• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.695-703
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• 2009

Currently, various cellular phone products with multiple functions and diverse designs are coming out in the cellular phone market. Companies have been developing their products for profit maximization, considering preferences of customers. In this article, we have created 18 profiles using the SPSS program and executed a survey to analyze preferences of college students. Also, we have grasped the relative importance of each attribute using conjoint analysis and executed clustering analysis to make market segmentation by binding the respondents who have similar partial value utilities. Lastly, by choice simulation, we have predicted market shares of 18 virtual products.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.386-389
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• 1999

In this study, visible and near infrared channels of NOAA/AVHRR data were used to classify land use and vegetation types over Korean peninsula. Analyzing forest stand structures and prediction of forest productivity using satellite data were also reviewed. Land use and land cover classification was made by unsupervised clustering methods. After monthly Normalized Difference Vegetation Index (NDVI) composite images were derived from April to November 1998, the derived composite images were used as temporal feature vector's in this clustering analysis. Visually interpreted, the classification result was satisfactory in overall for it matched well with the general land cover patterns. But subclassification of forests into coniferous, deciduous, and mixed forests were much confused due to the effects of low ground resolution of AVHRR data and without defined classification scheme. To investigate into the forest stand structures, digital forest type maps were used as an ancillary data. Forest type maps, which were compiled and digitalized by Forestry Research Institute, were registered to AVHRR image coordinates. Two data sets were compared and percent forest cover over whole region was estimated by multiple regression analysis. Using this method, other forest stand structure characteristics within the primary data pixels are expected to be extracted and estimated.

• Journal of the Korean Society for information Management
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• v.28 no.2
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• pp.65-78
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• 2011

The purpose of this study is to identify topic areas of academic library research using two informetric methods; word clustering and Pathfinder network. For the data analysis, 139 articles published in major library and information science journals from 2005 to 2009 were collected from the Korean Science Citation Index database. The keywords that represent research topics were gathered from two sections: an and titles in references. Results showed that reference titles usefully represent topics in detail, and combinings and reference titles can produce an expanded topic map.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.105-112
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• 2018

As data size increase, a single database is not enough to serve current volume of tasks. Since data is partitioned and stored into multiple databases, analysis should also support parallelism in order to increase efficiency. However, traditional analysis requires data to be transferred out of database into nodes where analytic service is performed and user is required to know both database and analytic framework. In this paper, we propose an efficient way to perform K-means clustering algorithm inside the distributed column-based database and relational database. We also suggest an efficient way to optimize K-means algorithm within relational database.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.333-340
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• 2000

Research on group decisions and electronic meeting systems have been increasing rapidly according to the widespread of Internet technology. Although various issues have been raised in empirical research, we will try to solve an issue on idea categorizing in the group decision making process of electronic meeting systems. Idea categorizing used at existing group decision support systems was performed in a top-down procedure and mostly b participants; manual work. This resulted in tacking as long in idea categorizing as it does for idea generating clustering an idea in multiple categories, and identifying almost similar redundant categories. However such methods have critical limitation in the electronic meeting systems, we suggest an intelligent idea categorizing methodology which is a bottom-up approach. This method consists of steps to present idea using keywords, identifying keywords' affinity, computing similarity among ideas, and clustering ideas. This methodology allows participants to interact iteratively for clear manifestation of ambiguous ideas. We also developed a prototype system, IIC (intelligent idea categorizer) and evaluated its performance using the comparision experimetn with other systems. IIC is not a general purposed system, but it produces a good result in a given specific domain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2539-2554
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• 2017

Regression-based image super resolution (SR) methods have shown great advantage in time consumption while maintaining similar or improved quality performance compared to other learning-based methods. In this paper, we propose a novel single image SR method based on hierarchical regression to further improve the quality performance. As an improvement to other regression-based methods, we introduce a hierarchical scheme into the process of learning multiple regressors. First, training samples are grouped into different clusters according to their geometry similarity, which generates the structure layer. Then in each cluster, a compact dictionary can be learned by Sparse Coding (SC) method and the training samples can be further grouped by dictionary atoms to form the detail layer. Last, a series of projection matrixes, which anchored to dictionary atoms, can be learned by linear regression. Experiment results show that hierarchical scheme can lead to regression that is more precise. Our method achieves superior high quality results compared with several state-of-the-art methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7C
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• pp.712-718
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• 2003

