• Journal of the Korean BIBLIA Society for library and Information Science
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• v.30 no.1
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• pp.181-207
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• 2019

This study analyzed social issues related to presidential records in press releases using semantic network analysis method. For this purpose, we 1) selected five major news medias in Korea - Chosun Ilbo, JoongAng Ilbo, Dong-A Ilbo, Hankyoreh, and Kyunghyang Newspaper; 2) collected relevant articles including the subject word "Presidential Records", and 3) analyzed issue trends based on timeline using semantic network. According to medias, the issue related to the presidential records were analyzed by comparing the specific keywords in terms of persons, entities, actions. At the results, It is possible to identify the reporting patterns and components of the presidential records related issues. And the difference of media characteristics according to news media tendency was derived.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.1-8
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• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.1
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• pp.179-187
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• 2019

With the recent development of small computing devices, IoT sensor network can be widely deployed and is now readily available with sensing, calculation and communi-cation functions at low cost. Sensor data management is a major component of the Internet of Things environment. The huge volume of data produced and transmitted from sensing devices can provide a lot of useful information but is often considered the next big data for businesses. New column-wise compression technology is mounted to the large data server because of its superior space efficiency. Since sensor nodes have narrow bandwidth and fault-prone wireless channels, sensor-based storage systems are subject to incomplete data services. In this study, we will bring forth a short overview through providing an analysis on IoT sensor networks, and will propose a new storage management scheme for IoT data. Our management scheme is based on RAID storage model using column-wise segmentation and compression to improve space efficiency without sacrificing I/O performance. We conclude that proposed storage control scheme outperforms the previous RAID control by computer performance simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.229-238
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• 2019

Before the scholarship loan system was implemented at the Korea Scholarship Foundation, the government's role was strengthened by the direct lending of student funds to banks and other financial institutions. However, the low repayment performance of student loans has raised concerns over the future of student loans and the government's financial burden. Moreover, since student loans are repaid even after graduating from college to support low-income families, it is highly unlikely that the repayment rate of student loans will improve unless the employment rate and income level of the borrower improve. In this paper, the final visualization graph is presented of the repayment amount of the student loan through the collection, storage, processing and analysis phase in the Big Data-based system. This could be the basis for visually checking the amount of student loans to come up with various ways to reduce the burden on the current student loan system.

• Journal of Industrial Convergence
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• v.18 no.6
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• pp.19-25
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• 2020

Big data analytics are indispensable today in various industries and public sectors. Therefore, in this study, we will utilize big data analysis to search for improvement plans for domestic tourism services using the LDA analysis method. In particular, we have tried an exploratory approach that can improve tourist satisfaction, which can improve revisit and service, especially in Seoul, which has the largest number of foreign tourists. In this study, we collected and analyzed statistical data of Seoul City and Korea Tourism Organization and Internet information such as SNS via R. And we utilized text mining methods including LDA. As a result of the analysis, one of the purposes of visiting South Korea by foreigners was gastronomic tourism. We will try to derive measures to improve the quality of services centered on gastronomic tourism.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.514-521
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• 2021

When calculating an intersection collision speed using a formula, it is very difficult to grasp the degree of deceleration of a vehicle after the collision unless there is road surface trace in the entire section where each vehicle moved from the point of collision to their final positions after the collision. A vehicle's motion trajectory shows an irregular curve after a collision due to the effects of inertia based on the driving characteristics of the vehicle, the eccentric force according to the collision site, and the collision speed. Therefore, it is very important to set the appropriate departure angle after a collision for accurate collision speed analysis. In this study, based on experimental collision data using a computer simulation (PC-Crash), the correlation between an appropriate vehicle departure angle and the post-collision speed was analyzed, and then, a regression analysis model was derived. Through this, we propose a method to calculate collision speed by applying only the vehicle departure angle in some types of collisions for traffic accidents at intersections.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.265-273
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• 2020

In this paper, to design a basic data science practice curriculum as a liberal arts subject for non-majors, we proposed an educational method using an Excel(spreadsheet) data analysis tool. Tools for data collection, data processing, and data analysis include Excel, R, Python, and Structured Query Language (SQL). When it comes to practicing data science, R, Python and SQL need to understand programming languages and data structures together. On the other hand, the Excel tool is a data analysis tool familiar to the general public, and it does not have the burden of learning a programming language. And if you practice basic data science practice with Excel, you have the advantage of being able to concentrate on acquiring data science content. In this paper, a basic data science practice curriculum for one semester and weekly Excel practice contents were proposed. And, to demonstrate the substance of the educational content, examples of Linear Regression Analysis were presented using Excel data analysis tools.

• Convergence Security Journal
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• v.20 no.4
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• pp.169-176
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• 2020

Recently, the era in which various services using smart devices are used is sometimes referred to as the so-called "smart era". Among these, Smart Home Service' have not only brought about significant changes in the residential environment and culture, but are evolving very rapidly. and The 'Smart Home Service' provides more convenient services to users through communication between various electronic products in general homes, and has a bright future in the future. In particular,'Smart Home Service' provides various services combined based on IoT(Internet of Things) technology and wired/wireless communication in connection between various devices. However, such a "smart home service" inherits the security vulnerabilities of the underlying technologies such as the Internet of Things and wired and wireless communication technologies, and accidents that lead to the leakage of personal information and invasion of privacy continue to occur. So, it is necessary to prepare a countermeasure and prevention against the weak factors of the underlying technologies. Therefore, this paper is expected to be used as basic data for future application technology development and countermeasure technology by examining various security vulnerability factors of 'Smart Home Service'.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.571-578
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• 2021

This study compares Online SW-based maker education in terms of self-efficacy, learning motivation, and interest after applying differently according to blended learning strategies. First, a SW maker program for blended learning was developed and applied as a live seminar-type class including real-time interactive and a support-providing class consisting of online content and Q&A. As a result of comparing the differences between students according to the two strategies divided into pre- and post- survey, in the self-efficacy part, there was a significant difference in the positive efficacy and the overall part, and in the learning motivation part, the live seminar form was significantly higher in the confidence part. In the interest part, the support-providing form showed a significantly higher average in the instrumental interest and nervous part. In order to maintain the effect of maker activities like existing face-to-face situations in Online learning, it is necessary to increase sharing time between students, an integrated learning environment, and sufficient provision of exploration time and learning materials.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.