• Journal of The Korean Astronomical Society
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• v.37 no.3
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• pp.119-129
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• 2004

A new method has been developed to solve the star cluster membership problem. It is based on synthetic photometry employing the Black Body concept as stellar radiation simulator. Synthetic color-magnitude diagram is constructed showing the main sequence band and the positions of binary star systems of combinations of various components through different photometric tracks. The method has been applied to the Hyades. The cluster membership problem has been re-appraised for the cluster (both single and binary) stars. For the binary members, the components' spectral types have been derived by the method. The results obtained agree very well with those found in literature, The method is simpler than the others and can be developed to undertake other cases as multiple star systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.166-171
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• 2013

The method proposed in this paper can improve the performance of the Boosting algorithm in machine learning. The proposed Boundary AdaBoost algorithm can make up for the weak points of Normal binary classifier using threshold boundary concepts. The new proposed boundary can be located near the threshold of the binary classifier. The proposed algorithm improves classification in areas where Normal binary classifier is weak. Thus, the optimal boundary final classifier can decrease error rates classified with more reasonable features. Finally, this paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Boundary AdaBoost in a simulation experiment of pedestrian detection using 10-fold cross validation.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• Journal of Astronomy and Space Sciences
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• v.29 no.1
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• pp.93-96
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• 2012

There are observational difficulties determining dynamical masses of binary star components in the upper HR diagram both due to the scarcity of massive binary systems and spectral and photometric contamination produced by the strong wind outflows in these systems. We discuss how variable X-ray emission in these systems produced by wind-wind collisions in massive binaries can be used to constrain the system parameters, with application to two important massive binaries, Eta Carinae and WR 140.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.565-565
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• 1995

• Journal of the Korean Society of Industry Convergence
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• v.7 no.1
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• pp.97-105
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• 2004

The Vapor-liquid equilibrium data for the binary system of 2-methyl-2-propanol-2-butanone are measured at subatmospheric pressure of 100, 200, 300, 400, 500, 600, 700 and 760 torr. This study shows that the relations between logarithmic values of relative valatility(log ${\alpha}$)and liquid phase composition(${\chi}$) in the above binary systems are expressed as a linear function. When the linear relationships of between logarithmic values of relative volatilities and liquid phase compositions in the binary systems of various pressure intersect at a point, this empirical equation can be applied to the systems of this kind. From these relations the vapor-liquid equilibrium data are estimated and compared with the measured values to be in a good agreement with in accuracy ${\pm}0.0021$ for the various pressure.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.60.3-60.3
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• 2019

Binary systems of a white dwarf showing mass transfer activities are classified into cataclysmic variables and symbiotic stars. In the case of cataclysmic variables, the companion is usually a late type main sequence star filling its Roche lobe, where material is transferred through the inner Lagrangian point to form an accretion disk around the white dwarf. The disk becomes unstable and highly viscous when the surface density exceeds the critical density, leading to dwarf nova outbursts. In contrast, symbiotic stars are wide binary systems having a giant as the mass donor. Some fraction of giant stellar wind is accreted to the white dwarf giving rise to various symbiotic activities. In particular, half of symbiotics show Raman O VI at 6830 and 7088, which are important spectroscopic probe of mass transfer process. Monitoring observations using 1 m class telescopes will produce valuable information regarding the mass loss and mass transfer to white dwarf stars, shedding much light on the last stage of stellar evolution of low and intermediate mass stars.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.117-122
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• 2009

In this paper, the performance of double binary turbo code is analyzed and simulated in ultra wide-band (UWB) systems employing multiple-antenna scheme. We consider both pulse position modulation-time hopping (PPM-TH) and pulse amplitude modulation-direct sequence (PAM-DS) UWB systems. The space time block code (STBC) scheme is adopted as a transmit diversity method. Also, receive diversity scheme is applied. And double binary turbo code is applied to the UWB system.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.8-17
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• 2014

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials that are provided by the KBO website. Using the collected raw data, we additionally prepared two more types of dataset, which are in ratio and binary format respectively. Dividing away-team's records by the records of the corresponding home-team generated the ratio dataset, while the binary dataset was obtained by comparing the record values. We applied seven classification techniques to three (raw, ratio, and binary) datasets. The employed data mining techniques are decision tree, random forest, logistic regression, neural network, support vector machine, linear discriminant analysis, and quadratic discriminant analysis. Among 21(= 3 datasets${\times}$7 techniques) prediction scenarios, the most accurate model was obtained from the random forest technique based on the binary dataset, which prediction accuracy was 84.14%. It was also observed that using the ratio and the binary dataset helped to build better prediction models than using the raw data. From the capability of variable selection in decision tree, random forest, and stepwise logistic regression, we found that annual salary, earned run, strikeout, pitcher's winning percentage, and four balls are important winning factors of a game. This research is distinct from existing studies in that we used three different types of data and various data mining techniques for win-loss prediction in Korean professional baseball games.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4007-4024
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• 2020

It is tremendously important to construct decision trees to use as a tool for knowledge representation from a given decision table. However, the usual algorithms may split the decision table based on each value, which is not efficient for numerical attributes. The methodology of this paper is to split the given decision table into binary groups as like the CART algorithm, that uses binary split to work for both categorical and numerical attributes. The difference is that it uses split for each attribute established by the directed acyclic graph in a dynamic programming fashion whereas, the CART uses binary split among all considered attributes in a greedy fashion. The aim of this paper is to study the effect of binary splits in comparison with each value splits when building the decision trees. Such effect can be studied by comparing the number of nodes, local and global misclassification rate among the constructed decision trees based on three proposed algorithms.