• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.6
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• pp.128-140
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• 2003

The purpose of this research was to present characteristics for the classification of biotopes and classification method of biotopes as basic data for ecological landscape planning in Metropolitan Daegu. The results of this study were as follows. 1) The study identified fifteen characteristics for classification of biotopes. Ecological landscape characteristics were divided into structural and functional factors. There are six structural factors such an inclination, and nine functional factors such as temperature. 2) The study area was separated into sixty eight biotope types. For example, an industrial district was divided into two biotope types: a biotope type of an industrial district with abundant green space, and a biotope type of an industrial district with scarce green space. 3) In the result of cluster analysis using the average linkage method between groups, biotope groups were divided into fifteen clusters and biotope groups were divided into seven clusters. Each cluster was named according to the features of a descriptive statistics analysis. For example, cluster 8 was identified as a biotope type with an impermeable pavement rate of more than 90 percent and an afforestation rate under 10 percent. 4) Fifteen biotope groups were converted to land use patterns for remote application and utilization of urban biotope in city planning. Biotope groups of a building area beyond an intermediate floor with an afforestation rate under 20-30 percent was converted to a land use pattern such as a tall apartment complex or commercial district. When examining the characteristics that were established in this research, there was a limit to achieve the objective of grade-classification because of a lack of related basic data. The research of landscape ecological characteristics for the classification of biotopes could not be completed due to a lack of time and resources, thus the study of ecological landscape characteristics will be accomplished over time.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.2
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• pp.55-64
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• 2000

In this paper, we propose an efficient datapath placement algorithm to minimize track density. Here, we consider each datapath element as a cluster, and merge the most strongly connected two clusters to a new cluster until only one cluster remains. As nodes in the two clusters to be merged are already linearly ordered respectively, we can merge two clusters with connecting them. The proposed algorithm produces circular linear ordering by connecting starting point and end point of the final cluster, and n different linear ordering by cutting between two contiguous elements of the circular linear ordering. Among the n different linear ordering, the linear ordering to minimize track density is final solution. In this paper, we show and utilize that if two clusters are strongly connected in a graph, the inner product of the corresponding vectors mapped in d-dimensional space using spectral method is maximum. Compared with previous datapath placement algorithm GA/S $A^{［2］}$, the proposed algorithm gives similar results with much less computation time.

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.33-39
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• 2004

In this paper. we design the learning algorithm of LVQ which is used Forward Counter Propagation Networks to improve classification performance of LVQ networks. The weights of Forward Counter Propagation Networks which is between input layer and cluster layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm. Finally. pattern vectors is classified into subclasses by neurons which is being in the cluster layer, and the weights of Forward Counter Propagation Networks which is between cluster layer and output layer is learned to classify the classified subclass, which is enclosed a class. Also. kr the number of classes is determined, the number of neurons which is being in the input layer, cluster layer and output layer can be determined. To prove the performance of the proposed learning algorithm. the simulation is performed by using training vectors and test vectors that ate Fisher's Iris data, and classification performance of the proposed learning method is compared with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional classification.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.203-209
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• 2016

In this paper, we introduce a fuzzy inference systems for nonlinear inference using fuzzy cluster. Typically, the generation of fuzzy rules for nonlinear inference causes the problem that the number of fuzzy rules increases exponentially if the input vectors increase. To handle this problem, the fuzzy rules of fuzzy model are designed by dividing the input vector space in the scatter form using fuzzy clustering algorithm which expresses fuzzy cluster. From this method, complex nonlinear process can be modeled. The premise part of the fuzzy rules is determined by means of FCM clustering algorithm with fuzzy clusters. The consequence part of the fuzzy rules have four kinds of polynomial functions and the coefficient parameters of each rule are estimated by using the standard least-squares method. And we use the data widely used in nonlinear process for the performance and the nonlinear characteristics of the nonlinear process. Experimental results show that the non-linear inference is possible.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.73-82
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• 2007