C.elegans are often used to study of function of gene, but it is difficult for human observation to distinguish the mutants of C.elegans. To solve this problem, the system, which can classify the mutant types automatically using the computer vision, is now studying. Tn previous work［1］, we described the preprocessing method for automated-classification system. In this paper, we introduce shape features, which can be extracted from an acquisition image. We divide the feature into two categories, which are related to size and posture of the worm, and each feature is described mathematically We validate the shape information experimentally. And we use hierarchical clustering algorithm for classification. It reveals that 4 mutants of the worm, which are used in experiment, can be classified with over 90% of success rate.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.3
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• pp.275-283
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• 2007

An experimental study was conducted to investigate the effect of the morphology of CNT (Carbon Nanotube) on the thermal conductivity of suspensions. The effective thermal conductivities of the samples were measured using a steady-state cut bar apparatus method. Enhancements based on the thermal conductivity of the base fluid are presented as functions of both the volume fraction and the temperature. Although functionalized SWNT (Single-Walled Carbon Nanotube) produced more stable and homogeneous suspensions, the addition of small amounts of surfactant to suspensions of 'as produced' SWNT demonstrated a greater increase in effective thermal conductivity than functionalized SWNT alone. The effective thermal conductivity enhancement corresponding to 1.0% by volume approached 10%, which was observed to be lower than expected, but more than twice the values, 3.5%, obtained for similar tests conducted using aluminum oxide suspensions. However, for suspensions of MWNT (Multi-Walled Carbon Nanotube), the degree of enhancement was measured to be approximately 37%. It was postulated that the effect of clustering, resulting from the multiple heat-flow passages constituted by interconnecting neighboring CNT clusters, played an important role in significant enhancement of effective thermal conductivity.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.63-76
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• 2000

Research on group decisions and electroinc meeting systems have been increasing rapidly according to the widespread of Internet technology. Although various issues have been raised in empirical research, we will try to solve an issue on idea categorizing in the group decision making process of elecronic meeting systems. Idea categorizing used at existing group decision support systems was performed in a top-down procedure and mostly participants\` by manual work. This resulted in tacking as long in idea categorizing as it does for idea generating, clustering an idea in multiple categories, and identifying almost similar redundant categories. However such methods have critical limitation in the electronic meeting systems, we suggest an intelligent idea categorizing methodology which is a bottom-up approach. This method consists of steps to present idea using keywords, identifying keywords\` affinity, computing similarity among ideas, and clustering ideas. This methodology allows participants to interact iteratively for clear manifestation of ambiguous ideas. We also developed a prototype system, IIC (intelligent idea categorizer) and evaluated its performance using the comparision experimetn with other systems. IIC is not a general purposed system, but it produces a good result in a given specific domain.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.168-175
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• 2006

An experimental study was conducted to investigate the effect of the morphology of CNT on the thermal conductivity of suspensions. The effective thermal conductivities of the samples were measured using asteady-state cut bar apparatus method. Enhancements based on the thermal conductivity of the base fluid are presented as functions of both the volume fraction and the temperature, Although functionalized SWNT produiced a more stable and homogeneous suspension, the addition of small amounts of surfactant to suspensions of 'as produced' SWNT demonstrated a greater increase in effective thermal conductivity than functionalized SWNT alone. The effective thermal conductivity enhancement corresponding to 1.0 percent by volume approached 10%, which was observed to be lower than expected, but more than twice the values, 3.5%, obtained for similar tests conducted using aluminum oxide suspensions. However, for suspensions of MWNT, the degree of enhancement was measured to be approximately 37%. It was postulated that the effect of clustering, resulting from the multiple heat-flow passages constituted by interconnecting neighboring CNT clusters, played an important role in significant enhancement of effective thermal conductivity.