• 전자공학회논문지B
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• 제31B권3호
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.281-282
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• 2007

We address a new representation of DCT/DFT/Wavelet matrices via one hybrid architecture. Based on an element inverse matrix factorization algorithm, we show that the OCT, OFT and Wavelet which based on Haar matrix have the similarrecursive computational pattern, all of them can be decomposed to one orthogonal character matrix and a special sparse matrix. The special sparse matrix belongs to Jacket matrix, whose inverse can be from element-wise inverse or block-wise inverse. Based on this trait, we can develop a hybrid architecture.

• 전자공학회논문지A
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• 제30A권5호
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• pp.52-62
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• 1993

This paper describes the implementation of a layout generation system for the gate matrix style to implement multi-level logic. To achieve improved layouts from general net lists, the proposed system performs flexible net binding for series nets. Also the system reassings gates by the heuristic information that shorter net lengths are better for the track minimization. By track minimizing with subdividing layout column information, the system decreases the number of necessary layout tracks. Experimental results show that the system generates more area-reduced (approximately 7.46%) layouts than those by previous gate matrix generation systems using net list inputs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2634-2648
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• 2020

In this paper, an adaptive sparse singular value decomposition (ASSVD) algorithm is proposed to estimate the signal matrix when only one data matrix is observed and there is high dimensional white noise, in which we assume that the signal matrix is low-rank and has sparse singular vectors, i.e. it is a simultaneously low-rank and sparse matrix. It is a structured matrix since the non-zero entries are confined on some small blocks. The proposed algorithm estimates the singular values and vectors separable by exploring the structure of singular vectors, in which the recent developments in Random Matrix Theory known as anisotropic Marchenko-Pastur law are used. And then we prove that when the signal is strong in the sense that the signal to noise ratio is above some threshold, our estimator is consistent and outperforms over many state-of-the-art algorithms. Moreover, our estimator is adaptive to the data set and does not require the variance of the noise to be known or estimated. Numerical simulations indicate that ASSVD still works well when the signal matrix is not very sparse.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2007년도 추계 종합학술대회 논문집
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• pp.501-505
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• 2007

최근에 한국과학기술정보연구원은 계량서지분석에 활용하기 위한 독립형 데이터 분석 시스템 Knowledge Matrix를 개발하였다. 본 논문에서는 개발된 시스템의 성능 수준을 이 분야에서 잘 알려진 분석툴인 VantagePoint와 BibTechMon과 비교분석 하였다. 기능 비교는 데이터, 행렬, 분석, 시각화, 데이터 전처리 부문에서 수행 하였다. 분석결과 각 분석툴의 특장점이 서로 다르지만 전반적으로 KnowledgeMatrix가 좀 더 우수한 기능을 보였다.

• Journal of electromagnetic engineering and science
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• 제6권4호
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• pp.244-252
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• 2006

A block Jacket transform and. its block inverse Jacket transformn have recently been reported in the paper 'Fast block inverse Jacket transform'. But the multiplication of the block Jacket transform and the corresponding block inverse Jacket transform is not equal to the identity transform, which does not conform to the mathematical rule. In this paper, new binary block Jacket transforms and the corresponding binary block inverse Jacket transforms of orders $N=2^k,\;3^k\;and\;5^k$ for integer values k are proposed and the mathematical proofs are also presented. With the aid of the Kronecker product of the lower order Jacket matrix and the identity matrix, the fast algorithms for realizing these transforms are obtained. Due to the simple inverse, fast algorithm and prime based $P^k$ order of proposed binary block inverse Jacket transform, it can be applied in communications such as space time block code design, signal processing, LDPC coding and information theory. Application of circular permutation matrix(CPM) binary low density quasi block Jacket matrix is also introduced in this paper which is useful in coding theory.