This paper proposes how to reduce the amount of data transmitted in each sensor and cluster head in order to lengthen the lifetime of sensor network by data aggregation of the continuous queries. The most important factor of refuting the sensor's energy dissipation is to reduce the amount of messages transmitted. The method proposed is basically to combine clustering, in-network data aggregation and hierarchical filtering. Hierarchical filtering is to divide sensor network by two tiers when filtering it. First tier performs filtering when transmitting the data from cluster member to cluster head, and second tier performs filtering when transmitting the data from cluster head to base station. This method is much more efficient and effective than the previous work. We show through various experiments that our scheme reduces the network traffic significantly and increases the network's lifetime than existing methods.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.12-22
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• 2014

This study was carried out to classify forest communities and to aggregate forest cover types for the complex and diversified natural forest areas of Hwangaksan, Bakseoksan, Deogyusan, and Jirisan in southern region of Baekdudaegan Mountains. The vegetation data were collected by point-centered quarter sampling method. Eight hundred fifty one sample points were subjected to cluster analysis to classify 18 forest communities, which were aggregated into 7 representative forest cover types on the basis of community similarity from composition of canopy species. They were mixed mesophytic forest cover type, the others deciduous forest cover type, Quercus variabilis-Quercus serrata cover type, Quercus mongolica cover type, Pinus densiflora cover type, Carpinus laxiflora cover type, and Abies koreana cover type. The Quercus mongolica cover type was most widely distributed in the study areas, and this cover type tended to occur in the place of higher altitude as latitude was getting lower. Mixed mesophytic forest and the others deciduous forest cover type were commonly distributed in the areas of valley, on the other hand, Quercus mongolica cover type and Pinus densiflora cover type tended to be distributed in the areas of ridge.

• Journal of Korea Foundry Society
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• v.26 no.6
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• pp.272-276
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• 2006

For the first time AZ91 (MgAl9Zn1) and AM60 (MgAl6) Mg alloy foams with homogeneous pore structures were prepared successfully via melting foaming method by using $CaCO_3$ powder as blowing agent. The possible foaming mechanisms and pore structures of these Mg alloy foams were discussed and investigated. The results show that Mg alloy melt can affect $CaCO_3$ decomposition behavior and AZ91 Mg alloy is relative easy to be foamed into metal foam with high porosity and big pore size.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.251-267
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• 2004

The purpose of this study is to generate the local level knowledge structure of a single document, similar to end-of-the-book indexes and table of contents of printed material through the use of term clustering and cluster representative term selection. Furthermore, it aims to analyze the functionalities of the knowledge structure. and to confirm the applicability of these methods in user-friend1y information services. The results of the term clustering experiment showed that the performance of the Ward's method was superior to that of the fuzzy K -means clustering method. In the cluster representative term selection experiment, using the highest passage frequency term as the representative yielded the best performance. Finally, the result of user task-based functionality tests illustrate that the automatically generated knowledge structure in this study functions similarly to the local level knowledge structure presented In printed material.

• Korean Journal of Materials Research
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• v.17 no.5
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• pp.250-255
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• 2007

The alloying effects on the electronic structures of Zinc are investigated using the relativistic $DV-X{\alpha}molecular$ orbital method in order to obtain useful information for alloy design. A new parameter which is the d obital energy level(Md) and the bonder order(Bo) of alloying elements in Zinc was introduced and used for prediction of the mechanical properties. The Md correlated with the atomic radius and the electronegativity of elements. The Bo is a measure of the strength of the covalent bond between M and X atoms. First-principles calculations of electronic structures were performed with a series of models composed of a MZn18 cluster and the electronic states were calculated by the discrete variational- $X{\alpha}method$ by using the program code SCAT. The central Zinc atom(M) in the cluster was replaced by various alloying elements. In this study energy level structures of pure Zinc and alloyed Zinc were calculated. From calculated results of energy level structures in MZn18 cluster, We found Md and Bo values for various elements of Zn. In this work, Md and Bo values correlated to the tensile strength for the Zn. These results will give some guide to design of zinc based alloys for high temperature applications and it is possible the excellent alloys design.