• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.248-257
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• 2002

Clustering is a method for grouping objects with similar properties into a same cluster. SVD(Singular Value Decomposition) is known as an efficient preprocessing method for clustering because of dimension reduction and noise elimination for a high dimensional and sparse data set like E-Commerce data set. However, it is hard to evaluate the worth of original attributes because of information loss of a converted data set by SVD. This research proposes a cluster feature selection method, called ENTROPY-SVD, to find important attributes for each cluster based on entropy weighting and SVD. Using SVD, one can take advantage of the latent structures in the association of attributes with similar objects and, using entropy weighting one can find highly dense attributes for each cluster. This paper also proposes a model-based collaborative filtering recommendation system with ENTROPY-SVD, called CFS-CF and evaluates its efficiency and utilization.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.212-218
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• 2013

This paper presents an SVD(Singular Value Decomposition)-Pseudo Spectrum method for SAR (Synthetic Aperture Radar) imaging. The purpose of this work is to improve resolution and target separability of SAR images. This paper proposes SVD-Pseudo Spectrum method whose advantages are noise robustness, reduction of sidelobes and high resolution of spectral estimation. SVD-Pseudo Spectrum method uses Hankel Matrix of signal components and SVD (Singular Value Decomposition) method. In this paper, it is demonstrated that the SVD-Pseudo Spectrum method shows better performance than the matched filtering method and the conventional super-resolution based multiple signal classification (MUSIC) method in SAR image processing. The targets to be separated are modeled, and this modeled data is used to demonstrate the performance of algorithms.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1585-1601
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• 2017

With the rapid development of computer technologies, a number of image modification methods have emerged, which have great impacts on the security of image information. Therefore, it is necessary to protect the integrity and authenticity of digital images, and digital watermarking technique consequently becomes a research hotspot. An effort is made to survey and analyze advancements of image watermarking algorithms based on singular value decomposition (SVD) in recent years. In the first part, an overview of watermarking techniques is presented and then mathematical theory of SVD is given. Besides, SVD watermarking model, features, and evaluation indexes are demonstrated. Various SVD-based watermarking algorithms, as well as hybrid watermarking algorithms based on SVD and other transforms for copyright protection, tamper detection, location, and recovery are reviewed in the last part.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.149-156
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• 2016

We proposed a blind watermarking scheme using singular vectors based on Discrete Wavelet Transform (DWT) and Redundant Discrete Wavelet Transform (RDWT) combined with Singular Value Decomposition (SVD) for copyright protection application. We replaced the 1st left and right singular vectors decomposed from cover image with the corresponding ones from watermark image to overcome the false-positive problem in current watermark systems using SVD. The proposed scheme realized the watermarking system without a false positive problem, and shows high fidelity and robustness.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.116-122
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• 1999

저차 동적계(특히 카오스계)에서 측정한 시계열의 잡음을 제거하기 위해서 SVD(Singular Value Decomposotion)을 이용한 새로운 간편하고 매우 효과적인 새로운 잡음 제거법이 소개되었다. 이 방법은 위상궤적(phase portraint)을 재구성하는데 중점을 두었으며, 궤적행렬(trajectory matrix)을 구성하는데 그 기본을 두었다. 이 궤적행렬에 SVD를 반복적으로 사용하여 신호와 잡음을 분리하였다. 이 방법은 Duffing계에서 측정한 잡음이 섞인 카오스 신호에 적용되었으며, 또한 실험에 의한 진폭변조된 신호에도 적용되었다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.165-170
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• 2013

When we build up a 3D model from the given 2D images, linear algorithms are often used to reduce computational cost or for initialization of nonlinear algorithms. However, contemporary linear algorithms have apparently linear structures, but virtually they are implemented using SVD. The SVD is also implemented using numerical analysis algorithms that need initialization. Moreover, solutions using SVD are more difficult to analyze than closed-form solutions. To avoid from such inconvenient numerical analysis algorithms of the contemporary methods and for convenient analysis of solutions, this paper proposes a convenient linear method that produces a closed-form solution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8455-8461
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• 2015

