• Bulletin of the Korean Mathematical Society
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• v.58 no.5
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• pp.1261-1278
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• 2021

Recently, a new kind of multiple zeta value of level two T(k) (which is called multiple T-value) was introduced and studied by Kaneko and Tsumura. In this paper, we define a kind of alternating version of multiple T-values, and study several duality formulas of weighted sum formulas about alternating multiple T-values by using the methods of iterated integral representations and series representations. Some special values of alternating multiple T-values can also be obtained.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.579-590
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• 2003

When we analyze incomplete data, i.e., data with missing values, we need treatment for the missing values. A common way to deal with this problem is to delete the cases with missing values. Various other methods have been developed. Among them are EM algorithm and regression algorithm which can estimate missing values and impute the missing elements with the estimated values. In this paper, we introduce multiple imputation software SOLAS which generates multiple data sets and imputes with them.

• Journal of Ecology and Environment
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• v.29 no.4
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• pp.361-364
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• 2006

The objective of the current study was to characterize and apply multiple regression model relating to vegetation values of the plant species over salt marshes. For each salt marsh community, vegetation and soil variables were investigated in the western coast and the southern coast in South Korea. Osmotic potential of soil and $Cl^-$ content of soil as independent variable had positive and negative influences on vegetation values. Multiple regression model showed that vegetation values of 14 coastal plant communities were determined by pH of soil, osmotic potential of soil and sand content. The multiple regression equation may be applied to the explanation of distribution and abundance of plant communities with exiting ordination plots.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.271-285
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• 2009

Multi-temporal satellite imagery can be changed into a transform image that emphasizes the changed area only through the application of various change detection techniques. From the transform image, an automated change detection model calculates the optimal threshold value for classifying the changed and unchanged areas. However, the model can cause undesirable results when the histogram of the transform image is unbalanced. This is because the model uses a single threshold value in which the sign is either positive or negative and its value is constant (e.g. -1, 1), regardless of the imbalance between changed pixels. This paper proposes an advanced method that can improve accuracy by applying separate threshold values according to the increased or decreased range of the changed pixels. It applies multiple threshold values based on the cumulative producer's and user's accuracies in the automated binary change detection model, and the analyst can automatically extract more accurate optimal threshold values. Multi-temporal IKONOS satellite imagery for the Daejeon area was used to test the proposed method. A total of 16 transformation results were applied to the two study sites, and optimal threshold values were determined using accuracy assessment curves. The experiment showed that the accuracy of most transform images is improved by applying multiple threshold values. The proposed method is expected to be used in various study fields, such as detection of illegal urban building, detection of the damaged area in a disaster, etc.

• Bulletin of the Korean Mathematical Society
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• v.44 no.4
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• pp.631-640
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• 2007

The purpose of this article is to deal with the multiple values and uniqueness of meromorphic functions with small functions in the whole complex plane. We obtain a more general theorem which improves and extends strongly the results of R. Nevanlinna, Li-Qiao, Yao, Yi, and Thai-Tan.

• Communications of the Korean Mathematical Society
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• v.20 no.2
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• pp.247-257
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• 2005

In this paper we calculate two special types of multiple zeta values, $\zeta(\{4\iota+2\}_n)\;and\;\zeta(\{4\iota\}_n)$ using the primitive roots of unity, which may be simpler and easier.

• Journal of the Korean Home Economics Association
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• v.34 no.2
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• pp.167-181
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• 1996

The purpose of this study was to predict the stability of the future marriage by understanding the values of marriage, child and sex role among pre-parents. For this purpose, the values of marriage, child and sex roles were compared between male (N = 340) and female (N = 363) university students, Tajeon. The data were analyzed by statistical methods, such as mean, standard deviation, t-Test, F-Test, Multiple-Regression and Duncan Multiple Range Test. The results of this study were as follows ; 1) There were significant differences between male and female students in the values of marriage, child and sex role. 2) The most important demographical variable influencing to the values of marriage, child and sex role was sex. 3) With reagard to the collections between the values of marriage and the positive values of child were negative. But the correlations between the values of marriage and the preference for a son were positive. The correlations between the preference for a son and the values of sex role were also positive. With reagard to the correlations between the values of marriage and the values of sex roles were positive.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.971-984
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• 2015

Imputation procedures fill-in missing values, thereby enabling complete data analyses. Fully efficient fractional imputation (FEFI) and multiple imputation (MI) create multiple versions of the missing observations, thereby reflecting uncertainty about their true values. Methods have been described for hypothesis testing with multiple imputation. Fractional imputation assigns weights to the observed data to compensate for missing values. The focus of this article is the development of tests of independence using FEFI for partially classified two-way contingency tables. Wald and deviance tests of independence under FEFI are proposed. Simulations are used to compare type I error rates and Power. The partially observed marginal information is useful for estimating the joint distribution of cell probabilities, but it is not useful for testing association. FEFI compares favorably to other methods in simulations.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.3
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• pp.49-61
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• 2015

We study the problem of multiple response optimization (MRO) and focus on the selection of input levels which will produce desirable output quality. We propose an interactive multiple objective optimization approach to the input design. The earlier interactive methods utilized for MRO communicate with the decision maker only using the response variable values, in order to improve the current response values, thereby resulting in the corresponding design solution automatically. In their interaction steps of preference articulation, no account is taken of any active changes in design variable values. On the contrary, our approach permits the decision maker to change the design variable values in its interaction stage, which makes possible the consideration of the preference or economics of the design variable side. Using some typical value functions, we also demonstrate that our method converges reasonably well to the known optimal solutions.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.105-110
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference. data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values.. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.