• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.298-299
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• 2014

The solution of transient response of complex stiffness system was obtained using a green function of this system. To derive the green function, governing equation of this systems was expressed in Steady Space and solved by the diagonalization. The solution of this system are written as a convolution integral form. The result that are calculated by the numerical integration process for transient responses was showed properly.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.3
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• pp.192-199
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• 2018

This paper proposes an improved discrete Fourier transform spread single sideband(DFT-s-SSB) orthogonal frequency division multiplexing(OFDM) system that solves the problems of conventional DFT-s-SSB OFDM systems. Conventional DFT-s-SSB systems use pulse amplitude modulation(PAM) for applying SSB modulation. The higher the modulation level, the worse is the BER performance. Further, transmission is possible only through the lower sideband(LSB) spectrum. When transmitting using the LSB and upper sideband(USB) spectra simultaneously, interference occurs and spectrum recovery is not performed correctly. To solve this problem, the proposed system applies the 2/3 convolution coding to improve the bit error rate(BER) performance, adjusts the DFT size, and selects the USB spectrum to utilize the remaining spectrum resources. In addition, when using this system in an environment that supports multiuser or limited bandwidth, it uses only half of the spectrum; therefore, it can utilize the remaining spectrum resources and improve the spectral efficiency.

• Economic and Environmental Geology
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• v.27 no.1
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• pp.41-47
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• 1994

A fast Hankel transform (FHT) algorithm (Anderson, 1982) is applied to numerical evaluation of many Green's tensor integrals encountered in three-dimensional electromagnetic modeling using integral equations. Efficient computation of Hankel transforms is obtained by a combination of related and lagged convolutions which are available in the FHT. We express Green's tensor integrals for a layered half-space, and rewrite those to a form of related functions so that the FHT can be applied in an efficient manner. By use of the FHT, a complete or full matrix of the related Hankel transform can be rapidly and accurately calculated for about the same computation time as would be required for a single direct convolution. Computing time for a five-layer half-space shows that the FHT is about 117 and 4 times faster than conventional direct and multiple lagged convolution methods, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.63-74
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• 2007

Fast Fourier transform(FFT) is powerful, fast computation framework for convolution in many image restoration application. However, an actually observed image acquired with finite aperture of the acquisition device from the infinite background and it lost data outside the cropped region. Because of these the boundary artifacts are produced. This paper reviewed and summarized the up to date the techniques that have been applied to reduce of the boundary artifacts. Moreover, we propose a new block-based fast image restoration using combined extrapolation and edge-tapering without boundary artifacts with reduced computational loads. We apply edgetapering to the inner blocks because they contain outside information of boundary. And outer blocks use half-convolution extrapolation. For this process it is possible that fast image restoration without boundary artifacts.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.205-208
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• 2022

Multi-exposure high dynamic range (HDR) image reconstruction, the task of reconstructing an HDR image from multiple low dynamic range (LDR) images in a dynamic scene, often produces ghosting artifacts caused by camera motion and moving objects and also cannot deal with washed-out regions due to over or under-exposures. While there has been many deep-learning-based methods with motion estimation to alleviate these problems, they still have limitations for severely moving scenes. They also require large parameter counts, especially in the case of state-of-the-art methods that employ attention modules. To address these issues, we propose a frequency domain approach based on the idea that the transform domain coefficients inherently involve the global information from whole image pixels to cope with large motions. Specifically we adopt Residual Fast Fourier Transform (RFFT) blocks, which allows for global interactions of pixels. Moreover, we also employ Depthwise Overparametrized convolution (DO-conv) blocks, a convolution in which each input channel is convolved with its own 2D kernel, for faster convergence and performance gains. We call this LFFNet (Lightweight Frequency Fusion Network), and experiments on the benchmarks show reduced ghosting artifacts and improved performance up to 0.6dB tonemapped PSNR compared to recent state-of-the-art methods. Our architecture also requires fewer parameters and converges faster in training.

• Journal of the Korean earth science society
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• v.25 no.2
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• pp.109-114
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• 2004

A wavelet method is applied to the analysis of gravity potential. One scaling function is proposed to generate wavelet. The scaling function is shown to be replaced to the Green’s function in gravity potential. The upward continuation can be expressed as a wavelet transform i.e. convolution with the scaling function. The scaling factor indicates the height variation. The multiscale edge detection is carried by connecting the local maxima of the wavelet transform at scales. The multiscale edge represents discontinuity of the geological structure. The multiscale edge method is applied to gravity data from Masan and Changwon.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.3
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• pp.126-131
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• 2014

In this study, QRS complex is detected by Wavelet Transform and it can be worked in 32bit fixed point operation thought optimization. First, ECG signal is passed though band pass filter. Second, it is transformed using one-band combined wavelet function from 3-band wavelet function. Third, it is passed though moving window integral. Finally, QRS complex is detected by decision rule. The proposed algorithm is evaluated using MIT-BIH arrhythmia database. Its all of process make progress 32-bit fixed-point operation and it makes table that high complexity operations like trigonometrical function. The detection algorithm evaluate through computer simulation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.1-11
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• 2023

In this paper, instead of using recurrent neural network, we compare a classification performance of time series imaging algorithms using convolution neural network. There are traditional algorithms that imaging time series data (e.g. GAF(Gramian Angular Field), MTF(Markov Transition Field), RP(Recurrence Plot)) in TSC(Time Series Classification) community. Furthermore, we compare STFT(Short Time Fourier Transform) algorithm that can acquire spectrogram that visualize feature of voice data. We experiment CNN's performance by adjusting hyper parameters of imaging algorithms. When evaluate with GunPoint dataset in UCR archive, STFT(Short-Time Fourier transform) has higher accuracy than other algorithms. GAF has 98~99% accuracy either, but there is a disadvantage that size of image is massive.

• Journal of The Korean Association of Information Education
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• v.15 no.3
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• pp.517-524
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• 2011

This paper presents a fast blind deconvolution method that produces a deblurring result from a single image in only a few seconds. The high speed of our method is enabled by considering the Discrete Fourier Transform (DFT), and its relation to filtering and convolution, and fast computation of Moore-Penrose inverse matrix. How can we predict the behavior of an arbitrary filter, or even more to the point design a filter to achieve certain specifications. The idea is to study the frequency response of the filter. This concept leads to an useful convolution formula. A Matlab implementation of our method usually takes less than one minute to deblur an image of moderate size, while the deblurring quality is comparable.