• Journal of The Korean Association For Science Education
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• v.29 no.2
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• pp.193-202
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• 2009

The purpose of this study was to investigate undergraduates' characteristics of problem-solving process through analysis of the response patterns on problem-solving laboratory. For this purpose, 18 freshmen taking a problem-solving-type general chemistry laboratory had been interviewed for the analysis of the characteristics of problem-solving process. According to the results, the students' responses have been classified into five types; trying to solve problems using new factors, trying to solve problems by finding missing factors in manual, recognizing problem-situations but just repeating the given process, not recognizing problem-situations but trying to solve doubts generated during execution, satisfying about results, and taking no further action. These results can be used as materials to suggest the role model of the students' laboratory execution and to look back on each students' execution.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.1
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• pp.1-16
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• 2017

This study presents an out-of-school problem-solving lesson we designed for American Indian students using a culturally relevant STEM topic. The lesson was titled "Shelter Design for Severe Weather Conditions." This shelter design lesson was developed based on an engineering design allowing us to integrate STEM topics within a traditional indigenous house-building context. This problem context was used to encourage students to apply their prior knowledge, experience, and community/cultural practice to solve problems. We implemented the lesson at a summer program on an American Indian reservation. Using the lesson, this study explores how American Indian students use cultural knowledge and experience to solve a STEM problem. We collected student data through pre- and post-STEM content knowledge tests, drawings and explanations of shelter models on the students' group worksheets, and classroom observations. We used interpretive and inductive methods to analyze the data. This study demonstrates that our culturally relevant, STEM problem-solving lesson helped the American Indian students solve a complex, real-world problem. This study examines how students' prior experiences and cultural knowledge affect their problem-solving strategies. Our findings have implications for further research on designing problem-solving lessons with culturally relevant STEM topics for students from historically marginalized populations.

• IE interfaces
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• v.24 no.1
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• pp.51-57
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• 2011

The traveling purchaser problem (TPP) is a generalization of the well-known traveling salesman problem (TSP), which has many real-world applications such as purchasing the required raw materials for the manufacturing factories and the scheduling of a set of jobs over some machines, and many others. In the last decade, TPP has received some attention of the researchers in the operational research area. However, all of the past researches for TPP are restricted on a single purchaser (vehicle). It could be the limitation to solve the real world problem. The purpose of this paper is to suggest the capacitated multiple TPP (CMTPP). It could be used in inbound logistics optimization in supply chain management area and many others. Since TPP is known as NP-hard, we also developed the heuristic algorithm to solve the CMTPP.

• Journal of the Korea Society for Simulation
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• v.14 no.4
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• pp.1-8
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• 2005

In a metal grating manufacturing process, the cutting operation allocates the gratings and cut them out from given panels or a plate sheets. Before the cutting operation an operator generates a cutting plan. The cutting plan should decide how pieces of metal rectangles i.e., gratings, are allocated and cut from the panel. This plan generation is a deal of weight on the production cost. the generation of cutting plan is similar to the general two-dimensional cutting problem. In this paper, we first define cutting problem and Af algorithm of Artificial Intelligence to solve the problem. Also, through a simulation, we compare the proposed cutting algorithm to an existing method in terms of material loss

• Journal of Communications and Networks
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• v.18 no.6
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• pp.884-891
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• 2016

The wireless mesh network (WMN) has attracted significant interests as a broadband wireless network to provide ubiquitous wireless access for broadband services. Especially with incorporating multiple orthogonal channels and multiple directional antennas into the WMN, each node can communicate with its neighbor nodes simultaneously without interference between them. However, as we allow more freedom, we need a more sophisticated algorithm to fully utilize it and developing such an algorithm is not easy in general. In this paper, we study a joint channel assignment, link scheduling, routing, and rate control problem for the WMN with multiple orthogonal channels and multiple directional antennas. This problem is inherently hard to solve, since the problem is formulated as a mixed integer nonlinear problem (MINLP). However, despite of its inherent difficulty, we develop an algorithm to solve the problem by using the generalized Benders decomposition approach [2]. The simulation results show the proposed algorithm provides the optimal solution to maximize the network utility, which is defined as the sum of utilities of all sessions.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1636-1648
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• 2006

In this paper, we propose a new optimization approach to the rate control problem in a wired-cum-wireless network. A primal-dual interior-point(PDIP) algorithm is used to find the solution of the rate optimization problem. We show a comparison between the dual-based(DB) algorithm and PDIP algorithm for solving the rate control problem in the wired-cum-wireless network. The PDIP algorithm performs much better than the DB algorithm. The PDIP can be considered as an attractive method to solve the rate control problem in network. We also present a numerical example and simulation to illustrate our conclusions.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.328-338
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• 1994

TSP(Traveling Salesman Problem) is a famous problem in Operations Research fields due to its applicability to various problems. It is also well-known that the problem is hard to solve in reasonable time, since it is in the NP-Complete class. Hence it is desired to develop heuristics which have polynominal complexity and also solve the problem to near-optimality. This paper presents a heuristic algorithm for TSP using the concept of dynamic programming. The proposed method has the complexity of O(N$\^$3/), and gives improved solutions than other well-known algorithms in our extensive computational experiments.

• Journal of Ocean Engineering and Technology
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• v.22 no.4
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• pp.1-7
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• 2008

The primary aim of the paper is to solve an unstable axisymmetric initial value problem of wave propagation when given initial data that is deteriorated by noise such as measurement error. To overcome the instability of the problem, Tikhonov's regularization, known as a non-iterative numerical regularization method, is introduced to solve the problem. The L-curvecriterion is introduced to find the optimal regularization parameter for the solution. It is confirmed that fairly stable solutions are realized and that they are accurate when compared to the exact solution.

• IE interfaces
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• v.20 no.4
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• pp.504-514
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• 2007

The multicast routing problem lies in the composition of a multicast routing tree including a source node and multiple destinations. There is a trade-off relationship between cost and delay, and the multicast routing problem of optimizing these two conditions at the same time is a difficult problem to solve and it belongs to a multi-objective optimization problem (MOOP). A multi-objective genetic algorithm (MOGA) is efficient to solve MOOP. A micro-genetic algorithm(${\mu}GA$) is a genetic algorithm with a very small population and a reinitialization process, and it is faster than a simple genetic algorithm (SGA). We propose a multi-objective micro-genetic algorithm (MO${\mu}GA$) that combines a MOGA and a ${\mu}GA$ to find optimal solutions (Pareto optimal solutions) of multicast routing problems. Computational results of a MO${\mu}GA$ show fast convergence and give better solutions for the same amount of computation than a MOGA.

• School Mathematics
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• v.11 no.1
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• pp.165-186
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• 2009

Problem-Based Learning has many implications on teaching and learning. Through the problem-Based Learning, students device their plans to solve the given problems and discuss with workers to find and share some ideas or mathematical contents needed to solve those problems. In this paper we studied characteristics of Problem-Based Learning problem and tried o find out the standard criterion of problem analysis that were appropriate to Problem-Based Learning. We applied them to analyze problems in textbooks and problems that were developed for Problem-Based Learning. Using he result, the further research questions and implications were suggested.