• Journal of the Korea Convergence Society
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• v.8 no.6
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• pp.209-218
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• 2017

Flipped Learning is being suggested which is well known as a teaching method which lets students learn the contents they will learn in advance through the advance online video and have a discussion through the team interaction in the main class for them to solve the assignment through the cooperation in a self-initiated way. Therefore, this study was intended to confirm if the flipped learning class could improve the students' learning ability and raising the interest in math by complementing the problem on the lecture-type class by applying the flipped learning class to the college basic math subject. As a result, in the unit test result, the average score of the experimental group was more than 20 higher than one of the control group indicating that Flipped Learning had a great effect on improving the learning ability, and as for the introspection journal analysis, many subjects from the experimental group showed the positive attitude toward math they felt difficult unlike ones from control group indicating that it was effective in improving the interest level.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.159-174
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• 2015

Dragomiretskiy and Zosso (2014) developed a new decomposition method, termed variational mode decomposition (VMD), which is efficient for handling the tone detection and separation of signals. However, VMD may be inefficient in the presence of missing data since it is based on a fast Fourier transform (FFT) algorithm. To overcome this problem, we propose a new approach based on a novel combination of VMD and hierarchical (or h)-likelihood method. The h-likelihood provides an effective imputation methodology for missing data when VMD decomposes the signal into several meaningful modes. A simulation study and real data analysis demonstrates that the proposed method can produce substantially effective results.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.251-258
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• 2019

In the future, various products are created in various fields using artificial intelligence. In this age, it is a very important problem to know the operation principle of artificial intelligence learning method and to use it correctly. This paper introduces artificial intelligence learning methods that have been known so far. Learning of artificial intelligence is based on the fixed point iteration method of mathematics. The GD(Gradient Descent) method, which adjusts the convergence speed based on the fixed point iteration method, the Momentum method to summate the amount of gradient, and finally, the Adam method that mixed these methods. This paper describes the advantages and disadvantages of each method. In particularly, the Adam method having adaptivity controls learning ability of machine learning. And we analyze how these methods affect digital signals. The changes in the learning process of digital signals are the basis of accurate application and accurate judgment in the future work and research using artificial intelligence.

• Structural Engineering and Mechanics
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• v.57 no.5
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• pp.837-859
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• 2016

An efficient shear deformation theory is developed for wave propagation analysis of an infinite functionally graded plate in the presence of thermal environments. By dividing the transverse displacement into bending and shear parts, the number of unknowns and governing equations of the present theory is reduced, and hence, makes it simple to use. The thermal effects and temperature-dependent material properties are both taken into account. The temperature field is assumed to be a uniform distribution over the plate surface and varied in the thickness direction only. Material properties are assumed to be temperature-dependent, and graded in the thickness direction according to a simple power law distribution in terms of the volume fractions of the constituents. The governing equations of the wave propagation in the functionally graded plate are derived by employing the Hamilton's principle and the physical neutral surface concept. There is no stretching.bending coupling effect in the neutral surface-based formulation, and consequently, the governing equations and boundary conditions of functionally graded plates based on neutral surface have the simple forms as those of isotropic plates. The analytic dispersion relation of the functionally graded plate is obtained by solving an eigenvalue problem. The effects of the volume fraction distributions and temperature on wave propagation of functionally graded plate are discussed in detail. It can be concluded that the present theory is not only accurate but also simple in predicting the wave propagation characteristics in the functionally graded plate. The results carried out can be used in the ultrasonic inspection techniques and structural health monitoring.

