• 제목/요약/키워드: set grouping

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성상도 집합 그룹핑 기반의 적응형 병렬 및 반복적 QRDM 검출 알고리즘 (Adaptive Parallel and Iterative QRDM Detection Algorithms based on the Constellation Set Grouping)

  • 마나르모하이센;안홍선;장경희;구본태;백영석
    • 한국통신학회논문지
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    • 제35권2A호
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    • pp.112-120
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    • 2010
  • 본 논문에서는 집합 그룹핑을 이용한 APQRDM (adaptive parallel QRDM) 알고리즘과 AIQRDM (adaptive iterative QRDM) 알고리즘을 제안한다. 제안된 검출 알고리즘은 집합 그룹핑을 이용하여 QRDM 알고리즘의 트리 검색 단계를 PDP (partial detection phases) 로 분할하여 수행한다. 기존 QRDM 알고리즘의 트리 검색 단계가 4개의 PDP로 나누어질 때, APQRDM 알고리즘은 기존 QRDM 알고리즘의 1/4 에 해당하는 검출 지연(latency) 을 가지며, AIQRDM 알고리즘은 기존 QRDM 알고리즘의 약 1/4에 해당하는 하드웨어 요구량을 가진다. 모의실험 결과는 $4{\times}4$ 시스템의 경우, APQRDM 알고리즘은 12dB의 Eb/N0에서 기존 QRDM 알고리즘의 약 43%에 해당하는 연산 복잡도를 가지며, AIQRDM 알고리즘은 0dB의 Eb/N0에서 기존 QRDM 알고리즘의 54%, AQRDM 알고리즘의 10%에 해당하는 연산 복잡도를 가짐을 보인다.

대체공정이 있는 기계-부품 그룹의 형성 - 자기조직화 신경망을 이용한 해법 - (Machine-Part Grouping with Alternative Process Plan - An algorithm based on the self-organizing neural networks -)

  • 전용덕
    • 산업경영시스템학회지
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    • 제39권3호
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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.

소집단 협력 학습을 위한 학생 그룹핑 시스템 (A Student Grouping System for Cooperative Learning in Small-Groups)

  • 장효원;김명
    • 컴퓨터교육학회논문지
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    • 제8권4호
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    • pp.15-24
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    • 2005
  • 소집단 협력 학습 시 집단 편성 방법은 학습 과정과 학습의 최종 결과에 큰 영향을 미친다. 따라서 소집단은 학습의 성격과 목적, 구성원의 능력, 적성, 흥미 등의 개인차를 고려하여 구성원의 상호작용이 최대화되도록 편성되어야 한다. 그러나 다차원적인 학생 데이터로부터 이러한 조건을 만족하는 소집단을 교사가 수작업으로 편성하기는 쉽지 않다. 본 연구에서는 학생 데이터와 소집단 편성을 위한 여러 조건들을 교사가 제공할 때, 각 조건에 대해 동질 또는 이질적인 특성을 가능한 맞춰서 소집단들을 편성해주는 학생 그룹핑 시스템을 설계하고 개발하였다. 이 시스템은 마이크로 소프트사의 엑셀과 연동되어 동작하며 편리한 사용자 인터페이스를 제공하므로 일선 교사들이 친숙하게 사용할 수 있으며, 교육 분야뿐 아니라 다양한 분야에서 활용될 수 있다.

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색상 그룹핑과 클러스터링을 이용한 회화 작품의 자동 팔레트 추출 (Automatic Color Palette Extraction for Paintings Using Color Grouping and Clustering)

  • 이익기;이창하;박재화
    • 한국정보과학회논문지:시스템및이론
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    • 제35권7호
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    • pp.340-353
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    • 2008
  • 화풍을 효과적이고 객관적으로 기술하는 한 방법으로 팔레트 추출에 대한 수학적 모델을 제시한다. 이 모델에서는 팔레트를 허용 오차 범위 내에서 회화 작품의 영상을 표현할 수 있는 주요 색상의 집합으로 정의하고 색상 그룹핑과 주요 색상 추출의 두 단계를 거처 팔레트 색상을 추출한다. 색상 그룹핑은 주어진 회화에 대해 적응적으로 색의 분해능을 조절하여 각 회화 작품을 이루는 기초 색상을 추출하며 다음 주요 색상 추출 단계에서 이것과 이것이 차지하는 영역에 대한 정보를 바탕으로 K-Means 클러스터링 알고리즘을 적용하여 팔레트를 얻는다. 실험을 통해 유명 화가의 작품을 대상으로 RGB와 CIE LAB 색상 모델을 사용하여 추출한 팔레트를 3차원 색 공간에 표시하였다. 팔레트 색상의 거리를 사용한 화가 분류 실험과 실사 영상의 색채 변환 실험 통해 이 방법이 화풍 분석과 그래픽 분야에 적용될 수 있음을 확인하였다.

