• Title/Summary/Keyword: Node Similarity

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DESIGN OF A BINARY DECISION TREE FOR RECOGNITION OF THE DEFECT PATTERNS OF COLD MILL STRIP USING GENETIC ALGORITHM

  • Lee, Byung-Jin;Kyoung Lyou;Park, Gwi-Tae;Kim, Kyoung-Min
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.208-212
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    • 1998
  • This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

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Design of a binary decision tree using genetic algorithm for recognition of the defect patterns of cold mill strip (유전 알고리듬을 이용한 이진 트리 분류기의 설계와 냉연 흠 분류에의 적용)

  • Kim, Kyoung-Min;Lee, Byung-Jin;Lyou, Kyoung;Park, Gwi-Tae
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.1
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    • pp.98-103
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    • 2000
  • This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

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Topic Similarity-based Event Routing Algorithm for Wireless Ad-Hoc Publish/Subscribe Systems (Ad-Hoc 무선 환경의 발행/구독 시스템을 위한 구독주제 유사도 기반의 이벤트 라우팅 알고리즘)

  • Nguyen, Hieu Trung;Oh, Sang-Yoon
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.10
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    • pp.11-22
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    • 2009
  • For a wireless ad-hoc network, event routing algorithm of the publish/subscribe system is especially important for the performance of the system because of the dynamic characteristic and constraint network of its own. In this paper, we propose a new hybrid event routing algorithm. TopSim for efficient publish/subscribe system on the wireless ad-hoc network by extending the ShopParent algorithm by considering not only network overheads to choose a Parent of the publish/subscribe tree, but also topic similarity which is closeness of subscriptions. Our evaluation shows our proposed TopSim performs better for the case where a new joining node subscribed to the multiple topics and there is a node among Parent candidate nodes who subscribe to the ones in the list of multiple topics (related topics).

Globally Optimal Recommender Group Formation and Maintenance Algorithm using the Fitness Function (적합도 함수를 이용한 최적의 추천자 그룹 생성 및 유지 알고리즘)

  • Kim, Yong-Ku;Lee, Min-Ho;Park, Soo-Hong;Hwang, Cheol-Ju
    • Journal of KIISE:Information Networking
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    • v.36 no.1
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    • pp.50-56
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    • 2009
  • This paper proposes a new algorithm of clustering similar nodes defined as nodes having similar characteristic values in pure P2P environment. To compare similarity between nodes, we introduce a fitness function whose return value depends only on the two nodes' characteristic values. The higher the return value is, the more similar the two nodes are. We propose a GORGFM algorithm newly in conjunction with the fitness function to recommend and exchange nodes' characteristic values for an interest group formation and maintenance. With the GORGFM algorithm, the interest groups are formed dynamically based on the similarity of users, and all nodes will highly satisfy with the information recommended and received from nodes of the interest group. To evaluate of performance of the GORGFM algorithm, we simulated a matching rate by the total number of nodes of network and the number of iterations of the algorithm to find similar nodes accurately. The result shows that the matching rate is highly accurate. The GORGFM algorithm proposed in this paper is highly flexible to be applied for any searching system on the web.

An Index Structure based on Space Partitions and Adaptive Bit Allocations for Multi-Dimensional Data (다차원 데이타를 위한 공간 분할 및 적응적 비트 할당 기반 색인 구조)

  • Bok, Kyoung-Soo;Kim, Eun-Jae;Yoo, Jae-Soo
    • Journal of KIISE:Databases
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    • v.32 no.5
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    • pp.509-525
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    • 2005
  • In this paper, we propose the index structure based on a vector approximation for efficiently supporting the similarity search of multi-dimensional data. The proposed index structure splits a region with the space partition method and allocates to the split region dynamic bits according to the distribution of data. Therefore, the index structure splits a region to the unoverlapped regions and can reduce the depth of the tree by storing the much region information of child nodes in a internal node. Our index structure represents the child node more exactly and provide the efficient search by representing the region information of the child node relatively using the region information of the parent node. We show that our proposed index structure is better than the existing index structure in various experiments. Experimental results show that our proposed index structure achieves about $40\%$ performance improvements on search performance over the existing method.