• 한국공간정보시스템학회 논문지
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• 제11권1호
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• pp.157-168
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• 2009

우리가 살아가는 삶속에 깊숙이 파고든 공간정보는 유비쿼터스 시대에 맞춰 지능형 공간정보로 진화하고 있다. 또한 이를 이용한 다양한 서비스 모델이 소개되고 있다. 하지만 이러한 서비스에 대하여 사용자와 제공자에게 동시에 설명할 수 있는 분류체계는 존재하지 않는다. 이에 다양한 지능형 공간정보 서비스들을 체계적으로 분류할 수 있는 체계가 필요한 실정이다. 본 연구에서는 지능형 공간정보의 개념을 소개하고 공간정보 서비스들의 특성을 고려한 서비스 분류 체계를 개발하였다. 개발된 지능형 공간정보 서비스분류 매트릭스는 지능수준 척도, 공간정보 정확도, 서비스 영역을 기준으로 하였다. 본 연구에서 제안한 서비스 분류 매트릭스는 두 가지 관점에서 활용될 수 있다. 첫째, 지능형 공간정보의 수요가 늘어나면서 유사한 기능만을 가진 채 실수요를 반영하지 못하고 중복된 서비스들이 나타나는 현실을 개선시킬 수 있다. 둘째, 공간정보 산업의 현황을 들여다보고 새로 진입하게 되는 서비스의 목표 선정이나 미래의 발전 방향을 제시할 수 있다. 이러한 활용을 토대로 서비스 분류 매트릭스는 새로운 블루오션 창출과 같이 공간정보 사업 활성화를 이루는데 도움이 될 수 있다. 하지만, 서비스 분류 매트릭스는 향후 개발될 다양한 서비스를 적용하는데 있어 문제점이 없도록 수정과 보완이 필요하다. 그리고 분류 매트릭스는 궁극적으로 서비스 로드맵을 작성하기 위한 자료로 활용되거나 참조모델로서 활용될 수 있도록 해야 할 것이다. 하여 U-City의 미래를 더욱 밝게 할 것이다.

• Journal of applied mathematics & informatics
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• 제17권1_2_3호
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• pp.585-590
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• 2005

An efficient procedure for finding the inverse of a nonsingular matrix is developed by solving directly for the UL decomposition of the inverse based on the entries of the original nonsingular matrix.

• 방송공학회논문지
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• 제16권5호
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• pp.782-792
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• 2011

In this paper, we address a new fast DCT-II/DFT/HWT hybrid transform architecture for digital video and fusion mobile handsets based on Jacket-like sparse matrix decomposition. This fast hybrid architecture is consist of source coding standard as MPEG-4, JPEG 2000 and digital filtering discrete Fourier transform, and has two operations: one is block-wise inverse Jacket matrix (BIJM) for DCT-II, and the other is element-wise inverse Jacket matrix (EIJM) for DFT/HWT. They have similar recursive computational fashion, which mean all of them can be decomposed to Kronecker products of an identity Hadamard matrix and a successively lower order sparse matrix. Based on this trait, we can develop a single chip of fast hybrid algorithm architecture for intelligent mobile handsets.

• Current Optics and Photonics
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• 제5권3호
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• pp.322-328
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• 2021

In this paper, a pretreatment method for a matrix in convex optimization is proposed to optimize the spectral reconstruction process of a disordered dispersion spectrometer. Unlike the reconstruction process of traditional spectrometers using Fourier transforms, the reconstruction process of disordered dispersion spectrometers involves solving a large-scale matrix equation. However, since the matrices in the matrix equation are obtained through measurement, they contain uncertainties due to out of band signals, background noise, rounding errors, temperature variations and so on. It is difficult to solve such a matrix equation by using ordinary nonstationary iterative methods, owing to instability problems. Although the smoothing Tikhonov regularization approach has the ability to approximatively solve the matrix equation and reconstruct most simple spectral shapes, it still suffers the limitations of reconstructing complex and irregular spectral shapes that are commonly used to distinguish different elements of detected targets with mixed substances by characteristic spectral peaks. Therefore, we propose a special pretreatment method for a matrix in convex optimization, which has been proved to be useful for reducing the condition number of matrices in the equation. In comparison with the reconstructed spectra gotten by the previous ordinary iterative method, the spectra obtained by the pretreatment method show obvious accuracy.