• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.177-190
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• 2001

전통적으로 많이 사용하는 군집분석의 방법들은 개체간의 거리를 고려하여 이들을 분류해 내는 것이며, 따라서 거리 측정 방법에 따라 여러 형태의 군집분석 방법이 나타나게 된다. 어떤 방법을 적용하던 간에 그 결과는 고정된 수치로써 나타난다. 다차원 자료의 구조파악이 몇 개의 수치로 나타나게 되면 어쩔 수 없이 정보의 손실이 발생하게 된다. 이를 보완하기 위해 시각적 매체를 동원하여 다차원 자료의 구조를 파악하는 연구가 있었으며, 이를 시각적 군집분석이라고 명명하고 있다. 본 연구에서는 시각적 군집분석에 대한 기본적 개념과 이를 위한 통계 도형의 활용, 구현방법 등에 대해 살펴보기로 한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3520-3537
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• 2014

Video copy detection methods have emerged in recent years for a variety of applications. However, the lack of efficiency in the usual retrieval systems restricts their use. In this paper, we propose a parallel implementation strategy for content based video copy detection (CBCD) by using a multi-core processor. This strategy can support video copy detection effectively, and the processing time tends to decrease linearly as the number of processors increases. Experiments have shown that our approach is successful in speeding up computation and as well as in keeping the performance.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3004-3006
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• 2000

In this paper, we propose an optimal design method of Fuzzy-Neural Networks model with parallel structure for complex and nonlinear systems. The proposed model is consists of a multiple number of FNN connected in parallel. The proposed FNNs with parallel structure is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. We use a HCM clustering and GAs to identify the structure and the parameters of the proposed model. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model. we use the time series data for gas furnace and the numerical data of nonlinear function.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1692-1711
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• 2016

Energy is a scarce resource in wireless sensor networks (WSNs). A variety of clustering protocols for WSNs, such as the zone-based stable election protocol-enhanced (ZSEP-E), have been developed for energy optimization. The ZSEP-E is a heterogeneous zone-based clustering protocol that focuses on unbalanced energy consumption with parallel formation of clusters in zones and election of cluster heads (CHs). Most ZSEP-E research has assumed probabilistic election of CHs in the zones by considering the maximum residual energy of nodes. However, studies of the diverse CH election parameters are lacking. We investigated the performance of the ZSEP-E in such scenarios using a fuzzy logic approach based on three descriptors, i.e., energy, density, and the distance from the node to the base station. We proposed an efficient ZSEP-E scheme to adapt and elect CHs in zones using fuzzy variables and evaluated its performance for different energy levels in the zones.

• The KIPS Transactions:PartC
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• v.8C no.6
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• pp.765-774
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• 2001

In this paper, we proposed the striping policy for the storage nodes in the Linux-based parallel media stream server. We newly developed a storage clustering architecture, and named it as a system RAID architecture. In this system, many storage cluster nodes are grouped to operate as a single server. This system uses unique striping policy to distribute multimedia files into the parallel storage nodes. If a service request occurs, each storage cluster node transmits striped files concurrently to the clients. This scheme can provide the fair distribution of the preprocessing load in all storage cluster nodes. The feature of this system is a relative striping policy based on the file types, service types, and the number of storage nodes to provide the best service.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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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

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.2
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• pp.87-93
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• 1989

Squared-error Pattern Clustering algorithm used in unsupervised pattern recognition and image processing application demands substantial processing time for operation of feature vector matrix. So, this paper propose the fast squared-error Pattern Clustering Processor using the Residue Number System which have been the nature of parallel processing and pipeline. The proposed Squared-error Pattern Clustering Processor illustrate satisfiable error rate for Cluster number which can be divide meaningful region and about 200 times faster than 80287 coprocessor from experiments result of image segmentation. In this result, it is useful to real-time processing application for large data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2520-2528
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• 2009

In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1040-1046
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• 2009

Thanks to the recent development of computing technology, the various forms of high-performance computers are available. Among them, the parallel-clustering CPU machines are realized for the high performance computing. These supercomputers (cluster computers) can be applied to various industries due to the advantages of lower price. Especially in the field of numerical flow simulation, use of supercomputers can produce results quickly, and various engineering problems can be reviewed effectively case by case. In this paper, an application of supercomputers (cluster computers) were examined for railroad industry of fire flow simulation by using parallel computational method. It make sure that the supercomputers are very useful tools for railroad engineering.