• 한국경영과학회지
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• 제8권2호
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• pp.27-43
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• 1983

The purpose of this paper is to analyze the structure and properties of the patterns by extracting the general patterns on socio-economic development from huge data by statistical analysis. We collected data concerning socio-logical, economical and technological aspects. Indicators used for this study amounted to a total of 136, and among them 39 were on science & technology. What is more, these indicators were set up with the resent data for the first half of the 1970's mainly, and 141 nations were selected as the sample. Some rinkage patterns to the total indicators were abstracted by cluster analysis based on the correlation matrix. And some rinkage patterns to the total countries were educed by applying cluster analysis of centroid method to the respective indicators.

• 한국환경과학회지
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• 제12권12호
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• pp.1215-1225
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• 2003

The purpose of this research was to investigate the characteristics of urban ecotopes and to classify ecotopes systematically from them. Total of 15 characteristics for classification of ecotopes were selected, and there were categorized 3 factors, that is abiotic, biotic and anthropological factors. The ecotope types in the study area were classified into 67. The classification of ecotope was made with SPSS for Windows Version 10.0 on the basis of the 15 characteristics. As the results of cluster analysis using the average linkage method between groups, groups of ecotope type were divided into 15 clusters. It was known that there was not a great difference in an affinity as the result of overlapping the maps of ecotope type and land use type. This research suggested characteristics for classification of ecotopes, but there was a limit to Set the objective method for grade classification because of lacking in the basic data, the research of characteristics will be accomplished continuously.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.456-459
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• 1999

In this paper, we propose a method of unconstrained handwritten numeral recognition using image dithering and multiple modular MLPs. The set of sample numeral patterns is subdivided into clusters which are extended by their radius. On each extended cluster, we constructed MLPs network as the expert recognizer of corresponding cluster. The gating network is also trained by an MLPs to weigh the outputs of expert MLPs. In training and test phase of the recognizer, we utilize the multiple dithered numeral images and the combination of the outputs for corresponding dithered images. Experimental results show that our recognition method works very well.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2001년도 봄 학술발표회 논문집
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• pp.61-68
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• 2001

A parallel nonlinear analysis program of prestressed concrete frame is migrated on a PC cluster system and a massively parallel processing system, CRAY T3E system, using MPI. The PC cluster system is configured with Pentium Ⅲ class PCs and fast ethernet. The CRAY T3E system is composed of a set of nodes each containing one Processing Element (PE), a memory subsystem and its distributed memory interconnect network. Parallel computing algorithms are implemented on element-wise processing parts including the calculation of stiffness matrix, element stresses and determination of material states, check of material failure and calculation of unbalanced loads. Parallel performance of the migrated program is evaluated through typical numerical examples.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.377-384
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• 2023

The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNs on the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory; the common part of the associated data set is mined by exchanging common information among the cluster head nodes, and the independent parts are updated within each cluster head node. To solve the non-convergence problem in the distributed computing, the approximate term is introduced into the optimization objective function to make the sub-optimization problem strictly convex. And the decompressed sensing signal reconstruction problem is addressed by the alternating direction multiplier method to realize the distributed signal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is located according to the characteristic information of the cross-power spectrum. The proposed method can accurately acquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance the communication quality of the experimental object.

• 디지털융복합연구
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• 제6권3호
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• pp.35-43
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• 2008

This research was to analyze information network which contributed Anyang Venture Valley's development. And mutual communication between regional innovated companies was the essential factor of this growth. Anyang city with its Knowledge Industry Promotion Agency, KIPA, has first set up communication network, facility and other structures to create basis of communication. For that, they were able to operate various networking and promotion of communication which led to the enforcement of regional innovation in venture businesses. During its innovation, KIPA played a key role as a link between regional Innovated companies, which enabled active interactions between them. Government can verify significance of establishing network base with mediation function for lively communication for promoting innovation capability through Anyang Venture Valley's case.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.118-124
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• 2004

This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is com-posed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.are given.

• 대한가정학회지
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• 제38권9호
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• pp.33-40
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• 2000

The purpose of this proper is to set up the sizing system of the lower, which would be a guide for obese women for selecting ready-to-wear of suitable size. This study was carried out on 130 obese women and was done by cluster analysis with two(waist girth and hip girth) or three(abdomen circumference, hip girth and crotch length) variables. The results were as fellows: First, it was found that 3-5 sizes were suitable by cluster analysis with two variable. Second, 3-4 sizes were suitable with three variables. Finally, the number of sizes to product was 5 with two variables as a feasible solution, and 4 with three variables.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.716-720
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• 2003

Order-picking process is one of the major operations in warehouses and distribution centers. In low-level picker-to-part system, picker usually combines orders (order batching) as much as possible so that the can pick a set of the combined orders at the same time. For midpoint routing policy, this paper develops an efficient order-batching algorithm based on cluster analysis. The algorithm is compared with a well known existing algorithm in terms of the total travel time and number of batches grouped. The test results show that the proposed heuristic performs better than the other.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.33-37
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• 2018

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.