• Proceedings of the Korea Association of Crystal Growth Conference
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• 2000.06a
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• pp.87-125
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• 2000

A new paradigm of crystal growth was suggested in a charged cluster model, where charged clusters of nanometer size are suspended in the gas phase in most thin film processes and are a major flux for thin film growth. The existence of these hypothetical clusters was experimentally confirmed in the diamond and silicon CVD processes as well as in gold and tungsten evaporation. These results imply new insights as to the low pressure diamond synthesis without hydrogen, epitaxial growth, selective deposition and fabrication of quantum dots, nanometer-sized powders and nanowires or nanotubes. Based on this concept, we produced such quantum dot structures of carbon, silicon, gold and tungsten. Charged clusters land preferably on conducting substrates over on insulating substrates, resulting in selective deposition. if the behavior of selective deposition is properly controlled, charged clusters can make highly anisotropic growth, leading to nanowires or nanotubes.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.151-161
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• 2016

ICT-convergence cluster is considered as critical policy means because it can create higher value-added products and services in the era of creative economy. Previous research has focused on comprehensive ICT-convergence cluster strategy based on Porter's diamond model. This paper adopted AIDA(Attention, Interest, Desire, Action) model and investigated a specific domain of government supporting policies related to non-R&D support. For two weeks, we gathered and analyzed 181 data from companies located in Chungbuk province. The results showed that support for technology, commercialization, and participation conditions positively leads to companies' interest in ICT-convergence cluster, which, in turn, makes positive impact on their intention to participate in it. It is significant that this paper verified AIDA model in the Government-to-Business(G2B) context. Future research will need to adapt AIDA model to national projects.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.777-784
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• 2006

This paper proposes a c-fuzzy model tree using partial least square method to predict the Chlorophyll-a concentration in each zone. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, each internal node is produced according to fuzzy membership values between centers and input attributes. Linear models are constructed by partial least square method considering input-output pairs remained in each internal node. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. On the other hands, prediction is performed with a linear model haying the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to water quality data set measured at several stations. Under various experiments, our proposed method shows better performance than conventional least square based model tree method.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.107-114
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• 2020

This paper studies a method for estimating the scale of a Hadoop cluster to process big data as a cost-effective manner. In the case of medical institutions, demands for cloud-based big data analysis are increasing as medical records can be stored outside the hospital. This paper first analyze the Amazon EMR framework, which is one of the popular cloud-based big data framework. Then, this paper presents a efficiency model for scaling the Hadoop cluster to execute a Mapreduce application more cost-effectively. This paper also analyzes the factors that influence the execution of the Mapreduce application by performing several experiments under various conditions. The cost efficiency of the analysis of the big data can be increased by setting the scale of cluster with the most efficient processing time compared to the operational cost.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.4
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• pp.336-346
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• 2013

Amaranth (Amaranthus sp. L.) is an important group of plants that includes grain, vegetable, and ornamental types. Centers of diversity for Amaranths are Central and South America, India, and South East Asia, with secondary centers of diversity in West and East Africa. The present study was performed to determine the genetic diversity and population structure of 75 amaranth accessions: 65 from South America and 10 from South Asia as controls using 14 SSR markers. Ninety-nine alleles were detected at an average of seven alleles per SSR locus. Model-based structure analysis revealed the presence of two subpopulations and 3 admixtures, which was consistent with clustering based on the genetic distance. The average major allele frequency and polymorphic information content (PIC) were 0.42 and 0.39, respectively. According to the model-based structure analysis based on genetic distance, 75 accessions (96%) were classified into two clusters, and only three accessions (4%) were admixtures. Cluster 1 had a higher allele number and PIC values than Cluster 2. Model-based structure analysis revealed the presence of two subpopulations and three admixtures in the 75 accessions. The results of this study provide effective information for future germplasm conservation and improvement programs in Amaranthus.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.246-252
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• 2017

In the defense acquisition, a company's goal is to maximize profits, and the government's goal is to allocate budgets efficiently. Each year, the government estimates the ratio of indirect cost sector to defense companies, and estimates the ratio to be applied when calculating cost of the defense articles next year. The defense industry environment is changing rapidly, due to the increasing trend of defense acquisition budgets, the advancement of weapon systems, the effects of the 4th industrial revolution, and so on. As a result, the cost structure of defense companies is being diversifying. The purpose of this study is to find an alternative that can enhance the rationality of the current methodology for estimating the ratio of indirect cost sector of defense companies. To do this, we conducted data analysis using the R language on the cost data of defense companies over the past six years in the Defense Integrated Cost System. First, cluster analysis was conducted on the cost characteristics of defense companies. Then, we conducted a regression analysis of the relationship between direct and indirect costs for each cluster to see how much it reflects the cost structure of defense companies in direct labor cost-based indirect cost rate estimates. Lastly a new ratio prediction model based on regularized regression analysis was developed, applied to each cluster, and analyzed to compare performance with existing prediction models. According to the results of the study, it is necessary to estimate the indirect cost ratio based on the cost character group of defense companies, and the direct labor cost based indirect cost ratio estimation partially reflects the cost structure of defense companies. In addition, the current indirect cost ratio prediction method has a larger error than the new model.

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.5-10
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• 1995

In this paper we present a clustering procedure based on the use of HMM in order to get multiple statistical models which can well absorb the variants of each speaker with different ways of saying words. The HMM-clustered models obtained from the developed technique are applied to the speaker independent isolated word recognition. The HMM clustering method splits off all observation sequences with poor likelihood scores which fall below threshold from the training set and create a new model out of the observation sequences in the new cluster. Clustering is iterated by classifying each observation sequence as belonging to the cluster whose model has the maximum likelihood score. If any clutter has changed from the previous iteration the model in that cluster is reestimated by using the Baum-Welch reestimation procedure. Therefore, this method is more efficient than the conventional template-based clustering technique due to the integration capability of the clustering procedure and the parameter estimation. Experimental data show that the HMM-based clustering procedure leads to $1.43\%$ performance improvements over the conventional template-based clustering method and $2.08\%$ improvements over the single HMM method for the case of recognition of the isolated korean digits.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.53-63
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• 2022

In the paper, we propose a TSCH-based scheduling scheme for IEEE 802.15.4e, which is able to perform the scheduling of its own network by avoiding collision from interference network cluster (INC). Firstly, we model a bipartite graph structure for presenting the slot-frame (channel-slot assignment) of TSCH. Then, based on the bipartite graph edge weight, we utilize the Hungarian assignment algorithm to implement a scheduling scheme. We have employed two features (maximization and minimization) of the Hungarian-based assignment algorithm, which can perform the assignment in terms of minimizing the throughput of INC and maximizing the throughput of own network. Further, in this work, we called the scheme "dual-stage Hungarian-based assignment algorithm". Furthermore, we also propose deep learning (DL) based deep neural network (DNN)scheme, where the data were generated by the dual-stage Hungarian-based assignment algorithm. The performance of the DNN scheme is evaluated by simulations. The simulation results prove that the proposed DNN scheme providessimilar performance to the dual-stage Hungarian-based assignment algorithm while providing a low execution time.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1213-1224
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• 2011

In high dimensionality where the number of variables are excessively larger than observations, it is required to remove the noninformative variables to cluster observations. Most model-based approaches for variable selection have been considered under the assumption of homoscedasticity and their models are mainly estimated by a penalized likelihood method. In this paper, a different approach is proposed to remove the noninformative variables effectively and to cluster based on the modified normal mixture model simultaneously. The validity of the model was provided and an EM algorithm was derived to estimate the parameters. Simulation studies and an experiment using real microarray dataset showed the effectiveness of the proposed method.