• Wind and Structures
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• v.16 no.5
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• pp.517-540
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• 2013

This paper presents applications of proper orthogonal decomposition in both the time and frequency domains based on both cross spectral matrix and covariance matrix branches to analyze multi-variate unsteady pressure fields on prisms and to study spanwise and chordwise pressure distribution. Furthermore, modification of proper orthogonal decomposition is applied to a rectangular spanwise coherence matrix in order to investigate the spanwise correlation and coherence of the unsteady pressure fields. The unsteady pressure fields have been directly measured in wind tunnel tests on some typical prisms with slenderness ratios B/D=1, B/D=1 with a splitter plate in the wake, and B/D=5. Significance and contribution of the first covariance mode associated with the first principal coordinates as well as those of the first spectral eigenvalue and associated spectral mode are clarified by synthesis of the unsteady pressure fields and identification of intrinsic events inside the unsteady pressure fields. Spanwise coherence of the unsteady pressure fields has been mapped the first time ever for better understanding of their intrinsic characteristics.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1841-1851
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• 2018

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.89-101
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• 2007

This paper investigates the effects of the fusion of multifrequency (C and L bands) polarimetric SAR data in land-cover classification. NASA JPL AIRSAR C and L bands data were used to supervised classification in an agricultural area to simulate the integration of ALOS PALSAR and Radarsat-2 SAR data to be available. Several scattering features derived from target decomposition based on eigen value/vector analysis were used as input for a support vector machines classifier and then the posteriori probabilities for each frequency SAR data were integrated by applying a probabilistic ratio model as a decision level fusion methodology. From the case study results, L band data had the proper amount of penetration power and showed better classification accuracy improvement (about 22%) over C band data which did not have enough penetration. When all frequency data were fused for the classification, a significant improvement of about 10% in overall classification accuracy was achieved thanks to an increase of discrimination capability for each class, compared with the case of L band Shh data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2068-2082
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• 2023

With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multistage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.103-109
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• 2015

In this paper, a novel method for the classification of term and preterm birth is proposed based on time-frequency analysis of electrohysterogram (EHG) using multivariate empirical mode decomposition (MEMD). EHG is a promising study for preterm birth prediction, because it is low-cost and accurate compared to other preterm birth prediction methods, such as tocodynamometry (TOCO). Previous studies on preterm birth prediction applied prefilterings based on Fourier analysis of an EHG, followed by feature extraction and classification, even though Fourier analysis is suboptimal to biomedical signals, such as EHG, because of its nonlinearity and nonstationarity. Therefore, the proposed method applies prefiltering based on MEMD instead of Fourier-based prefilters before extracting the sample entropy feature and classifying the term and preterm birth groups. For the evaluation, the Physionet term-preterm EHG database was used where the proposed method and Fourier prefiltering-based method were adopted for comparative study. The result showed that the area under curve (AUC) of the receiver operating characteristic (ROC) was increased by 0.0351 when MEMD was used instead of the Fourier-based prefilter.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.633-638
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• 2011

In this paper, we consider resource allocation with proportional fairness in the downlink orthogonal frequency division multiple access relay networks, in which relay nodes operate in decode-and-forward mode. A joint optimization problem is formulated for relay selection, subcarrier assignment and power allocation. Since the formulated primal problem is nondeterministic polynomial time-complete, we make continuous relaxation and solve the dual problem by Lagrangian dual decomposition method. A near-optimal solution is obtained using Karush-Kuhn-Tucker conditions. Simulation results show that the proposed algorithm provides superior system throughput and much better fairness among users comparing with a heuristic algorithm.

• ETRI Journal
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• v.43 no.1
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• pp.152-162
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• 2021

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.1
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• pp.29-37
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• 2005

One of the principal disadvantages of multicarrier modulation technique is the sensitivity to the frequency offset introduced by Doppler shift. This frequency offset introduces inter-carrier interference (ICI) among the multiplicity of carriers in the multicarrier modulated signal. However, Doppler spread induced by temporal channel variations can Provide another means for diversity. In this paper, we propose a modified multicarrier code division multiple access (CDMA) system to exploit Doppler diversity as well as multipath diversity. The key work of our framework is a canonical time-frequency-based decomposition of the mobile wireless channel into series of independent fading channel. The decomposition naturally leads to a time-frequency generalization of the Rake receiver that exploits both multipath and Doppler diversity.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1500-1511
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• 2017

The purpose of this paper is to study a high-performance control scheme for neutral-point-clamping three-level (NPC-3L) inverter fed dual three-phase permanent magnet synchronous motor (PMSM) drives by considering some asymmetric factors such as the non-identical parameters in phase windings. To implement this, the system model is analyzed for dual three-phase PMSM drives with asymmetric factors based on the vector space decomposition (VSD) principle. Based on the equivalent circuits, PI controllers with feedforward compensation are used in the d-q subspace for regulating torque, where the cut-off frequency of the PI controllers are set at the twice the fundamental frequency for compensating both the additional DC component and the second order component caused by asymmetry. Meanwhile, proportional resonant (PR) controllers are proposed in the x-y subspace for suppressing the possible unbalanced currents in the phase windings. A dual three-phase space vector modulation (DT-SVM) is designed for the drive, and the balancing factor is designed based on the numerical fitting surface for balancing the DC link capacitor voltages. Experimental results are given to demonstrate the validity of the theoretical analysis and the proposed control scheme.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.98-105
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• 2021

Using the frequency-based decomposition, I decompose the consumption growth to explain well-known patterns of stock returns in the Korean market. To be more specific, the consumption growth is decomposed by its half-life of shocks. The component over four years of half-life is called the business-cycle consumption component, and the components with half-lives under four years are short-run components. I compute the long-run and short-run components of stock excess returns as well and use component-by-component sensitivities to price stock portfolios. As a result, the business-cycle consumption risk with half-life of over four years is useful in explaining the cross-section of size-book-to-market portfolios and size-momentum portfolios in the Korean stock market. The short-run components have their own pricing abilities with mixed direction, so that the restricted one short-term factor model is rejected. The explanatory power with short- and long-run components is comparable to that of the Fama-French three-factor model. The components with one- to four-year half-lives are also helpful in explaining the returns. The results about the long-run components emphasize the importance of long-run component in consumption growth to explain the asset returns.