• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.253-268
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• 2011

In this paper, we estimate systematic risk from credit migration (or transition) matrices under "Asymptotic Single Risk Factor" model. We analyzed transition matrices issued by KR(Korea Ratings) and concluded that systematic risk implied on credit migration somewhat coincide with the real economic cycle. Especially, we found that systematic risk implied on credit migration is better than that implied on the default rate. We also emphasize how to conduct a stress test using systematic risk extracted from transition migration. We argue that the proposed method in this paper is better than the usual method that is only considered for the conditional probability of default(PD). We found that the expected loss critically increased when we explicitly consider the change of credit quality in a given portfolio, compared to the method considering only PD.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.105-110
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• 2000

Since the advent of high resolution satellite image, possibilities of applying various human interpretation mechanism to these images have increased. Also many studies about these possibilities in many fields such as computer vision, pattern recognition, artificial intellegence and remote sensing have been done. In this field of these studies, texture is defined as a kind of quantity related to spatial distribution of brightness and tone and also plays an important role for interpretation of images. Especially, methods of obtaining texture by statistical model have been studied intensively. Among these methods, texture measurement method based on cooccurrence matrix is highly estimated because it is easy to calculate texture features compared with other methods. In addition, these results in high classification accuracy when this is applied to satellite images and aerial photos. But in the existing studies using cooccurrence matrix, features have been chosen arbitrarily without considering feature variation. And not enough studies have been implemented for appropriate resolution selection in which cooccurrence matrix can extract texture. Therefore, this study reviews the concept of cooccurrence matrix as a texture measurement method, evaluates usefulness of several features obtained from cooccurrence matrix, and proposes appropriate resolution by investigating variance trend of several features.

• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.978-993
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• 2013

It can be advantage as well as disadvantage to use the spatial weight matrix in a spatial regression model; it would benefit from explicitly quantifying spatial relationships between geographical units, but necessarily involve subjective judgment while specifying the matrix. We took Incheon City as a study area and investigated how the fitness of a spatial regression model changed by constructing various spatial weight matrices. In addition, we explored neighborhood segmentation in the study area and analyzed any influence of it on the model adequacy of two basic spatial regression models, i.e., spatial lagged and spatial error models. The results showed that it can help to improve the adequacy of models to specify the spatial weight matrix strictly, that is, interpreting the neighborhood as small as possible when estimating land price. It was also found that the spatial error model would be preferred in the area with serious spatial heterogeneity. In such area, we found that its spatial heterogeneity can be alleviated by delineating sub-neighborhoods, and as a result, the spatial lagged model would be preferred over the spatial error model.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.147-153
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• 2007

The conjugate gradient (CG) method is one of the most efficient algorithms for solving a linear system of equations. In addition to being used as a linear equation solver, it can be applied to a least-squares problem. When the CG method is applied to large-scale three-dimensional inversion of magnetotelluric data, two approaches have been pursued; one is the linear CG inversion in which each step of the Gauss-Newton iteration is incompletely solved using a truncated CG technique, and the other is referred to as the nonlinear CG inversion in which CG is directly applied to the minimization of objective functional for a nonlinear inverse problem. In each procedure we only need to compute the effect of the sensitivity matrix or its transpose multiplying an arbitrary vector, significantly reducing the computational requirements needed to do large-scale inversion.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.1-11
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• 2008

Stiffness estimation of a shear building due to local damages is usually achieved though structural analysis based on the assumed material properties and idealized numerical modeling of structure. Conventional numerical modeling, however, frequently causes an inevitable error in the structural response and this makes it difficult to exactly predict the damage state in structure. To solve this problem, this paper introduces a damage detection technique for shear building using genetic algorithm. The introduced algorithm evaluates the damage in structure using a flexibility matrix since the flexibility matrix can exactly be obtained from the field test in spite of using a few lower dynamic modes of structure. The introduced algorithm is expected to be more effectively used in damage detection of structures rather than conventional method using the stiffness matrix. Moreover, even in cases when an accurate measurement of structural stiffness cannot be expected, the proposed technique makes it possible to estimate the absolute change in stiffness of the structure on the basis of genetic algorithm. The validity of the proposed technique is demonstrated though numerical analysis using OPENSEES.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.615-625
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• 2020

