• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.129-137
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• 2002

Two-sample problem is frequently discussed problem in statistics. In this paper we consider the hypothese methods for the general two-sample problem and suggest the bootstrap methods. And we show that the modified Kolmogorov-Smirnov test is more efficient than the Kolmogorov-Smirnov test.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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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.

• Journal of Elementary Mathematics Education in Korea
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• v.16 no.2
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• pp.269-293
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• 2012

This study is intended to figure out how the 6th grade students carry out newly added standards regard to the problem solving in the revised mathematics curriculum in 2007 and 2009, which are 'finding useless information in a given problem', 'finding insufficient information in a given problem', and 'posing new problem by changing conditions of the given problem.' In order to achieve this goal, we examined the characteristics of 200 elementary students' problem posing. We constructed and used the survey sheet which consisted of 6 items relevant to 'finding useless information in a given problem', 'finding insufficient information in a given problem', and 'posing new problem by changing conditions of the given problem.'

• Management & Information Systems Review
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• v.5
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• pp.33-59
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• 2000

Problem definition is as important as problem solving in decision making. Decision makers, however, tend to see problem partly or to define it unclearly. The researchers on decision making tend to lay a focus only on the Process of solving the given problem. This dissertation empirically studied the Problem diagnosis and definition in the decision making process. This study developed three kinds of DSS(Decision Support System) for the research and analyzed the effectiveness of the problem diagnosis and definition using DSS developed in this study. The three kinds of DSS are K-T(Kennel and Tregoe) DSS which is a method of table-styled information Presentation, and F-N(Fact-Net) DSS and C-E(Cause and Effect) DSS which are methods of graphic-styled information presentation. The empirical study was conducted twice; the pilot test and the main test. The samples of experiment are 218 students for the pilot test and 259 students for the main test. The author used K-T DSS and F-N DSS for the pilot test, and the revised K-T DSS and C-E DSS for the main test. A questionnaire survey method was included in the main test process The result of the study shows that the group using DSS in problem diagnosis and definition is more effective than the group not using DSS. A table-styled information presentation DSS, K-T, turns out more effective than a graphic-styled information presentation DSS, F-N, K-T DSS and C-E DSS showed no significant differences in the effectiveness in the main test. These results indicate that the use of DSS in the stage of problem diagnosis and definition is very effective and the methods and types of system design are a significant factors for DSS development.

• The Journal of Korean Association of Computer Education
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• v.17 no.3
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• pp.53-63
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• 2014

An important element to consider in information subjects is problem solving skills. It is problem solving skills that emphasize "practical usage of learning" in which knowledge of information subjects is integrated along with the cross curricular characteristics of the information subjects. What is usually considered paramount in a subject is knowledge, so the question is, should problem solving be considered more important in information subjects? This study discusses the foundation of subjects based on knowledge in information science, and the specifics of the 2014 amendment to the U.K. curriculum. And based on the core subjects of the U.K. curriculum, this study also offers educational implications as to how knowledge and problem solving skills should be discussed.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.991-997
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• 2006

Recently, Liu (2005) proposed a special type of weighting function under a given preference index level with the minimal variability similar to the minimal variability OWA operator weights problem proposed by Fuller and Majlender (2003). He solved this problem using a result of classical optimal control theory. In this note, we give a direct elementary proof of this problem without using any known results.

• Journal of The Korean Association of Information Education
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• v.11 no.4
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• pp.407-416
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• 2007

This study is to examine the effects of problem-based learning on elementary school students' ability in coping with the dysfunction of information. In order to conduct research, prevention education for elementary school students using problem-based learning is designed and the designed teaching method is applied to an experimental group, but tradition teaching is conducted to a comparative group. Therefore, it is possible to understand the effects of problem-based learning on students' ability in coping with the dysfunction of information, ICT application ability and learning achievement. The study has a meaning to give correct understanding of the dysfunction of information and prevention consciousness, offering significant information to teachers for improving teaching methods for the dysfunction of prevention education.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.173-181
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• 2016

In this paper, we address a new robust optimization problem in a multiuser multiple-input single-output broadcasting system with simultaneous wireless information and power transmission, where a multi-antenna base station (BS) sends energy and information simultaneously to multiple users equipped with a single antenna. Assuming that perfect channel-state information (CSI) for all channels is not available at the BS, the uncertainty of the CSI is modeled by an Euclidean ball-shaped uncertainty set. To optimally design transmit beamforming weights and receive power splitting, an average total transmit power minimization problem is investigated subject to the individual harvested power constraint and the received signal-to-interference-plus-noise ratio constraint at each user. Due to the channel uncertainty, the original problem becomes a homogeneous quadratically constrained quadratic problem, which is NP-hard. The original design problem is reformulated to a relaxed semidefinite program, and then two different approaches based on convex programming are proposed, which can be solved efficiently by the interior point algorithm. Numerical results are provided to validate the robustness of the proposed algorithms.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.9-16
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• 2016

Collaborative filtering(CF) uses the purchase or item rating history of other users, but does not need additional properties or attributes of users and items. Hence CF is known th be the most successful recommendation technology. But conventional CF approach has some significant weakness, such as the new user problem. In this paper, we propose a approach using word embedding with skip-gram for learning distributed item representations. In particular, we show that this approach can be used to capture precise item for solving the "new user problem." The proposed approach has been tested on the Movielens databases. We compare the performance of the user based CF, item based CF and our approach by observing the change of recommendation results according to the different number of item rating information. The experimental results shows the improvement in our approach in measuring the precision applied to new user problem situations.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.616-631
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• 2019

With the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.