• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.871-874
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• 1998

It is most important in small signal stability analysis of large scale power systems to compute only the dominant eigenvalues selectively with numerical stability and efficiency. Hessenberg process is numerically very stable and identifies the largest eigenvalues in magnitude. Hence, transformed system matrix must be used with the process. Inverse transformation with complex shift provides high selectivity centered on the shift, but does not possess the desired property of computing the dominant mode first. Thus, advantage of high selectivity of the transformation can be fully utilized only when the complex shift is given close to the dominant eigenvalues. In this paper, complex shift is determined by Fourier transforming the results of dynamic simulation with PTI's PSS/E transient simulation program. The convergence in Hessenberg process is accelerated using the iterative scheme. Overall, a numerically stable and very efficient small signal stability program is obtained. The stability and efficiency of the program has been validated against New England 10-machines 39-bus system and KEPCO system.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.1
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• pp.1-8
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• 2014

Digital watermarking techniques are attracting attention as a proper solution to protect copyright for multimedia data. This paper proposes a new audio watermarking method based on Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) for copyright protection. In our proposed watermarking method, the original audio is transformed into DCT domain and divided into two parts. Synchronization code is applied on the signal in first part and 2 levels DWT domain is applied on the signal in second part. The absolute value of DWT coefficient is divided into arbitrary number of segments and calculates the energy of each segment and middle peak. Watermarks are then embedded into each middle peak. Watermarks are extracted by performing the inverse operation of watermark embedding process. Experimental results show that the hidden watermark data is robust to re-sampling, low-pass filtering, re-quantization, MP3 compression, cropping, echo addition, delay, and pitch shifting, amplitude change. Performance analysis of the proposed scheme shows low error probability rates.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Polymer(Korea)
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• v.33 no.2
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• pp.183-188
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• 2009

Structural rearrangements of uni-axially deformed polyethylenes containing 1-octene comonomer and HDPE upon heating were investigated by time-resolved small and wide angle X-ray scattering techniques. During heating, structural changes including crystal transformation and lamellar rearrangement noted were very different depending on the comonomer contents. At low comonomer content below 2 wt%, inverse martensitic transformation of crystal lattice from monoclinic to orthorhombic cell and the rearrangement of broken lamellar units into more ordered and perfect lamellar stacks were noted with the temperature increase. At high contents above 9.5 wt%, however, polyethylene copolymers showed neither the crystal transformation nor lamellar rearrangement that can be attributed to low crystallinity and high content of branch units.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.2
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• pp.41-48
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• 2023

In this study, simplified linear numerical method that can simulate wave generation and transformation by a moving bottom is introduced. Numerical analysis is conducted in wave number domain after continuity equation, linear dynamic and kinematic free surface boundary conditions and linear kinematic bottom boundary condition are Fourier transformed, and the results are expressed in space domain by an inverse Fourier transform. In the wavenumber domain, the dynamic free water surface boundary condition and the kinematic free water surface boundary condition are numerically calculated, and the velocity potential in the mean water level (z = 0) satisfies the continuity equation and the kinematic bottom boundary condition. Wave generation and transformation are investigated when the triangular and rectangular shape of bottoms move periodically. The results of the simplified numerical method are compared with the results of previous analytical solutions and agree well with them. Stability of numerical results according to the calculation time interval (Δt) and the calculation wave number interval (Δk) was also investigated. It was found that the numerical results were appropriate when Δt ≤ T(period)/1000 and Δk ≤ π/100.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.1-8
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• 2000

A spectrum minimizing the frequency-domain Kullback-Leibler information number has been proposed and used to modify a spectrum estimate. Some numerical examples have illustrated the KL spectrum estimate is superior to the initial estimate, i.e., the autocovariances obtained by the inverse Fourier transformation of the KL spectrum estimate are closer to the sample autocovariances of the given observations than those of the initial spectrum estimate. Also, it has been shown that a Gaussian autoregressive process associated with the KL spectrum is the closest in the timedomain Kullback-Leibler sense to a Gaussian white noise process subject to given autocovariance constraints. In this paper a corresponding conditional probability theorem is presented, which gives another rationale to the KL spectrum.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.221-229
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• 2004

The Continuous Wavelets Transform project signal f(t) to "Time-scale"plan utilizing the time varied function which called "wavelets". This Transformation permit to analyze scale time dependence of signal f(t) thus the local or global scale properties can be extracted. Moreover, the signal f(t) can be reconstructed stably by utilizing the Inverse Continuous Wavelets Transform. In this paper, the EEG signal is analyzed by wavelets coherence method and the De-noising procedure is represented.

• Kyungpook Mathematical Journal
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• v.59 no.4
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• pp.821-834
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• 2019

The reductivity of a spherical curve is the minimal number of times a particular local transformation called an inverse-half-twisted splice is required to obtain a reducible spherical curve from the initial spherical curve. It is unknown if there exists a spherical curve whose reductivity is four. In this paper, an unavoidable set of configurations for a spherical curve with reductivity four is given by focusing on 5-gons. It has also been unknown if there exists a reduced spherical curve which has no 2-gons and 3-gons of type A, B and C. This paper gives the answer to this question by constructing such a spherical curve.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.10
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• pp.42-50
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• 2005

Based upon the derived analytical model and equation of motion for the general rotor system with anisotropic stator and asymmetric rotor treated as a periodically time-varying system, the series of equations are structured by associating with the time modulated coefficients. The frequency response functions (FRFs) expressed by physical parameters are derived in such a convenient way from the direct inverse matrices of the Fourier transformation of those series of equations, from which the characteristics are analyzed and the properties are suggested.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.1
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• pp.65-73
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• 2007

Pattern classification of single and multiple discharge sources was applied using a wavelet image signal method in which a feature extraction was applied using a hidden sub-image. A feature extracting method that used vertical and horizontal images using an MSD method was applied to an averaging process for the scale of pulses for the phase. A feature extracting process for the preprocessing of the input of a neural network was performed using an inverse transformation of the horizontal, vertical, and diagonal sub-images. A back propagation algorithm in a neural network was used to classify defective signals. An algorithm for wavelet image processing was developed. In addition, the defective signal was classified using the extracted value that was quantified for the input of a neural network.