• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.505-508
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• 2016

In city, tracing of power transmission lines is difficult due to compound installation of overhead and underground lines, transposition, bad view caused by trees or big buildings. It is hard problem for electrical technician on site to trace power transformers or power lines to serve customers in 3 phase -4 wires power distribution systems. It is necessary that the correct and fast tracing method is required for load balancing among distribution lines. Old technology use to trace lines with high power impulse injection. Our proposed method uses to trace lines with very small power high frequency signal injection. Simulation models for 3-phase power transformers, 3-phase wire lines, and customer loads are described to investigate the transmission characteristics of high frequency power line carrier. Distribution lines have only a limited ability to carry higher frequencies. Typically power transformers in the distribution system prevent propagating the higher frequency carrier signal. The proposed method uses the limited propagation ability to identify the power transformer to serve customers. The system consists of a transmitter and a receiver with power-line communication module. Some experiments are conducted to verify the theoretical concepts in a big commercial building. Also some simulations are done to help and understand the concepts by using MATLAB Simulink simulator.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.4
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• pp.419-425
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• 2014

The current work investigated the variation of numerical solutions according to the grid number, the distance of the first grid point off the ship surface, turbulence modeling and discretization. The subject vessel is KVLCC. A commercial code, Gridgen V15 and FLUENT were used the generation of the ship hull surface and spatial system and flow computation. The first part of examination, the effect of solutions were accessed depending on the grid number, turbulence modeling and discretization. The second part was focus on the suitable selection of the distance of the first grid point off the ship surface: $Y_P+$. When grid number and discretization were fixed the same value, the friction resistance showed differences within 1 % but the pressure resistance showed big differences 9 % depending on the turbulence modeling. When $Y_P+$ were set 30 and 50 for the same discretization, friction resistance showed almost same results within 1 % according to the turbulence modeling. However, when $Y_P+$ were fixed 100, friction resistance showed more differences of 3 % compared to $Y_P+$ of 30 and 50. Whereas pressure resistance showed big differences of 10 % regardless of turbulence modeling. When turbulence modeling and discretization were set the same value, friction, pressure and total resistance showed almost same result within 0.3 % depending on the grid number. Lastly, When turbulence modeling and discretization were fixed the same value, the friction resistance showed differences within 5~8 % but the pressure resistance showed small differences depending on the $Y_P+$.

• The Journal of Bigdata
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• v.5 no.1
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• pp.11-27
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• 2020

In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.

• Smart Media Journal
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• v.8 no.4
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• pp.58-63
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• 2019

The growth of the mobile device market has changed the education market and led to the quantitative growth of various media education. In particular, smart devices, which have better interaction than existing PCs or consoles, can develop more user-friendly content, allowing various types of educational content and inducing changes in traditional education methods for consumers. Although many researchers recently suggest viable development methods or marketing elements of contents, development companies, and developers, until now, merely rely on the human senses. Therefore, it is necessary to study the actual user's smart-device based usability and experience environment. This study aims to propose an intuitive statistical processing method for analyzing the usability of game-type educational contents in terms of form, for popular games that have been released as a basis for analyzing the user experience environment. In particular, because the game industry has a sufficient number of similar examples, it is possible to conduct research based on big data and to use them for immediate decision-making between multiple co-developers through the analysis method proposed by the research. It is expected to become an analytical model that can communicate with other industries because it is effective in securing data sources.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.49-58
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• 2019

In the era of the 4th industrial revolution, the development of information technology brought various benefits, but it also increased social interest in privacy issues. As the possibility of personal privacy violation by big data increases, academic discussion about privacy management has begun to be active. While the traditional view of privacy has been defined at various levels as the basic human rights, most of the recent research trends are mainly concerned only with the information privacy of online privacy protection. This limited discussion can distort the theoretical concept and the actual perception, making the academic and social consensus of the concept of privacy more difficult. In this study, we analyze the privacy concept that is exposed on the internet based on 12,000 news data of the portal site for the past one year and compare the difference between the theoretical concept and the socially accepted concept. This empirical approach is expected to provide an understanding of the changing concept of privacy and a research direction for the conceptualization of privacy for current situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.337-347
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• 2018

Weather causes much of the risk of agricultural activity. For efficient farming, we need to use weather information. Modern agriculture has been developed to create high added value through convergence with state-of-the-art Information and Communication Technology (ICT). This study deals with the quality control algorithms of weather monitoring equipment through Ubiquitous Sensor Network (USN) observational equipment for efficient cultivation of cabbage. Accurate weather observations are important. To achieve this goal, the Korea Meteorological Administration, for example, developed various quality control algorithms to determine regularity of the observation. The research data of this study were obtained from five USN stations, which were installed in Anbandegi and Gwinemi from 2015 to 2017. Quality control algorithms were developed for flat line check, temporal outliers check, time series consistency check and spatial outliers check. Finally, the quality control algorithms proposed in this study can also identify potential abnormal observations taking into account the temporal and spatial characteristics of weather data. It is expected to be useful for efficient management of highland cabbage production by providing quality-controlled weather data.

• Journal of the Korean Institute of Gas
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• v.23 no.2
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• pp.74-81
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• 2019

Expansion turbine system to recover the electricity energy from natural gas transmission stations is a well-known technique. The turbo-expander efficiency depends on the ratio of the natural gas flow rates to the design flow rate of the turbo-expander. However, if there is a big difference of the natural gas flow rate through the pressure letdown station because of seasonal supply pattern, that is, high flow rate in winter while low flow rate in summer, single turbo-expander system is not so efficient as to recover the pressurized energy from the low flow-rate natural gas. Therefore, we have proposed a new concept of double turbo-expander system: one is a big capacity and the other a small capacity. Here we have theoretically computed the electric powers at the pressure reduction from 18.5 bar to 7.5 bar depending on the inlet conditions of temperature and flow rate. The calculated electricity generation has been increased by 30% from 12.4 MW in a single turbo expander to 16.1 MW in the proposed double turbo-expander system when a minimal design efficiency of 0.72 is applied.

• Journal of the Economic Geographical Society of Korea
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• v.21 no.4
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• pp.335-349
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• 2018

This study analyzes the effect of the changes in traffic environments such as transportation speeds on the time-distance accessibility for the public transportation passengers. To do this, we use passenger transaction databases of the Seoul metropolitan public transportation system: one week for each of the three years (2011, 2013, and 2015). These big data contain the information about time and space on the traffic trajectories of every passenger. In this study, the time-distances of links between subway stations and bus stops of the public transportation system at each time are calculated based on the actual travel time extracted from the traffic-card transaction database. The changes in the time-distance accessibility of the integrated transportation network from the experimental results can be summarized in two aspects. First, the accessibility tends to decline as the year goes by. This is because the transportation network becomes more complicated and then the average moving speed of the vehicles is lowered. Second, the accessibility tends to increase on the weekend in the analysis of accessibility changes by day. This tendency is because the bus speeds on bus routes on the weekend are faster than other days. In order to analyze the accessibility changes, we illustrate graphs of the vehicle speeds and the numbers of passengers by year and day.