• 제목/요약/키워드: Matching Tree

검색결과 171건 처리시간 0.024초

불일치 절점을 가지는 경우의 축약된 모델을 이용한 동특성 변경법 (Structural Dynamics Modification using Reduced Model for Having Non-matching Nodes)

  • 강옥현;박윤식;박영진
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2005년도 추계학술대회논문집
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    • pp.830-833
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    • 2005
  • SDM(Structural Dynamics Modification) is to improve dynamic characteristics of a structure, more specifically of a base structure, by adding or deleting auxiliary(modifying) structures. In this paper, I will focus on the optimal layout of the stiffeners which are attached to the plate to maximize 1st natural frequency. Recently, a new topology method was proposed by yamazaki. He uses growing and branching tree model. I modified the growing and branching tree model. The method is designated modified tree model. To expand the layout of stiffeners, I will consider non-matching problem. The problem is solved by using local lagrange multiplier without the mesh regeneration. Moreover The CMS(Component mode synthesis) method is employed to reduce the computing time of eigen reanalysis using reduced componet models.

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Autonomous Deployment in Mobile Sensor Systems

  • Ghim, Hojin;Kim, Dongwook;Kim, Namgi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제7권9호
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    • pp.2173-2193
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    • 2013
  • In order to reduce the distribution cost of sensor nodes, a mobile sensor deployment has been proposed. The mobile sensor deployment can be solved by finding the optimal layout and planning the movement of sensor nodes with minimum energy consumption. However, previous studies have not sufficiently addressed these issues with an efficient way. Therefore, we propose a new deployment approach satisfying these features, namely a tree-based approach. In the tree-based approach, we propose three matching schemes. These matching schemes match each sensor node to a vertex in a rake tree, which can be trivially transformed to the target layout. In our experiments, the tree-based approach successfully deploys the sensor nodes in the optimal layout and consumes less energy than previous works.

트리검색 기법을 이용한 희소신호 복원기법 (Sparse Signal Recovery Using A Tree Search)

  • 이재석;심병효
    • 한국통신학회논문지
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    • 제39A권12호
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    • pp.756-763
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    • 2014
  • 본 논문에서는 트리검색 기반의 GTMP (matching pursuit with greedy tree search)이라는 새로운 희소신호 복원기법을 제안한다. 트리검색은 비용함수 (cost function)를 최소화함으로써 희소신호 복원 성능을 향상시키기 위해 적용하였다. 또한 각 노드마다 트리제거 (tree pruning)기법을 이용하여 효율적인 알고리듬을 개발하였다. 본 논문에서는 알고리듬의 성능분석을 통해 희소신호에서 영(0)이 아닌 값의 위치를 정확히 찾아내는 조건을 도출하였다. 그리고 실험을 통해 GTMP가 기존의 희소신호 복원기법에 비해 성능이 향상되었음을 보였다.

Case-Based Reasoning Cost Estimation Model Using Two-Step Retrieval Method

  • Lee, Hyun-Soo;Seong, Ki-Hoon;Park, Moon-Seo;Ji, Sae-Hyun;Kim, Soo-Young
    • 토지주택연구
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    • 제1권1호
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    • pp.1-7
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    • 2010
  • Case-based reasoning (CBR) method can make estimators understand the estimation process more clearly. Thus, CBR is widely used as a methodology for cost estimation. In CBR, the quality of case retrieval affects the relevance of retrieved cases and hence the overall quality of the reminding capability of CBR system. Thus, it is essential to retrieve relevant past cases for establishing a robust CBR system. Case retrieval needs the following tasks to obtain appropriate case(s); indexing, search, and matching (Aamodt and Plaza 1994). However, the previous CBR researches mostly deal with matching process that has limits such as accuracy and efficiency of case retrieval. In order to address this issue, this research presents a CBR cost model for building projects that has two-step retrieval process: decision tree and nearest neighbor methods. Specifically, the proposed cost model has indexing, search and matching modules. Features in the model are divided into shape-based and scale-based attributes. Based on these, decision tree is established for facilitating the search task and nearest neighbor method was utilized for matching task. In regard to applying nearest neighbor method, attribute weights are assigned using GA optimization and similarity is calculated using the principle of distance measuring. Thereafter, the proposed CBR cost model is developed using 174 cases and validated using 12 test cases.

