• The Korean Journal of Ecology
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• v.20 no.5
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• pp.303-313
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• 1997

A new evaluation technique, i.e. multicriterion matrix technique, on the vegetation assessment was proposed and compared with several techniques having been previously used in the environmental impact assessment. Four criterias and 10 subcriterias were selected for two evaluation indices such as vegetation naturalness value and vegetation class. These criterias were characterized by syntaxonomical informations of hemeroby concept and potential vegetation, hierarchical system between criterias, and ordinal scale of vegetation naturalness valuse. Vegetation naturalness values were classified into 11 ordinal levels and condensed to five vegetation classes for facilitating practical use. In the example study two sites were compared by using two indices. This technique could have useful applications for ssessment of regional vegetation. A vegetation map of naturalness described by combination of two indices was proposed in order to illustrate regional vegetation naturalness.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.251-263
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• 2004

In this paper, algorithms for computing the minimal polynomial and the common minimal polynomial of resultant matrices over any field are presented by means of the approach for the Grobner basis of the ideal in the polynomial ring, respectively, and two algorithms for finding the inverses of such matrices are also presented. Finally, an algorithm for the inverse of partitioned matrix with resultant blocks over any field is given, which can be realized by CoCoA 4.0, an algebraic system over the field of rational numbers or the field of residue classes of modulo prime number. We get examples showing the effectiveness of the algorithms.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.113-123
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• 2006

Normal mixture model(NMM) frequently used to cluster genes on microarray gene expression data. In this paper some of component means of NMM are modelled by a linear regression model so that its design matrix presents the pattern between sample classes in microarray matrix. This modelling for the component means by given design matrices certainly has an advantage that we can lead the clusters that are previously designed. However, it suffers from 'overfitting' problem because in practice genes often are highly dimensional. This problem also arises when the NMM restricted by the linear model for component-means is fitted. To cope with this problem, in this paper, the use of the factor analyzer NMM restricted by linear model is proposed to cluster genes. Also several design matrices which are useful for clustering genes are provided.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.309-320
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• 2007

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. It causes low prediction accuracy of the minority class because classifiers tend to assign instances to major classes and ignore the minor class to reduce overall misclassification rate. In order to solve the data imbalance problem, there has been proposed a number of techniques based on resampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.29-32
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• 2008

In this paper, we propose one unsupervised classification technique using the degree of polarization (DoP) and the co-polarized phase-difference (CPD) statistics, instead of the entropy and alpha. It is shown that the DoP is closely related to the entropy, and the CPD to the alpha. The DoP explains the feature how much the effect of multiple reflections is contained. Hence, the DoP could be used as an important factor for classifying classes. The CPD can also be computed from the measured Mueller matrix elements. For the smooth surface scattering, the CPD is about $0^{\circ}$, and for dihedral-type scattering, the CPD is about $180^{\circ}$. A DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification results are compared with the existing Entropy-alpha diagram as well as the IPL-AirSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest. Based on the DoP and CPD analysis, a simple three-component decomposition technique was also proposed.

• Journal of the Korean School Mathematics Society
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• v.3 no.1
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• pp.59-67
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• 2000

This study has been done to help teach mathematics on the spot of education by providing the history of mathematics and illustrations concerning mathematics, which were rearranged for the level of the second grade students in highschool and intented to interest students in mathematics classes. The contents of teaching, according to each unit (Matrix, Sequence, Limit, Differentiation, Integration, Probability, Statistics) include the life of the representative mathematician, the historical background centered on episodes, questions linked with reality, questions making sensations in history and something for maxim in mathematics. If such contents are properly used, they are expected to be able to stimulate students' curiosity, and to be effective in improving students' learning ability in mathematics by causing them to show their active attitudes toward learning mathematics.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.111-116
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• 2007

The design problem of partial pole assignment (PPA) in two classes of frequency-domain MIMO models by constant gain feedback is investigated in this paper. Its aim is to design a constant gain feedback which changes only a subset of the open-loop eigenvalues, while the rest of them are kept unchanged in the closed-loop system. A near general parametric expression for the feedback gain matrix in term of a set of design parameter vectors and the set of the closed-loop poles, and a simple parametric approach for solving the proposed problem are presented. The set of poles do not need to be previously prescribed, and can be set undetermined and treated together with the set of parametric vectors as degrees of design freedom provided by the approach. An illustrative example shows that the proposed parametric method is simple and effective.

• Bulletin of the Korean Chemical Society
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• v.34 no.7
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• pp.2143-2147
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• 2013

Matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is a very effective method for lipid mass fingerprinting. However, MALDI MS suffered from spectral complexities, differential ionization efficiencies, and poor reproducibility when analyzing complex lipid mixtures without prior separation steps. Here, we aimed to find optimal MALDI sample preparation methods which enable selective or class-wide mass fingerprinting of two totally different lipid classes. In order to achieve this, various matrices with additives were tested against the mixture of phosphatidylcholine (PC) and cerebrosides (Cers) which are abundant in animal brain tissues and also of great interests in disease biology. Our results showed that, from complex lipid mixtures, 2,4,6-trihydroxyacetophenone (THAP) with $NaNO_3$ was a useful MALDI matrix for the class-wide fingerprinting of PC and Cers. In contrast, THAP efficiently generated PC-focused profiles and graphene oxide (GO) with $NaNO_3$ provided Cer-only profiles with reduced spectral complexity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.731-739
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• 1998

The probabilistic relaxation(PR) scheme based on the conditional probability and probability space partition has the important property that when its compatibility coefficient matrix (CCM) has uniform components it can classify m-dimensional probabilistic distribution vectors into different classes. When consistency or inconsistency measures have been defined, the properties of PRs are completely determined by the compatibility coefficients among labels of labeled objects and influence weight among labeled objects. In this paper we study the properties of PR in which both compatibility coefficients and influence weights are uniform, and then a learning rule for such PR system is derived. Experiments have been performed to verify the effectiveness of the learning rule.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.292-295
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• 2004

Recently, polymer-clay hybrid materials have received considerable attention from both a fundamental research and application point of view. This organ-inorganic hybrid, which contains a nanoscale dispersion of the layered silicates, is a material with greatly improved thermal and mechanical characteristics. Two classes of nanocomposites were synthesized using an unsaturated polyester resin as the matrix and sodium montmorillonite as well as an organically modified montmorillonite as the reinforcing agents. X -ray diffraction pattern of the composites showed that the interlayer spacing of the modified montmorillonite were exfoliated in polymer matrix. The mechanical properties also supported these findings, since in general, tensile strength, modulus with modified montmorillonite were higher than the corresponding properties of the composites with unmodified montmorillonite. Adding organically modified clay improved the tensile strength of unsaturated polyester by $22\%$ and the tensile modulus of unsaturated polyester was also improved by $34\%$.