• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.72.3-73
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• 2021

Baryon Acoustic Oscillations (BAO) are caused by acoustic density waves in the early universe and act as a standard ruler in the clustering pattern of galaxies in the late Universe. Measuring the BAO feature in the 2-point correlation function of a sample of galaxies allows us to estimate cosmological distances to the galaxies mean redshift, , which is important for testing and constraining the cosmology model. The BAO feature is also expected to appear in the higher order statistics. In this work we measure the generalized spatial N-point point correlation functions up to 4th order. We made measurements of the 2, 3, and 4-point correlation functions in the SDSS-III DR12 CMASS data, comprising of 777,202 galaxies. The errors and covariances matrices were estimated from 500 mock catalogues. We created a theoretical model for these statistics by measuring the N-point functions in halo catalogues produced by the approximate Lagrangian perturbation theory based simulation code, PINOCCHIO. We created simulations using initial conditions with and without the BAO feature. We find that the BAO is detected to high significance up to the 4-point correlation function.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.189-196
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• 2011

In this paper, we propose a logical control-based actor-critic algorithm as an efficient approach for the approximation of the capacitated fab scheduling problem. We apply the average reward temporal-difference learning method for estimating the relative value functions of system states, while avoiding deadlock situation by Banker's algorithm. We consider the Intel mini-fab re-entrant line for the evaluation of the suggested algorithm and perform a numerical experiment by generating some sample system configurations randomly. We show that the suggested method has a prominent performance compared to other well-known heuristics.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.197-207
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• 2022

The Naval Combat System operates in multiple software components for efficient functional processing. Data transmission and reception between components is performed in real time using DDS(Data Distribution Service). Reliable Post-Analysis requires the collection of all DDS messages. However, Software workload and development costs increase because common functions for all messages must be developed directly. In the paper, We propose an improved Post-Analysis based on the Feature Model and a new development system using it. Functions for the modified message were applied as variable domains of the Feature Model. When Build Center updates Post-Analysis, it automatically processes all software tasks associated with Post-Analysis. The proposed development system simplified the overall work procedure, and work time was significantly reduced compared to the existing development system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.30-36
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• 2004

The exchange of model design data among heterogeneous CAD systems is very difficult because each CAD system has different data structures suitable for its own functions. STEP represents product information in a common computer-interpretable form that is required to remain complete and consistent when the product information is needed to be exchanged among different computer systems. However, STEP has complex architecture to represent point, line, curve and vectors of element. Moreover it can't represent geometry data of feature based models. In this study, a structure of XML document that represents geometry data of feature based models as neutral format has been developed. To use the developed XML document, a converter also has been developed to exchange modules so that it can exchange feature based data models among heterogeneous CAD systems. Developed XML document and Converter have been applied to commercial CAD systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.299-302
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of$.$10 persons show that the proposed method yields high recognition rates.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.434-440
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• 2003

The exchange of model design date among heterogeneous CAD systems is a difficult task because each system has different data structures suitable for its own functions. STEP has been able to represent product information as a common computer-interpretable form that is required to remain complete and consistent when the product informant is needed to be exchanged among different computer system. However, STEP has difficult architecture in is representing point, line, curve and vectors of element, more over it can't represent geometry data of feature based models. In this study, a structure of XML document that represents geometry data of feature based models as neutral format has been developed. To use the developed XML document, a Converter has also been developed to exchange modules so that it can exchange feature based data models among heterogeneous CAD systems. Aa for evaluation of the developed XML document and Converter, Solidworks and SolidEdge are selected.

• Journal of Digital Convergence
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• v.9 no.1
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• pp.115-124
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• 2011

The property of the mobile phone is taking important role to choose it. In the present situation, exploring, comparing and analyzing the important properties of regular mobile phone(feature phone) and smart phone are very meaningful study. Therefore, the survey was carried out to get the properties of feature phone and smart phone and analyze the difference of those phones. And proposed the important variables for customer satisfaction which must be given priority. The result showed that 'design' and 'Quality' are important to both mobile phone user groups. The problems with mobile phones currently in use were 'poor performance' to feature phone users and 'expensive charge' and 'poor A/S' to smart phone users. Two groups also showed significant difference with the customer satisfactions, and smart phone user group showed higher satisfaction. For smart phone user group, four factors are induced from the properties but 'Hardware Quality' (representing 'call Quality', 'A/S', 'Convenience to use', 'Battery life') and 'Design & Function'(representing 'Internet', 'Convergence Functions', 'Design, 'Color') have significant and positive effects on Customer Satisfaction.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.822-826
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.137-146
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• 2005

In parametric approach to a signature verification, it generally uses so many redundant features unsuitable for each individual signature that it causes harm, instead. This paper proposes a method of determining personalized weights of a feature set in signature verification with parametric approach by identifying the characteristics of each feature. For an individual signature, we define a degree of how difficult it is for any other person to forge the one's (called 'DFD' as the Degree of Forgery Difficulty). According to the statistical characteristics and the intuitional characteristics of each feature, the standard features are classified into four types. Four types of DFD functions are defined and applied into the distance calculation as a personalized weight factor. Using this method, the error rate of signature verification is reduced and the variation of the performance is less sensitive to the changes of decision threshold.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.131-139
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• 2013

Support vector machines(SVM) may perform poorly in the presence of noise variables; in addition, it is difficult to identify the importance of each variable in the resulting classifier. A feature selection can improve the interpretability and the accuracy of SVM. Most existing studies concern feature selection in the linear SVM through penalty functions yielding sparse solutions. Note that one usually adopts nonlinear kernels for the accuracy of classification in practice. Hence feature selection is still desirable for nonlinear SVMs. In this paper, we compare the performances of nonlinear feature selection methods such as component selection and smoothing operator(COSSO) and kernel iterative feature extraction(KNIFE) on simulated and real data sets.