• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.108-112
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• 2000

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.10-14
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• 2006

Many clustering methods have been studied. For the most part of these methods may be needed to determine the number of clusters. But, there are few methods for determining the number of population clusters objectively. It is difficult to determine the cluster size. In general, the number of clusters is decided by subjectively prior knowledge. Because the results of clustering depend on the number of clusters, it must be determined seriously. In this paper, we propose an efficient method for determining the number of clusters using hybrid' self organizing map and new criterion for evaluating the clustering result. In the experiment, we verify our model to compare other clustering methods using the data sets from UCI machine learning repository.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.31-36
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• 2010

Many clustering algorithms have been used in diverse fields. When we need to group given data set into clusters, many clustering algorithms based on similarity or distance measures are considered. Most clustering works have been based on hierarchical and non-hierarchical clustering algorithms. Generally, for the clustering works, researchers have used clustering algorithms case by case from these algorithms. Also they have to determine proper clustering methods subjectively by their prior knowledge. In this paper, to solve the subjective problem of clustering we make empirical comparisons of popular clustering algorithms which are hierarchical and non hierarchical techniques using Silhouette measure. We use silhouette information to evaluate the clustering results such as the number of clusters and cluster variance. We verify our comparison study by experimental results using data sets from UCI machine learning repository. Therefore we are able to use efficient and objective clustering algorithms.

• Journal of the Korean housing association
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• v.18 no.6
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• pp.1-13
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• 2007

The purpose of the study was to identify the housing plan responding to lifestyles of residents living in Center of Ulsan city. A total of 230 date sets were analyzed after collecting questionnaire from 284 households using convenient sampling method. For date analysis, descriptive statistics. cross analysis, One-Way ANOVA, factor analysis, cluster analysis were performed by SPSS program. The research centered on the possibility of categorizing lifestyles of residents based upon their living awareness. The results showed that there are four major categorizes of residents's lifestyles. According to classification of their lifestyles, This study tried to analyse the characteristics of residents based upon the characteristics of the households, housing satisfaction. The analysis instrument of the lifestyle concept will be useful to develop the new strategies and to plan the new multi-family houses.

• Journal of Families and Better Life
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• v.19 no.6
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• pp.221-239
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• 2001

The purpose of this research was to identify arid to compare stereotypes toward the elderly held by adolescents and middle-aged adults. Stereotypes toward the elderly were examined at two levels: to assess students′and middle-aged adults′beliefs about the traits of the elderly and to categorize the traits into stereotypes. Traits groupings were analyzed with hierarchical cluster analysis. The main results of this study were as follows: First, both adolescents and middle-aged adults believed the negative stereotypes were more characteristic of the elderly than the positive ones. Second, middle-aged adults reported more complex negative stereotype sets of the elderly than adolescents. Third, Adolescents reported even more negative physical appearance trails of elderly persons than the middle-aged. Fourth, the traits endorsed as characteristics of elderly persons were often contradictory within two age groups. For example, adolescents perceived "sacrifice" as a positive trait of the elderly, while middle-aged adults perceived it as a negative one.

• Bulletin of the Korean Chemical Society
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• v.25 no.12
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• pp.1855-1858
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• 2004

Molecular geometries for the cationic and neutral species of OXO (X=Cl, Br, and I) are optimized using the Hartree-Fock (HF) theory, the second order Moller-Plesset perturbation theory (MP2), the density functional theory with the B3LYP hybrid functional (B3LYP), and the coupled cluster theory using single and double excitation with a perturbational treatment of triplet excitation (CCSD[T]) methods, with two basis sets of triple zeta plus polarization quality. The single point calculations for these species are performed at the CCSD(T,Full) level. The harmonic vibrational frequencies for these species are calculated at the HF, MP2, B3LYP and CCSD(T) levels. The adiabatic ionization potential for OIO is calculated to be 936.7 kJ/mol at the CCSD(T,Full) level and the correct value is estimated to be around 945.4 kJ/mol.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.485-494
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• 2004

An algorithm is proposed to identify multiple outliers in linear regression. It is based on the clustering of residuals from the least median of squares estimation. A cut-height criterion for the hierarchical cluster tree is suggested, which yields the optimal clustering of the regression outliers. Comparisons of the effectiveness of the procedures are performed on the basis of the classic data and artificial data sets, and it is shown that the proposed algorithm is superior to the one that is based on the least squares estimation. In particular, the algorithm deals very well with the masking and swamping effects while the other does not.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.363-372
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• 2016

Two-stage sample designs are commonly used for household surveys in Korea using as clusters the enumeration districts (EDs). Since clustering decomposes the population variation into within- and between-cluster variations, the sample sizes allocated in stages can affect the overall precision. Alternative clusters are often considered due to diverse reasons such as the EDs' limitation in size, being out-of-date, and in-assessibility to their household lists. In addition, the EDs are currently under development by the Statistics Korea as an joint effort toward their transition from the traditional practice to the register census from 2015. We present an approach for evaluating the difference in the precision of the mean estimators of the sets of the cluster units in between a hierachical and nested form, where the design effect is used to reflect the effect of the clustering and the sample allocation. We also demonstrate our approach using the U.S. Census counts from the year 2000 for Anne Arundel County in Maryland. Our research shows that the within-cluster variance can be significantly different for survey variables and thus the choice of cluster units and the associated sample allocation scheme should reflect the corresponding variance decomposition due to clustering.

• Journal of the Korean association of regional geographers
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• v.15 no.4
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• pp.510-523
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• 2009

Geodemographics(GD) can be defined as an analytical approach of socio-economic and behavioral data about people to investigate geographical patterns. GD is based on the assumptions that demographical and behavioral characteristics of people who live in the same neighborhood are similar and then the neighborhoods can be categorized with spatial classifications with the geographical classifications. Thus, this paper, in order to identify the applicability of the geographical classification of the GD, explores the concepts of the geodemographics into Seoul city areas with Korea census data sets that contain key characteristics of demographic profiles in the area. Then, this paper attempt to explain each area classification profile by using clustering techniques with Ward's and k-means statistical methods. For this as as as, this paper employs 2005 Census dataset released by Korea National Statistics Office and the neighborhood unit is based on Dong level, the smallest administrative boundary unit in Korea. After selecting and standardizing variables, several areas are categorized by the cluster techniques into 13, this paps as distinctive cluster profiles. These cluster profiles are used to cthite a short description and expand on the cluster names. Finally, the results of the classification propose a reasonable judgement for target area types which benefits for the people who make a spatial decision for their spatial problem-solving.