• Archives of design research
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• v.11
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• pp.22-29
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• 1995

I have to find standpoint of sight moulding of Chi I Sung Hwa(seven stars picture) analysis of graphic systems of a symbol sight native to our nation. And I will comprehend emotion of folkways by simple and graphic lines and colors in mathematical Grid of which ancestor had expressed in gauge moulding consciousness. This papers aim is to make a contribution to lead by on part of communication design. About structural analysis of pictorial graphic side. I) Mathematical thought of the Orient and space constitution are first basically the Orient expressed number notion of mathematics of unlimitedness and notion of zero so called space and empty second can analigize a diagonal expansion method by development of symmetry notion to basic the dual principle of the negative and positive by degrees development expressed space division method by direction notion. 2) About the proportion analysis it based the golden section globularity and in modern layout it takes vision center of position, after appointing the brow of sacred image of Chil Sung Hwa as center point of proportion and applied to the point proportion and so analigized the posibility of established. Rule in union of each elements and rule of forms about picture image. 3) Mathematical structure analysis to search a unified principle at the balanced arrangement and rule of forms it analigized the standard the rule of forms. it analigized the standard the rule of forms to body module of basic movement of protagonist and follower above basic forms of grid that is the basis of design system.

• Geophysics and Geophysical Exploration
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• v.15 no.2
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• pp.75-84
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• 2012

The sea layer in marine Controlled-Source Electromagnetic (mCSEM) survey changes the conventional definition of apparent resistivity which is used in the land CSEM survey. Thus, the development of a new algorithm, which computes apparent resistivity for mCSEM survey, can be an initiative of mCSEM data interpretation. First, we compared and analyzed electromagnetic responses of the 1D stratified gas hydrate model and the half-space model below the sea layer. Amplitude and phase components showed proper results for computing apparent resistivity than real and imaginary components. Next, the amplitude component is more sensitive to the subsurface resistivity than the phase component in far offset range and vice versa. We suggested the induction number as a selection criteria of amplitude or phase component to calculate apparent resistivity. Based on our study, we have developed a numerical algorithm, which computes appropriate apparent resistivity corresponding to measured mCSEM data using grid search method. In addition, we verified the validity of the developed algorithm by applying it to the stratified gas hydrate models with various model parameters. Finally, by constructing apparent resistivity pseudo-section from the mCSEM responses with 2D numerical models simulating gas hydrate deposits in the Ulleung Basin, we confirmed that the apparent resistivity can provide the information on the geometric distribution of the gas hydrate deposit.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.2
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• pp.311-316
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• 2016

Network intrusion detection system based on machine learning method such as artificial neural network is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features, which guarantees accuracy and efficienty, from generally used many features to detect network intrusion requires extensive computing resources. In this paper, we deal with a optimal feature selection problem to determine 6 denial service attacks and normal usage provided by NSL-KDD data. We propose a optimal feature selection algorithm. Proposed algorithm is based on the multi-start local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In order to evaluate the performance of our proposed algorithm, comparison with a case of all 41 features used against NSL-KDD data is conducted. In addtion, comparisons between 3 well-known machine learning methods (multi-layer perceptron., Bayes classifier, and Support vector machine) are performed to find a machine learning method which shows the best performance combined with the proposed feature selection method.

• Journal of the Korean Chemical Society
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• v.53 no.6
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• pp.653-662
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• 2009

Developing effective tools for predicting absorption, distribution, metabolism, excretion properties and toxicity (ADME/T) of new chemical entities in the early stage of drug design is one of the most important tasks in drug discovery and development today. As one of these attempts, support vector machines (SVM) has recently been exploited for the prediction of ADME/T related properties. However, two problems in SVM modeling, i.e. feature selection and parameters setting, are still far from solved. The two problems have been shown to be crucial to the efficiency and accuracy of SVM classification. In particular, the feature selection and optimal SVM parameters setting influence each other, which indicates that they should be dealt with simultaneously. In this account, we present an integrated practical solution, in which genetic-based algorithm (GA) is used for feature selection and grid search (GS) method for parameters optimization. hERG ion-channel inhibitor classification models of ADME/T related properties has been built for assessing and testing the proposed GA-GS-SVM. We generated 6 different models that are 3 different single models and 3 different ensemble models using training set - 1891 compounds and validated with external test set - 175 compounds. We compared single model with ensemble model to solve data imbalance problems. It was able to improve accuracy of prediction to use ensemble model.

