• Korean Journal of Mathematics
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• v.9 no.2
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.315-320
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• 2010

Classifying multi-class pattern plays an important role in grouping of records in database systems, detection of faults in the VLSI circuits and so on. In this paper, we propose an algorithm for the construction of multi-class pattern classifier with minimum memory capacity using MACA(Multiple Attractor Cellular Automata) and the subspace concept for given multi-class patterns.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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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.

• Journal of the Chungcheong Mathematical Society
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• v.19 no.1
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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 $\mathcal{F}(C_{a,b}[0,T])$.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.3
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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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• v.56 no.1
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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.

• Journal of the Korean Home Economics Association
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• v.29 no.2
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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.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.4
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• pp.451-455
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• 2008

In this paper, the k-nearest templates method is proposed to combine multiple classifiers effectively. First, the method decomposes training samples of each class into several subclasses based on the outputs of classifiers to represent a class as multiple models, and estimates a localized template by averaging the outputs for each subclass. The distances between a test sample and templates are then calculated. Lastly, the test sample is assigned to the class that is most frequently represented among the k most similar templates. In this paper, C-means clustering algorithm is used as the decomposition method, and k is automatically chosen according to the intra-class compactness and inter-class separation of a given data set. Since the proposed method uses multiple models per class and refers to k models rather than matches with the most similar one, it could obtain stable and high accuracy. In this paper, experiments on UCI and ELENA database showed that the proposed method performed better than conventional fusion methods.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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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.