• 한국컴퓨터정보학회논문지
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• 제3권2호
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• pp.37-44
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• 1998

본 연구는 프로그래밍이 문제해결학습에 미치는 효과를 문헌을 통해 조사하고 있다. 컴퓨터는 이제껏 타매체가 실현하지 못했던 분석적인 사고를 함양하는 것으로 알려져 있다. 특히 프로그래밍을 하면서 자신의 사고 과정에 주목하게 됨으로서 문제해결수업에 가장 적절한 매체로 주목받고 있다. 그러나 실제로 이를 뒷받침할 현장 검증 자료가 산출되지않은 상황에서 이 가정을 검토하기 위해 프로그래밍의 특성과 문제해결학습의 공통분모를 검토하고 학교 현장에서 위의 전제를 실현시키기 위한 교육과정의 개선을 제시하고 있다.

• International journal of advanced smart convergence
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• 제5권2호
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• pp.8-17
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• 2016

Scratch-based programming has come to be known as an effective programming tool because of its graphic instruction modules, which are designed to be assembled like the famous LEGO building blocks. These building block-like structures allow users to more easily program applications without using other more difficult programming languages such as C or Java, which are text-based. Therefore, it poses a good opportunity for application in educational settings, especially in primary schools. This paper presents an effective approach to developing an educational robot for use in elementary schools. Furthermore, we present the method for scratch programming based on the external modules need for the implementation of robot motion. Lastly, we design a systematic curriculum, titled "Play with a Robot," and propose guidelines to using the educational programming language Scratch.

• Journal of applied mathematics & informatics
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• 제24권1_2호
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• pp.449-460
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• 2007

Several fuzzy approaches can be considered for solving multi-objective transportation problem. This paper presents a fuzzy goal programming approach to determine an optimal compromise solution for the multiobjective transportation problem. We assume that each objective function has a fuzzy goal. Also we assign a special type of nonlinear (hyperbolic) membership function to each objective function to describe each fuzzy goal. The approach focuses on minimizing the negative deviation variables from 1 to obtain a compromise solution of the multiobjective transportation problem. We show that the proposed method and the fuzzy programming method are equivalent. In addition, the proposed approach can be applied to solve other multiobjective mathematical programming problems. A numerical example is given to illustrate the efficiency of the proposed approach.

• Journal of applied mathematics & informatics
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• 제28권3_4호
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• pp.597-610
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• 2010

For a class of nonlinear bilevel programming problems in which the follower's problem is linear, the paper develops a genetic algorithm based on the optimality conditions of linear programming. At first, we denote an individual by selecting a base of the follower's linear programming, and use the optimality conditions given in the simplex method to denote the follower's solution functions. Then, the follower's problem and variables are replaced by these optimality conditions and the solution functions, which makes the original bilevel programming become a single-level one only including the leader's variables. At last, the single-level problem is solved by using some classical optimization techniques, and its objective value is regarded as the fitness of the individual. The numerical results illustrate that the proposed algorithm is efficient and stable.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.707-712
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• 1994

An Enhanced Shopfloor Oriented Programming(AESOP) system is developed as a programming utillity of the CNC turning machine. The developed system is specially designed to give a beginner the convenience for CNC part programming with graphical interaction between a machine operator and the AESOP system. The combination of process-oriented cycles and various contour programming as well as an immediate tool path verification support the easiness and swiftness of a part program generation in the shopfloor. Since the AESOP system has been designed to operate on the basis of MS-Windows in the PC-embedded CNC system, it is also useful for the training of the part programming by utilizing provailing personal computers in the educational department.

• 오토저널
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• 제14권1호
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• pp.64-73
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• 1992

본 논문은 비선형 goal programming을 이용하여 구조 최적화 문제들의 해를 얻는 방법을 제시한 다. 이 방법은 다기준 최적화의 도구로 사용되는데 그 까닭은 goal programming이 목적함수와 제한조건 등을 정의하는데 있어서 발생하는 난점들을 제거해 주며, 또한 설계목적의 우선 순위를 다루는 능력을 갖추고 있기 때문이다. 비선형 goal programming에 대한 구조 최적화의 모델이 제시된다. Hooke-Jeeves의 pattern 탐색, 수정된 Hooke-Jeeves의 Pattern 탐색, 그리고 Powell의 공액방향 탐색방향을 이용한 세 가지의 비선형 goal programming 들이 개발되고 토의된다. 설계 도구로서 이 방법의 유효성을 논증하기 위하여 사례의 해를 모색하며 이를 다른 연구결과와 비교 검토한다.

• 한국자동차공학회논문집
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• 제3권6호
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• pp.199-211
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• 1995

As international market pressures increase, product life cycles are becoming shorter at same time requirements for productivity and flexibility are becoming higher. Major improvement in robot programming user interface is central to increasing productivity and flexibility for automatic manufacturing environments. New computer technologies have the potential to accomplish this improvement. In this study we have developed a system which proves that interactive computer graphics and geometric modeling have matured to the point where their artful application into an integrated system can rsult in radically new and powerful user interface for robot programming. With the graphic user interface environment the system efficiently utilizes the existing CAD data and gemetric modeling techniques for off-line programming and simulations. Also the system successfully generates robot control programs performing the desired tasks given through off-line programming.

• 한국컴퓨터정보학회논문지
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• 제20권9호
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• pp.21-29
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• 2015

The set partitioning problem is a well-known NP-hard combinatorial optimization problem, and it is formulated as an integer programming model. This paper proposes an Integer Programming-based Local Search for solving the set partitioning problem. The key point is to solve the set partitioning problem as the set covering problem. First, an initial solution is generated by a simple heuristic for the set covering problem, and then the solution is set as the current solution. Next, the following process is repeated. The original set covering problem is reduced based on the current solution, and the reduced problem is solved by Integer Programming which includes a specific element in the objective function to derive the solution for the set partitioning problem. Experimental results on a set of OR-Library instances show that the proposed algorithm outperforms pure integer programming as well as the existing heuristic algorithms both in solution quality and time.

• International Journal of Control, Automation, and Systems
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• 제2권3호
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• pp.263-278
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• 2004

This paper reviews dynamic programming (DP), surveys approximate solution methods for it, and considers their applicability to process control problems. Reinforcement Learning (RL) and Neuro-Dynamic Programming (NDP), which can be viewed as approximate DP techniques, are already established techniques for solving difficult multi-stage decision problems in the fields of operations research, computer science, and robotics. Owing to the significant disparity of problem formulations and objective, however, the algorithms and techniques available from these fields are not directly applicable to process control problems, and reformulations based on accurate understanding of these techniques are needed. We categorize the currently available approximate solution techniques fur dynamic programming and identify those most suitable for process control problems. Several open issues are also identified and discussed.

• 한국CDE학회논문집
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• 제14권6호
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.