Verification of the adequacy of domestic low-level radioactive waste grouping analysis using statistical methods

  • Lee, Dong-Ju;Woo, Hyunjong;Hong, Dae-Seok;Kim, Gi Yong;Oh, Sang-Hee;Seong, Wonjun;Im, Junhyuck;Yang, Jae Hwan
    • Nuclear Engineering and Technology
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    • 제54권7호
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    • pp.2418-2426
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    • 2022
  • The grouping analysis is a method guided by the Korea Radioactive Waste Agency for efficient analysis of radioactive waste for disposal. In this study, experiments to verify the adequacy of grouping analysis were conducted with radioactive soil, concrete, and dry active waste in similar environments. First, analysis results of the major radionuclide concentrations in individual waste samples were reviewed to evaluate whether wastes from similar environments correspond to a single waste stream. As a result, the soil and concrete waste were identified as a single waste stream because the distribution range of radionuclide concentrations was "within a factor of 10", the range that meet the criterion of the U.S. Nuclear Regulatory Commission for a single waste stream. On the other hand, the dry active waste was judged to correspond to distinct waste streams. Second, after analyzing the composite samples prepared by grouping the individual samples, the population means of the values of "composite sample analysis results/individual sample analysis results" were estimated at a 95% confidence level. The results showed that all evaluation values for soil and concrete waste were within the set reference values (0.1-10) when five-package and ten-package grouping analyses were conducted, verifying the adequacy of the grouping analysis.

Group Technology Cell Formation Using Production Data-based P-median Model

  • 원유경
    • 한국경영과학회:학술대회논문집
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    • 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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    • pp.375-380
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    • 2003
  • This study is concerned with the machine part grouping m cellular manufacturing. To group machines into the set of machine cells and parts into the set of part families, new p-median model considering the production data such as the operation sequences and production volumes for parts is proposed. Unlike existing p-median models relying on the classical binary part-machine incidence matrix which does not reflect the real production factors which seriously impact on machine-part grouping, the proposed p-median model reflects the production factors by adopting the new similarity coefficient based on the production data-based part-machine incidence matrix of which each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. Computation test compares the proposed p median model favorably.

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Hierarchical Age Estimation based on Dynamic Grouping and OHRank

  • Zhang, Li;Wang, Xianmei;Liang, Yuyu
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제8권7호
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    • pp.2480-2495
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    • 2014
  • This paper describes a hierarchical method for image-based age estimation that combines age group classification and age value estimation. The proposed method uses a coarse-to-fine strategy with different appearance features to describe facial shape and texture. Considering the damage to continuity between neighboring groups caused by fixed divisions during age group classification, a dynamic grouping technique is employed to allow non-fixed groups. Based on the given group, an ordinal hyperplane ranking (OHRank) model is employed to transform age estimation into a series of binary enquiry problems that can take advantage of the intrinsic correlation and ordinal information of age. A set of experiments on FG-NET are presented and the results demonstrate the validity of our solution.

최대 로트 그룹핑 문제의 복잡성 (On the Hardness of the Maximum Lot Grouping Problem)

  • 황학진
    • 대한산업공학회지
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    • 제29권4호
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    • pp.253-258
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    • 2003
  • We consider the problem of grouping orders into lots. The problem is modelled by a graph G=(V,E), where each node ${\nu}{\in}V$ denotes order specification and its weight ${\omega}(\nu)$ the orders on hand for the specification. We can construct a lot simply from orders of single specification. For a set of nodes (specifications) ${\theta}{\subseteq}V$, if the distance of any two nodes in $\theta$ is at most d, it is also possible to make a lot using orders on the nodes. The objective is to maximize the number of lots with size exactly $\lambda$. In this paper, we prove that our problem is NP-Complete when $d=2,{\lambda}=3$ and each weight is 0 or 1. Moreover, it is also shown to be NP-Complete when $d=1,{\lambda}=3$ and each weight is 1,2 or 3.

Image segmentation and line segment extraction for 3-d building reconstruction

  • Ye, Chul-Soo;Kim, Kyoung-Ok;Lee, Jong-Hun;Lee, Kwae-Hi
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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    • pp.59-64
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    • 2002
  • This paper presents a method for line segment extraction for 3-d building reconstruction. Building roofs are described as a set of planar polygonal patches, each of which is extracted by watershed-based image segmentation, line segment matching and coplanar grouping. Coplanar grouping and polygonal patch formation are performed per region by selecting 3-d line segments that are matched using epipolar geometry and flight information. The algorithm has been applied to high resolution aerial images and the results show accurate 3-d building reconstruction.

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