• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.97-104
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• 2008

Most of engine control systems for helicopter turboshaft engines are equipped with dual sensors. For the system with dual redundancy, analytic methods are used to detect faults based on the system dynamical model. Helicopter engine dynamics are affected by aerodynamic torque induced from the dynamics of the main rotor. In this paper an engine model including the rotor dynamics is constructed for the T700-GE-700 turboshaft engine powering UH-60 helicopter. The singular value decomposition(SVD) method is applied to the developed model in order to detect sensor faults. The SVD method which do not need an additional computation to generate residual uses the characteristics that the system outputs in direction of the left singular vector if an input is applied in direction of the right singular vector. Simulations show that the SVD method works well in detecting and isolating the sensor faults.

• Journal of the Korean Society for Railway
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• v.5 no.3
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• pp.174-180
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• 2002

In multibody dynamics, DAE(Differential Algebraic Equations) that combine differential equations of motion and kinematic constraint equations should be solved. To solve these equations, either coordinate partitioning method or constraint stabilization method is commonly used. The most typical coordinate partitioning methods are LU decomposition, QR decomposition, and SVD(singular value decomposition). The objective of this research is to suggest a hybrid coordinate partitioning method in the dynamic analysis of multibody systems containing singular configurations. Two coordinate partitioning methods, i.e. LU decomposition and QR decomposition for constrained multibody systems, are combined for a new hybrid coordinate partitioning method. The proposed hybrid method reduces the simulation time while keeping accuracy of the solution.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.705-712
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• 2005

Biplot is a graphical display of the rows and columns of an n${\times}$p data matrix. In particular, Gabriel (1995) suggested the MANOVA biplot using singular value decomposition (SVD) with the averages of response variables according to treatment groups. But his biplot may cause wrong results by disregarding them when there exist covariate effects. In this paper, we will provide the MANCOA biplot based on the SVD with the parameter estimates for MANCOVA model when there exist covariate effects.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.285-290
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• 2005

Biplot is a graphical display of the rows and columns an $n{\time}p$ data matrix. In particular, Gabriel(1981) suggested The MANOVA BIPLOT using singular value decomposition (SVD) with the averages of response variables according to treatment groups. But his biplot may cause wrong results by disregarding them when there exists covariate effects. In this paper, we will provide the MANCOVA BIPLOT based on the SVD with the parameter estimates for MANCOVA model when there exist covariate effects.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.276-286
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• 2013

SVD-based deconvolution algorithm has been known as the most effective technique for CT perfusion image analysis. In this algorithm, in order to reduce noise effects, SVD coefficients smaller than a certain threshold are removed. As the truncation threshold, either a fixed value or a variable threshold yielding a predetermined OI (oscillation index) is frequently employed. Each of these two thresholding methods has an advantage to the other either in accuracy or efficiency. In this paper, we propose a Monte Carlo simulation method to evaluate the accuracy of the two methods. An extension of the proposed method is presented as well to measure the effects of image smoothing on the accuracy of the thresholding methods. In this paper, after the simulation method is described, experimental results are presented using both simulated data and real CT images.

• East Asian mathematical journal
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• v.40 no.3
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• pp.307-318
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• 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 Intelligence and Information Systems
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• v.22 no.4
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• pp.1-18
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• 2016

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.122-128
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• 2015

As multimedia information is spread over cyber networks, problems such as protection of legal rights and original proof of an information owner raise recently. Various image transformations of DCT, DFT and DWT have been used to embed a watermark as a token of ownership. Recently, SVD being used in the field of numerical analysis is additionally applied to the watermarking methods. A watermarking method is proposed in this paper using Gabor cosine and sine transform as well as SVD for embedding and extraction of watermarks for digital images. After delivering attacks such as noise addition, space transformation, filtering and compression on watermarked images, watermark extraction algorithm is performed using the proposed GCST-SVD method. Normalized correlation values are calculated to measure the similarity between embedded watermark and extracted one as the index of watermark performance. Also visual inspection for the extracted watermark images has been done. Watermark images are inserted into the lowest vertical ac frequency band. From the experimental results, the proposed watermarking method using the singular vectors of SVD shows large correlation values of 0.9 or more and visual features of an embedded watermark for various attacks.

• KIEE International Transactions on Power Engineering
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• v.4A no.1
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• pp.33-41
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• 2004

Knowledge of partial discharge (PD) is important to accurately diagnose and predict the condition of insulation. The PD phenomenon is highly complex and seems to be random in its occurrence. This paper indicates the possible use of chaos theory for the recognition and distinction concerning PD signals. Chaos refers to a state where the predictive abilities of a systems future are lost and the system is rendered aperiodic. The analysis of PD using deterministic chaos comprises of the study of the basic system dynamics of the PD phenomenon. This involves the construction of the PD attractor in state space. The simulation results show that the variance of an orthogonal axis in an attractor of chaos theory increases according to the magnitude and the number of PDs. However, it is difficult to clearly identify the characteristics of the PDs. Thus, we calculated the magnitude on an orthogonal axis in an attractor using singular value decomposition (SVD) and principal component analysis (PCA) to extract the numerical characteristics. In this paper, we proposed the condition monitoring method for gas insulated switchgear (GIS) using SVD for efficient calculation of the variance. Thousands of simulations have proven the accuracy and effectiveness of the proposed algorithm.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.725-734
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• 2003

In this paper, we propose the text summarization method using PCA (Principal Component Analysis) and SVD (Singular Value Decomposition). The proposed method presents a summary by extracting significant sentences based on the distances between thematic words and sentences. To extract thematic words, we use both word frequency and co-occurence information that result from performing PCA. To extract significant sentences, we exploit Euclidean distances between thematic word vectors and sentence vectors that result from carrying out SVD. Experimental results using newspaper articles show that the proposed method is superior to the method using either word frequency or only PCA.