• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.222-229
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• 2003

In this paper, we address a layout design problem, PTN[2], for determining an appropriate 2-class-based dedicated storage layout in a class of unit load storage systems. Our strong conjecture is that PTNI2] is NP-hard. Restricting PTN[2], we provide three solvable cases of PTN[2] in which an optimal solution to the solvable cases is one of the partitions based on the PAI(product activity index)-nonincreasing ordering. However, we show with a counterexample that a solution based on the PAI-non increasing ordering does not always give an optimal solution to PTN[2]. Utilizing the derived properties, we construct an effective heuristic algorithm for solving PTN[2] based on a PAI-non increasing ordering with performance ratio bound. Our algorithm with O($n^2$) is effective in the sense that it guarantees a better class-based storage layout than a randomized storage layout in terms of the expected single command travel time.

• The Mathematical Education
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• v.55 no.1
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• pp.73-89
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• 2016

The purpose of this study was to analyze middle school students' problem posing types and strategies. we analyzed problems posed by 120 middle school students during mathematics class focused on problem posing activities in various aspects. Students' posed problems were classified into five types: not a problem(NP), non-math(NM), impossible(IM), insufficient(IN), sufficient(SU) and each of the posed problems. Students used three kinds of problem posing strategies such as goal manipulation(GM), assumption manipulation(AM), and condition manipulation(CM), and in posing one problem, one or more than two strategies were used. According to the prior studies, problem posing can contributes to the development of students' problem solving ability, creativity, mathematical aptitude, and a broader understanding of mathematical concepts. However, we found that some students had difficulties in posing problems or limited understandings of that. We hope the results of the study contribute to encouraging problem posing activities in mathematics instruction.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1446-1452
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• 2014

Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.99-102
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• 2003

In this paper we propose an adaptive learning method for HMM classifiers by using splitting and merging techniques to overcome the problem of the conventional teaming, where one HMM classifier per class has been trained, individually. The experimental results demonstrate a possibility that the proposed mechanism could be applied for applications of having multiple clusters in a class.

• East Asian mathematical journal
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• v.36 no.2
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• pp.229-251
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• 2020

Problem Based learning(PBL) is a teaching and learning method to increase mathematical ability and help achieving mathematical concepts and principles through problem solving using the learner's mathematical prerequisite knowledge. In addition, the recent instructional situations or environments have focused on the learner's self construction of his learning and its process. In spite of such a quite attention, it is not easy to apply and execute PBL program actually in class. Especially, there are some difficulties in actually applying and practicing PBL in the areas of mathematics education in not only secondary school but also in college. Its reason is that in order to conduct PBL instruction constantly in real or experimental class there is no more concrete and detailed instructional content during the consistent and long period. However, to whom is related to mathematics education including instructors called scaffolders, investigation and recognition on the degree of the learner's acquisition of mathematical thinking skills and strategies is an very important work. By the reason, in this study, the instructional content was to be explored and developed to be conducted during 15 weeks in one semester, which was based on Problem Based Learning environment by the subject of the theories relevant to mathematics education in the college of education.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.89-102
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• 2023

This study was a one group, pre-post test design experimental study to identify the effects of problem-based learning applied to adult nursing subjects on meta-cognition, problem solving, professional self-concept and self-directed learning of nursing students. The participants were 60 fourth grade students who had registered for adult nursing class from a nursing university in D metropolitan city, the data were collected from September to December, 2022. The adult nursing class model was designed based on the ADDIE model suitable for PBL. The class period was conducted for 15 weeks, with 8 weeks of lectures, 2 weeks of exams, and 5 weeks of Barrow and Myers 5-step PBL learning. The collected data were analyzed using the SPSS/WIN 20.0 Program, and paired t-test was used to test the differences between variables before and after the intervention. There was a statistically significant difference in metacognition(t=-8.04, p<.001), problem solving(t=-4.08, p<.001), professional self-concept(t=-4.67, p<.001) and self-directed learning(t=-4.69, p<.001) between pre and post problem based learning. Therefore, our result recommend that to apply problem-based learning in various major subjects to strengthen nursing students' metacognition, problem-solving, professional self-concept, and self-directed learning skills.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.351-363
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• 2017

It is organized to evaluate the effect of nursing students' learning attitude, learning motivation, problem solving ability, and class participation, after applying team-based learning to basic nursing classes. The subjects were 103 people in the second year of the G region. The data collection was from September 1 to December 5, 2016 by using t-test, one-way ANOVA, paired t-test and Pearson's correlation coefficient. Result is that the ability to solve problems increased, showing a statistically significant difference. Learning attitude and motivation of learning increased and the degree of participation didn't change, but there were no statistically significant differences. The learning attitude shows positive correlation with learning motivation, problem-solving skills and participation. After team based-learning, learning attitude showed a positive correlation with learning motivation, problem solving ability and class participation. Learning motivation shows positive correlation with class participation and so does problem solving ability with participation. Based on the results of this study, in order to improve the practical ability, it is necessary to activate the self-directed active learning method such as team base for nursing major study.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.311-316
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• 2019

Classification problem is to predict the class to which an input data belongs. One of the most popular methods to do this is training a machine learning algorithm using the given dataset. In this case, the dataset should have a well-balanced class distribution for the best performance. However, when the dataset has an imbalanced class distribution, its classification performance could be very poor. To overcome this problem, we propose an over-sampling scheme that balances the number of data by using Conditional Generative Adversarial Networks (CGAN). CGAN is a generative model developed from Generative Adversarial Networks (GAN), which can learn data characteristics and generate data that is similar to real data. Therefore, CGAN can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Experiments using actual collected data show that the over-sampling technique using CGAN is effective and that it is superior to existing over-sampling techniques.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.151-165
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• 2022

The aim of this study is to examine the effectiveness of non-face-to-face classes conducted due to the COVID-19 crisis. In order to achieve this goal, a non-face-to-face class with flipped learning was applied in one subject of clothing major held at 'S' University in Cheongju, Korea. In addition, this study tried to analyze the differences between pre- and post-non-face-to-face classes in problem analysis ability, resource/information/technology literacy, convergent thinking ability as core competencies, and overall learning satisfaction. As a result, after participating in the non-face-to-face class in which the flipped learning was applied, the students recognized that their abilities improved in the three problem-solving competency sub-areas, and their overall learning satisfaction also increased. The effectiveness of non-face-to-face classes in the field of clothing and fashion has been mainly measured in fashion design and clothing construction courses. However, based on the results of this study, it can be suggested that non-face-to-face classes in a theory-oriented lecture-type class can be effective methods for improving students' core competencies such as problem-solving skills if teaching-learning methods such as flipped learning are applied. Therefore, the results of this study will be useful data for designing differentiated non-face-to-face class strategies in a theory-oriented lecture-type class to improve the core competencies of college students.