• Journal of The Korean Astronomical Society
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• v.35 no.4
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• pp.197-208
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• 2002

Five contemporary pre-main sequence (PMS) evolution model grids are compared with the photo-metric data for a nearly complete sample of low-mass members in NGC 2264. From amongst the grids compared, the models of Baraffe et al. (1998) prove to be the most reliable in mass-age distribution. To overcome the limited mass range of the models of Baraffe et al. we derived a simple transformation relation between the mass of a PMS star from Swenson et al. (1994) and that from Baraffe et al., and applied it to the PMS stars in NGC 2264 and the Orion nebula cluster (ONC). The resulting initial mass function (IMF) of the ONC shows that the previous interpretation of the IMF is not a real feature, but an artifact caused by the evolution models adopted. The IMFs of both clusters are in a good agreement with the IMF of the field stars in the solar neighborhood. This result supports the idea proposed by Lada, Strom, & Myers (1993) that the field stars originate from the stars that are formed in clusters and spread out as a result of dynamical dissociation. Nevertheless, the IMFs of OB associations and young open clusters show diverse behavior. For the low-mass regime, the current observations suffer from difficulties in membership assignment and sample incompleteness. From this, we conclude that a more thorough study of young open clusters is necessary in order to make any definite conclusions on the existence of a universal IMF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4336-4354
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• 2018

Fuzzy C-means (FCM) algorithm is a most usually technique for medical image segmentation. But conventional FCM fails to perform well enough on magnetic resonance imaging (MRI) data with the noise and intensity inhomogeneity (IIH). In the paper, we propose a Gamma correction conditional FCM algorithm with spatial information (GcsFCM) to solve this problem. Firstly, the pre-processing, Gamma correction, is introduced to enhance the details of images. Secondly, the spatial information is introduced to reduce the effect of noise. Then we introduce the effective neighborhood mechanism into the local space information to improve the robustness for the noise and inhomogeneity. And the mechanism describes the degree of participation in generating local membership values and building clusters. Finally, the adjustment mechanism and the spatial information are combined into the weighted membership function. Experimental results on four image volumes with noise and IIH indicate that the proposed GcsFCM algorithm is more effective and robust to noise and IIH than the FCM, sFCM and csFCM algorithms.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.277-283
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• 2014

Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.543-548
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• 2002

It is well known that FCM is an indispensible tool for fuzzy clustering. The problems of using FCM are 1) it is sensitive to the initial random membership functions and 2) FCM inherently requires the number of clusters. Hence we need to run FCM algorithms with an appropriate validity measure until we find a suitable number of clusters. In this paper, we suggest the Majority-Voting FCM with implied validity measure. With this algorithm, we can solve the aforementioned problems. The working simulation results are provided. The contributions are 1) MV-FCM algorithm and 2) its definitive capability of being an excellent validity measure.

• Korean Journal of Breeding Science
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• v.42 no.1
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• pp.11-22
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• 2010

The population structure of a domesticated species is influenced by the natural history of the populations of its pre-domesticated ancestors, as well as by the breeding system and complexity of breeding practices implemented by humans. In the genetic and population structure analysis of 122 South Asia collections using 29 simple sequence repeat (SSR) markers, 362 alleles were detected, with an average of 12.5 per locus. The average expected heterozygosity and polymorphism information content (PIC) for each SSR locus were 0.74 and 0.72,respectively. The model-based structure analysis revealed the presence of three clusters with the 91.8% (shared > 75%) membership, with 8.2% showing admixture. The genetic distances of Clusters 1-3 were 0.55, 0.56, and 0.68, respectively. Polymorphic information content followed the same trend (Cluster 3 had the highest value and Cluster 1 had smallest value), with genetic distances for each cluster of 0.52, 0.52, and 0.65, respectively. This result could be used for supporting rice breeding programs in South Asia countries.

• Korean Journal of Breeding Science
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• v.43 no.1
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• pp.14-22
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• 2011

In this study, 15 simple sequence repeat (SSR) markers were used to analyze the population structure of 55 mungbean accessions (34 from South Asia, 20 from West Asia, 1 sample from East Asia). A total of 56 alleles were detected, with an average of 3.73 per locus. The mean of major allele frequency, expected heterozygosity and polymorphic information content for 15 SSR loci were 0.72, 0.07 and 0.33 respectively. The mean of major allele frequency was 0.79 for South Asia, and 0.74 for West Asia. The mean of genetic diversity and polymorphic information content were almost similar for South Asian and West Asian accessions (genetic diversity 0.35 and polymorphic information content 0.29). Model-based structure analysis revealed the presence of three clusters based on genetic distance. Accessions were clearly assigned to a single cluster in which >70% of their inferred ancestry was derived from one of the model-based populations. 47 accessions (85.56%) showed membership with the clusters and 8 accessions (14.54%) were categorized as admixture. The results could be used to understanding the genetic structure of mungbean cultivars from these regions and to support effective breeding programs to broaden the genetic basis of mungbean varieties.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.397-410
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• 2014

This study sought to identify types of health behavior clusters among Korean adults and their related factors. A secondary analysis of 1,441 subjects, aged 19 to 64, in the 2009 Korean National Health and Nutrition Examination Survey (KNHANES IV-3) was conducted. A cluster analysis was used to identify types of clusters related to physical activity, smoking, and alcohol drinking. A complex samples chi square test and multivariate logistic regression were performed to analyze the associations between types of health behavior clusters and sample's characteristics using SPSS WIN 21. Five clusters were identified: health promotion, smoking, alcohol drinking, passive attitude, and risky behavior. The passive attitude cluster had the most subjects, with 47.7% of subjects as members. Socio-demographic factors, hypertension, and depressive symptoms were associated with membership in the alcohol drinking, smoking, passive attitude, or risky behavior cluster rather than the health promotion cluster. The findings of this study suggest that integrated health promotion programs incorporating multiple strategies need to be investigated. In addition, further studies should explore psychosocial factors that affect health behavior clusters, such as stress, self-efficacy, social support, and social networks.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.8
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• pp.84-97
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• 2012

In wireless sensor networks, cluster structure brings on many advantages such as load balancing, energy saving, and distributed key management, and so on. To transform a physical network into the cluster structure, sensor nodes should invoke a cluster formation protocol. During the protocol operation, if some nodes are compromised and they do not conform to the protocol, an inconsistency of membership in a cluster happen. This splits the cluster and consequently increases the number of clusters and decreases the number of members in the cluster. In this paper, we propose a scheme which well copes with such a problem. First, our scheme generates two hop clusters where hop distance between any two nodes is at most two. Besides, our scheme employs verification of two hop distant nodes to prevent the cluster split induced by compromised nodes. Last, our scheme mainly employs broadcast transmissions to reduce energy consumption of nodes. Simulation results have proven that our scheme reduces the number of clusters and more secure and energy-efficient than other scheme.

• Journal of The Korean Astronomical Society
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• v.47 no.4
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• pp.137-145
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• 2014

The astrophysical parameters of four unstudied open star cluster candidates; Ivanov 2, 7, 9, and Harvard 9; are estimated for the first time using the PPMXL database. The stellar density distributions and color-magnitude diagrams for each cluster are used to determine the geometrical structure (cluster center, limited radius, core and tidal radii, the distances from the Sun, from the Galactic center and from the Galactic plane). Also, the main photometric parameters (age, distance modulus, color excesses, membership, total mass, relaxation time, luminosity and mass functions) are estimated.