• Korean Journal of Mathematics
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• 제9권2호
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• pp.133-147
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• 2001

In this paper, we introduce the concepts of multiple $L_p$ analytic Fourier-Feynman transform ($1{\leq}p$ < ${\infty})$ and a convolution product of functionals on abstract Wiener space and verify the existence of the multiple $L_p$ analytic Fourier-Feynman transform for functionls in the Fresnel class. Moreover, we verify that the Fresnel class is closed under the $L_p$ analytic Fourier-Feynman transformation and the convolution product, respectively. And we establish some relationships among the multiple $L_p$ analytic Fourier-Feynman transform and the convolution product on the Fresnel class.

• 대한산업공학회지
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• 제25권1호
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• pp.141-149
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• 1999

In this paper, we consider multiple-class queueing systems in which the server starts a set-up as soon as the number of customers in the "start-up class" reaches threshold N. After the set-up the server starts his service. We obtain the Laplace-Stieltjes transform and the mean of the waiting times of each class of customers for FCFS and non-preemptive priority disciplines.

• 한국전자통신학회논문지
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• 제5권3호
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• pp.315-320
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• 2010

다중 클래스로 이루어진 패턴을 분류하는 것은 데이터 베이스 시스템에서 기록을 그룹화하거나 VLSI 회로에서 어디에 결함이 있는지를 찾는 것 등에서 중요한 역할을 한다. 본 논문에서는 주어진 다중 클래스 패턴을 MACA(Multiple Attractor Cellular Automata)와 부분공간의 개념을 이용하여 가능한 최소 메모리량을 필요로 하는 다중 클래스 패턴 분류기를 구성하는 알고리즘을 제안한다.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.21-31
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• 2023

In this paper, we focused on the problem of evaluating multi-class classification accuracy and simulation of multiple classifier performance metrics. Multi-class classifiers for sentiment analysis involved many challenges, whereas previous research narrowed to the binary classification model since it provides higher accuracy when dealing with text data. Thus, we take inspiration from the non-linear Support Vector Machine to modify the algorithm by embedding dynamic hyperplanes representing multiple class labels. Then we analyzed the performance of multi-class classifiers using macro-accuracy, micro-accuracy and several other metrics to justify the significance of our algorithm enhancement. Furthermore, we hybridized Enhanced Convolution Neural Network (ECNN) with Dynamic Support Vector Machine (DSVM) to demonstrate the effectiveness and efficiency of the classifier towards multi-class text data. We performed experiments on three hybrid classifiers, which are ECNN with Binary SVM (ECNN-BSVM), and ECNN with linear Multi-Class SVM (ECNN-MCSVM) and our proposed algorithm (ECNNDSVM). Comparative experiments of hybrid algorithms yielded 85.12 % for single metric accuracy; 86.95 % for multiple metrics on average. As for our modified algorithm of the ECNN-DSVM classifier, we reached 98.29 % micro-accuracy results with an f-score value of 98 % at most. For the future direction of this research, we are aiming for hyperplane optimization analysis.

• 충청수학회지
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• 제19권1호
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• pp.79-99
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• 2006

In this paper, we define a class of functional defined on a very general function space $C_{a,b}[0,T]$ like a Fresnel class of an abstract Wiener space. We then define the multiple $L_p$ analytic generalized Fourier-Feynman transform and the generalized convolution product of functionals on function space $C_{a,b}[0,T]$. Finally, we establish some relationships between the multiple $L_p$ analytic generalized Fourier-Feynman transform and the generalized convolution product for functionals in $\mathcal{F}(C_{a,b}[0,T])$.

• 충청수학회지
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• 제26권3호
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• pp.657-669
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• 2013

A general class of two-point optimal fourth-order methods is proposed for locating multiple roots of a nonlinear equation. We investigate convergence analysis and computational properties for the family. Special and simple cases are considered for real-life applications. Numerical experiments strongly verify the convergence behavior and the developed theory.

• Kyungpook Mathematical Journal
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• 제56권1호
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• pp.57-68
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• 2016

In this work, we shall give the degree of approximation for functions belonging to $H{\ddot{o}}lder$ class by matrix summability method of multiple Fourier series in the $H{\ddot{o}}lder$ metric.

• 대한가정학회지
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• 제29권2호
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• pp.135-154
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• 1991

The purposes of this study are: 1) To find out overall family life issues percieved by the middle-classhousewives in their forties. 2) To examine detailed aspects related to middle years crises, leisure activities, children issues, family economy issues, and housing issues. 3) To clarify solutions to, and provide basic data on family issues raised by the middle-class families. The middle-class housewives in their forties living in the Seoul area were the subject of the survey. The sample size analysed in this study was 422. Data were analysed by the frequency, mean, percentile, standard deviation, X2-test, analysis of variance, multiple classification analysis, analysis of multiple regression, and Scheffe-test as a post-hoc analysis. The conclusions are as follows: First, the middle-class housewives tend to give more importance on children issues, especially on academic achievement and career development. Second, family cohesion of middle-class families is comparatively high and intra-familial conflict is low, and middle years crisis of housewives is comparatively low, too. Third, the stability of middle-class families can be found in household economic management patterns. one fourth of the families own stocks and two fifths of the families own real estate except their own dwelling house. Be based on their property income add to their labor income, middle-class families are showed their economic stability, however, intra-class inequality is found, too. Fourth, the great part of middle-class families that possess their own house, tend to be unsatisfied with their housig scale, and a half of the families expect to enlarge their housing scale for more comfortable and convient living.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제14권4호
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• pp.451-455
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• 2008

본 논문에서는 다중 분류기를 효과적으로 결합하기 위하여 k-최근접 템플릿방법을 제안한다. 이는 하나의 클래스를 여러개의 템플릿으로 모델링하기 위하여 분류기의 출력값을 기반으로 각 클래별 학습 샘플들을 여러개의 하위클래스로 분해하고, 각 하위클래스별 분류기 출력값의 평균을 계산하여 지역화된 템플릿을 생성한다. 그 뒤 평가샘플과 각 템플릿간의 거리를 계산하고, k개의 최근접 템플릿들 중 가장 많은 비율을 차지하는 클래스로 평가샘플을 분류한다. 본 논문에서는 클래스 분해를 위해 C-means 클러스터링 알고리즘을 이용하였으며, k값은 주어진 데이타 셋의 클래스 내 밀집도와 클래스 간 분리도에 따라 자동으로 결정하였다. 제안하는 방법은 각 클래스별로 여러 개의 모델을 사용하며, 이들 중 가장 유사한 하나의 모델과 매칭하는 대신 k개의 모델을 참조하기 때문에 안정적이고 높은 분류성능을 획득할 수 있다. 본 논문에서는 UCI와 ELENA데이타베이스를 이용한 실험을 통해 제안하는 방법이 기존의 결합 방법들에 비해 우수한 분류성능을 보임을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.15-35
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• 2020

In research on behavioral studies, significant attention has been paid to the stage-sequential process for multiple latent class variables. We now explore the stage-sequential process of multiple latent class variables using the multivariate latent class profile analysis (MLCPA). A latent profile variable, representing the stage-sequential process in MLCPA, is formed by a set of repeatedly measured categorical response variables. This paper proposes the extended MLCPA in order to explain an association between the latent profile variable and the latent group variable as a form of a two-dimensional contingency table. We applied the extended MLCPA to the National Longitudinal Survey on Youth 1997 (NLSY97) data to investigate the association between of developmental progression of depression and substance use behaviors among adolescents who experienced Authoritarian parental styles in their youth.