• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.412-415
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• 2008

매일 쏟아져 나오는 방대한 양의 기상자료는 현재의 대기상태를 대표하기도 하지만 그 지역의 지형적 특성을 나타내고 있다. 이번 연구는 수원지역의 일일 기상자료를 토대로 지형적 특성과 그에 따른 기상현상(바람, 안개)알고자 한다. K-means를 이용 특정 기상현상끼리 군집화하여 지형적 특성과 비교하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.17-18
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• 2008

다차원 데이터들에 대한 거리기반 클러스터링에서는 데이터의 전체 차원을 고려한 거리 정보를 이용하여 근접한 것들을 인접하게 만든다. 마이크로어레이 데이터의 경우에는 일부 차원 관점에서 유사한 지역 클러스터를 찾는 것이 분석에서 유용한 경우가 있다. 이 논문에서는 마이크로어레이 데이터에 대한 지역 클러스터를 찾는 방법을 제안한다.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.109-115
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• 2001

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2186-2188
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• 1998

In this paper, Fundamental rules governing group intelligence "arrangement" behavior of multiple number of autonomous mobile robots are represented by a small number of fuzzy rules. Complex lifelike behavior is considered as local interactions between simple individuals under small number of fundamental rules. The fuzzy rules for arrangement are generated from clustering the input-output data obtained from the arrangement algorithm. Simulation shows the fuzzy rules successfully realizes fundamental rules of the flocking group behavior.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.11
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• pp.1060-1073
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• 2013

In this paper, we present a cluster-based routing scheme for efficiently delivering data to group mobile users by extracting and clustering mobile user group simply from beacon message information in wireless ad-hoc networks. First, we propose an online-clustering mechanism that uses a local neighbor table on each node by recursively transmitting to neighbor nodes, and forms a group table where a set of listed nodes are classified as group members, without incurring much overhead. A node that appears the most frequently from neighbor tables throughout the network is selected as the cluster-head node, serving as a data gateway for the intra-cluster. Second, we design an inter-cluster routing that delivers data from stationary data sources to the selected cluster-head node, and a intra-cluster routing to deliver from the cluster-head node to users. Simulation results based on ns-2 in the ad-hoc networks consisting of 518 stationary nodes and 20 mobile nodes show that our proposed clustering mechanism achieves high clustering accuracy of 96 % on average. Regarding routing performance, our cluster-based routing scheme outperforms a naive one-to-one routing scheme without any clustering by reducing routing cost up to 1/20. Also, our intra-cluster routing utilizing a selected cluster-head node reduces routing cost in half as opposed to a counterpart of the intra-cluster routing through a randomly-selected internal group member.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.11
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• pp.1170-1181
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• 2012

Wireless Sensor Networks(WSNs) are used in diverse applications. In general, sensor nodes that are easily deployed on specific areas have many resource constrains such as battery power, memory sizes, MCUs, RFs and so on. Hence, first of all, the efficient energy consumption is strongly required in WSNs. In terms of event states, event-driven deliverly model (i.e. surveillance and reconnaissance applications) has several characteristics. On the basis of such a model, clustering algorithms can be mostly used to manage sensor nodes' energy efficiently owing to the advantages of data aggregations. Since a specific node collects packets from its child nodes in a network topology and aggregates them into one packet to relay them once, amount of transmitted packets to a sink node can be reduced. However, most clustering algorithms have been designed without considering can be reduced. However, most clustering algorithms have been designed without considering characteristics of event-driven deliverly model, which results in some problems. In this paper, we propose enhanced clustering algorithms regarding with both targets' movement and energy efficiency in order for applications of surveillance and reconnaissance. These algorithms form some clusters to contend locally between nodes, which have already detected certain targets, by using a method which called CHEW (Cluster Head Election Window). Therefore, our proposed algorithms enable to reduce not only the cost of cluster maintenance, but also energy consumption. In conclusion, we analyze traces of the clusters' movements according to targets' locations, evaluate the traces' results and we compare our algorithms with others through simulations. Finally, we verify our algorithms use power energy efficiently.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.564-579
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• 2008

It is often challenging to evaluate local spatial association among onedimensional vectors generally representing paired-location events where two points are physically or functionally connected. This is largely because of complex process of such geographic phenomena itself and partially representational complexity. This paper addresses an alternative way to identify spatially autocorrelated paired-location events (or vectors) at a local scale. In doing so, we propose a statistical algorithm combining univariate point pattern analysis for evaluating local clustering of origin-points and similarity measure of corresponding vectors. For practical use of the suggested method, we present an empirical application using transactions data in a local housing market, particularly recorded from 2004 to 2006 in Franklin County, Ohio in the United States. As a result, several locally characterized similar transactions are identified among a set of vectors showing various local moves associated with communities defined.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.3
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• pp.492-507
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• 2008

Many classical network analysis methods approach networks in aspatial perspectives. Measuring network reliability and finding critical nodes in particular, the analyses consider only network connection topology ignoring spatial components in the network such as node attributes and edge distances. Using local network autocorrelation measure, this study handles the problem. By quantifying similarity or clustering of individual objects' attributes in space, local autocorrelation measures can indicate significance of individual nodes in a network. As an application, this study analyzed internet backbone networks in the United States using both classical disjoint product method and Getis-Ord local G statistics. In the process, two variables (population size and reliability) were applied as node attributes. The results showed that local network autocorrelation measures could provide local clusters of critical nodes enabling more empirical and realistic analysis particularly when research interests were local network ranges or impacts.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.588-591
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• 2004

This paper describes a new approach to Internet host mobility. We argue that local mobility, the performance of existing mobile host protocol can be significantly improved. It proposes Fast Moving Detection scheme that based on neighbor AP channel information and moving detection table. And, it composes Local Area Clustering Path (LACP) domain that collected in AP's channel information and MN interface information. It stored the roaming table to include channel information and moving detection. Those which use the proposal scheme will need to put LACP information into the beacon or probe frame. Each AP uses scheme to inform available channel information to MN. From the simulation result, we show that the proposed scheme is advantageous over the legacy schemes in terms of the burst blocking probability and the link utilization.

• World Technopolis Review
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• v.5 no.2
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• pp.98-107
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• 2016

By highlighting the efficiencies gained from regional specialization, the cluster concept has distracted economic development officials from their traditional role of diversifying regional and local economies. Clustering was a viable strategy for much of the 18 years following its original appearance in the literature. Now, two events cast doubt on the continued viability of cluster-based specialization. First, the digital convergence has blurred the boundaries that once separated one industry from another. An industry cluster strategy becomes difficult when the industry cannot be defined. Second, many cluster initiatives fail. Combining literature search with the system-theoretic notions of efficiency and redundancy, we find many factors moderate cluster success. This implies regions facing uncertain success in their cluster-building efforts should thoroughly understand their unique circumstances and build upon them. Regions with successful clusters are advised to aim for multiple related clusters or superclusters.