• The KIPS Transactions:PartC
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• v.14C no.7
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• pp.589-596
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• 2007

Although flooding has the disadvantages like a transmission of duplicated packets and a packet collision, it has been used frequently to find a path between a source and a sink node in a wireless ad hoc network. Clustering is one of the techniques that have been proposed to overcome those disadvantages. In this paper, we propose a new flooding mechanism in ad hoc networks using cluster formation based on the link density which means the number of neighbors within a node's radio reach. To reduce traffic overhead in the cluster is to make the number of non-flooding nodes as large as possible. Therefore, a node with the most links in a cluster will be elected as cluster header. This method will reduce the network traffic overhead with a reliable network performance. Simulation results using NS2 show that cluster formation based on the link density can reduce redundant flooding without loss of network performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.423-426
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• 2007

클러스터링은 주어진 데이터 포인트들을 주어진 개수의 그룹으로 나누는 비지도 학습의 한 방법이다. 클러스터링의 방법 중 하나로 널리 알려진 퍼지 클러스터링은 하나의 포인트가 모든 클러스터에 서로 다른 정도로 소속될 수 있도록 함으로써 각 포인트가 하나의 클러스터에만 속할 수 있도록 하는 K-means와 같은 방법에 비해 자연스러운 클러스터 형태의 유추가 가능하고, 잡음에 강한 장점이 있다. 이 논문에서는 기존의 퍼지 클러스터링 방법 중 소속도(membership)와 전형성(typicality)을 동시에 계산해 낼 수 있는 Possibilistic Fuzzy C-Means (PFCM) 방법에 Gath-Geva (GG)의 방법 을 적용하여 PFCM을 확장한다. 제안한 방법은 PFCM의 장점을 그대로 가지면서도, GG의 거리 척도에 의해 클러스터들 사이의 경계를 강조함으로써 분류 목적에 적합한 소속도를 계산할 수 있으며, 전형성은 가우스 형태의 분포에서 생성된 포인트들의 분포 함수를 정확하게 모사함으로써 확률 밀도 추정의 방법으로도 사용될 수 있다. 또한 GG 방법은 Gustafson-Kessel 방법과 달리 클러스터에 포함된 포인트의 개수가 확연히 차이 나는 경우에도 정확한 결과를 얻을 수 있다는 사실을 실험 결과를 통해 확인할 수 있었다.

• 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.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.14-24
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• 2010

Because WSNs operate with limited resources of sensor nodes, its life is extended by cluster-based routing methods. In this study, we use data on direction, distance, density and residual energy in order to maximize the energy efficiency of cluster-based routing methods. Through this study, we expect to minimize the frequency of isolated nodes when selecting a new cluster head autonomously using information on the direction of the upper cluster head, and to reduce energy consumption by switching sensor nodes, which are included in both of the new cluster and the previous cluster and thus do not need to update information, into the sleep mode and updating information only for newly included sensor nodes at the setup phase using distance data. Furthermore, we enhance overall network efficiency by implementing secure and energy-efficient communication through key management robust against internal and external attacks in cluster-based routing techniques. This study suggests the modified cluster head selection scheme which uses the conserved energy in the steady-state phase by reducing unnecessary communications of unchanged nodes between selected cluster head and previous cluster head in the setup phase, and thus prolongs the network lifetime and provides secure and equal opportunity for being cluster head.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.3-6
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• 2009

최근 차량통신 환경에서의 위성/지상파 DMB 등 차량통신에 대한 관심이 증대되고, 전 세계적으로 기술개발이 활발하게 진행되고 있다. 그러나 좁고 긴 도로 내에 차량이 수시로 들락거려 일정한 통신 구심점을 찾는 것이 쉽지 않고, 빠르게 이동하는 차량의 특성으로 채널 특성이 좋지않 으며, 차량통신을 위한 인프라가 충분히 설치되지 않을 경우 V2I 통신의 어려움 등이 문제점으로 지적될 수 있다. 본 논문에서는 차량통신 네트워크의 효율적 운용을 위한 적응적 클러스터링(Clustering) 기법을 제안하도록 한다. 제안된 적응적 클러스터링 방법에서는 차량의 속도, 위치, 차량 간 거리에 의하여 수시로 클러스터가 적응적으로 재구성된다. 이를 위해 효율적인 클러스터해드 설정이 이루어지며, 차량통신 인프라, 클러스터해드를 중심으로 통신이 이루어진다. 제안된 기법은 제한된 인프라에서도 이중 홉 통신 및 적응적 클러스터 재구성을 통하여 차량의 네트워크 연결성을 높인다. 컴퓨터 모의실험을 통하여 적응적 클러스터링 방법의 성능을 알아보도록 한다. 모의실험을 통하여 빠른 속도로 움직이는 차량의 네트워크 접근성을 확인하고, 차량 밀도 및 인프라 간 거리에 따른 성능변화를 확인한다.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.182-183
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• 2007

