• Journal of applied mathematics & informatics
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• 제29권1_2호
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• pp.475-483
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• 2011

In this paper, we study the oscillatory and asymptotic behavior of a class of second order nonlinear differential inequality with perturbation and establish several theorems by using classification and analysis, which develop and generalize some known results.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2011년도 정기 학술대회
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• pp.278-281
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• 2011

The global coordinates generated from Isomap algorithm provide a simple way to analyze and manipulate high dimensional observations in terms of their intrinsic nonlinear degrees of freedom. Thus, Isomap can find globally meaningful coordinates and nonlinear structure of complex data sets, while neither principal component analysis (PCA) nor multidimensional scaling (MDS) are successful in many cases. It is demonstrated that the adapted Isomap algorithm successfully enhances the quality of pattern classification for damage identification in various numerical examples.

• 한국결정성장학회지
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• 제12권6호
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• pp.323-328
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• 2002

The model of the shortest chemical bonds is applied for the classification of acentric simple and binary Mo(VI) and W(VI) oxides. It is shown that on the plane of the shortest chemical bonds the compounds are located into the rosette of three intersected ellipses. The correlation between the optical nonlinearity and combination of the bond lengths is discussed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.522-526
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• 1993

The first objective of this study is to derive an automated method that minimizes the number of spline regions and optimizes the locations of the knots to provide and adequate fit of a given nonlinear function. This has been accomplished by the development of the Optimal Spline Method discussed herein. The second objective is to apply the derived automated method to an important application. This objective has been accomplished by the successful application of the Optimal Spline Method to ship classification.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2164-2167
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• 2003

In this paper we propose effective speech recognizer through recognition experiments for three feature parameters(PCA, ICA and MFCC) using SVM(Support Vector Machine) classifier In general, SVM is classification method which classify two class set by finding voluntary nonlinear boundary in vector space and possesses high classification performance under few training data number. In this paper we compare recognition result for each feature parameter and propose ICA feature as the most effective parameter

• 사상체질의학회지
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• 제22권1호
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• pp.17-25
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• 2010

1. Objectives: Voice diagnosis has been used to classify individuals into the Sasang constitution in SCM(Sasang Constitution Medicine) and to recognize his/her health condition in TKM(Traditional Korean Medicine). In this paper, we purposed a new speech classification algorithm for Sasang constitution. 2. Methods: This algorithm is based on the SVM(Support Vector Machine) technique, which is a classification method to classify two distinct groups by finding voluntary nonlinear boundary in vector space. It showed high performance in classification with a few numbers of trained data set. We designed for this algorithm using 3 SVM classifiers to classify into 4 groups, which are composed of 3 constitutional groups and additional indecision group. 3. Results: For the optimal performance, we found that 32.2% of the voice data were classified into three constitutional groups and 79.8% out of them were grouped correctly. 4. Conclusions: This new classification method including indecision group appears efficient compared to the standard classification algorithm which classifies only into 3 constitutional groups. We find that more thorough investigation on the voice features is required to improve the classification efficiency into Sasang constitution.

• Journal of information and communication convergence engineering
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• 제17권1호
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• pp.14-20
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• 2019

Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.

• 대한조선학회논문집
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• 제42권5호
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• pp.479-486
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• 2005

The final goal of shift alignment design is that the bearing reaction forces or mean pressures are within design boundaries for various service conditions of a ship. However, it is found that calculated bearing load can be substantially variable according to the locations of the effective support points of after sterntube bearing which are determined by simple calculation or assumption suggested by classification societies. A new analysis method for shaft alignment calculation is introduced in order to resolve these problems. Key concept of the new method is featured by adopting both nonlinear elastic and multi-support elements to simulate a bearing support Hertz contact theory is basically applied for nonlinear elastic stiffness calculation instead of the projected area method suggested by most of classification societies. Three loading conditions according to the bearing offset and the hydrodynamic moment and twelve models according to the locations of the effective support points of sterntube bearings are prepared to carry out quantitative verifications for an actual shafting system of 8000 TEU class container vessel. It is found that there is relatively large difference between assumed and calculated effective support points.

• 대한원격탐사학회지
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• 제29권5호
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• pp.509-516
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• 2013

초분광 영상에 내재된 비선형 현상을 다루기 위해서는 과거에 주로 사용되었던 선형 피처 추출 방법은 적합하지 않았다. 따라서 최근 Manifold learning이라 불리우는 비선형 피처 추출 방법이 초분광 원격탐사 분야를 비롯 여러 분야에서 관심이 증가되고 있다. Manifold learning 방법 중 널리 이용되는 Isomap은 분류와 분광 혼합 분석 등의 분야에서 좋은 결과를 보여주지만, 지나치게 복잡하고 높은 계산량은, 특히 원격탐사 자료와 같이 자료의 크기가 큰 경우 문제가 된다. 따라서 자료의 일부분을 이용하는 랜드마크 기법이 해결책으로 제안 되었다. 본 연구에서는 좀 더 통제가 가능한 랜드마크 추출을 위해 자료를 구성하는 최대 부피를 지닌 Simplex를 이용하여 랜드마크를 선택하는 방법을 제안한다. 초분광 영상을 이용하여 랜드마크의 개수, 선택 방법에 따른 분류 정확도와 편차, 그리고 처리 시간을 비교하였고, 그 결과 제안된 랜드마크 선택 기법은 분류 정확도, 처리시간 모두에서 효율적인 결과를 보여주었다.

• 통합자연과학논문집
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• 제13권4호
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.