• The Journal of Korean Association of Computer Education
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• v.12 no.4
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• pp.57-64
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• 2009

Protein structure comparison is a task that requires a lot of computing resources because many atoms in proteins need to be processed. To address the issue, Grid computing environment has been employed for processing time-consuming jobs in a distributed manner. However, controling the Grid computing environment may not be easy for non-experts. In this paper, we present a JXTA-based system for protein structure comparison that can be easily controled by non-experts. To search proteins similar to a query protein, the geometric hashing algorithm that consists of preprocessing and recognition was employed. Experimental results indicate that the system can find the correct protein structure for a given query protein structure and the proposed system can be easily extended to solve the protein docking problem. It is expected that the proposed system can be useful for non-experts, especially users who do not have sophisticated knowledge of distributed systems in general such as college students who major in biology or chemistry.

• Atmosphere
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• v.25 no.3
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• pp.529-541
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• 2015

Korea Institute of Atmospheric Prediction System (KIAPS) has been developing a global numerial prediction model and data assimilation system. We has implemented LETKF (Local Ensemble Transform Kalman Filter, Hunt et al., 2007) data assimilation system to NCAR CAM-SE (National Center for Atmospheric Research Community Atmosphere Model with Spectral Element dynamical core, Dennis et al., 2012) that has cubed-sphere grid, known as the same grid system of KIAPS Integrated Model (KIM) now developing. In this study, we have assimilated Global Positioning System Radio Occultation (GPS-RO) bending angle measurements in addition to conventional data within ensemble-based data assimilation system. Before assimilating bending angle data, we performed a vertical unit conversion. The information of vertical localization for GPS-RO data is given by the unit of meter, but the vertical localization method in the LETKF system is based on pressure unit. Therefore, with a clever conversion of the vertical information, we have conducted experiments to search for the best vertical localization scale on GPS-RO data under the Observing System Simulation Experiments (OSSEs). As a result, we found the optimal setting of vertical localization for the GPS-RO bending angle data assimilation. We plan to apply the selected localization strategy to the LETKF system implemented to KIM which is expected to give better analysis of GPS-RO data assimilation due to much higher model top.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.2
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• pp.1-10
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• 2005

An unstructured parallel overset mesh method has been developed for the simulation of unsteady flows around multiple bodies in relative motion. For this purpose, an efficient and robust search method is proposed for the unstructured grid system. A new data-structure is also proposed to handle the variable number of data on parallel sub-domain boundary. The interpolation boundary is defined for data communication between grid systems. An interpolation method to retain second-order spatial accuracy and to treat the points inside the neighboring solid bodies are also suggested. A single store separating from the Eglin/Pylon configuration is calculated and the result is compared with experimental data for validation. Simulation of unsteady flows around multiple bodies in relative motion is also performed.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.911-923
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• 2016

Machine learning becomes increasingly important in the big data era. Logistic regression is a type of classification in machine leaning, and has been widely used in various fields, including medicine, economics, marketing, and social sciences. Rhipe that integrates R and Hadoop environment, has not been discussed by many researchers owing to the difficulty of its installation and MapReduce implementation. In this paper, we present the MapReduce implementation of Gradient Descent algorithm and Newton-Raphson algorithm for logistic regression using Rhipe. The Newton-Raphson algorithm does not require a learning rate, while Gradient Descent algorithm needs to manually pick a learning rate. We choose the learning rate by performing the mixed procedure of grid search and binary search for processing big data efficiently. In the performance study, our Newton-Raphson algorithm outpeforms Gradient Descent algorithm in all the tested data.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.12
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• pp.987-997
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• 2019

Walk score, walkablity index of built environmental condition, has developed and used in everyday life in United States. The purpose of this study is to produce walk score in Seoul, and to develop computer-based walk score system for improving it's usage. This study covers city of Seoul, and the unit of spatial analysis is 100m × 100m grid cell. This study uses same methodology with walk score in US, the Geographic Information Systems(GIS) is used for calculating the values of walk score(N=58,062). This study implemented Java-based system that utilizes walk score dataset(latitude, longitude, and walk score). This system provided search functions including both lat/long-based and address-based options. Meanwhile, this system was designed to provide the closest value of walk score in dataset if location did not match the certain points in dataset. This study has significance to provide walk score system being easily available to all.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.