• Journal of Elementary Mathematics Education in Korea
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• v.15 no.1
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• pp.19-38
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• 2011

The purpose of this study was to analyze the responses and the behavioral characteristics between mathematically promising students and normal students in solving open-ended problems. For this study, 55 mathematically promising students were selected from the Science Education Institute for the Gifted at Seoul National University of Education as well as 100 normal students from three 6th grade classes of a regular elementary school. The students were given 50 minutes to complete a written test consisting of five open-ended problems. A post-test interview was also conducted and added to the results of the written test. The conclusions of this study were summarized as follows: First, analysis and grouping problems are the most suitable in an open-ended problem study to stimulate the creativity of mathematically promising students. Second, open-ended problems are helpful for mathematically promising students' generative learning. The mathematically promising students had a tendency to find a variety of creative methods when solving open-ended problems. Third, mathematically promising students need to improve their ability to make-up new conditions and change the conditions to solve the problems. Fourth, various topics and subjects can be integrated into the classes for mathematically promising students. Fifth, the quality of students' former education and its effect on their ability to solve open-ended problems must be taken into consideration. Finally, a creative thinking class can be introduce to the general class. A number of normal students had creativity score similar to those of the mathematically promising students, suggesting that the introduction of a more challenging mathematics curriculum similar to that of the mathematically promising students into the general curriculum may be needed and possible.

• Communications of Mathematical Education
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• v.21 no.1 s.29
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• pp.1-17
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• 2007

There are many students around us, who are mathematically promising but have not taken some instructions with a program for the gifted. Providing a certain opportunity for them to take a differentiate program from one given normal students in a regular classroom has some limitations. If ever, offering a learning program developed with the connection of regular curriculum can lead them to reveal their potential. I think it is desirable that the effect of the program is more increasing when study in school keeps a reciprocal relationship to study in home for increasing the students' promising. In this paper, it is discussed to develop and implement the teaching learning program for the integration and connection of school and home for the Mathemaically Promising Students.

• Research in Mathematical Education
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• v.12 no.4
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• pp.259-270
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• 2008

The change of the developed countries to highly technological societies continuously requires that they nurture and use the full potential of mathematically and scientifically talented people. As this is a process that should start early in order to be efficient, the main responsibility of identifying and addressing the specific needs of these people is assigned to public school systems and, in particular, to elementary teachers. In this regard, three significant areas of concern arise and are discussed in this paper: (a) The complexity in identifying mathematically promising and mathematically talented elementary students; (b) The highly responsible and difficult task for elementary teachers to differentiate and serve the mathematically promising students within an inclusive classroom; and (c) The need of teachers with specialized training and mathematics knowledge in pre-high school grades. The last one should be considered predominantly as a logical consequence of the first two. The main goal and, hence, the purpose of the paper is to promote understanding of this crucial necessity of mathematics specialists and to advocate for a change in this direction.

• Journal of Educational Research in Mathematics
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• v.17 no.1
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• pp.51-66
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• 2007

The purpose of this study is to analyse the cases of the posed problems while the mathematically gifted students are playing the NIM game. The findings of a qualitative case study have led to the conclusions as follows. Most of all mathematically gifted students in the elementary school are not intend to suggest the solutions of the posed problem unless the teacher or the 'problem is requested. But a higher level of promising children were changing each data components of a problem in a consistent way and restructuring the problems while controlling their cognitive process. This is compared to that a relatively lower level of promising children tends to modify one or two data components instantly without trying to look at the whole structure. And we gave 2 suggestions to teach the mathematically gifted students in the problem posing.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.119-123
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• 2001

The CDMA system with time division duplex mode (CDMA/TDD system) is a highly attractive solution to support the next generation cellular mobile systems providing multimedia services where the traffic unbalance between downlink and uplink exists. In this paper, the capacity of the CDMA/TDD system is analyzed in general multicell environments. For this analysis, the interference for a time slot is analyzed, and a time slot and channel allocation problem is mathematically formulated and solved using simulated annealing technique. Computational experiments provide a promising result.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1348-1356
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• 2017

Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.

• Asia pacific journal of information systems
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• v.15 no.2
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.10
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• pp.1340-1346
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• 2014

Previous studies of reliable server assignment considered only to assign the same cost of server, that is, homogeneous servers. In this paper, we generally deal with reliable server assignment with different server costs, i.e., heterogeneous servers. We formulate this problem as a nonlinear integer programming mathematically. Our problem is defined as determining a deployment of heterogeneous servers to maximize a measure of service availability. We propose two metaheuristic algorithms (tabu search and particle swarm optimization) for solving the problem of reliable server assignment. From the computational results, we notice that our tabu search outstandingly outperforms particle swarm optimization for all test problems. In terms of solution quality and computing time, the proposed method is recommended as a promising metaheuristic for a kind of reliability optimization problems including reliable sever assignment and e-Navigation system.

• Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.233-247
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• 2015

In this paper, the cutting force calculation of ball-end mill processing was modeled mathematically. All derivations of cutting forces were directly based on the tangential, radial, and axial cutting force components. In the developed mathematical model of cutting forces, the relationship of average cutting force and the feed per flute was characterized as a linear function. The cutting force coefficient model was formulated by a function of average cutting force and other parameters such as cutter geometry, cutting conditions, and so on. An experimental method was proposed based on the stable milling condition to estimate the cutting force coefficients for ball-end mill. This method could be applied for each pair of tool and workpiece. The developed cutting force model has been successfully verified experimentally with very promising results.

• IE interfaces
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• v.22 no.3
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• pp.252-262
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• 2009

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.