• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.171-182
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• 2015

The general wavelet transform has profitable property in non-stationary signal analysis specially. The tunable Q-factor wavelet transform is a fully-discrete wavelet transform for which the Q-factor Q and the asymptotic redundancy r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The transform is based on a real valued scaling factor and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its over-sampling rate, with modest over-sampling rates being sufficient for the analysis/synthesis functions to be well localized. This paper describes filter design of 2D discrete-time wavelet transform for which the Q-factor is easily specified. With the advantage of this transform, perfect reconstruction filter design and implementation for performance improvement are focused in this paper. Hence, the 2D transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. Therefore, application for performance improvement in multimedia communication field was evaluated.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.181-188
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• 2002

It is well known that double integration of measured acceleration records are very difficult, particularly in the case of measurements on civil engineering structures. The measured accelerations on civil structures usually contain non-gaussian and low-frequency noises as well as acceleration records are non-stationary. For this type of signals, wavelet transform can be useful because of its inherent processing abilities for non-stationary signals. In this paper, the do-noising algorithm using the wavelet transform are slightly extended to process non-gaussian and low frequency noises, using median filter concepts. The example studies show that the integration can be improved using proposed method.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.26-30
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• 2005

A rotor run-up or run-down process provide more useful information for modal analysis than normal operation conditions. A traditional difficulty associated with rotor run-up or run-down analysis is the non-stationary nature of vibration data. This paper compares Short-Time Fourier Transform (STFT) and the wavelets analysis in these non-stationary signal analyses. An Adaptive Wavelet Analysis (AWT) is proposed to analyze these signals. Although simulations and experiments in a simple rotor-bearing system show that both STFT and AWT can be used to analyze non-stationary vibration signals in rotor dynamics, proposed AWT provides better results than STFT analysis. From the amplitude-frequency curve obtained by AWT, the modal frequency and damping ratio are calculated. This paper also analyzes the characteristics of signals when the shaft touches the outer hoop in a run-up process. The AWT can give a good result in this complex dynamic analysis of the touching process.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.169-172
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• 1999

Recently, the wavelet transform has been a new and powerful tool for signal processing. It is more suitable specially for the feature extraction and detection of non-stationary signals than traditional methods such as, the Fourier Transform(FT), the Fast Fourier Transform(FFT) and the Least Square Method etc. because of the characteristic of the multi-scale analysis and time-frequency domain localization. The wavelet transform has been developed for the analysis of PD pulse signal to raise in the progress of insulation degradation. In this paper, the wavelet transform was applied to one foundational method for feature extraction. For the obtain experimental data, a computer-aided partial discharge measurement system with a single acoustic sensor was used. If we are applying to the neural network method the accumulated data through the extracted feature, it is expected that we can detect the PD pulse signal in the insulation materials on the on-line.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.827-832
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• 2003

There has been a number methods for the presentation of time-frequency analysis of non-stationary signal. In this paper, STFT(short time Fourier transform), wavelet transform, Wigner distribution, and higher order Wigner distribution are discussed in details with simulation signals. They are also applied to the analysis of the wave propagation of a semi finite beam. Wigner distribution and higher order Wigner distribution have good time-frewuency resolutions. Wavelet transform is required for impact analysis but should be applied carefully. STFT suffers from time-frequency resolutions. Each method is has its advantage and disadvantage depending on each application signals.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.26-32
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• 2007

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform(WFT or STFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform(WT) is used to decompose the acoustic emission(AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.342-348
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• 2005

This study was carried out fusion-welded butt joints in SWS 490A high strength steel subjected to tensile test that load-deflection curve. The windowed or short-time Fourier transform (WFT or SIFT) makes possible for the analysis of non-stationary or transient signals into a joint time-frequency domain and the wavelet transform (WT) is used to decompose the acoustic emission (AE) signal into various discrete series of sequences over different frequency bands. In this paper, for acoustic emission signal analysis to use a continuous wavelet transform, in which the Gabor wavelet base on a Gaussian window function is applied to the time-frequency domain. A wavelet transform is demonstrated and the plots are very powerful in the recognition of the acoustic emission features. As a result, the technique of acoustic emission is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1181-1196
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• 2011

Image fusion is the process of combining information from two or more source images of a scene into a single composite image with application to many fields, such as remote sensing, computer vision, robotics, medical imaging and defense. The most common wavelet-based fusion is discrete wavelet transform fusion in which the high frequency sub-bands and low frequency sub-bands are combined on activity measures of local windows such standard deviation and mean, respectively. However, discrete wavelet transform is not translation-invariant and it often yields block artifacts in a fused image. In this paper, we propose a robust image fusion based on the stationary wavelet transform to overcome the drawback of discrete wavelet transform. We use the activity measure of interquartile range as the robust estimator of variance in high frequency sub-bands and combine the low frequency sub-band based on the interquartile range information present in the high frequency sub-bands. We evaluate our proposed method quantitatively and qualitatively for image fusion, and compare it to some existing fusion methods. Experimental results indicate that the proposed method is more effective and can provide satisfactory fusion results.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4E
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• pp.161-166
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• 2003

We present an adaptive wavelet packet based speech enhancement method with robust voice activity detection (VAD) in non-stationary noise environment. The proposed method can be divided into two main procedures. The first procedure is a VAD with adaptive wavelet packet transform. And the other is a speech enhancement procedure based on the proposed VAD method. The proposed VAD method shows remarkable performance even in low SNRs and non-stationary noise environment. And subjective evaluation shows that the performance of the proposed speech enhancement method with wavelet bases is better than that with Fourier basis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.12
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• pp.561-566
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• 2005

This paper introduces a compression technique for power qualify disturbance signal via discrete wavelet transform(DWT). The proposed approach is based on a previous estimation of the stationary component of power quality disturbance signal, so that it could be subtracted from the original signal in order to reduce a dynamic range of signal and generate transient events signal, which is subsequently applied to the compression technique. The compression techniques is performed through the difference signal decomposition, thresholding of wavelet coefficients, and signal reconstruction. It presents the relation between compression efficiency and threshold. It shouts that the wavelet transform leads to a power quality data compression approach with high compression efficiency, small compression error and good de-nosing effect.