• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.5
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• pp.142-148
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• 1996

It is shown that a simple vector analysis method can provide beam cone shapes for laser bemas non-paraxially input to dielectric boundaries with inclination. Acceptance coen shapes for angled-endface fibers are calculated by the method. Beam cone shapes inside InP substrates are also calculated by the method for the coupling of an optical fiber and an InP-based photodiode using a Si v-groove. The effectiveness and errors of the recently suggested matrix method for inclined boundaries are also studied.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.933-943
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• 2008

Spectral analysis of a strictly stationary r-vector valued time series is considered under the assumption that some of the observations are missed due to some random failure. Statistical properties and asymptotic moments are derived. Asymptotic normality is discussed.

• 한국전산유체공학회:학술대회논문집
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• 1995.10a
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• pp.132-137
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• 1995

Numerical analysis is conducted on the deformation of free surface of magnetic fluid. Steady magnetic fields are induced by a circular current loop. Governing equations of magnetic fields are solved by using the concept of vector potential. The free surface of magnetic fluid is formed by the balance of surface force, gravity, pressure difference, magnetic normal pressure and magnetic body force. The deformations of free surface of magnetic fluid are qualitatively clarified. And, the patterns of steady non-uniform magnetic fields induced by a circular current loop are quantitatively presented.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.605-616
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• 2004

In this paper we consider the proportional hazard models for survival analysis in the microarray data. For a given vector of response values and gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1997.10a
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• pp.151-158
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• 1997

The main concern of numerical dynamic analysis of large structures is to find an acceptable solution with fewer mode shapes and less computational efforts. component mode method utilizes substructure technique to reduce the degrss of freedom but have a disadvantage to not consider the dynamic characteristics of loads. Ritz Vector method consider the load characteristics but requires many integrations and errors are accumulated. In this study, to prove the effectiveness of component mode method, Lanczos algorithm are introduced. To prove the effectiveness of this method, example structures areanalyzed and the results are compared with SAP90.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.129-132
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• 2000

We propose a new method to deal with the optimized auto-tuning for the PID controller which is used to the process -control in various fields. First of all, in this method, initial values of DC motor are determined by the Ziegler-Nichols method. Finally, after studying the parameters of PID controller by input vector of multiple regression analysis, when we give new K, L, T values to multiple regression model, the optimized parameters of PID controller is found by multiple regression analysis program.

• Proceedings of the KSR Conference
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• 1998.11a
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• pp.429-436
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• 1998

In this paper, we derive the algorithm for calculating contact point between wheel and rail and develop the method for track modeling. The proposed methods use travelling distance to represent track center line poistion vector and track orientation with respect to Newtonian reference frame. The proposed methods can be easily used in multibody dynamic analysis. Two numerical examples are given to verify the validity of the proposed methods.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.555-562
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• 2016

Semantic Role Labeling (SRL) is to determine the semantic relation of a predicate and its argu-ments in a sentence. But Korean semantic role labeling has faced on difficulty due to its different language structure compared to English, which makes it very hard to use appropriate approaches developed so far. That means that methods proposed so far could not show a satisfied perfor-mance, compared to English and Chinese. To complement these problems, we focus on suffix information analysis, such as josa (case suffix) and eomi (verbal ending) analysis. Korean lan-guage is one of the agglutinative languages, such as Japanese, which have well defined suffix structure in their words. The agglutinative languages could have free word order due to its de-veloped suffix structure. Also arguments with a single morpheme are then labeled with statistics. In addition, machine learning algorithms such as Support Vector Machine (SVM) and Condi-tional Random Fields (CRF) are used to model SRL problem on arguments that are not labeled at the suffix analysis phase. The proposed method is intended to reduce the range of argument instances to which machine learning approaches should be applied, resulting in uncertain and inaccurate role labeling. In experiments, we use 15,224 arguments and we are able to obtain approximately 83.24% f1-score, increased about 4.85% points compared to the state-of-the-art Korean SRL research.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.117-128
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• 2017

Purpose: This study analyzes automobile quality review data to develop alternative analytical method of informal data. Existing methods to analyze informal data are based mainly on the frequency of informal data, however, this research tries to use correlation information of each informal data. Method: After sentimental analysis to acquire the user information for automobile products, three classification methods, that is, $na{\ddot{i}}ve$ Bayes, random forest, and support vector machine, were employed to accurately classify the informal user opinions with respect to automobile qualities. Additionally, Word2vec was applied to discover correlated information about informal data. Result: As applicative results of three classification methods, random forest method shows most effective results compared to the other classification methods. Word2vec method manages to discover closest relevant data with automobile components. Conclusion: The proposed method shows its effectiveness in terms of accuracy and sensitivity on the analysis of informal quality data, however, only two sentiments (positive or negative) can be categorized due to human errors. Further studies are required to derive more sentiments to accurately classify informal quality data. Word2vec method also shows comparative results to discover the relevance of components precisely.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.134-138
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• 2006

Depression is the most common and widespread mood disorder. About 20% of the population might suffer a major, incapacitating episode of depression during their lifetime. This disorder can be classified into two types: major depressive disorders and bipolar disorder. Since pharmaceutical treatments are different according to types of depression disorders, correct and fast classification is quite critical for depression patients. Yet, classical statistical method, such as minnesota multiphasic personality inventory (MMPI), have some difficulties in applying to depression patients, because the patients suffer from concentration. We used electroencephalogram (EEG) analysis method fer classification of depression. We extracted nonlinearity of information flows between channels and estimated approximate entropy (ApEn) for the EEG at each channel. Using these attributes, we applied two types of data mining classification methods: decision tree and possibilistic support vector machines (PSVM). We found that decision tree showed 85.19% accuracy and PSVM exhibited 77.78% accuracy for classification of depression, 30 patients with major depressive disorder and 24 patients having bipolar disorder.