• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.237-250
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• 2017

A new field of e-sports gains the great popularity in Korea as well as abroad. AOS (aeon of strife) genre games are quickly gaining popularity with gamers from all over the world and the game companies hold game competitions. The e-sports broadcasting teams and webzines use a variety of statistical indicators. In this paper, as an AOS genre game, League of Legends game data is used for statistical analysis using the indicators to predict the outcome. We develop new indicators with the factor analysis to improve existing indicators. Also we consider discriminant function, neural network model, and SVM (support vector machine) for make winning forecasting models. As a result, the new position indicators reflect the nature of the role in the game and winning forecasting models show more than 95 percent accuracy.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.192-200
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• 2019

This paper is analyzed causality using cointegration test and impact response after deriving a causality between direct overseas purchasing and sale and macroeconomic variables. The model used for the empirical analysis is the vector error correlation model. The model is used the macroeconomic variables such as the consumer price index and the GDP, and e-commerce variables such as direct overseas purchasing, direct overseas sales and online shopping amount. According to empirical analysis, the direct overseas purchasing has the causality with the consumer price index, and GDP has the causality with direct overseas purchasing and online. According to the impact response analysis of the VECM, the direct overseas purchasing has a positive effect on the CPI and GDP, but the direct overseas sales has a negative effect on the CPI and GDP. In addition, both direct overseas purchasing and sales have a negative effect on online shopping, but it has been shown that the direct overseas purchasing has a bigger negative effect on online shopping.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• Horticultural Science & Technology
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• v.32 no.6
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• pp.845-852
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• 2014

Among various abiotic stress factors, soil salinity decreases the photosynthetic rate, growth, and yield of plants. Recently, many genes have been reported to enhance salt tolerance. The objective of this study was to characterize the Brassica rapa Salt Stress Resistance (BrSSR) gene, of which the function was unclear, although the full-length sequence was known. To characterize the role of BrSSR, a B. rapa Chinese cabbage inbred line ('CT001') was transformed with pSL94 vector containing the full length BrSSR cDNA. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis showed that the expression of BrSSR in the transgenic line was 2.59-fold higher than that in the wild type. Analysis of phenotypic characteristics showed that plants overexpressing BrSSR were resistant to salinity stress and showed normal growth. Microarray analysis of BrSSR over-expressing plants confirmed that BrSSR was strongly associated with ERD15 (AT2G41430), a gene encoding a protein containing a PAM2 motif (AT4G14270), and GABA-T (AT3G22200), all of which have been associated with salt tolerance, in the co-expression network of genes related to salt stress. The results of this study indicate that BrSSR plays an important role in plant growth and tolerance to salinity.

• Korea Trade Review
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• v.43 no.3
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• pp.125-145
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• 2018

The Baltic Shipping Exchange is reporting the Baltic Dry Index (BDI) which represents the average charter rate for bulk carriers transporting major cargoes such as iron ore, coal, grain, and so on. And the current BDI index is reflected in the proportion of capesize 40%, panamax 30% and spramax 30%. Like mentioned above, the capesize plays a major role among the various sizes of bulk carriers and this study is to analyze the influence of the factors influencing on charter rate of capesize carriers which transport iron ore and coal as the major cargoes. For this purpose, this study verified causality between variables using Vector Error Correction Model (VECM) and tried to derive a long-run equilibrium model between the dependent variable and independent variables. Regression analysis showed that every six independent variable has a significant effect on the capesize charter rate, even at the 1% level of significance. Charter rate decreases by 0.08% when capesize total fleet increases by 1%, charter rate increases by 0.04% when bunker oil price increases by 1%, and charter rate decreases by 0.01% when Yen/Dollar rate increases by 1%. And charter rate increases by 0.02% when global GDP increases by one unit (1%). In addition, the increase in cargo volume of iron ore and coal which are major transportation items of capesize carriers has also been shown to increase charter rates. Charter rate increases by 0.11% in case of 1% increase in iron ore cargo volume, and 0.09% in case of 1% increase in coal cargo volume. Although there have been some studies to analyze the influence of factors affecting the charterage of bulk carriers in the past, there have been few studies on the analysis of specific size vessels. At present moment when ship size is getting bigger, this study carried out research on capesize vessels, which are biggest among bulk carriers, and whose utilization is continuously increasing. This study is also expected to contribute to the establishment of trade policies for specific cargoes such as iron ore and coal.

• Information Systems Review
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• v.21 no.1
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• pp.23-38
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• 2019

The social network service (SNS) is one of the important marketing channels, so many companies actively exploit SNSs by posting SNS messages with appropriate content and style for their customers. In this paper, we focused on the psychological distances embedded in the SNS messages and developed a method to measure the psychological distance in SNS message by mixing a traditional content analysis, natural language processing (NLP), and machine learning. Through a traditional content analysis by human coding, the psychological distance was extracted from the SNS message, and these coding results were used for input data for NLP and machine learning. With NLP, word embedding was executed and Bag of Word was created. The Support Vector Machine, one of machine learning techniques was performed to train and test the psychological distance in SNS message. As a result, sensitivity and precision of SVM prediction were significantly low because of the extreme skewness of dataset. We improved the performance of SVM by balancing the ratio of data by upsampling technique and using data coded with the same value in first content analysis. All performance index was more than 70%, which showed that psychological distance can be measured well.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.477-481
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• 2007

The purpose of this study is to provide the ideal forecasting model of cadastral survey work load through the Economeatric Analysis of Time Series, Granger Causality and VAR Model Analysis, it suggested the forecasting reference materials for the total amount of cadastral survey general work load. The main result is that the derive of the environment variables which affect cadastral survey general work load and the outcome of VAR(vector auto regression) analysis materials(impulse response function and forecast error variance decomposition analysis materials), which explain the change of general work load depending on altering the environment variables. And also, For confirming the stability of time series data, we took a unit root test, ADF(Augmented Dickey-Fuller) analysis and the time series model analysis derives the best cadastral forecasting model regarding on general cadastral survey work load. And also, it showed up the various standards that are applied the statistical method of econometric analysis so it enhanced the prior aggregate system of cadastral survey work load forecasting.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.99-104
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• 2010

MDPREF(Multidimensional Preference Analysis) is a program for analysis of preferences. It is what is known as a vector model. This means that the objective of the MDPREF analysis is to identify a perceptual map displaying subject(attribute) vectors. To form the subject vectors visually, lines are drawn from the origin of the plot to each subject point. We analysis the defense industry infrastructure in fire power area by using MDPREF.

• The KSFM Journal of Fluid Machinery
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• v.19 no.1
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• pp.45-51
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• 2016

This study conducted CFD analysis on the mean velocity vector of distribution of the ejector driven pipe while changing the inlet velocity to 1 m/s at the diameter ratio of diffuser of 1:3, 1:2.25, 1:1.8 with the end position of driven pipe at 1, 1.253, 1.333, 1.467 respectively, which used $k-{\varepsilon}$/High Reynolds Number for the turbulence model, SIMPLE method for the analysis algorithm, and PIV experiment to verify the CFD analysis. As a result of the CFD analysis the optimum diameter ratio of ejector driven pipe was 1:3, the optimum end position of driven pipe was 1.333 for the diameter ratio of 1:3, 1:2.25, 1:1.8 and the PIV experiment obtained the same result as the CFD analysis. Therefore, the numerical analysis of the flow characteristics of ejector can be used for the optimum design implementation on ejector system.