• Journal of the Korean Chemical Society
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• v.44 no.1
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• pp.68-73
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• 2000

In this study, tlhe relationships of high school students' abilities to solve chemistry problems with cognitive variables (logical thinking ability, mental capacity. and learning strategy) and affective variables(self-efficacy, self-concept of ability, learning goal, and attitude toward science) were investigated. The proportion of variance due to the variables for algorithmic and conceptual problem solving ability was studied by a multiple regression analysis. The results indicated that, among the cognitive variables, the logical thinking ability significantly predicted the algorithmic problem solving ability, and the learning strategy was the best predictor of conceptual problem solving ability although not significant. Among the affective variables studied, the self-concept of alility was the significant predictor of both algorithmic and conceptual problem solving abilities. The seif-efficacy was significantly correlated with conceptual problem solving ability, but it had no predictive power.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.143-161
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• 2023

The purpose of this study was to investigate whether students' creative problem-solving skills improve through project-based courses. Rather than measuring overall competencies, the focus was specifically on measuring the improvement of problem-solving skills. The study involved a literature review, the design of project-based courses, and the assessment and analysis of students' competencies before and after the courses. The results of the study are as follows: Firstly, the measurement of students' creative problem-solving skills before and after the project-based courses indicated an overall improvement in the five dimensions of problem identification, analytical thinking, flexible thinking, alternative generation, and a proactive attitude. Secondly, significant correlations were found among these five dimensions, particularly strong correlations were observed between problem identification and alternative generation, problem identification and analytical thinking, and analytical thinking and alternative generation. To generalize the findings of this study, further research is needed to apply similar approaches in different types of courses and across various universities and departments.

• Journal of Convergence for Information Technology
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• v.10 no.2
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• pp.193-200
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• 2020

This study proposes the Field Oriented Capacity Upgrade Program, an educational model for improving clinical practice of occupational therapy. The effectiveness of the self-directed learning ability and problem solving ability of students was compared by comparing the proposed educational model with the traditional teaching method. The research method was divided into the experimental group who participated in the model education and the control group who participated in the lecture class, and conducted similar experimental studies using the nonequivalent control group pretest-posttest design. In addition, the study participants conducted a preliminary and follow-up survey of a total of 135 students who participated in the education over three semesters. As a result of training using the proposed learning method, the experimental group improved self-directed learning ability and problem solving ability by 22% and 18%, respectively, compared to the control group. This study developed and proposed a new teaching-learning method to improve clinical practice of occupational therapy, and it is meaningful as a basic data of teaching-learning method to improve the ability required in various jobs.

• The Mathematical Education
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• v.57 no.2
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• pp.137-155
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• 2018

From the assumption that an individual's working memory capacity is limited, the cognitive load theory is concerned with providing adequate instructional design so as to avoid overloading the learner's working memory. Based on the cognitive load theory, this study aimed to provide implications for effective problem-based collaborative teaching and learning design by analyzing the level of middle school students' cognitive load which is perceived according to the problem types(short answer type, narrative type, project) in the process of collaborative problem solving in middle school function part. To do this, this study analyzed whether there is a relevant difference in the level of cognitive load for the problem type according to the math achievement level and gender in the process of cooperative problem solving. As a result, there was a relevant difference in the task burden and task difficulty perceived according to the types of problems in both first and second graders in middle schools students. and there was no significant difference in the cognitive effort. In addition, the efficacy of task performance differed between first and second graders. The significance of this study is as follows: in the process of collaborative problem solving learning, which is most frequently used in school classrooms, it examined students' cognitive load according to problem types in various aspects of grade, achievement level, and gender.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.180-187
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• 2001

This paper proposes an efficient evolutionary programming algorithm for solving a generation expansion planning(GEP) problem known as a highly-nonlinear dynamic problem. Evolutionary programming(EP) is an optimization algorithm based on the simulated evolution (mutation, competition and selection). In this paper, new algorithm is presented to enhance the efficiency of the EP algorithm for solving the GEP problem. By a domain mapping procedure, yearly cumulative capacity vectors are transformed into one dummy vector, whose change can yield a kind of trend in the cost value. To validate the proposed approach, this algorithm is tested on two cases of expansion planning problems. Simulation results show that the proposed algorithm can provide successful results within a resonable computational time compared with conventional EP and dynamic programming.

• Computational Structural Engineering : An International Journal
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• v.3 no.1
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• pp.49-59
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• 2003

This paper discusses the enhancement of the buckling capacity of column strips by use of piezoelectric layer. The analytical model for obtaining the buckling capacity of the piezoelectric coupled column with general boundary conditions modelled with different types of springs applied at the ends of the column is derived the first time. Based on this proposed model, the buckling capacity of the column strips can be accurately predicted by solving an eigenvalue problem. The computational results show the great potential of the piezoelectric materials in enhancing the buckling capacity of the column strips. The optimal locations of the piezoelectric layer for higher buckling capacity are also obtained for the columns with. standard pinned-pinned, fixed-free, and fixed-pinned structures. In addition, the buckling capacity and the increase of buckling capacity are discussed for those columns with the general boundaries as well. This research may provide a benchmark for the buckling analysis of the piezoelectric coupled strips.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• Korean Management Science Review
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• v.10 no.1
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• pp.207-216
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• 1993

The purpose of this paper is to develop a branch and bound algorithm for the transportation problem with a limited number of company owned vehicles. First, we find an initial solution by solving quasi-assignment subproblem induced by relaxing constraints of the vehicle capacity and illegal tours elimination equations. Second, we build routing from the assignment, and if there is a routings which violates relaxed constraints, we introduce branches of the subproblem in order to remove it. After all branches are searched, we get the optimal solution.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.6
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• pp.241-246
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• 1984

The power system scheduling process is formulated as an optimization problem with linear inequality constraints. AC Loadflow method is used for the problem solution and line losses are considered. The constraints under consiceration are generator power limits, load schedding limits and line capacity limits. In solving the objective function the Dual Relaxation method is adopted. Tests indicate that the method is practical for real time applications.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.81-98
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• 1997

Given a rooted tree T with profits and node demands, the capacitated subtree of a tree problem (GSTP) consists of finding a rooted subtree of maximum profit, subject to having total demand no larger than the given capacity H. We first define the so-called critical item for CSTP and find an upper bound on the optimal value of CSTP in O(n$^{2}$) time, where n is the number of nodes in T. We then present our branch and bound algorithm for solving CSTP and illustrate the algiruthm by using an example. Finally, we implement our branch-and-bound algorithm and compare the computational results with those for both CPLEX and a dynamic programming algorithm. The comparison shows that our branch-and-bound algorithm performs much better than both CPLEX and the dynamic programming algorithm, where n and H are the range of [50, 500] and [5000, 10000], respectively.