• Journal of the Economic Geographical Society of Korea
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• v.25 no.4
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• pp.514-530
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• 2022

Industrial clusters are being promoted in various ways to enhance industrial competitiveness around the world. This study aims to examine the formation and development process of regional industrial clusters in Bavaria, which are strengthening the competitiveness of local industrial enterprises and leading the continuous development of related industries in Germany, which shows stable industrial growth amidst global competition. To this end, this study first theoretically overviews the regional industrial clusters, followed by a case study of the development process and characteristics of cluster promotion policy in Bavaria, Germany. In particular, this study seeks to identify the formation and organization system of industrial clusters in Bavaria. Based on these analysis results, this study examines the main characteristics and success factors of regional industrial clusters in Bavaria, Germany, and tries to derive policy implications for creating and fostering industrial clusters in the future.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.536-543
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• 2004

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.35-54
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• 2009

The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.89-89
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• 2014

While the current consensus view holds that galaxy mergers are commonplace, it is sometimes speculated that Globular Clusters (GCs) may also have undergone merging events, possibly resulting in massive objects with a strong metallicity spread such as Omega Centauri. Galaxies are mostly far, unresolved systems whose mergers are most likely wet, resulting in observational as well as modeling difficulties, but GCs are resolved into stars that can be used as discrete dynamical tracers, and their mergers might have been dry, therefore easily simulated with an N-body code. It is however difficult to determine the observational parameters best suited to reveal a history of merging based on the positions and kinematics of GC stars, if evidence of merging is at all observable. To overcome this difficulty, we investigate the applicability of supervised and unsupervised machine learning to the automatic reconstruction of the dynamical history of a stellar system. In particular we test whether statistical clustering methods can classify simulated systems into monolithic versus merger products. We run direct N-body simulations of two identical King-model clusters undergoing a head-on collision resulting in a merged system, and other simulations of isolated King models with the same total number of particles as the merged system. After several relaxation times elapse, we extract a sample of snapshots of the sky-projected positions of particles from each simulation at different dynamical times, and we run a variety of clustering and classification algorithms to classify the snapshots into two subsets in a relevant feature space.

• Journal of the Korean Home Economics Association
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• v.43 no.1 s.203
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• pp.105-113
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• 2005

The purpose of this study was to suggest a standard sizing system for college female students' head wears according to their head types. The subjects were 193 college women, of 20 to 25 years-old. The subjects were directly measured anthropometrically and indirectly analyzed photographically. In previous tudy (Lim, 2004), 3 clusters as their head types were categorized. the sizing system, which had frequencies more than $4\%$ was classified 6 cases, 7 cases and 9 cases, respectively, by head 3 types. 3 types of size system, which were 56-28-38, 56-30-38 and 57-28-38(Eds note: which of the measurements are head girth, surface length 1 and surface length 2), which were included in 3 clusters. Although head girths were as the same, head surface length was different in size. On the contrary, head surface length was same, head girth was different. The result will contribute to fitness of head wear fitness of consumer, and the amount of production.

• Journal of the Korean Institute of Educational Facilities
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• v.24 no.4
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• pp.15-24
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• 2017

This paper aims to investigate in what extent subject-centered clusters are different from one another in terms of message system, which is composed of curriculum, pedagogy and evaluation. For this, Bernstein's pedagogic transmission code(i.e., classification and framing) and school typology(i.e., open-type or close-type) have been explored, and then applied into Shimin Junior School, Japan, in order to find out substantial characteristics between subject-centered clusters. In this case study, VGA(visibility graph analysis), as one of syntactical methodologies in space syntax theory, has been used to measure to what degree they are actually different. Throughout in-depth investigation of spatial configurations, it can be said that the square of clusters is strongly connected and integrated very well, so that it acts as an anchor place for school life within a cluster. However, it works in different ways according to message systems. In the subjects like Japanese and Science whose message system are characterized by strong classification and strong framing, integration values are relatively low, and this means that it is hard to expect cross-referencing activities through the subject squares. On the contrary, the subject of Social Studies defined by weak classification and weak framing shows the highest mean integration values, and this can be expected that there are inter-changeable learning activities in the square.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1059-1065
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• 2020

In hospitals, nurses are subjectively determining the urine status to check the kidneys and circulatory system of patients whose statuses are related to patients with kidney disease, critically ill patients, and nursing homes before and after surgery. To improve this problem, this paper proposes a urine spectrum analysis system which clusters urine test results based on a hybrid machine learning model consists of unsupervised learning and supervised learning. The proposed system clusters the spectral data using unsupervised learning in the first part, and classifies them using supervised learning in the second part. The results of the proposed urine spectrum analysis system using a mixed model are evaluated with the results of pure supervised learning. This paper is expected to provide better services than existing medical services to patients by solving the shortage of nurses, shortening of examination time, and subjective evaluation in hospitals.

• ETRI Journal
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• v.13 no.2
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• pp.34-41
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• 1991

In this paper, a new distributed parallel algorithm for pattern classification based upon Self-Organizing Neural Network(SONN)[10-12] is developed. This system works without any information about the number of clusters or cluster centers. The SONN model showed good performance for finding classification information, cluster centers, the number of salient clusters and membership information. It took a considerable amount of time in the sequential version if the input data set size is very large. Therefore, design of parallel algorithm is desirous. A new distributed parallel algorithm is developed and experimental results are presented.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.70.2-70.2
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• 2014

Galaxy clusters, the largest gravitationally bound systems, are an important means to place constraints on cosmological models. Moreover, they are excellent places to test galaxy evolution models in connection to the environments. To this day, massive clusters have been found unexpectedly(Kang & Im 2009, Durret et al. 2011, Tashikawa et al. 2012) and evolution of galaxies in cluster have been still controversial (Elbaz et al. 2007, Cooper et al. 2008, Tran et al. 2009). Finding galaxy cluster candidates in a wide, deep imaging survey data will enable us to solve the such issues of modern extragalactic astronomy. We have used multi-wavelength data from the UKIRT Infrared Deep Sky Survey Deep Extragalactic Survey (UKIDSS DXS/J and K bands), Spitzer Wise-area InfraRed Extragalactic survey (SWIRE/two mid-infrared bands), the Panoramic Survey Telescope and Rapid Response System (PAN-STARRS/ g, r, i, z, y bands) and Infrared Medium-deep Survey(IMS/J band). We report new candidates of galaxy clusters and properties of their member galaxies in one of the wide and deep survey fields ELAIS-N1, European Large Area ISO Survey North1, covering sky area of $8.75deg^2$.

• Journal of The Korean Astronomical Society
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• v.45 no.3
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• pp.71-84
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• 2012

We present a photometric study of the globular clusters (GCs) in the Virgo giant elliptical galaxy M86 based on Washington $CT_1$ images. The colors of the GCs in M86 show a bimodal distribution with a blue peak at ($C-T_1$) = 1.30 and a red peak at ($C-T_1$) = 1.72. The spatial distribution of the red GCs is elongated similar to that of the stellar halo, while that of the blue GCs is roughly circular. The radial number density profile of the blue GCs is more extended than that of the red GCs. The radial number density profile of the red GCs is consistent with the surface brightness profile of the M86 stellar halo. The GC system has a negative radial color gradient, which is mainly due to the number ratio of the blue GCs to the red GCs increasing as galactocentric radius increases. The bright blue GCs in the outer region of M86 show a blue tilt: the brighter they are, the redder their mean colors get. These results are discussed in comparison with other Virgo giant elliptical galaxies.