• 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 information and communication convergence engineering
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• v.10 no.1
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• pp.40-44
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• 2012

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.231-238
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• 2003

This paper presents a virtual grouping method used in multi-user network games, which reduces a response time and losses of response data. The proposed method divides each group into virtual groups and transmits data in them after dividing an overall map on a game into several fixed regions and grouping them. And this paper derives the optimal number of groups minimizing a given cost function. The proposed method if shown to be useful by comparing with a general grouping method.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.677-683
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• 2017

Apache Storm is a representative real-time distributed processing system, which is able to process data streams quickly over distributed servers. Storm currently provides several stream grouping methods to distribute data traffic to multiple servers. Among them, the shuffle grouping may cause a processing delay problem and the local-or-shuffle grouping used to solve the problem may cause the problem of concentrating the traffic on a specific node. In this paper, we propose the locality-aware grouping to solve the problems that may arise in the existing Storm grouping methods. Experimental results show that the proposed locality-aware grouping is considerably superior to the existing shuffle grouping and the local-or-shuffle grouping. These results show that the new grouping is an excellent approach considering both the locality and load balancing which are limitations of the existing Storm.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.649-660
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• 1998

Steel making in Mini Mill consists of three major processing stages: molten steel making in an electric arc fuenace, slab casting in a continuous caster, and hot rolling in a finishing mill. Each processing stage has its own lot grouping criterion. However, these criteria in three stages are conflicting with each other. Therefore, delveloping on efficient lot grouping algorithm to enhance the overall productivity of the Mini Mill is an extremely difficult task. The algorithm proposed in this paper is divided into three steps hierarchically: change grouping, cast grouping, and roll grouping. An efficient charge grouping heuristic is developed by exploiting the characteristics of the orders, the processing constraints and the requirements for the downstream stages. In order to maximaize the productivity of the continuous casters, each cast must contain as many charges as possible. Based on the constraint satisfaction problem technique, an efficient cast grouping heuristic is developed. Each roll consists of two casts satisfying the constraints for rolling. The roll grouping problem is formulated as a weighted non-bipartite matching problem, and an optimal roll grouping algorithm is developed. The proposed algorithm is programmed with C language and tested on a SUN Workstation with real data obtained from the H steel works. Through the computational experiment, the algorithm is verified to yield quite satisfactory solutions within a few minutes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.293-302
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• 2004

Wireless LAN is a rather mature communication technology that connects mobile terminals. A typical wireless LAN system is composed of one AP and more than one terminals, which is called a BSS. Terminals near to an AP can receive high rate data but terminals far from an AP may not receive data with guaranteed high speed rate because received signal strength is weakened. This paper proposes a method to allow high speed data transmission by grouping terminals and using part of wireless terminals as repeaters. We compare and analyze proposed grouping algorithms based on Depth First Search and Breadth First Search via simulations. A grouping algorithm based on Breadth First Search is shown to be more effective in term of efficiency and coverage.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.359-370
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• 2019

Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables.

• Agricultural and Biosystems Engineering
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• v.6 no.1
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• pp.8-14
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• 2005

This study was carried out to develop a real-time grouping system of chlorophyll contents of rice crop canopy for precision agriculture. The system measured reflected light energy of a rice canopy on a paddy field from visual to near-infrared range and analyzed the collected information of chlorophyll contents of rice crop canopy with given position data. The four filters, 560 nm $({\pm}10nm)$, 650 nm $({\pm}25nm)$, 700 nm $({\pm}12nm)$, and 850 nm $({\pm}40nm)$, were used for a multiple regression to estimate the chlorophyll contents of rice crop canopy. Every $0.2m^2$ area of the open field was inspected at a distance of 1 m above the rice canopy. According to the results of verification test, the chlorophyll content grouping by a commerical chlorophyll meter (SPAD) and by the developed system showed 58.7% match for five-stage chlorophyll contents of rice crop canopy grouping and 93.5% for the $five{\pm}1-stage$ grouping. In addition, the results showed 63.0% match for three-stage grouping and 100.0% for the $three{\pm}1-stage$ grouping.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3336-3346
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• 2022

In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.501-512
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• 2006

XML is the most popular platform-independent data expression which is used to communicate between loosely coupled heterogeneous systems such as B2B Applications or Workflow systems. The powerful query language XQuery has been developed to support diverse needs for querying XML documents. XQuery is designed to configure results from diverse data sources into a uniquely structured query result. Therefore, it became the standard for the XML query language. Although the latest XQuery supports heavy search functions including iterations, the grouping mechanism for data is too primitive and makes the query expression difficult and complex. Therefore, this work is focused on supporting the groupby clause in the query expression to process XQuery grouping. We suggest it to be a more efficient way to process grouping for restructuring and aggregation functions on XML data. We propose an XQuery EBNF that includes the groupby clause and implemented an XQuery processing system with grouping functions based on the eXist Native XML Database.