• Journal of National Security and Military Science
• /
• s.1
• /
• pp.269-310
• /
• 2003

AS Extensible Markup Language(XML) is emerging as the data format of the Internet era, there are increasing needs to efficiently transform between database and XML documents. In this paper, we propose a schema transformation method from relational database to XML database. To transform the schema, we represent input schema as Entity-Relationship diagram. Entity-Relationship model translator scans the input Entity-Relationship diagram using BFS (breadth First Search) and translates the diagram into hierarchical structure model. The XML Schema generator produces XML Scema code using the transformed hierarchical structure model. The proposed method has a merit that having reusability facility of XML Schema property in comparison with existing researches.

• Journal of Korean Academy of Fundamentals of Nursing
• /
• v.18 no.4
• /
• pp.452-462
• /
• 2011

Purpose: The purpose of this study was to examine the factors affecting body image among undergraduate students. Method: The research design for this study was a descriptive survey design using a convenience sampling. Data collection was done using self-report questionnaires with 319 undergraduate students located in 3 cities, Seoul, Gangneung and Seosan. Pearson correlation coefficients and hierarchical multiple regression with the SPSS Win 12.0 Program were used to analyze the data. Results: In the hierarchical multiple regression analysis, gender, height, weight and college major were controlled. Body surveillance and body shame significantly predicted 72.3% of appearance orientation. Sociocultural attitudes toward appearance and self-esteem significantly predicted 33.5% of appearance evaluation. Self-esteem and body surveillance significantly predicted 15.9% of health orientation. Self-esteem significantly predicted 23.3% of health evaluation. Conclusion: Findings from this study provide a comprehensive understanding of body image and related factors in undergraduate students in Korea. However, further study with a larger random sample and more a detailed research design is necessary.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.217-220
• /
• 2001

The fuzzy equalization method does not require the usual learning step for generating fuzzy rules. However it is heavily depend on the given input-output data set. So, we adapt an hierarchical scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership functions obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to the Rice taste data and got better results than previous ones.

• Proceedings of the KIEE Conference
• /
• 2000.11d
• /
• pp.672-674
• /
• 2000

There are various ways in classification methodologies of data mining such as neural networks but the result should be explicit and understandable and the classification rules be short and clear. Rough set theory is a effective technique in extracting knowledge from incomplete and inconsistent information and makes an offer classification and approximation by various attributes with effect. This paper discusses granularity of knowledge for reasoning of uncertain concepts by using generalized rough set approximations based on hierarchical granulation structure and uses hierarchical classification methodology that is more effective technique for classification by applying core to upper level. The consistency rules with minimal attributes is discovered and applied to classifying real data.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.3
• /
• pp.755-762
• /
• 2015

One of the main objectives of the U.S. Census Bureau is the proper estimation of median household income for small areas. These estimates have an important role in the formulation of various governmental decisions and policies. Since direct survey estimates are available annually for each state or county, it is desirable to exploit the longitudinal trend in income observations in the estimation procedure. In this study, we consider Fay-Herriot type small area models which include time-specific random effect to accommodate any unspecified time varying income pattern. Analysis is carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. We have evaluated our estimates by comparing those with the corresponding census estimates of 1999 using some commonly used comparison measures. It turns out that among three types of time-specific random effects the small area model with a time series random walk component provides estimates which are superior to both direct estimates and the Census Bureau estimates.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.1207-1208
• /
• 2008

We analyzed the performance of hierarchical cellular system with multi traffic(voice traffic, data traffic). We executed the computer simulation by the number of reserved channel for handover. For new call, the more the number of channel reserved, the higher the block probability of call became. The blocking probability of data traffic was higher, compared with that of voice traffic

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.729-733
• /
• 2003

The presence of local features within clusters incurred by multi-modal nature of data prohibits many conventional clustering techniques from working properly. Especially, the clustering of datasets with non-Gaussian distributions within a cluster can be problematic when the technique with implicit assumption of Gaussian distribution is used. Current study proposes a simple tandem clustering method composed of k-means type algorithm and hierarchical method to solve such problems. The multi-modal dataset is first divided into many small pre-clusters by k-means or fuzzy k-means algorithm. The pre-clusters found from the first step are to be clustered again using agglomerative hierarchical clustering method with Kullback- Leibler divergence as the measure of dissimilarity. This method is not only effective at extracting the multi-modal clusters but also fast and easy in terms of computation complexity and relatively robust at the presence of outliers. The performance of the proposed method was evaluated on three generated datasets and six sets of publicly known real world data.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.29 no.1
• /
• pp.43-56
• /
• 2004

The purpose of clustering is to partition a set of objects into several clusters based on some appropriate similarity measure. In most cases, clustering is considered without any prior information on the number of clusters or the structure of the given data, which makes clustering is one example of very complicated combinatorial optimization problems. In this paper we propose a general-purpose clustering method that can determine the proper number of clusters as well as efficiently carry out clustering analysis for various types of data. The method is composed of two stages. In the first stage, two different hierarchical clustering methods are used to get a reasonably good clustering result, which is improved In the second stage by ASA(accelerated simulated annealing) algorithm equipped with specially designed perturbation schemes. Extensive experimental results are given to demonstrate the apparent usefulness of our ASA clustering method.

• Communications for Statistical Applications and Methods
• /
• v.10 no.1
• /
• pp.19-30
• /
• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.579-581
• /
• 1988

This paper proposes the logic simulation for hierarchical logic network with function descriptor base data structure and algorithm on which a macro cell is considered as a logic elements. Function descriptor base data structure is useful when many logic elements of which type is same exist in a network, for it lessens the computer memory size used during the simulation. And the proposed simulation algorithm may improve the simulation speed.