• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.348-351
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• 1995

This note proposes a robust LQR method for systems with structured real parameter uncertainty based on Riccati equation approach. Emphasis is on the reduction of design conservatism in the sense of quadratic performance by utilizing the uncertainty structure. The class of uncertainty treated includes all the form of additive real parameter uncertainty, which has the multiple rank structure. To handle the structure of uncertainty, the scaling matrix with block diagonal structure is introduced. By changing the scaling matrix, all the possible set of uncertainty structures can be represented. Modified algebraic Riccati equation (MARE) is newly proposed to obtain a robust feedback control law, which makes the quadratic cost finite for an arbitrary scaling matrix. The remaining design freedom, that is, the scaling matrix is used for minimizing the upper bound of the quadratic cost for all possible set of uncertainties within the given bounds. A design example is shown to demonstrate the simplicity and the effectiveness of proposed method.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.47-48
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• 2005

Multidimensional scaling is a multivariate technique for constructing a configuration of n points in Euclidean space using information about the distances between the objects. This can be done by the singular value decomposition of the data matrix. But it is known that the singular value decomposition is not resistant. In this study, we provide a resistant version of the multidimensional scaling.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.21-27
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• 2019

The video compression standard required a processing technique for a high resoluion image and increased the coding size to increase the resolution of the image. Accurate motion estimation and increased coding size provide high accuracy and compression rate, but there is a problem of increased computational complexity. In this paper, we use DCT - based motion estimation in the frequency domain to reduce complexity. However, we found that the DCT and quantization process used in a general video encoder are applied to the frequency domain encoder, resulting in problems caused by the scaling process. Therfore, in this paper, we extract the scaling matrix that can be applied in the DCT step and resolve the, and improve the performance of motion estimation using increased coding size.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.619-627
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• 2014

Distances or dissimilarities among units are assumed to be symmetric in most cases of multidimensional scaling(MDS); consequently, it is not an easy task to deal with asymmetric distances. Current asymmetric MDS still face difficulties in the interpretation of results. This study proposes a simpler asymmetric MDS that utilizes the order statistic of an asymmetric matrix. The proposed Web method demonstrates that some influences among objects are visualized by direction, size and shape of arrow to ease the interpretability of users.

• Kyungpook Mathematical Journal
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• v.47 no.1
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• pp.133-150
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• 2007

In a development of efficient primal-dual interior-points algorithms for self-scaled convex programming problems, one of the important properties of such cones is the existence and uniqueness of "scaling points". In this paper through the identification of scaling points with the notion of "(metric) geometric means" on symmetric cones, we extend several well-known matrix inequalities (the classical L$\ddot{o}$wner-Heinz inequality, Ando inequality, Jensen inequality, Furuta inequality) to symmetric cones. We also develop a theory of spectral geometric means on symmetric cones which has recently appeared in matrix theory and in the linear monotone complementarity problem for domains associated to symmetric cones. We derive Nesterov-Todd inequality using the spectral property of spectral geometric means on symmetric cones.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.117-122
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• 2008

This paper presents a multi-robot localization based on multidimensional scaling (MDS) in spite of the existence of incomplete and noisy data. While the traditional algorithms for MDS work on the full-rank distance matrix, there might be many missing data in the real world due to occlusions. Moreover, it has no considerations to dealing with the uncertainty due to noisy observations. We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nystr$\ddot{o}$m approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

• Journal of the Korean Mathematical Society
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• v.46 no.2
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• pp.281-294
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• 2009

Accuracy of the scaling function is very crucial in wavelet theory, or correspondingly, in the study of wavelet filter banks. We are mainly interested in vector-valued filter banks having matrix factorization and indicate how to choose block central symmetric matrices to construct multi-wavelets with suitable accuracy.

• Journal of the Korean earth science society
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• v.37 no.5
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• pp.277-285
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• 2016

We introduce a depth scaling strategy to improve the accuracy of frequency-domain elastic full waveform inversion (FWI) using the new pseudo-Hessian matrix for seismic data without low-frequency components. The depth scaling strategy is based on the fact that the damping factor in the Levenberg-Marquardt method controls the energy concentration in the gradient. In other words, a large damping factor makes the Levenberg-Marquardt method similar to the steepest-descent method, by which shallow structures are mainly recovered. With a small damping factor, the Levenberg-Marquardt method becomes similar to the Gauss-Newton methods by which we can resolve deep structures as well as shallow structures. In our depth scaling strategy, a large damping factor is used in the early stage and then decreases automatically with the trend of error as the iteration goes on. With the depth scaling strategy, we can gradually move the parameter-searching region from shallow to deep parts. This flexible damping factor plays a role in retarding the model parameter update for shallow parts and mainly inverting deeper parts in the later stage of inversion. By doing so, we can improve deep parts in inversion results. The depth scaling strategy is applied to synthetic data without lowfrequency components for a modified version of the SEG/EAGE overthrust model. Numerical examples show that the flexible damping factor yields better results than the constant damping factor when reliable low-frequency components are missing.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.431-440
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• 2022

Hanwoo cattle are a unique and historical breed in Korea that have been genetically improved and maintained by the national evaluation and selection system. The aim of this study was to provide information that can help improve the accuracy of the estimated breeding values in Hanwoo cattle by showing the difference between the imputation reference chip platforms of genomic data and the scaling factor of the genetic relationship matrix (GRM). In this study, nine sets of data were compared that consisted of 3 reference platforms each with 3 different scaling factors (-0.5, 0 and 0.5). The evaluation was performed using MTG2.0 with nine different GRMs for the same number of genotyped animals, pedigree, and phenotype data. A five multi-trait model was used for the evaluation in this study which is the same model used in the national evaluation system. Our results show that the Hanwoo custom v1 platform is the best option for all traits, providing a mean accuracy improvement by 0.1 - 0.3%. In the case of the scaling factor, regardless of the imputation chip platform, a setting of -1 resulted in a better accuracy increased by 0.5 to 1.6% compared to the other scaling factors. In conclusion, this study revealed that Hanwoo custom v1 used as the imputation reference chip platform and a scaling factor of -0.5 can improve the accuracy of the estimated breeding value in the Hanwoo population. This information could help to improve the current evaluation system.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.2
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• pp.335-357
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• 2007

There has been many studies representing intellectual structures with multidimensional scaling(MDS) However MDS configuration is limited in representing local details and explicit structures. In this paper, we identified two components of MDS mapping approach; one is MDS algorithm and the other is preparation of data matrix. Various combinations of the two components of MDS mapping are compared through some measures of fit. It is revealed that the conventional approach composed of ALSCAL algorithm and Euclidean distance matrix calculated from Pearson's correlation matrix is the worst of the compared MDS mapping approaches. Otherwise the best approach to make MDS map is composed of PROXSCAL algorithm and z-scored Euclidean distance matrix calculated from Pearson's correlation matrix. These results suggest that we could obtain more detailed and explicit map of a knowledge domain through careful considerations on the process of MDS mapping.