The document-term frequency matrix is a term extracted from documents in which the group information exists in text mining. In this study, we generated the document-term frequency matrix for document classification according to research field. We applied the traditional term weighting function term frequency-inverse document frequency (TF-IDF) to the generated document-term frequency matrix. In addition, we applied term frequency-inverse gravity moment (TF-IGM). We also generated a document-keyword weighted matrix by extracting keywords to improve the document classification accuracy. Based on the keywords matrix extracted, we classify documents using a deep neural network. In order to find the optimal model in the deep neural network, the accuracy of document classification was verified by changing the number of hidden layers and hidden nodes. Consequently, the model with eight hidden layers showed the highest accuracy and all TF-IGM document classification accuracy (according to parameter changes) were higher than TF-IDF. In addition, the deep neural network was confirmed to have better accuracy than the support vector machine. Therefore, we propose a method to apply TF-IGM and a deep neural network in the document classification.

• Journal of Service Research and Studies
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• v.14 no.3
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• pp.172-187
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• 2024

This paper deals with asymptotic approximations analysis of M/M/s and M/D/s queues. For M/M/s queue, we observe "economies of scale" under the fixed utilization ρ and the fixed probability α that customer waits in system, how the average system size vary according to the number of servers s increasing. Simulation results show that as s increases, the number of servers who are idling increases, that is, the slack n-E[Q_n] diverges. In addition, through changing the waiting probability α under the M/M/s system, α was not highly sensitive to the behavior of the system size. And, it is shown that using ${\rho}_n\,=\,1-k/\sqrt{n}$ to handle heavy-traffic regime is only appropriate for k = 1 by observing the effect on the performance of the system with different values of k. For the M/D/s queue, two approximations are used to evaluate the expected system size under the fixed ρ and α. Simulations and comparison of these two approximations show that Cosmetatos' approximation performs quite well when the number of servers is small and traffic intensity is heavy, but it overestimates the true value for the large number of servers. Meanwhile, the modified approximation gives good results for the steady state count of the system although the number of servers grows large.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.1
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• pp.35-46
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• 2004

In comparison with edge-emitting lasers(EELs), predicting the output power and slope efficiency of Vertical-Cavity Surface-Emitting Lasers(VCSELs) is very difficult due to the absorption loss in DBR layers. However, by using transfer matrix method(TMM), we've made possible to calculate such parameters of multi-layer structures like VCSELs. In this paper, we've calculated the threshold gain, threshold current and slope efficiency through the methodology based on TMM. Also TMM is the way of customizing the VCSEL structure for the desired threshold current and slope efficiency by changing the number of DBR mirror layers.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.769-776
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• 2014

It is necessary to detect the feature points existing simultaneously in both images and then find the corresponding relationship between the detected feature points. We propose a new feature detector based on geometric mean of two eigenvalues of gradient matrix which is able to measure the change of pixel intensities. The corner response of the proposed detector is proportional to the geometric mean and also the difference of two eigenvalues in the case of same geometric mean. We analyzed the localization error of the feature detection using aerial image and artificial image with various types of corners. The localization error of the proposed detector was smaller than that of the typical corner detector, Harris detector.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.1
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• pp.157-163
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• 1986

A method to improve the Finite Elements Model and to identify structure stiffness damages utilizing measured dynamic characteristics is suggested. In order to show the effectiveness of the proposed method, a sample structure is adopted and tested. It is found that; (1) the Finite Elements Model can be improved with experimentally obtained dynamic characteristics satisfactorily and (2) the test results are also very helpful to search the stiffness damaged part of the sample structure via plotting the error matrix[E], especially it is aboved that the weighted error matrix is very effective to detect even the stiffness changes are relatively small.