Single-Query Probabilistic Roadmap Planning Algorithm using Remembering Exploration Method (기억-탐험 방법을 이용한 단일-질의 확률 로드맵 계획 알고리즘)

  • Kim, Jung-Tae;Kim, Dae-Jin
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.4
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    • pp.487-491
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    • 2010
  • In this paper we propose a new single-query path planning algorithm for working well in high-dimensional configuration space. With the notice of the similarity between single-query algorithms with exploration algorithms, we propose a new path planning algorithm, which applies the Remembering Exploration method, which is one of exploration algorithms, to a path-planning problem by selecting a node from a roadmap, finding out the neighbor nodes from the node, and then inserting the neighbor nodes into the roadmap, recursively. For the performance comparison, we had experiments in 2D and 3D environments and compared the time to find out the path. In the results our algorithm shows the superior performance than other path planning algorithms.

A Ranking Technique of XML Documents using Path Similarity for Expanded Query Processing (확장된 질의 처리를 위해 경로간 의미적 유사도를 고려한 XML 문서 순위화 기법)

  • Kim, Hyun-Joo;Park, So-Mi;Park, Seog
    • Journal of KIISE:Databases
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    • v.37 no.2
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    • pp.113-120
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    • 2010
  • XML is broadly using for data storing and processing. XML is specified its structural characteristic and user can query with XPath when information from data document is needed. XPath query can process when the tern and structure of document and query is matched with each other. However, nowadays there are lots of data documents which are made by using different terminology and structure therefore user can not know the exact idea of target data. In fact, there are many possibilities that target data document has information which user is find or a similar ones. Accordingly user query should be processed when their term usage or structural characteristic is slightly different with data document. In order to do that we suggest a XML document ranking method based on path similarity. The method can measure a semantic similarity between user query and data document using three steps which are position, node and relaxation factors.

Local Information-based Betweenness Centrality to Identify Important Nodes in Social Networks (사회관계망에서 중요 노드 식별을 위한 지역정보 기반 매개 중심도)

  • Shon, Jin Gon;Kim, Yong-Hwan;Han, Youn-Hee
    • KIPS Transactions on Computer and Communication Systems
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    • v.2 no.5
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    • pp.209-216
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    • 2013
  • In traditional social network analysis, the betweenness centrality measure has been heavily used to identify the relative importance of nodes in terms of message delivery. Since the time complexity to calculate the betweenness centrality is very high, however, it is difficult to get it of each node in large-scale social network where there are so many nodes and edges. In this paper, we define a new type of network, called the expanded ego network, which is built only with each node's local information, i.e., neighbor information of the node's neighbor nodes, and also define a new measure, called the expended ego betweenness centrality. Through the intensive experiment with Barab$\acute{a}$si-Albert network model to generate the scale-free networks which most social networks have as their embedded feature, we also show that the nodes' importance rank based on the expanded ego betweenness centrality has high similarity with that based on the traditional betweenness centrality.

An Application of Network Autocorrelation Model Utilizing Nodal Reliability (집합점의 신뢰성을 이용한 네트워크 자기상관 모델의 연구)

  • Kim, Young-Ho
    • Journal of the Economic Geographical Society of Korea
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    • v.11 no.3
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    • pp.492-507
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    • 2008
  • Many classical network analysis methods approach networks in aspatial perspectives. Measuring network reliability and finding critical nodes in particular, the analyses consider only network connection topology ignoring spatial components in the network such as node attributes and edge distances. Using local network autocorrelation measure, this study handles the problem. By quantifying similarity or clustering of individual objects' attributes in space, local autocorrelation measures can indicate significance of individual nodes in a network. As an application, this study analyzed internet backbone networks in the United States using both classical disjoint product method and Getis-Ord local G statistics. In the process, two variables (population size and reliability) were applied as node attributes. The results showed that local network autocorrelation measures could provide local clusters of critical nodes enabling more empirical and realistic analysis particularly when research interests were local network ranges or impacts.

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SOMk-NN Search Algorithm for Content-Based Retrieval (내용기반 검색을 위한 SOMk-NN탐색 알고리즘)

  • O, Gun-Seok;Kim, Pan-Gu
    • Journal of KIISE:Databases
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    • v.29 no.5
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    • pp.358-366
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    • 2002
  • Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.