visual resolution, color expression and methods of measuring image quality but also on transmission rate on home network that has a limited bandwidth. The present study proposes a new motion vector estimation algorithm for transmitting, processing and controlling image data, which is the core part of contents in home network situation and, using algorithm, implements a real time monitoring system of multi dimensional images transmitted from multiple cameras. Image data of stereo cameras to be transmitted in different environment in angle, distance, etc. are preprocessed through reduction, magnification, shift or correction, and compressed and sent using the proposed metamorphosis hierarchical motion vector estimation algorithm for the correction of motion. The proposed algorithm adopts advantages and complements disadvantages of existing motion vector estimation algorithms such as whole range search, three stage search and hierarchical search, and estimates efficiently the motion of images with high variation of brightness using an atypical small size macro block. The proposed metamorphosis hierarchical motion vector estimation algorithm and implemented image systems can be utilized in various ways in ubiquitous environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.543-546
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• 2017

Recently, people are using the data analysis in order to follow the new trend in information and communication technology. Media plays an important role to expand the new issue in our society, especially affected to establish social awareness about science and technology. So, We find some major technologies (Machine Learning & Blockchains) of future communication and information based on the 200 news articles through two data analysis methods such as keyword analysis and sentiment analysis. We look forward this paper to constantly develop the technology of information and communication as the guiding frame of the new scientific world.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.57-65
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• 2023

Various studies to enhance the accuracy of rice production forecasting are focused on improving the accuracy of the models. In contrast, there is a relative lack of research regarding the data itself, which the prediction models are applied to. When applying the same dependent variable and prediction model to two different sets of rice production data composed of distinct features, discrepancies in results can occur. It is challenging to determine which dataset yields superior results under such circumstances. To address this issue, by identifying potential influential features within the data before applying the prediction model and centering the modeling around these, it is possible to achieve stable prediction results regardless of the composition of the data. In this study, we propose a method to adjust the composition of the data's features in order to select optimal base variables, aiding in achieving stable and consistent predictions for rice production. This method makes use of the Korea Meteorological Administration's ASOS data. The findings of this study are expected to make a substantial contribution towards enhancing the utility of performance evaluations in future research endeavors.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.18-34
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• 2024

This study aimed to examine the effects of a science-learning program based on data visualization on the science inquiry and creative problem-solving abilities of elementary school science-gifted students. Accordingly, this research developed a data visualization science-learning program using Tableau, which had twelve sessions. The subjects encompassed 61 students in three gifted classes taught by the researcher. The scientific inquiry ability test and creative problem-solving ability test modified to suit the environment and situation were given to the subjects before and after the treatment. The results confirmed that science learning based on data visualization had no significant impact on basic science inquiry skills. Among the subdomains, significant results were obtained only in the reasoning subdomain. Moreover, integrative inquiry ability was significantly affected, unlike basic inquiry abilities. Among the five subdomains, significant differences were observed in three subdomains (data conversion, data interpretation, and variable control). However, concerning the generation of hypotheses and the control of variables, students exhibited confusion regarding the process of variable control and the exact concept of hypothesis development. This study also evaluated the effects of the program's application on creative problem-solving abilities and found a significant impact. Additionally, it was significantly different in all four subdomains. The results were interpreted to be owing to the students' mastery of Tableau's features, collaborative learning through discussion and debate, and the thematic impact of the data visualization program emphasizing procedural thinking. Finally, this study presented implications for science learning based on data visualization and the future direction of education.