• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.2
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• pp.7-14
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• 2016

In this paper presents a low power algorithm using a feasible cluster generation method considered glitch. The proposed algorithm is a method for reducing power consumption of a given circuit. The algorithm consists of a feasible cluster generation process and glitches removal process. So that glitches are not generated for the node to which the switching operation occurs most frequently in order to reduce the power consumption is a method for generating a feasible cluster. A feasible cluster generation process consisted of a node value set, dividing the node, the node aligned with the feasible cluster generation. A feasible cluster generation procedure is produced from the highest number of nodes in the output. When exceeding the number of OR-terms of the inputs of the selected node CLB prevents the signal path is varied by the evenly divided. If there are nodes with the same number of outputs selected by the first highest number of nodes in the input produces a feasible cluster. Glitch removal process removes glitches through the path balancing in the same manner as [5]. Experimental results were compared with the proposed algorithm [5]. Number of blocks has been increased by 5%, the power consumption was reduced by 3%.

• Journal of Korea Multimedia Society
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• v.17 no.10
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• pp.1160-1170
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• 2014

Clustering methods are very useful in many fields such as data mining, classification, and object recognition. Both the supervised and unsupervised grouping approaches can classify a series of sample data with a predefined or automatically assigned cluster number. However, there is no constraint on the number of elements for each cluster. Numbers of cluster members for each cluster obtained from clustering schemes are usually random. Thus, some clusters possess a large number of elements whereas others only have a few members. In some areas such as logistics management, a fixed number of members are preferred for each cluster or logistic center. Consequently, it is necessary to design a clustering method that can automatically adjust the number of group elements. In this paper, a k-means based clustering method with a fixed number of cluster members is proposed. In the proposed method, first, the data samples are clustered using the k-means algorithm. Then, the number of group elements is adjusted by employing a greedy strategy. Experimental results demonstrate that the proposed clustering scheme can classify data samples efficiently for a fixed number of cluster members.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6081-6088
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• 2012

An unequal clustering method in wireless sensor networks is the technique that forms the cluster of different size. This method decreases whole energy consumption by solving the hot spot problem. In this paper, I propose a layer-based dynamic unequal clustering using the unequal clustering model. This method decreases whole energy consumption and maintain that equally using optimal cluster's number and cluster head position. I also show that proposed method is better than previous clustering method at the point of network lifetime.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2603-2608
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• 2009

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the clustering method based NMF(non-negative matrix factorization) and refinement of documents in cluster by using coherence of cluster. The proposed method can improve the quality of document clustering because the re-assigned documents in cluster by using coherence of cluster based similarity between documents, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

• International Journal of Contents
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• v.4 no.1
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• pp.16-19
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• 2008

In this paper, we propose to new dispatcher method, which doesn't depend on an operating system of the server, and the direct routing method, by which a server answers a client's request at first hand. And, propose new web clustering scheme based on the contents on the web where web servers composed of cluster, with each different contents, answer client's request. The other purposes are to reduce overhead of the dispatcher through load balance, and to minimize the time to take in responding to a client's request. The performance of new web cluster scheme was improved by about 39% than that of the existing RR method. It was identified that the performance of the proposed web cluster method was extraordinary improved comparing with that of the existing RR method as a whole.

• The KIPS Transactions:PartC
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• v.14C no.3 s.113
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• pp.277-284
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• 2007

The routing algorithm many used in the wireless sensor network features the clustering method to reduce the amount of data transmission from the energy efficiency perspective. However, the clustering method results in high energy consumption at the cluster head node. Dynamic clustering is a method used to resolve such a problem by distributing energy consumption through the re-selection of the cluster head node. Still, dynamic clustering modifies the cluster structure every time the cluster head node is re-selected, which causes energy consumption. In other words, the dynamic clustering approaches examined in previous studies involve the repetitive processes of cluster head node selection. This consumes a high amount of energy during the set-up process of cluster generation. In order to resolve the energy consumption problem associated with the repetitive set-up, this paper proposes the Round-Robin Cluster Header (RRCH) method that fixes the cluster and selects the head node in a round-robin method The RRCH approach is an energy-efficient method that realizes consistent and balanced energy consumption in each node of a generated cluster to prevent repetitious set-up processes as in the LEACH method. The propriety of the proposed method is substantiated with a simulation experiment.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.167-176
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• 2009

Traditional cluster-based routing method is a representative method for increasing the energy efficiencies. In these cluster-based routing methods, the selected cluster head collect/aggregate the information and send the aggregated information to the base station. But they have to solve the unnecessary energy dissipation of frequent information exchange between the cluster head and whole member nodes in cluster. In this paper, we minimize the frequency of the information exchange for reducing the unnecessary transmit/receive frequencies as calculate the overlapped area or number of overlapped member nodes between the selected cluster head and previous cluster head in the setup phase. So, we propose the modified cluster selection protocol method that optimizes the energy dissipation in the setup phase and reuses the saved energy in the steady-state phase efficiently that prolongs the whole wireless sensor network lifetime by uniformly selecting the cluster head.

• Proceedings of the IEEK Conference
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• summer
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.3
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• pp.203-216
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• 2003

In this paper a new feature extraction and target classification method is proposed for the recognition part of FLIR(Forwar Looking Infrared)-image-based ATR system. Proposed feature extraction method is "cluster(=set of classes)-based"version of previous fisherfaces method that is known by its robustness to illumination changes in face recognition. Expecially introduced class clustering and cluster-based projection method maximizes the performance of fisherfaces method. Proposed target image classification method is based on the mixture of experts model which consists of RBF-type experts and MLP-type gating networks. Mixture of experts model is well-suited with ATR system because it should recognizee various targets in complexed feature space by variously mixed conditions. In proposed classification method, one expert takes charge of one cluster and the separated structure with experts reduces the complexity of feature space and achieves more accurate local discrimination between classes. Proposed feature extraction and classification method showed distinguished performances in recognition test with customized. FLIR-vehicle-image database. Expecially robustness to pixelwise sensor noise and un-wanted intensity variations was verified by simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.224-242
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• 2010

Data routing in wireless sensor networks must be energy-efficient because tiny sensor nodes have limited power. A cluster-based hierarchical routing is known to be more efficient than a flat routing because only cluster-heads communicate with a sink node. Existing hierarchical routings, however, assume unrealistically large radio transmission ranges for sensor nodes so they cannot be employed in real environments. In this paper, by considering the practical transmission ranges of the sensor nodes, we propose a clustering and routing method for hierarchical sensor networks: First, we provide the optimal ratio of cluster-heads for the clustering. Second, we propose a d-hop clustering scheme. It expands the range of clusters to d-hops calculated by the ratio of cluster-heads. Third, we present an intra-cluster routing in which sensor nodes reach their cluster-heads within d-hops. Finally, an inter-clustering routing is presented to route data from cluster-heads to a sink node using multiple hops because cluster-heads cannot communicate with a sink node directly. The efficiency of the proposed clustering and routing method is validated through extensive simulations.