• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제22권1호
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• International Journal of Quality Innovation
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• 제5권1호
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• pp.10-22
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• 2004

The paper outlines a comprehensive three-step approach to the development of advanced strategies for quality control of a complex machining process. The research framework is a developed concept for Integrated Supervisory Process Control. The promising results obtained demonstrate a non-traditional approach to the deployment of quality and productivity requirements set by the system users, whereby the efficiency and systematization of the development of quality control strategies can be significantly improved.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.44-51
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• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• 한국실내디자인학회논문집
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• 제26호
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• pp.49-56
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• 2001

Science, technology and machine aesthetics were the main theme of architectural design in the first machine age. Modern architecture used the theory of science as its framework. But the theory of science was used not as a professional way, but as a pseudo-scientific way by modern architects. It was because that modern architects relied on the spiritual, mystical realm of science and mathematics. Moreover, the avant-garde movement in modernism used the theory of science and mathematics for its complicated and ailenated nature and scientific image. The pseudo-scientifism in architectural design in the first machine age was expressed mainly as a concentration on the 4th dimensional space and the use of symbolic image of science and technology.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제21권2호
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• pp.63-73
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• 2017

Segmenting the image into multiple regions is at the core of image processing. Many segmentation formulations of an images with multiple regions have been suggested over the years. We consider segmentation algorithm based on the multi-phase level set method in this work. Proposed method gives the best result upon other methods found in the references. Moreover it can segment images with intensity inhomogeneity and have multiple junction. We extend our method (GLIF) in [T. Dultuya, and M. Kang, Segmentation with shape prior using global and local image fitting energy, J.KSIAM Vol.18, No.3, 225-244, 2014.] using a multiphase level set formulation to segment images with multiple regions and junction. We test our method on different images and compare the method to other existing methods.

• 대한수학회보
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• 제45권1호
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• pp.157-167
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• 2008

A mapping f : $M{\rightarrow}N$ between Hilbert $C^*$-modules approximately preserves the inner product if $$\parallel<f(x),\;f(y)>-<x,y>\parallel\leq\varphi(x,y)$$ for an appropriate control function $\varphi(x,y)$ and all x, y $\in$ M. In this paper, we extend some results concerning the stability of the orthogonality equation to the framework of Hilbert $C^*$-modules on more general restricted domains. In particular, we investigate some asymptotic behavior and the Hyers-Ulam-Rassias stability of the orthogonality equation.

• ETRI Journal
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• 제38권2호
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• pp.397-404
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• 2016

In group-oriented applications, it is often required to verify a group of signatures/messages. The individual verification of signed messages in such applications comes at a high cost in terms of computations and time. To improve computational efficiency and to speed up the verification process, a batch verification technique is a good alternative to individual verification. Such a technique is useful in many real-world applications, such as mail servers, e-commerce, banking transactions, and so on. In this work, we propose a new, efficient identity-based signature (IDS) scheme supporting batch verifications. We prove that the proposed IDS scheme and its various types of batch verifications is tightly related to the Computational Diffie.Hellman problem under a random oracle paradigm. We compare the efficiency of the proposed scheme with related schemes that support batch verifications.

• 한국지능시스템학회논문지
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• 제20권1호
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• pp.1-6
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• 2010

Ying[M.S. Ying, Linguistic quantifiers modeled by Sugeno integrals, Artificial Intelligence 170(2006) 581-606] studied a framework for modeling quantifiers in natural languages in which each linguistic quantifier is represented by a family of fuzzy measures and the truth value of a quantified proposition is evaluated by using Sugeno integral. In this paper, we consider interval-valued fuzzy measures and interval quantifiers which are the generalized concepts of fuzzy measures and quantifiers, respectively. We also investigate logical properties of a first order language with interval quantifiers modeled by the Sugeno integral with respect to an interval-valued fuzzy measures.

• Journal of applied mathematics & informatics
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• 제27권3_4호
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• pp.843-856
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• 2009

In possibility framework, we propose two risk measures named Fuzzy Value-at-Risk and Fuzzy Conditional Value-at-Risk, based on Credibility measure. Two portfolio optimization models for fuzzy portfolio selection problems are formulated. Then a chaos genetic algorithm based on fuzzy simulation is designed, and finally computational results show that the two risk measures can play a role in possibility space similar to Value-at-Risk and Conditional Value-at-Risk in probability space.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.57-76
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• 2007

Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.