• 한국초등수학교육학회지
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• 제15권1호
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• pp.19-38
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• 2011

본 연구의 목적은 개방형 수학 문제 해결 과정에서 수학 영재교육 대상 학생과 일반 학생의 문제해결 전략과 그 해결 과정에서 보이는 행동 특성을 비교 분석하는 것이다. 이 분석을 토대로 일반 수학 수업에서의 영재교육 대상 학생들을 위한 창의성을 강조한 수업의 가능성을 탐구하였다. 이를 위해 수학 영재교육 대상 학생집단과 일반 학생 집단을 다단계 군집표집하여 수학 영재교육 대상 학생 55명과 일반 학생 100명을 선정하여 다양한 해법이 가능한 개방형 문제를 6개월 동안 제시하여 해결 전략 및 행동 특성을 분석하였다. 행동특성은 수업 관찰과 활동지 분석 및 개별 면담을 사용하였다. 연구결과 수학 영재 교육 대상 학생들이 일반 학생들에 비하여 다양한 전략을 보여 주었으나 많은 수학 영재교육 대상 학생도 고차원적 조작 능력이 미흡하였다. 또한 수학 영재교육 대상 학생의 행동 특성은 일반에 비하여 집착력이 강하고 다양한 해법을 추구하는 면에서 뛰어났다. 그런데 과제의 특성에 따라서 반응의 양상이 다르게 나타나므로 수학 영재교육 대상 학생의 수준과 능력에 맞게 다양한 유형의 과제를 개발하여 제시할 필요가 있다.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제21권1호
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• pp.1-17
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• 2007

우리 주변에는 수학영재 교육 수혜자는 아니지만 수학적으로 유망성이 있는 학생들이 많이 있다. 이들의 수학 유망성을 계발 신장시키기 위해서는 정규교육과정과 차별화된 학습 프로그램과 교육의 기회를 제공하는 것이 바람직하지만 현실적으로 한계가 있다. 학교교육과 가정교육은 상호보완적인 관계를 가질 경우 교육의 효과는 상승한다고 볼 때, 학교와 가정을 연계한 개별학습 프로그램과 집단학습 프로그램을 제공하는 것은 수학 유망성을 신장시키는데 기여하리라 생각한다. 따라서 본고에서는 수학적 유망성이 있는 학생들을 위해 학교와 가정을 연계한 통합 연결형 학습 프로그램의 개발과 그 활용 방안에 대해서 살펴보고자 한다.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제12권4호
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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.

• 대한수학교육학회지:수학교육학연구
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• 제17권1호
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• pp.51-66
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• 2007

본 연구의 목적은 초등학교 수학영재들이 NIM 게임이라는 특수한 과제가 주어졌을 때 그것의 정보 또는 구조를 변경하여 만든 문제들의 사례들을 분석하는 것이다. 그 결과 우수한 수학영재들은 정보 구성 요소를 일관성 있게 변화시키거나 문제를 재구조화하였다. 교육청부설 과학영재교육원 소속 학생들은 대부분 Brown & Walter가 설정한 'What-if-not' 전략에 따른 문제 만들기의 III수준에서 머물고 있지만, 대학부설 과학영재교육원 소속 학생들 중에는 IV수준에 도달하는 학생들도 있었다. 특히, 학생들의 사고 수준이 높을수록 표면적으로 드러난 특정한 수치를 다른 값으로 변경하거나 수치 값의 범위를 변경하는 방법에 치중하지 않고 문제의 구조를 파악하고 또 메타인지적 과정을 통해 각각의 요소를 체계적으로 분류하면서 보다 다양하고 확장된 유형의 문제를 만들어 간다는 점을 밝혔다. 그리고, 문제 만들기 수업에서 활용할 수 있는 2가지의 지도방안을 제안하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2001년도 추계학술대회 논문집
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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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• 제12권4호
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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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• 제15권2호
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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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• 제38권10호
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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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• 제2권4호
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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.

• 산업공학
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• 제22권3호
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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.