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

• Communications of the Korean Mathematical Society
• /
• v.16 no.3
• /
• pp.487-508
• /
• 2001

We consider the Cauchy problem for Laplacian. Using the single layer representation, we obtain an equivalent system of boundary integral equations. We show the singular values of the ill-posed Cauchy operator decay exponentially, which means that a small error is exponentially amplified in the solution of the Cauchy problem. We show the decaying rate is dependent on the geometry of he domain, which provides the information on the choice of numerically meaningful modes. We suggest a pseudo-inverse regularization method based on singular value decomposition and present various numerical simulations.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.8
• /
• pp.3-11
• /
• 2001

This paper describes an implementation of inverse filter using SVD in order to recover the input in multi-channel system. The matrix formulation in SISO system is extended to MIMO system. In time and frequency domain we investigates the inversion of minimum phase system and non-minimum phase system. To execute an effective inversion of non-minimum phase system, SVD is introduced. First of all we computes singular values of system matrix and then investigates the phase property of system. In case of overall system is non-minimum phase, system matrix has one (or more) very small singular value (s). The very small singular value (s) carries information about phase properties of system. Using this property, approximate inverse filter of overall system is founded. The numerical simulation shows potentials in use of the inverse filter.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.24 no.5
• /
• pp.481-489
• /
• 2011

In this paper presents new force method by using singular value decomposition. The existing force method has some advantages about analysis of truss structures such as it is easier basic concept than finite element method, which apply to analyze truss structures. However, this method has complex formulation for analysis. Therefore, in this study proposes new force method using singular value decomposition, which is both having easy basic concept and simple computation than existing force method. The proposed method is illustrated through numerical examples.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.11
• /
• pp.928-937
• /
• 2013

This paper presents a novel contrast enhancement method based on singular value decomposition and image pyramid. The proposed method consists mainly of four steps. The proposed algorithm firstly decomposes image into band-pass images, including basis image and detail images, to improve both the global contrast and the local detail. In the global contrast process, singular value decomposition is used for contrast enhancement; the local detail scheme uses weighting factors. In the final image composition process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. Experimental results show that the proposed algorithm improves contrast performance and enhances detail compared to conventional methods.

• Journal of the Korean Magnetics Society
• /
• v.17 no.6
• /
• pp.259-264
• /
• 2007

This paper describes the mathematical modeling method for the static magnetic field signature generated by a magnetic scale model. we proposed the equivalent dipole modeling method utilizing a singular value decomposition technique from magnetic field signatures by magnetic sensors are located special depths below the scale model. The proposed dipole modeling method was successfully verified through comparisons with the real measured values in our non-magnetic laboratory. Using the proposed method, it is possible to predict and analyze static magnetic field distributions at any difference depths generated from the real ships as well as a scale model ship.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.3
• /
• pp.117-124
• /
• 2023

In the machine vision inspection equipment, diameter measurement is important process in inspection of cylindrical object. However, machine vision inspection equipment requires complex algorithm processing such as camera distortion correction and perspective distortion correction, and the increase in processing time and cost required for precise diameter measurement. In this paper, we proposed the algorithm for diameter measurement of cylindrical object using the laser displacement sensor. In order to fit circle for given four input outer points, grid search algorithms using root-mean-square error and mean-absolute error are applied and compared. To solve the limitations of the grid search algorithm, we finally apply the singular-value-decomposition based circle fitting algorithm. In order to compare the performance of the algorithms, we generated the pseudo data of the outer points of the cylindrical object and applied each algorithm. As a result of the experiment, the grid search using root-mean-square error confirmed stable measurement results, but it was confirmed that real-time processing was difficult as the execution time was 10.8059 second. The execution time of mean-absolute error algorithm was greatly improved as 0.3639 second, but there was no weight according to the distance, so the result of algorithm is abnormal. On the other hand, the singular-value-decomposition method was not affected by the grid and could not only obtain precise detection results, but also confirmed a very good execution time of 0.6 millisecond.

• East Asian mathematical journal
• /
• v.40 no.3
• /
• pp.307-318
• /
• 2024

This paper explores the application of matrix factorization, specifically CUR decomposition, in the clustering of Korean language documents by topic. It addresses the unique challenges of Natural Language Processing (NLP) in dealing with the Korean language's distinctive features, such as agglutinative words and morphological ambiguity. The study compares the effectiveness of Latent Semantic Analysis (LSA) using CUR decomposition with the classical Singular Value Decomposition (SVD) method in the context of Korean text. Experiments are conducted using Korean Wikipedia documents and newspaper data, providing insight into the accuracy and efficiency of these techniques. The findings demonstrate the potential of CUR decomposition to improve the accuracy of document clustering in Korean, offering a valuable approach to text mining and information retrieval in agglutinative languages.

• Journal of Communications and Networks
• /
• v.10 no.1
• /
• pp.63-70
• /
• 2008

Narrow-band interference (NBI) from the coexisting narrow-band services affects the performance of ultra wideband (UWB) systems considerably due to the high power of these narrow-band signals with respect to the UWB signals. Specifically, IEEE 802.11a systems which operate around 5 GHz and overlap the band of UWB signals may interfere with UWB systems significantly. In this paper, we suggest a novel NBI suppression technique based on singular value decomposition (SVD) algorithm in time hopping UWB (TH-UWB) systems. SVD is used to approximate the interference which then is subtracted from the received signals. The algorithm precision and closed-form bit error rate (BER) expression are derived in the wireless multipath channel. Comparing with the conventional suppression methods such as a notch filter and a RAKE receiver, the proposed method is simple and robust and especially suitable for UWB systems.

• The Korean Journal of Applied Statistics
• /
• v.25 no.1
• /
• pp.225-237
• /
• 2012

This study proposes a quantification algorithm for a PLS method with several sets of variables. We called the quantification method for PLS with more than 2 sets of data a generalization. The basis of the quantification for PLS method is singular value decomposition. To derive the form of singular value decomposition in the data with more than 2 sets more easily, we used the constraint, $a^ta+b^tb+c^tc=3$ not $a^ta=1$, $b^tb=1$, and $c^tc=1$, for instance, in the case of 3 data sets. However, to prove that there is no difference, we showed it by the use of 2 data sets case because it is very complicate to prove with 3 data sets. The keys of the study are how to form the singular value decomposition and how to get the coordinates for the plots of variables and observations.