• Steel and Composite Structures
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• v.25 no.2
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• pp.157-175
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• 2017

The novelty of this work is the use of a new displacement field that includes undetermined integral terms for analyzing thermal buckling response of functionally graded (FG) sandwich plates. The proposed kinematic uses only four variables, which is even less than the first shear deformation theory (FSDT) and the conventional higher shear deformation theories (HSDTs). The theory considers a trigonometric variation of transverse shear stress and verifies the traction free boundary conditions without employing the shear correction factors. Material properties of the sandwich plate faces are considered to be graded in the thickness direction according to a simple power-law variation in terms of the volume fractions of the constituents. The core layer is still homogeneous and made of an isotropic material. The thermal loads are assumed as uniform, linear and non-linear temperature rises within the thickness direction. An energy based variational principle is employed to derive the governing equations as an eigenvalue problem. The validation of the present work is checked by comparing the obtained results the available ones in the literature. The influences of aspect and thickness ratios, material index, loading type, and sandwich plate type on the critical buckling are all discussed.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.253-263
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• 2021

Machine learning is the process of constructing a cost function using learning data used for learning and an artificial neural network to predict the data, and finding parameters that minimize the cost function. Parameters are changed by using the gradient-based method of the cost function. The more complex the digital signal and the more complex the problem to be learned, the more complex and deeper the structure of the artificial neural network. Such a complex and deep neural network structure can cause over-fitting problems. In order to avoid over-fitting, a weight decay regularization method of parameters is used. We additionally use the value of the cost function in this method. In this way, the accuracy of machine learning is improved, and the superiority is confirmed through numerical experiments. These results derive accurate values for a wide range of artificial intelligence data through machine learning.

• Education of Primary School Mathematics
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• v.22 no.1
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• pp.1-12
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• 2019

This study examined the attention and attention shift of general students and mathematically gifted students about pattern by the types of mathematical patterns. For this purpose, we analyzed eye movements during the problem solving process of 5th general and mathematically gifted students using eye tracker. The results were as follows: first, there was no significant difference in attentional style between the two groups. Second, there was no significant difference in attention according to the generation method between the two groups. The diversion was more frequent in the incremental strain generation method in both groups. Third, general students focused more on the comparison between non-contiguous terms in both attributes. Unlike general students, mathematically gifted students showed more diversion from geometric attributes. In order to effectively guide the various types of mathematical patterns, we must consider the distinction between attention and attention shift between the two groups.

• Education of Primary School Mathematics
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• v.24 no.2
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• pp.103-114
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• 2021

In this study I recognized the problems with the use of the terms 'quotient' and 'reminder' in the division of decimal and explored ways to improve them. The prior studies and current textbooks critically analyzed because each researcher has different views on the use of the terms 'quotient' and 'reminder' because of the same view of the values in the division calculation. As a result of this study, I proposed to view the result 'q' and 'r' of division of decimals by division algorithms b=a×q+r as 'quotient' and 'reminder', and the amount equal to or smaller to q the problem context as a final 'result value' and the residual value as 'remained value'. It was also proposed that the approximate value represented by rounding the quotient should not be referred to as 'quotient'.

• The Mathematical Education
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• v.61 no.4
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• pp.631-651
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• 2022

It has been alerted that Korean students' mathematical affective achievement is very low. In order to solve this problem, various policies related to mathematical affective domains have been promoted, but it is necessary to examine various existing policies and explore the direction for improving them in more essential aspects. Based on previous studies that the growth mindset helps to increase students' affective achievement, this study focused on improving students' math-related growth mindset and ultimately exploring policies that can increase mathematical affective achievement. Therefore, the current status of mathematical affective achievement of Korean students was examined, and the policies and related cases in the mathematical affective domain were investigated. Based on the results, some keywords were derived and then the directions of policy for improving the math-related growth mindset and the affective achievement of students were suggested.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.363-369
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• 2020

This study intends to examine effects of python education for adolescents. 6 primary studies were chosen through careful search process and investigated through meta-analysis. Research findings were as follows. The total effect size was 0.684. Second, the effect sizes of dependent variables were academic achievement 0.871, cognitive domain 0.625, and affective domain 0.428 in order. Third, for cognitive domain, the effect sizes were self-efficacy 0.833, problem-solving 0.283, computing thinking 0.276, and coding competency 0.251 in order. Fourth, for affective domain, the effect sizes were learning interest 0.560 and programming interest 0.417 in order. Fifth, regarding school level, the effect sizes were middle school 0.851, high school 0.585, and college 0.435 in order. Finally, for subject areas, the effect sizes were mathematics 1.057, design 0.595, information 0.585, and software 0.28 in order.