• Communications of Mathematical Education
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• v.36 no.1
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• pp.139-170
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• 2022

In relation to differentiated mathematics education, Russia has a longer experience in research and practice than Korea. The mathematics curriculum for 10-11 grades currently in use in Russia is a level-specific curriculum and consists of a basic level and an advanced level. And in Russia mathematics textbooks for 10-11 grades are also textbooks for each level. In this study, we analyzed basic level textbook and advanced level textbook written by the same author group among the textbooks 'Algebra and Introduction of Mathematical Analysis' of the 10th grade in Russia. To analyze the main learning contents and textbook descriptions that were added in advanced level the 'real numbers' and 'complex numbers' sections were studied. The main contents of basic and advanced level textbooks for 'functions', 'trigonometric functions', 'trigonometric equations', 'conversions of trigonometric expressions', and 'derivatives', which are included in both basic and advanced textbooks were compared and analyzed, and the descriptive characteristics of the definitions and theorems presented in the two levels of textbooks were also compared and analyzed. From the results of this study, it is expected that various information on the contents of various level textbooks of mathematics, the differences between textbooks for each level, and strategies for the composition of textbooks for various level can be accumulated.

• Journal of applied mathematics & informatics
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• v.30 no.3_4
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• pp.561-570
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• 2012

Fourier transform is well known for trigonometric systems. It is also a very useful tool for the construction of wavelets. The method of constructing wavelets has evolved as times went by. We review some methods. Then we do some calculations on wavelets defined by its Fourier transform.

• Journal of applied mathematics & informatics
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• v.31 no.1_2
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• pp.263-276
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• 2013

We discuss the effects of the ${\delta}^2$ and Lubkin acceleration methods on the partial sums of Fourier Series. We construct continuous, even H$\ddot{o}$lder continuous functions, for which these acceleration methods fail to give convergence. The constructed functions include some interesting trigonometric series whose properties were investigated by Hardy and Littlewood.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.72-77
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• 2008

Stability is a very important part which we must consider in structural design. In this paper, we take advantage of finite element method, and study about parametrical instability of star-dome structures, which is subjected to harmonically pulsating load. When calculating stiffness matrix, we consider elastic stiffness and geometrical stiffness simultaneously. In equation of motion, we represent displacements and accelerations by trigonometric series expansions, and then obtain Hill's infinite determinants. After first order approximation, we can get first and second order dynamic instability region finally.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.139-147
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• 2009

We study the neural network approximation to a function in $C^1$[0, 1] and its derivative. In [3], we used even trigonometric polynomials in order to get an approximation order to a function in $L_p$ space. In this paper, we show the simultaneous approximation order to a function in $C^1$[0, 1] using a Bernstein polynomial and a feedforward neural network. Our proofs are constructive.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.137-149
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• 1997

In this paper we propose a logdensity estimation based on a Fourier expansion. The basis functions consisting of trigonometric functions are determinded by stepwise addition and deletion and the Bayes Information Criterion, where the maximum likelihood method is used to estimate the parameters. Numericla examples using real data and simulated data are provided to show the performance of proposed method.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.27-32
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• 2000

To overcome boundary problems with wavelet regression, we propose a simple method that reduces bias at the boundaries. It is based on a combination of wavelet functions and low-order polynomials. The utility of the method is illustrated with simulation studies and a real example. Asymptotic results show that the estimators are competitive with other nonparametric procedures.

• Bulletin of the Korean Mathematical Society
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• v.53 no.1
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• pp.127-138
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• 2016

Differential subordination and superordination preserving properties for univalent functions in the open unit disk with an operator involving generalized Bessel functions are derived. Some particular cases involving trigonometric functions of our main results are also pointed out.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.251-259
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• 2002

Lagged regressor models with general stationary errors independent of the regressors are considered. The regressor process is unstable having characteristic roots on the unit circle. If the order of the lag matches the number of roots on the unit circle, the ordinary least squares estimator (OLSE) is asymptotically efficient in that it has the same limiting distribution as the generalized least squares estimator (GLSE) under the same normalization. This result extends the well-known result of Grenander and Rosenblatt (1957) for asymptotic efficiency of the OLSE in deterministic polynomial and/or trigonometric regressor models to a class of models with stochastic regressors.

• Bulletin of the Korean Mathematical Society
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• v.55 no.2
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• pp.367-378
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• 2018

In this paper, we study m-isometric Toeplitz operators $T_{\varphi}$ with rational symbols. We characterize m-isometric Toeplitz operators $T_{\varphi}$ by properties of the rational symbols ${\varphi}$. In addition, we give a necessary and sufficient condition for Toeplitz operators $T_{\varphi}$ with analytic symbols ${\varphi}$ to be m-expansive or m-contractive. Finally, we give some results for m-expansive and m-contractive Toeplitz operators $T_{\varphi}$ with trigonometric polynomial symbols ${\varphi}$.