• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.113-116
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• 2002

It is required to network to make more intelligent packet processing and forwarding for increasing bandwidth and various services. Classification provides these intelligent to network which is acquired by increasing number of rules in classification rule set. In this Paper, we propose a classification algorithm efficient to scalable rule set ahead as well as Present small rule set. This algorithm has competition to existing methods by performance and advantage that it is mixed with another algorithm because il does not change original shape of rule set.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.133-138
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• 2002

Several well-known soil classification charts, like made by Robertson(1986,1990), Olsen(1981), existed already. In Korea, Lee Sun-Jae(1997) made new classification chart based on Unified Soil Classification System with locality In this study, 6 classification charts were applied respective area. Even exact decision is impossible which one is correct, this study can give useful guideline.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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• pp.159-166
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• 1994

A new pattern classification algorithm using cepstrum to analyze lung sounds for the classification of pattern with pulmonary and bronchial disorders is proposed. To evaluate the perfomance of the proposed method, the results are compared to the pattern classification with the AR modeling method. In the experiment lung sounds recorded for the training of physician used. As a results, the accuracy of the cepstrum classification is 92.3 % and AR modeling is the 53.8 %, therefore cepstrum modeling method has very high performance than AR and it turned out to be a very efficient algorithm.

• Korean Journal of Remote Sensing
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• v.6 no.1
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• pp.11-24
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• 1990

This is the study about the meteorological satellite cloud image classification by objective methods. For objective cloud classification, linear discriminant analysis was tried. In the linear discriminant analysis 27 cloud characteristic parameters were retrieved from GMS infrared image data. And, linear cloud classification model was developed from major parameters and cloud type coefficients. The model was applied to GMS IR image for weather forecasting operation and cloud image was classified into 5 types such as Sc, Cu, CiT, CiM and Cb. The classification results were reasonably compared with real image.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.629-638
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• 2012

We propose some methods to obtain optimal thresholds and classification functions by using various cutoff criterion based on the bivariate ROC curve that represents bivariate cumulative distribution functions. The false positive rate and false negative rate are calculated with these classification functions for bivariate normal distributions.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.4
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• pp.633-642
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• 2012

We give the complete classification of binary formally self-dual even codes of length 18. There are exactly 26568 inequivalent such codes. This completes the classification of binary formally self-dual even codes of length up to 18.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.23-28
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• 2002

Support vector machine(SVM) has been very successful in classification, regression, time series prediction and density estimation. In this paper, we will propose SVM for fuzzy data classification.

• Korean Journal of Environment and Ecology
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• v.22 no.6
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• pp.666-678
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• 2008

The needs for drawing up of biotope map is rapidly spreaded over each local government recently in Korea, according as enhancing of interest about biotope, which is recognized to practical instrument for concretely being able to considering natural environment and ecosystem on all sorts of development plan. However, there are not yet the standard suggestion on biotope types and classification systems and biotope classification criteria. Therefore, each other methodologies are applied to each of local autonomies. First, under such critical mind the biotope types and classification systems were drafted by a review on biotope types, biotope classification systems, and biotope classification criteria of the preceded case studies until now at the inside and outside of the country. And then the purpose of this study is to derive biotope types and biotope classification systems applicable to the whole Korean region through continual feed back such as field surveys in selected representative areas and consultations. As a result of reviewing the case examples, first, the biotope classification systems were mixed two steps system with three steps system and those were composed mostly of the structure of two steps: large and small. Second, land-use, soil pavement ratio, green cover ratio, and vegetation usually were applied to the biotope classification criteria. This study suggests that the biotope classification system is consisted of four steps system: large(biotope class), medium(biotope group), small(biotope type) and detail(sub-biotope type), and the biotope types are classified into 13 types of large step, 45 types of medium step and 127 types of small step. However, this study suggests that the new biotope types on small step or detail step should be continually supplemented with the foundation of classification system proposed in this study because the biotope type classification should consider regional characteristics.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.205-214
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• 2015

In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.2
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• pp.80-91
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• 2003

In this research, re-using band ratio data was proposed and examined as a method of raising the accuracy in landcover classification which is using satellite data.In order to determine the band which is used to calculation in the classified item, the six bands except the band 6 were combined with the band in which combination is possible and the landcover classification by MLC of supervised classification was carried out. In the result of landcover classification which is combined with forty nine combination, Two bands which were mostly used by band combination in the accuracy belonged inside the 10th place of a higher rank were selected and also calculated. landcover classification were performed again after the calculation result had been recombinated from the research. In addition, the new landcover classification result was compared and examined with the landcover classification using the old data. From the result of which was compared and examined the new landcover classification data recombinated calculation result with landcover classification using the original data, The classification accuracy of the new landcover classification data recombinated calculation result became low in ground but became improved in the all class. Specially The accuracy to urban area is very improved. therefore, it determined that reusing band ratio data is very useful when we need to analyze landcover classification and land information to urban area after that.