본 논문에서는 전형적인 비선형 회귀문제를 다루기 위해 슈뢰딩거 방정식에 의해 표현되는 Hilbert공간에서 수행되는 Quantum 클러스터링과 Mountain 함수를 이용하여, 수치적인 입출력데이터로부터 TSK 형태의 자동적인 퍼지 if-then 규칙의 생성방법을 제안한다. 여기서 슈뢰딩거 방정식은 분석적으로 확률함수로부터 유도되어질 수 있는 포텐셜 함수를 포함한다. 이 포텐셜의 최소점들은 데이터의 특성을 포함하는 클러스터 중심들과 관련되어진다. 그러나 이들 클러스터 중심들은 데이터의 수와 같으므로 퍼지 규칙을 생성하기 어려울 뿐만 아니라 수렴속도가 느린 문제점을 가지고 있다. 이러한 문제점들을 해결하기 위해서, 본 논문에서는 밀도 척도에 기초한 클러스터 중심의 근사적인 추정에 대해 간단하면서 효과적인 Mountain 함수를 이용하여 효과적인 클러스터 중심을 얻음과 동시에 적응 뉴로-퍼지 네트워크의 자동적인 퍼지 규칙을 생성하도록 한다. 자동차 MPG 예측문제에 대한 시뮬레이션 결과는 제안된 방법이 기존 문헌에서 제시한 예측성능보다 더 좋은 특성을 보임을 알 수 있었다.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.1-20
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• 2024

Regarding the discourse on the correlation between governmental financial support and firm performance, much emphasis has been placed on the role of individual corporate characteristics as well as spatial features. However, there is a notable scarcity of empirical research examining the integrated impact of corporate and cluster characteristics on managerial performance. This study addresses this gap by empirically analyzing the financial and non-financial outcomes resulting from specific allocations of governmental financial support. Additionally, it explores corporate and cluster characteristics predicted to moderate the influence between governmental financial support and firm performance. The analysis employs a two-level hierarchical linear model (HLM) at individual and group levels. The data, reorganized based on business registration numbers at the firm and cluster levels, ultimately utilized panel data from 83,395 firms and 641 clusters. The research findings indicate that governmental financial support demonstrates a positive effect (+) on both sales and patents for firms, suggesting its effectiveness in complementing market failures. Results from the hierarchical linear model analysis show that when combined with human capital capacity, absorptive capacity, and cluster network density, governmental financial support exhibits significant positive effects on sales. This study contributes theoretical and practical insights by analyzing the relationship between governmental financial support and firm performance using a two-level hierarchical linear model. It highlights the role of corporate characteristics such as human capital and absorptive capacity, along with cluster characteristics like cluster network density, in moderating the effects of governmental financial support on firm performance.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.228-242
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• 2005

The projected clustering seeks to find clusters in different subspaces within a high dimensional dataset. We propose an algorithm to discover near optimal projected clusters without user specified parameters such as the number of output clusters and the average cardinality of subspaces of projected clusters. The objective function of the algorithm computes projected energy, quality, and the number of outliers in each process of clustering. In order to minimize the projected energy and to maximize the quality in clustering, we start to find best subspace of each cluster on the density of input points by comparing standard deviations of the full dimension. The weighting factor for each dimension of the subspace is used to get id of probable error in measuring projected distances. Our extensive experiments show that our algorithm discovers projected clusters accurately and it is scalable to large volume of data sets.

• Journal of the Mineralogical Society of Korea
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• v.20 no.4
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• pp.313-325
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• 2007

In order to have better insights into adsorption of organic molecules on kaolinite surfaces, we performed quantum chemical calculations of interaction between three different model clusters of kaolinite siloxane surfaces and benzyl alcohol, with emphasis on the effect of size and lattice topology of the cluster on the variation of electron density and magnetic shielding tensor. Model cluster 1 is an ideal silicate tetrahedral surface that consists of 7 hexagonal rings, and model cluster 2 is composed of 7 ditrigonal siloxane rings with crystallographically distinct basal oxygen atoms in the cluster, and finally model cluster 3 has both tetrahedral and octahedral layers. The Mulliken charge analysis shows that siloxane surface of model cluster 3 undergoes the largest electron density transfer after the benzyl alcohol adsorption and that of model cluster 1 is apparently larger than that of model cluster 2. The difference of Mulliken charges of basal oxygen atoms before and after the adsorption is positively correlated with hydrogen bond strength. NMR chemical shielding tensor calculation of clusters without benryl alcohol shows that three different basal oxygen atoms (O3, O4, and O5) in model cluster 2 have the isotropic magnetic shielding tensor as $228.2{\pm}3.9,\;228.9{\pm}3.4,\;and\;222.3{\pm}3.0ppm$, respectively. After the adsorption, the difference of isotropic chemical shift varies from 1 to 5.5 ppm fer model cluster 1 and 2 while model cluster 2 apparently shows larger changes in isotropic chemical shift. The chemical shift of oxygen atoms is also positively correlated with electron density transfer. The current results show that the adsorption of benzyl alcohol on the kaolinite siloxane surfaces can largely be dominated by a weak hydrogen bonding and electrostatic force (charge-charge interaction) and demonstrate the importance of the cluster site and the lattice topology of surfaces on the adsorption behavior of the organic molecules on clay surfaces.

• Journal of the Korean Chemical Society
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• v.59 no.1
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• pp.9-17
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• 2015

The density functional theory (DFT) calculations on $(H_2O)_n@C_{60}$, (n=1-10) complexes have been performed to elucidate hydrogen interaction between fullerene and water clusters. The optimized geometries, harmonic vibrational frequencies, and binding energies are predicted at various levels of theory. The harmonic vibrational frequencies for the molecules considered in this study show all real numbers implying true minima. We also compare the H-bond interaction between $(H_2O)_n$ and $(H_2O)_n@C_{60}$, (n=1-10) clusters.