• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.6-11
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• 2021

Routing protocols play a pivotal role in the energy management and lifespan of any Wireless Sensor Network. Lower network lifetime has been one of the biggest concerns in LEACH protocol due to dead nodes. The LEACH protocol suffers from uneven energy distribution problem due to random selection of a cluster head. The cluster head has much greater responsibility compared to other non- cluster head nodes and consumes greater energy for its roles. This results in early dead nodes due to energy lost for the role of cluster- head. This study proposes an approach to balance the energy consumption of the LEACH protocol by using a semi-deterministic opportunity coefficient to select the cluster head. This is calculated in each node with the battery energy level and node ID. Ultimately, based on the opportunity cost, cluster head will be selected and broadcasted for which other nodes with higher opportunity cost will agree. It minimizes the chances of nodes with lower battery level being elected as cluster head. Our simulation experiments demonstrate that cluster heads chosen using our proposed algorithm perform better than those using the legacy LEACH protocol.

• The Korean Journal of Zoology
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• v.24 no.2
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• pp.91-98
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• 1981

It has been confirmed that two dendrograms resulted from two similarity matrices, average taxonomic distance and correlation coefficient matrices, are different with each other when cluster analyses were performed with 571 adults of deer mice, Peromyscus maniculatus using 30 morphometric characters. To choose one of two similarity matrices mentioned above in order to construct a dendrogram representing phenetic relationships among taxa, an objective method using the result from principal component analysis as a standard result to compare with two matrices has been suggested.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.53.1-53.1
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• 2011

We analyze the halo and galaxy catalogs from the Millennium simulations at redshifts z=0, 0.5, 1 to determine the alignment profiles of cluster galaxies in terms of the matter density correlation coefficient and discuss a cosmological implication our result has for breaking parameter degeneracies. For each selected cluster, we measure the alignment between the major axes of the pseudo inertia tensors from all satellites within cluster's virial radius and from only those satellites within some smaller radius. Then we average the measured values over the similar-mass sample to determine the cluster galaxy alignment profile as a function of top-hat scale difference at each redshift. It is shown that the alignment profile of cluster galaxies is well approximated by a power-law of the nonlinear density correlation coefficient that is independent of the power spectrum normalization and bias factor. The alignment profile of cluster galaxies is found to have higher amplitude and lower power-law index when averaged over the larger-mass sample and to have rather weak redshift-dependence. This result is consistent with the picture that the satellite galaxies retain the memory of the external tidal fields right after merging and infalling into the clusters but they gradually lose the initial alignment tendency as the cluster's relaxation proceeds. Demonstrating that the nonlinear density correlation coefficient varies sensitively with the density parameter and neutrino mass fraction, we discuss a potential power of the cluster galaxy alignment profile as an independent probe of cosmology.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.373-383
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• 2018

The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

• The Bulletin of The Korean Astronomical Society
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• v.16
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• pp.19.2-19.2
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• 1991

• Atmosphere
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• v.16 no.4
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• pp.259-267
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• 2006

In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.4
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• pp.337-341
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• 2004

Morphological and genetic diversity of thirty nine safflower germplasm were collected and evaluated by Principal Component Analysis (PCA) and Random Amplified Polymorphic DNA (RAPD) method. Stem length and seeding to flowering days of the safflower germplasm showed $26\~117cm\;and\;76\~179$ days of variation respectively. USA originated germplasm showed higher oil content as $39\%$, but that of Japanese showed lower as $26\%$. PCA made three different cluster groups according to some agronomic characteristics of safflower. Korea originated germplasm showed similar cluster group with that of collected from USA in the PCA of stem length. But in the seeding to flowering days, it showed similar cluster pattern with that of collected from Japan rather than USA. In the experiment of RAPD analysis, total five primers showed polymorphism at the several chromosomal loci. Korea, China Japan and South Central Asia originated germplasm were differently classified with USA and South West Asia originated germplasm with lower similarity coefficient value (0.47). Most of Korea originated germplasm were grouped with South Central Asia originated germplasm with higher similarity coefficient value (0.74) conferring similar genetic background between both of them. China and Japan originated germplasm were dendrogramed with Korea originated germplasm at the 0.65 and 0.50 similarity coefficient values respectively. Some common results were expected from both of PCA and RAPD analysis, but lower genetic heritability caused by relative higher portion of environmental variance and environment by genotype interaction at the expression of those of agronomic characteristics made constraint to find any reliable results.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.19 no.1
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• pp.1-15
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• 2007

In this study 8 nodes parallel linux cluster system is constructed and tested for the evaluation of computational efficiency and reliability of the Yellow Sea tidal hydrodynamics prior to compute storm surge inundation along the west coast of the Korean Peninsular. Computational efficiency increases up to 7 times based on NPB bench-marking test. Simulated results by pADCIRC on reproduction of the Yellow Sea tidal hydrodynamics resemble well with previous studies. According to model parameter tests, bottom friction coefficient, which should be appropriately represented shallow depth along the west coast, is essential factor in simulation.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.371-385
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• 1995

The yield of semiconductor chips is dependent not only on the average defect density but also on the distribution of defects over a wafer. The distribution of defects leads to consider a cluster index. This paper briefly reviews the existing yield prediction models ad proposes a new cluster index, which utilizes the information about the defect location on a wafer in terms of the coefficient of variation. An extensive simulation is performed under a variety of defect distributions and a yield prediction model is derived through the regression analysis to relate the yield with the proposed cluster index and the average number of defects per chip. The performance of the proposed simulation-based yield prediction model is compared with that of the well-known negative binomial model.

• Journal of Plant Biotechnology
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• v.42 no.4
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• pp.401-408
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• 2015

Horticultural traits and genetic relationship were evaluated for 83 melon (Cucumis melo L.) cultivars. Survey of a total of 36 characteristics for seedling, leaf, stem, flower, fruit, and seed and subsequent multiple analysis of variance (MANOVA) were conducted. Principal component analysis (PCA) showed that 8 principle components including fruit weight, fruit length, fruit diameter, cotyledon length, seed diameter, and seed length accounted for 76.3% of the total variance. Cluster analysis of the 83 melon cultivars using average linkage method resulted in 5 clusters at coefficient of 0.7. Cluster I consisted of cultivars with high values for fruit-related traits, Cluster II for soluble solid content, and Cluster V for high ripening rate. Genotyping of the 83 cultivars was conducted using 15 expressed-sequence tagged-simple sequence repeat (EST-SSR) from the Cucurbit Genomics Initiative (ICuGI) database. Analysis of genetic relatedness by UPGMA resulted in 6 clusters. Mantel test indicated that correlation between morphological and genetic distance was very low (r = -0.11).