서픽스트리 클러스터링 방법과 블라스트를 통합한 유전자 서열의 클러스터링과 기능검색에 관한 연구 (A Study on Clustering and Identifying Gene Sequences using Suffix Tree Clustering Method and BLAST)

  • 한상일;이성근;김경훈;이주영;김영한;황규석
    • 제어로봇시스템학회논문지
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    • 제11권10호
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    • pp.851-856
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    • 2005
  • The DNA and protein data of diverse species have been daily discovered and deposited in the public archives according to each established format. Database systems in the public archives provide not only an easy-to-use, flexible interface to the public, but also in silico analysis tools of unidentified sequence data. Of such in silico analysis tools, multiple sequence alignment [1] methods relying on pairwise alignment and Smith-Waterman algorithm [2] enable us to identify unknown DNA, protein sequences or phylogenetic relation among several species. However, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST was combined with a clustering tool. Our clustering and annotating tool is summarized as the following steps: (1) construction of suffix tree; (2) masking of cross-matching pairs; (3) clustering of gene sequences and (4) annotating gene clusters by BLAST search. The system was successfully evaluated with 22 gene sequences in the pyrubate pathway of bacteria, clustering 7 clusters and finding out representative common subsequences of each cluster

기능 도메인 예측을 위한 유전자 서열 클러스터링 (Gene Sequences Clustering for the Prediction of Functional Domain)

  • 한상일;이성근;허보경;변윤섭;황규석
    • 제어로봇시스템학회논문지
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    • 제12권10호
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    • pp.1044-1049
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    • 2006
  • Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

차영상과 4진트리 구조를 이용한 가변 블럭정합 알고리즘에 관한 연구 (A study on variable block matching algorithm using differential image and quad tree)

  • 정일화;이대영
    • 한국통신학회논문지
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    • 제21권11호
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    • pp.2768-2775
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    • 1996
  • 가변블럭정합(Variable Block Matching: VBM) 알고리즘은 영상블럭의 복잡도에 따라 다른 블럭크기를 사용함으로써 복잡한 영역이나 경계부근에서의 이동벡터 추정시 효과적이지만 많은 계산량이 요구되므로 이러한 계산량의 문제점을 해결하기 위해서 최적임계치에 의한 차영상신소를 이용하여 4진 크리구조를 구성하고 각각의 블럭에 대해 여러가지 고속블럭정합 알고리즘을 응용하여 움직임을 추정하는 방법을 제안하였다.

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Sparse Signal Recovery via Tree Search Matching Pursuit

  • Lee, Jaeseok;Choi, Jun Won;Shim, Byonghyo
    • Journal of Communications and Networks
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    • 제18권5호
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    • pp.699-712
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    • 2016
  • Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin.

접미사 배열을 이용한 Suffix-Prefix가 일치하는 모든 쌍 찾기 (Finding All-Pairs Suffix-Prefix Matching Using Suffix Array)

  • 한선미;우진운
    • 정보처리학회논문지A
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    • 제17A권5호
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    • pp.221-228
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    • 2010
  • 최근 문자열 연산들이 계산 생물학 및 인터넷의 보안, 검색 분야에 응용되면서 효율적인 문자열 연산을 위한 다양한 자료구조와 알고리즘이 연구되고 있다. suffix-prefix가 일치하는 모든 쌍 찾기는 두 개 이상의 문자열이 주어질 때 각 쌍의 문자열에 대해 가장 긴 suffix와 일치하는 prefix를 찾는 것으로 가장 짧은 슈퍼스트링을 검출하는 근사 알고리즘에서 사용될 뿐만 아니라 생물정보학, 데이터 압축 분야에서도 중요하게 사용된다. 본 논문에서는 접미사 배열을 이용하는 suffix-prefix가 일치하는 모든 쌍 찾기 알고리즘을 제안하며 O($k{\cdot}m$) 시간 복잡도를 가진다. 접미사 배열 알고리즘이 접미사 트리 알고리즘 보다 소요 시간과 메모리 면에서 더 우수함을 실험을 통해서 제시한다.

Improving Bagging Predictors

  • Kim, Hyun-Joong;Chung, Dong-Jun
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2005년도 추계 학술발표회 논문집
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    • pp.141-146
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    • 2005
  • Ensemble method has been known as one of the most powerful classification tools that can improve prediction accuracy. Ensemble method also has been understood as ‘perturb and combine’ strategy. Many studies have tried to develop ensemble methods by improving perturbation. In this paper, we propose two new ensemble methods that improve combining, based on the idea of pattern matching. In the experiment with simulation data and with real dataset, the proposed ensemble methods peformed better than bagging. The proposed ensemble methods give the most accurate prediction when the pruned tree was used as the base learner.

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