• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.170-181
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• 2007

The purpose of computing economic depreciation value is to find valuation of assets closely in line with market prices. The valuation of industrial assets are called Engineering Valuation. The two representative techniques for such valuation are Hulten-Wykoff Method, which estimates real value using regression equations, and T-factor Method devised at Iowa State University. The two are all empirical methods for computing service life (duration period). In this paper, we derived the service life by empirical methods using national wealth statistics, and also by more conventional methods such as original group method and retirement method. The results from each method are compared with one another. We also computed economic service life from these results. In S. Korea where amount of asset value statistics is still insufficient, the most effective method for empirically computing economic service life turns out to be the one using national wealth statistics. In addition, we also present economic relationship between depreciation value computed by using Hulten-Wykoff Method and depreciation value computed by using T-factor Method.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.275-291
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• 1997

In the case that an abstract concept was measured indirectly by using its indicators, many researcher have obtained its score by using the simple mean, the first principal component, or the first factor, etc. In this paper, an scoring method named as the representative component scoring system was suggested as an alternative and its validity and applicability were studied.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.591-598
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• 2010

A questionnaire may consist of duplicated or similar items. This study finds the duplicated or similar items by using the MDS and the cluster analysis of response patterns. By identifying the characteristics of the cluster, those items are combined into a representative item. The similarity of items is measured by the mutual information.

• International Journal of Contents
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• v.1 no.2
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• pp.26-30
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• 2005

According to the fast growth of information on the Internet, it is becoming increasingly difficult to find and organize useful information. To reduce information overload, it needs to exploit automatic text classification for handling enormous documents. Support Vector Machine (SVM) is a model that is calculated as a weighted sum of kernel function outputs. This paper describes a document classifier for web documents in the fields of Information Technology and uses SVM to learn a model, which is constructed from the training sets and its representative terms. The basic idea is to exploit the representative terms meaning distribution in coherent thematic texts of each category by simple statistics methods. Vector-space model is applied to represent documents in the categories by using feature selection scheme based on TFiDF. We apply a category factor which represents effects in category of any term to the feature selection. Experiments show the results of categorization and the correlation of vector length.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.229-238
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• 2003

K-means clustering has been widely used in many applications, such that pattern analysis, data analysis, market research and so on. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters, because it is more primitive and explorative. In this paper we propose a new method of k-means clustering using the grid-based representative value(arithmetic and trimmed mean) for sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Ecology and Resilient Infrastructure
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• v.3 no.3
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• pp.162-168
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• 2016

The objective of this study was to estimate the roadkill occurrence of water deer (Hydropotes inermis), a representative roadkill species in South Korea. For this estimation, I analyzed national road statistics and roadkill statistics, and then reviewed case studies that estimated the number of deer roadkill in other countries to apply the estimating methods to our case. As a result, the estimated number of water deer vehicle collision was at least 60,000 per year in South Korea.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.77-88
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• 2003

Cluster analysis has been widely used in many applications, such that data analysis, pattern recognition, image processing, etc. But clustering requires many hours to get clusters that we want, because it is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It is more fast than any traditional clustering method and maintains accuracy. It reduces running time by using grid-based sample and keeps accuracy by using representative point, a center of gravity.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.555-563
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• 2011

Osteoarthritis (OA) and rheumatoid arthritis (RA) are most popular types of arthritis in Korea. This study compared health-related quality of life (HRQoL) of homedwelling people with OA and RA in Korea. Data were drawn from the Korean nationwide representative survey. Subjects were 3,352 people with arthritis over 19 years of age (2,953 OA respondents and 399 RA respondents). Good HRQoL in OA respondents was dierentiated with limitation of mobility, perceived health status, age, economic status, presence of arthralgia, gender, medical coverage, and educational level. Good HRQoL in RA respondents was dierentiated with limitation of mobility, perceived health status, economic status, educational status, and presence of arthralgia. In conclusion, HRQoL and predictors of good HRQoL among people with arthritis diers for OA or RA. These results can be of use in development of health programs and clinical interventions for community-dwelling people with arthritis.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.507-516
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• 2007

Multinomial logistic regression is probably the most popular representative of probabilistic discriminative classifiers for multiclass classification problems. In this paper, a kernel variant of multinomial logistic regression is proposed by combining a Newton's method with a bound optimization approach. This formulation allows us to apply highly efficient approximation methods that effectively overcomes conceptual and numerical problems of standard multiclass kernel classifiers. We also provide the approximate cross validation (ACV) method for choosing the hyperparameters which affect the performance of the proposed approach. Experimental results are then presented to indicate the performance of the proposed procedure.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.217-226
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• 2005

Cluster analysis has been widely used in many applications, such as data analysis, pattern recognition, image processing, etc. But clustering requires many hours to get clusters that we want, because it is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It reduces running time by using grid-based sample and keeps accuracy by using representative point, a center of gravity.