• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.275-280
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• 2015

In this paper, we studied for modified MUSIC algorithm of direction of arrival (DOA)estimation. Modified MUSIC algorithm search optimal covariance matrix using singular value decomposition and Bayes method, and desired signals are estimated by updating weight. In order to estimation of desired signals, we used sub spatial method of MUSIC algorithm. General MUSIC algorithm can estimate a desired signal in case of non-correlation signal. But, general MUSIC algorithm in case of correlation signal can not estimate a desired signals and resolution is decreased. Though simulation in case of correlation signal, we analyze to compare proposed MUSIC algorithm with general MUSIC algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.7
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• pp.1346-1353
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• 2001

This paper presents an algorithm for the design of 2 dimension separable denominator digital filter(SDDF). The proposed algorithm is based on the reduced dimensional decomposition not only 2 dimension SDDF's but also of given 2 dimension specification. The frequency domain design of 2 dimension separable denominator digital filters based on the reduced dimensional decomposition can be realized when the given 2 dimension frequency specification are optimally decomposed into a pair of 1 dimension digital filter specification via singular value decomposition. the algorithm is computationally efficient and numerically stable. In case of the low pass filter, the approximation error of the proposed design algorithm is $e_{m}$=5.17, $e_{r1}$ =8.78, $e_{r2}$=7.34, while in case of band pass filter, the approximation error is $e_{m}$=13.00, $e_{r1}$=62.76, $e_{r2}$=62.7676.7676

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.338-344
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• 2017

In this paper, two-way amplify-and-forward relay methods are investigated where two terminals and one relay node are equipped with multiple antennas. In two-way relay channels, it is assumed that one terminal can eliminate its own self-interference but the other cannot. For this channel, we first maximize the sum-rate performance by employing an iterative gradient descent (GD) algorithm. Then, a simple singular value decomposition (SVD) based block triangularization is developed to null the self-interference. Simulation results show the proposed methods outperform the conventional schemes for various environments.

• 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 the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.752-757
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• 2006

In the case of the requirement of high speed transmission, OFDM is a powerful technique employed in communications systems suffering from frequency selective fading. In this paper, we apply an optimal adaptive bitloading algorithm technique. The BER performance of the fixed-rate SISO and adaptive SISO is simulated. Specially, we can decompose the MIMO channel into the SISO channel by making use of the singular value decomposition(SVD) assuming channel knowledge in a multipath environment. As a results of simulation, we confirmed that the BER enhancement of MIMO-OFDM system with the bitloadins algorithm was superior to the SISO-OFDM system.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.3
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• pp.154-161
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• 2013

In this paper, Gabor cosine and sine transform considered as human visual filter is applied to watermarking methods for digital images. Four algorithms by using singular values or principal components of SVD in the frequency domain are proposed for watermark embedding and extraction. Two dimensional image is used as an embedded watermark. To measure the similarity between the embedded watermark image and the extracted one, a normalized correlation value is computed for the comparison of the four proposed methods with various attacks. Extracted watermark images are also provided for visual inspection. The proposed GCST-SVD method which embeds a watermark image into the lowest vertical or horizontal ac frequency band can provide useful watermarking algorithm with high correlation values and visual watermark features from experimental results for various attacks.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.491-498
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• 2015

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

• Korean Journal of Optics and Photonics
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• v.4 no.4
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• pp.363-372
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• 1993

An optimization technique with variable orthogonalization and SVD(singular value decomposition) is examined in a double-Gauss type photographic lens design and its convergence and stability are compared with ordinary least squares and DLS(damped least squares) method. It is known that there are close relationship between the stability of optimization and condition number of nomal equation, the ratio between maximum and minimum of eigenvalues. In this study, the stability is greatly improved by limiting the condition number, the SVD, as expeded. The case of DLS with small damping, orthogonalization and SVD shows the most rapid convergence and stability. It means that the unstability of DLS method with small damping is overcome by using the variable orthogonalization and SVD.