• Journal of Korean Ophthalmic Optics Society
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• v.6 no.2
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• pp.121-127
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• 2001

While SVD and Gaussian elimination method were applied to the additive damped least squares(DLS), the convergence and the stability of the optimization process were examined in a triplet-type camera lens-system where the condition number is well conditioned. DLS with SVD method generated a suitable merit function but this merit function may be trapped in a local minimum by the nonlinearity of error function. Therefore, the least camera lens-system was further designed by the global optimization method is grid method, and this method is adopted to get merit function that convergent to global minimum without local minimum trapping.

• ETRI Journal
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• v.29 no.2
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• pp.237-239
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• 2007

Interference from IEEE 802.11a systems affects ultra-wideband (UWB) systems significantly. In this letter, we suggest a novel narrow-band interference (NBI) suppression technique based on the singular value decomposition (SVD) algorithm in time-hopping pulse amplitude modulation (TH-PAM) UWB systems. The SVD algorithm is used to approximate the interference which then is subtracted from the received signals. In contrast to the conventional notch filter and rake receiver, our method is more effective and the receiver complexity can be greatly reduced.

• 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 the Acoustical Society of Korea
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• v.34 no.3
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• pp.234-239
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• 2015

Non-Negative Matrix Factorization (NMF) is a method for updating dictionary and gain in alternating manner. Due to ease of implementation and intuitive interpretation, NMF is widely used to detect and separate overlapping sound events. However, NMF that utilizes non-negativity constraints generates parts-based representation and this distinct property leads to a dictionary containing fragmented acoustic events. As a result, the presence of shared basis results in performance degradation in both separation and detection tasks of overlapping sound events. In this paper, we propose a new method that utilizes K-Singular Value Decomposition (K-SVD) based dictionary to address and mitigate the part-based representation issue during the dictionary learning step. Subsequently, we calculate the gain using NMF in sound event detection step. We evaluate and confirm that overlapping sound event detection performance of the proposed method is better than the conventional method that utilizes NMF based dictionary.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.26-33
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• 2014

For wireless communication systems of (and beyond) LTE-Advanced, multiple-input multiple-output (MIMO) with an increased number of antennas will be utilized for system throughput improvement. When using such an increased number of antenna, an excessive amount of overhead in channel state information (CSI) feedback can be a serious problem. In this paper, we propose methods which reduce the CSI feedback overhead, particularly including application strategies for multi-rank transmission targeted for two or more reception antennas. To reduce the information which is instantaneously transmitted from the reception node to the transmission node, we present a beamforming method utilizing singular value decomposition (SVD) based on channel estimation of partitioned antenna arrays. Since the SVDs for partial matrices of the channel may lose the characteristics of the original unpartitioned matrix, we explain an appropriate scheme to cope with this problem.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.279-286
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• 2006

One of the widely used methods for product recommendation in Internet storefronts is matching product features against target customer profiles. When using this method, it's very important to choose a suitable subset of features for recommendation efficiency and performance, which, however, has not been rigorously researched so far. In this paper, we utilize a dataset collected from a virtual shopping experiment in a Korean Internet book shopping mall to compare several popular methods from other disciplines for selecting features for product recommendation: the vector-space model, TFIDF(Term Frequency-Inverse Document Frequency), the mutual information method, and the singular value decomposition(SVD). The application of SVD showed the best performance in the analysis results.

• Korean Journal of Optics and Photonics
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• v.6 no.3
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• pp.178-187
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• 1995

The method that determines the appropriate damping factor is studied for a lens design. When suitable damping factor is applied to the additive damped least-squares (DLS) method, the convergence and the stability of the optimization process are examined in a triplet-type photographic lens design. We calculate eigenvalues of the product of the Jacobian matrix of error functions by using the singular value decomposition (SVD) method. We adopt the median of eigenvalues as an appropriate damping factor. The convergence and the stability of the optimization process are improved by choosing the adequate damping factor for the optimization of a photographic lens. It is known that the numerical inaccuracy in the calculation of normal equation is overcome by using the orthogonal transformations of the Jacobian matrix. Therefore, a combination of the method for setting a proper damping factor and the orthogonal transformations of the Jacobian matrix is good for application to the design of an aspheric lens with high-order terms. terms.

• 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.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1628-1639
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• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• 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.