In this paper, we propose new search algorithms using SVD(Singular Value Decomposition) for fast search of Raman spectrum. In the proposed algorithms, small number of the eigen vectors obtained by SVD are chosen in accordance with their respective significance to achieve computation reduction. By introducing pilot test, we exclude large number of data from search and then, we apply partial distance search(PDS) for further computation reduction. We prepared 14,032 kinds of chemical Raman spectrum as the library for comparisons. Experiments were carried out with 7 methods, that is Full Search, PDS, 1DMPS modified MPS for applying to 1-dimensional space data with PDS(1DMPS+PDS), 1DMPS with PDS by using descending sorted variance of data(1DMPS Sort with Variance+PDS), 250-dimensional components of the SVD with PDS(250SVD+PDS) and proposed algorithms, PSP and PSSP. For exact comparison of computations, we compared the number of multiplications and additions required for each method. According to the experiments, PSSP algorithm shows 64.8% computation reduction when compared with 250SVD+PDS while PSP shows 157% computation reduction.

• Biomedical Science Letters
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• v.11 no.2
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• pp.129-134
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• 2005

Three different deconvolution techniques for quantifying cerebral blood flow (CBF) from whole brain $T2^{\ast}-weighted$ bolus tracking images were implemented (parametric Fourier transform P-FT, parametric single value decomposition P-SVD and nonparametric single value decomposition NP-SVD). The techniques were tested on 206 regions from 38 hyperacute stroke patients. In the P-FT and P-SVD techniques, the tissue and arterial concentration time curves were fit to a gamma variate function and the resulting CBF values correlated very well $(CBF_{P-FT}\;=\;1.02{\cdot}CBF_{p-SVD},\;r^2\;=\;0.96)$. The NP-SVD CBF values correlated well with the P-FT CBF values only when a sufficient number of time series volumes were acquired to minimize tracer time curve truncation $(CBF_{P-FT}\;=\;0.92{\cdot}CBF_{NP-SVD},\;r^2\;=\;0.88)$. The correlation between the fitted CBV and the unfitted CBV values was also maximized in regions with minimal tracer time curve truncation $(CBV_{fit}\;=\;1.00{\cdot}CBV_{ Unfit},\;^r^2\;=\;0.89)$. When a sufficient number of time series volumes could not be acquired (due to scanner limitations) to avoid tracer time curve truncation, the P-FT and P-SVD techniques gave more reliable estimates of CBF than the NP-SVD technique.

• Bulletin of the Korean Chemical Society
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• v.24 no.7
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• pp.971-974
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• 2003

By utilizing singular value decomposition (SVD) and shift averaged Harr wavelet transform (WT) with a set of Daubechies wavelet coefficients (1/2, -1/2), a method that can simultaneously eliminate an unwanted large solvent peak and noise peaks from NMR data has been developed. Noise elimination was accomplished by shift-averaging the time domain NMR data after a large solvent peak was suppressed by SVD. The algorithms took advantage of the WT, giving excellent results for the noise elimination in the Gaussian type NMR spectral lines of NMR data pretreated with SVD, providing superb results in the adjustment of phase and magnitude of the spectrum. SVD and shift averaged Haar wavelet methods were quantitatively evaluated in terms of threshold values and signal to noise (S/N) ratio values.

• Journal of the Korean Magnetic Resonance Society
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• v.4 no.2
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• pp.116-124
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• 2000

Singular value decomposition (SVD) has been used during past few decades in the advanced NMR data processing and in many applicable areas. A new modified SVD, piecewise polynomial truncated SVD (PPTSVD) was developed far the large solvent peak suppression and noise elimination in U signal processing. PPTSVD consists of two algorithms of truncated SVD (TSVD) and L$_1$ problems. In TSVD, some unwanted large solvent peaks and noises are suppressed with a certain son threshold value while signal and noise in raw data are resolved and eliminated out in L$_1$ problem routine. The advantage of the current PPTSVD method compared to many SVD methods is to give the better S/N ratio in spectrum, and less time consuming job that can be applicable to multidimensional NMR data processing.