• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.1
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• pp.89-96
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• 2013

This paper presents an improved clutter signal removal algorithm using Singular Value Decomposition(SVD). For indoor positioning system using IR-UWB Radar, the target signal is extracted from received signal. We use clutter signal removal algorithm using SVD for target signal extraction. Clutter signal removal algorithm using SVD has the advantage of operation but the disadvantage of high computational complexity. In this paper, we propose a method to improve computational complexity. As the experimental results, it is confirmed that the method presented in this paper improve the computational complexity of clutter removal algorithm using SVD.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.273-280
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• 2003

Recommender System can help users to find products to Purchase. A representative method for recommender systems is collaborative filtering (CF). It predict products that user may like based on a group of similar users. User information is based on user's ratings for products and similarities of users are measured by ratings. As user is increasing tremendously, the performance of the pure collaborative filtering is lowed because of high dimensionality and scarcity of data. We consider the effect of dimension deduction in collaborative filtering to cope with scarcity of data experimentally. We suggest that SVD improves the performance of collaborative filtering in comparison with pure collaborative filtering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2C
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• pp.155-160
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• 2010

In this paper, we proposed a forensic mark algorithm which can embed the distributor's information at each distribution step to trace the illegal distribution path. For this purpose, the algorithm has to have the high capacity payload for embedding the copyright and user information at each step, and the embedded information at a step should not interfere with the information at other step. The proposed algorithm can trace the multilevel distribution because the forensic mark is generated by digital hologram and embedded in the DWT-SVD domain. For the high capacity embedding, the off-axis hologram is generated from the forensic mark and the hologram is embedded in the HL, LH, HH bands of the DWT to reduce the signal interference. The SVD which is applied the holographic signal enhanced the detection performance and the safety of the forensic mark algorithm. As the test results, this algorithm was able to embed 128bits information for the copyright and user information at each step. In this paper, we can embed total 384bits information for 3 steps and the algorithm is also robust to the JPEG compression.

• 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 Drive and Control
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• v.2 no.1
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• pp.18-24
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• 2005

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.176-184
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• 1998

We present a sequential factorization method using singular value decomposition (SVD) for recovering both the three-dimensional shape of an object and the motion of camera from a sequence of images. We employ paraperpective projection [6] for camera model to handle significant translational motion toward the camera or across the image. The proposed mthod not only quickly gives robust and accurate results, but also provides results at each frame becauseit is a sequential method. These properties make our method practically applicable to real time applications. Considerable research has been devoted to the problem of recovering motion and shape of object from image [2] [3] [4] [5] [6] [7] [8] [9]. Among many different approaches, we adopt a factorization method using SVD because of its robustness and computational efficiency. The factorization method based on batch-type computation, originally proposed by Tomasi and Kanade [1] proposed the feature trajectory information using singular value decomposition (SVD). Morita and Kanade [10] have extenened [1] to asequential type solution. However, Both methods used an orthographic projection and they cannot be applied to image sequences containing significant translational motion toward the camera or across the image. Poleman and Kanade [11] have developed a batch-type factorization method using paraperspective camera model is a sueful technique, the method cannot be employed for real-time applications because it is based on batch-type computation. This work presents a sequential factorization methodusing SVD for paraperspective projection. Initial experimental results show that the performance of our method is almost equivalent to that of [11] although it is sequential.

• Journal of Broadcast Engineering
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• v.13 no.3
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• pp.289-298
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• 2008

We propose an SNR-scalable coding algorithm for three-dimensional mesh sequences based on singular value decomposition (SVD). SVD achieves a coding gain by representing a mesh sequence with a small number of basis vectors and singular values. First, we introduce a bit plane coding scheme and derive a quantitative relationship between each bit plane and the reconstructed image quality. Using the relationship, we develop a rate-distortion (RD) optimized coding algorithm. Moreover, we propose prediction techniques to exploit the spatio-temporal correlations in real mesh sequences. Simulation results demonstrate that the proposed algorithm provides significantly better RD performance than conventional SVD coders.

• Earthquakes and Structures
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• v.25 no.5
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• pp.343-358
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• 2023

The superelastic viscous damper (SVD) is a hybrid passive control device comprising a viscoelastic damper and shape memory alloy (SMA) cables connected in series. The SVD is an innovative damper through which a large amount of seismic energy can dissipate. The current study assessed the seismic collapse induced by steel moment-resisting frames (SMRFs) equipped with SVDs and compared them with the performance of special MRFs and buckling restrained brace frames (BRBFs). For this purpose, nonlinear dynamic and incremental dynamic analysis (IDA) were conducted in OpenSees software. Both 5- and 9-story special MRFs, BRBFs, and MRFs equipped with the SVDs were examined. The results indicated that the annual exceedance rate for maximum residual drifts of 0.2% and 0.5% for the BRBFs and MRFs with SVDs, respectively, were considerably less than for SMRFs with reduced-beam section (RBS) connections and that the seismic performances of these structures were enhanced with the use of the BRB and SVD. The probability of collapse due to residual drift in the SVD, BRB, and RBS frames in the 9-story structure was 1.45, 1.75, and 1.05 times greater than for the 5-story frame.

• 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.