• Journal of Fisheries and Marine Sciences Education
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• v.19 no.3
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• pp.390-402
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• 2007

According to the paradigm of Neo-liberalism, the issue of ability grouping has grown more and more in education of Korea. And because of the influence of ability grouping, now ability grouping is enforcing partially in the subjects of English and Mathematics. But ability grouping is going to expand to the all subjects. So, it is very important that how teachers and students are recognize about partial ability grouping in the subjects of English and Mathematics. Because that information about partial ability grouping can guide direction for the future educational policy. The purpose of this study was to actually analyze teacher's and students' recognition of partial ability grouping in the subjects of English and Mathematics. To accomplish this purpose, 622 middles school students and 552 teachers were sampled. As a tool of investigation, questionnaires about teacher's and students' recognition of partial ability grouping had made by researcher of this study were used. And as processing of data, t-test, F-test, Scheff-test were used. The result of this study is as follow. First, teachers who are experiencing ability grouping recognized more negative about ability grouping than teachers who are not experiencing ability grouping. Second, students who have low ability recognized more negative about ability grouping than students who have high ability. Third, teachers who are experiencing ability grouping recognized more ineffective about ability grouping than teachers who are not experiencing ability grouping. Fourth, students who have low ability recognized more ineffective about ability grouping than students who have high ability.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.109-125
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• 2011

A folksonomy is a new classification approach for organizing information by users to freely attach one or more tags to various resources published on the web. Recently, in order to provide useful services and organize the folksonomy data, many collaborative tagging systems based on folksonomy offer additional functionalities for grouping each elements of a folksonomy. In this paper, organization framework for grouped folksonomy is proposed. That is, we suggest the grouped folksonomy model that is an extended folksonomy with the concept of "group" and fundamental operations(Group Aggregation, Group Composition, Group Intersection, Group Difference) for grouping of folksonomy elements. Also, we developed a supporting tool(GFO) that constructs grouped folksonomy and executes fundamental operations. And we introduce some cases using the fundamental operations for grouping of each elements of folksonomy. Based on suggested our approach, we can construct grouped folksonomy and organize and extract useful information from the folksonomy data by grouping each elements of a folksonomy.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.243-248
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• 2018

The most important factor for collaborative learning to be effective is the selection of tools that constitute groups. Grouping is to facilitate collaborative learning, learners form groups based on various characteristics. If a group of students fails to form properly due to the selection of the wrong tools, problems can arise where complaints from students can lead to lectures and the effects of learning. In this paper, we have implemented a group of configuration tools that considered improving learning effects and diagnosing bulling tendency. We have proposed a group composition tool that can take into consideration the learning effect and also diagnose the tendency of the bullring by constructing the group according to the teacher's preference by inputting the class preference and the student's grade through the sociometry survey. We expect that the teacher will be able to grasp the students' friendship in advance and cope with the bulling that can happen in the class, as well as the cooperative learning that can lead the class to improve the learning effect.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1235-1242
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• 2019

While several machine learning technique has been implemented for Android malware categorization, there is still difficulty in analyzing due to overfitting problem and including of un-executable code, etc. In this paper, we introduce our implemented tool to address these problems. Tool is consists of approximately 1,500 lines of Java code, and perform Flow analysis on set of APIs, or on control flow graph. Our tool groups all the API by its relationship and only perform analysis on actually executing code. Using our tool, we grouped 39032 APIs into 4972 groups, and 12123 groups with result of including class names. We collected 7,000 APKs from 7 families and evaluated our feature reduction technique, and we also reduced features again with selecting APIs that have frequency more than 20%. We finally reduced features to 263-numbers of feature for our collected APKs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.911-917
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• 2009

This paper introduces a sequential clustering technique as a tool for an effective grouping of transmission systems. The interconnected network system retains information about the location of each line. With this information, this paper aims to carry out initial clustering through the transmission usage rate, compare the similarity measures of regional information with the similarity measures of location price, and introduce the techniques of the clustering method. This transmission usage rate uses power flow based on congestion costs and similarity measurements using the FCM(Fuzzy C-Mean) algorithm. This paper also aims to prove the propriety of the proposed clustering method by comparing it with existing clustering methods that use the similarity measurement system. The proposed algorithm is demonstrated through the IEEE 39-bus RTS and Korea power system.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.7
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• pp.340-353
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• 2008

A computational color palette extraction model is introduced to describe paint brush objectively and efficiently. In this model, a color palette is defined as a minimum set of colors in which a painting can be displayed within error allowance and extracted by the two step processing of color grouping and major color extraction. The color grouping controls the resolution of colors adaptively and produces a basic color set of given painting images. The final palette is obtained from the basic color set by applying weighted k-means clustering algorithm. The extracted palettes from several famous painters are displayed in a 3-D color space to show the distinctive palette styles using RGB and CIE LAB color models individually. And the two experiments of painter classification and color transform of photographic image has been done to check the performance of the proposed method. The results shows the possibility that the proposed palette model can be a computational color analysis metric to describe the paint brush, and can be a color transform tool for computer graphics.

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.1-18
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• 1996

FMS operating system consists of several subsystems in general. i.e. tool grouping subsystem. tool/job assignment subsystem. job dispatching subsystem, and papers dealing with each subsystem were published by many researchers [1], [4], [6], [8], [9], [10], [11], [12], [13], [14], [15], [16]. This paper mainly discusses about tool/job assignment subsystem as a job loading procedure. that occupies the kernel position of overall FMS operating system. Its performance is evaluated through simulation experiments of an integrated operating system under a typical FMS hardware configuration implemented in many machining factories, which is composed of the proposed procedure as well as a job dispatching procedure including several heuristic dispatching rules in terms of rule-base.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.111-128
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• 2005

This paper examines the validity of the newly developed IT consulting SERVQUAL measurement tool. In an attempt to measure Ire IS customers' expectations and perceived quality of the services they received, the researchers developed a diagnostic tool of SERVQUAL based on the solid theoretical background, which can specifically be applied to the IT consulting service sector. This on-going research so far, has been applied to six (6) different organizations that have received IT consulting services over the past years. From the preliminary data collected, the correlation and the factor analyses were conducted to understand the underlying concept and refinement of the measurement tool. Although the correlation analysis showed a little tendency of collinearity among some of the variables, all showed sound relationship of the proposed hypotheses. The exploratory factor analytic approach was chosen because it does not set any priori constraints on the estimation of components or the number of components to be extracted. The number of different factor solutions was extracted and tested to see which solution represents better grouping of the variables. The Crombach's Alpha was computed on different combinations of the factor solutions to ensure validity. The results show 8-dimensional IT consulting SERVQUAL measures which they are, assurance, knowledge & skill, customer relationship, support, empathy, process management, expertise, and education, seem more appropriate than the originally proposed 6 dimensions. The study approach was non-experimental cross-sectional research design. The longitudinal design of follow-up studies to periodically revise and refine current measure is strongly recommended for fine tuning of the tool.