• 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.

• 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.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.49-50
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• 2011

본 논문에서는 WT(Wavelet Transform)와 SVD(Singular Value Decomposition)를 함께 사용한 WSVD(Wavelet Singular Value Decomposition)를 이용하여 발전기 탈락 시의 전압 변동 특성을 분석하였다. WSVD 특성 분석을 위해 부산 지역의 345kV급 송전계통을 EMTP-RV로 모델링하였으며, 이 계통모델에서 발전기 탈락을 모의하였다. MATLAB을 통해 이 때 측정된 전압의 WSVD를 계산하여 발전기 탈락에 따른 특성을 분석하였다.

• Journal of computational fluids engineering
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• v.22 no.1
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• pp.59-66
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• 2017

The guideline of selecting the number of snapshot dataset, $N_s$ in proper orthogonal decomposition(POD) was presented via the analysis of Eigen values based on the singular value decomposition(SVD). In POD, snapshot datasets from the solutions of Euler or Navier-Stokes equations are utilized to SVD and a reduced order model(ROM) is constructed as the combination of Eigen vectors. The ROM is subsequently applied to reconstruct the flowfield data with new set of flow conditions, thereby enhancing the computational efficiency. The overall computational efficiency and accuracy of POD is dependent on the number of snapshot dataset; however, there is no reliable guideline of determining $N_s$. In order to resolve this problem, the order of maximum to minimum Eigen value ratio, O(R) from SVD was analyzed and presented for the decision of $N_s$; in case of steady flow, $N_s$ should be determined to make O(R) be $10^9$. For unsteady flow, $N_s$ should be increased to make O(R) be $10^{11\sim12}$. This strategy of selecting the snapshot dataset was applied to two dimensional NACA0012 airfoil and vortex flow problems including steady and unsteady cases and the numerical accuracies according to $N_s$ and O(R) were discussed.

• 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.

• 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.