• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.3
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• pp.133-138
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• 2006

This paper presents an Optimal Power Flow (OPF) algorithm using Interior Point Method (IPM) to swiftly and precisely perform the five minute dispatch. This newly suggested methodology is based on Affine Scaling Interior Point Method which is favorable for large-scale problems involving many constraints. It is also eligible for OPF problems in order to improve the calculation speed and the preciseness of its resultant solutions. Big-M Method is also used to improve the solution stability. Finally, this paper provides relevant case studies to confirm the efficiency of the proposed methodology.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.103-112
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• 2014

In recent years, the rapid diffusion of smart devices and growth of internet usage and social media has led to a constant production of huge amount of valuable data set that includes personal information, buying patterns, location information and other things. IT and Production Infrastructure has also started to produce its own data with the vitalization of M2M (Machine-to-Machine) and IoT (Internet of Things). This analysis study researches the applicable effects of Structured and Unstructured Big Data in various business circumstances, and purposes to find out the value creation method for a corporation through the Structured and Unstructured Big Data case studies. The result demonstrates that corporations looking for the optimized big data utilization plan could maximize their creative values by utilizing Unstructured and Structured Big Data generated interior and exterior of corporations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.140-141
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• 2017

Big data analysis, in the large amount of data stored as the data warehouse which it refers the process of discovering meaningful new correlations, patterns, trends and creating new values. Thus, Big data analysis is an effective analysis of various big data that exist all over the world such as social big data, machine to machine (M2M) sensor data, and corporate customer relationship management data. In the big data era, it has become more important to effectively analyze not only structured data that is well organized in the database, but also unstructured big data such as the internet, social network services, and explosively generated web documents, e-mails, and social data in mobile environments. By the way, a meta analysis refers to a statistical literature synthesis method from the quantitative results of many known empirical studies. We reviewed a total of 750 samples among 50 studies published on the topic related as IDT between 2000 and 2017 in Korea.

• Annual Conference of KIPS
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• 2017.04a
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• pp.540-543
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• 2017

It will be possible to solve some of the major issues in our society and economy with the emerging Big Data used across 21st century global digital economy. One of the main areas where big data can be quite useful is the medical and health area. IT technology is being used extensively in this area and expected to expand its application field further. However, there is still room for improvement in the usage of Big Data as it is difficult to search unstructured data contained in Big Data and collect statistics for them. This limits wider application of Big Data. Depending on data collection and analysis method, the results from a Big Data can be varied. Some of them could be positive or negative so that it is essential that Big Data should be handled adequately and appropriately adapting to a purpose. Therefore, a Big Data has been constructed in this study to applying Crawling technique for data mining and analyzed with R. Also, the data were visualized for easier recognition and this was effective in developing an individualized health plan from different angles.

• Journal of the Korean Ceramic Society
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• v.47 no.4
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• pp.308-311
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• 2010

Porous SiC samples were prepared with three types of wood (poplar, pine, big cone pine) by simply embedding the wood charcoal in a powder mixture of Si and $SiO_2$ at 1600 and $1700^{\circ}C$. The basic engineering properties such as density, porosity, pore size and distribution, and strength were characterized. The samples showed full conversion to mostly $\beta$-SiC with good retention of the cellular structure of the original wood. More rigid SiC struts were developed for $1700^{\circ}C$. They showed similar bulk density ($0.5{\sim}0.6\;g/cm^3$) and porosity (81~84%) irrespective of the type of wood. The poplar sample showed three pore sizes (1, 8, $60\;{\mu}m$) with a main size of $60\;{\mu}m$. The pine sample showed a single pore size ($20\;{\mu}m$). The big cone pine sample showed two pore sizes (10, $80\;{\mu}m$) with a main size of $10\;{\mu}m$. The bend strength was 2.5 MPa for poplar, 5.7 MPa for pine, 2.8 MPa for big cone pine, indicating higher strength with pine.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• Journal of the Korea Convergence Society
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• v.6 no.4
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• pp.243-248
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• 2015

U-Healthcare is a convergence service with medical care and IT which enables to examine, manage and maintain the patient's health any time and any place. For communication conducted in U-Healthcare service, the transmission methods are used that patient's medical checkup analysis results or emergency data are transmitted to hospital server using wireless communication method. At this moment when the attacker who executes the malicious access makes DRDoS(Distributed Reflection DoS) attack to U-Healthcare devices or BS(Base Station), various damages occur that contextual information of urgent patients are not transmitted to hospital server. In order to deal with this problem, this study suggests DRDoS attack scenario and countermeasures against DRDoS and converges with Big Data which could process large amount of packets. When the attacker attacks U-Healthcare devices or BS(Base Station), DB is interconnected and the attack is prevented if it is coincident. This study analyzes the attack method that could occur in U-Healthcare devices or BS which are remote medical service and suggests countermeasures against the security threat using Big Data.

• Journal of the Korean Solar Energy Society
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• v.40 no.1
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• pp.15-24
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• 2020

Building energy consumption generally depends on living patterns of residents and outdoor air temperature changes. Although outdoor air temperature changes effect on building energy consumption, there is no calibration method for the comparison before and after Green Remodeling or BEMS installation etc., Big data of building energy consumption are collected and managed by 『National Integrated Management System of Building Energy』 in Korea, and they are utilized for the development of a calibration method for individual buildings as shown as the calibration method for small-area building stocks in the previous research. This study aims to develope a calibration method using big data of building energy consumption of individual buildings and outdoor air temperature changes, and to propose application of appropriate calibration methods for individual buildings or small-area building stocks according to the calibration purpose and conditions.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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• pp.478-483
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• 2012

The speed performances of ice sea trial on the Arctic(2010 & 2011) area were shown different results depend on the ice floe size. Penetration phenomena of level ice was not happened on medium ice floe and tore up by the impact force because the mass of medium ice floe is similar to the mass of Araon which is Korean ice breaking research vessel and did not shut up by the ice ridge or iceberg. The sea trial on the Amundsen sea was performed at the big floe which is classified by WMO(World Meteorological Organization). Three measurements of ice properties and five results of speed trial were obtained with different ice thicknesses and engine powers. To evaluate speed of level ice trial and model test results at the same ice thickness and engine power, the correction method of HSVA(Hamburg Ship Model Basin) was used. The thickness, snow effect, flexural strength and friction coefficient were corrected to compare the speed of sea trial. The analyzed speed at 1.03m thickness of big floe was 5.85 knots at 10MW power and it's 6.10 knots at 1.0m ice thickness and the same power. It's bigger than the results of level ice because big floe was also slightly tore up by the impact force of vessel based on the observation of recorded video.

• New & Renewable Energy
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• v.20 no.1
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• pp.95-101
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• 2024

Since 1990, the Renewable Big Data Research Lab at the Korea Institute of Energy Technology has been observing solar radiation at 16 sites across South Korea. Serving as the National Reference Standard Data Center for Renewable Energy since 2012, it produces essential data for the sector. By 2020, it standardized meteorological year data from 22 sites. Despite user demand for data from approximately 260 sites, equivalent to South Korea's municipalities, this need exceeds the capability of measurement-based data. In response, our team developed a method to derive solar radiation data from satellite images, covering South Korea in 400,000 grids of 500 m × 500 m each. Utilizing satellite-derived data and ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), we produced standard meteorological year data for 1,000 sites. Our research also focused on data measurement traceability and uncertainty estimation, ensuring the reliability of our model data and the traceability of existing measurement-based data.