• 수산해양교육연구
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• 제19권3호
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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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• 제10권1호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권3_4호
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• pp.231-238
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• 2003

본 논문은 다중 사용사 네트워크 게임에서 응답 시간과 응답 데이타 손실을 줄일 수 있는 가상 그룹핑 방법을 제안한다. 제안하는 방법은 게임상의 모든 맵을 일정 크기의 영역으로 분할하고, 이들 각각의 영역을 그룹으로 묶은 후 각각의 그룹을 가상의 그룹으로 분할하고 각각의 가상 그룹에 데이타를 전송하는 방법이다. 또한 된 논문에서는 주어진 비용 함수를 최소화하는 최적 그룹수를 유도하였고, 제안된 방법이 기존의 그룹핑 방법과 비교하여 유용함을 보였다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권12호
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• pp.677-683
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• 2017

Apache Storm이란 대표적인 실시간 분산 처리 시스템으로써, 분산 서버를 통해 실시간 데이터를 빠르게 처리하는 특징을 갖는다. 기존에 Storm은 다수의 서버에 트래픽을 분배할 때, 셔플(Shuffle) 그룹핑은 처리 지연 문제가 발생하며 이를 개선한 로컬(Local-or-Shuffle) 그룹핑은 트래픽이 특정 노드에 편중되는 문제가 발생할 수 있다. 본 논문은 이러한 기존 Storm 그룹핑에서 발생할 수 있는 문제를 해결하기 위한 지역성 고려(Locality-aware) 그룹핑을 제안한다. 실험에서는 제안하는 지역성 고려 그룹핑이 기존의 셔플 그룹핑 및 로컬 그룹핑에 비해 우수함을 확인하였다. 본 논문은 기존의 Storm의 한계점인 지역성과 로드 밸런싱을 동시에 고려한 우수한 결과라 사료된다.

• 대한산업공학회지
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• 제24권4호
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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.

• 한국통신학회논문지
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• 제29권3A호
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• pp.293-302
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• 2004

무선랜은 무선으로 근거리 단말들을 연결하는 통신 기술로, 일반적인 무선랜의 구성은 하나의 AP와 하나 이상의 단말 기기가 BSS를 구성한다. 무선랜 서비스 영역에서 AP와 거리가 가까운 단말은 고속의 데이터 전송률을 보장받을 수 있지만 AP에서 거리가 멀리 떨어진 단만은 신호의 세기가 약해지므로 고속데이터 전송률의 보장을 받을 수 없는 단점을 가지고 있다. 본 논문에서는 무선 단말 중 일부를 중계 단말로 이용함으로써 그룹화를 통해 고속데이터 전송을 가능하게 하는 방법을 제안하고, 시뮬레이션을 통해 무선 단말의 그룹화를 위해 제안한 깊이우선탐색 알고리즘과 넓이우선탐색 알고리즘을 비교, 분석하였다. 그 결과 넓이우선탐색 알고리즘이 무선 단말을 위한 그룹화에 보다 효과적인 알고리즘임을 보였다.

• Communications for Statistical Applications and Methods
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• 제26권4호
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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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• 제6권1호
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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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• 제54권9호
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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.

• 정보처리학회논문지D
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• 제13D권4호
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• pp.501-512
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• 2006

현재 널리 채택되고 있는 XML은 플랫폼에 의존하지 않는 데이터 표현 형식으로 B2B 응용 프로그램이나 워크플로우 상황에서처럼 느슨하게 연결된(loosely coupled) 이기종 시스템 간에 정보를 교환하는 데 매우 유용하게 사용되고 있다. XML의 이러한 장점 때문에 점차 증가하는 XML에 대한 관리 및 검색에 대한 요구 사항에 대처할 수 있도록 강력한 질의 언어인 XQuery가 만들어졌다. 문서의 검색을 위한 질의 언어인 XQuery는 다양한 데이터 소스로부터 가져온 XML 데이터를 고유한 구조를 가진 질의 결과로 구성할 수 있도록 설계되었으며 현재 XML 질의 언어의 표준이다. XQuery는 반복문 등을 포함하는 강력한 검색 기능을 지원하나 데이터를 그룹화 하는 경우에는 질의 표현이 상대적으로 어렵고, 복잡한 형태를 취한다. 따라서 본 논문에서는 XQuery에 그룹화 처리를 위한 명시적인 groupby절을 도입한 질의 표현 기법을 모색함으로써 XML 데이터의 재구성과 집계 함수 처리를 위한 그룹화를 보다 효율적으로 처리할 수 있도록 하였다. 이를 위해서 XQuery에 groupby절을 도입하기 위한 EBNF(Extended Backus-Naur Form)를 제안하고, 네이티브 XML 데이터베이스인 eXist 기반의 XQuery 그룹화 질의 처리 시스템을 구현하였다.