• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.135-141
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• 1999

Recently, advance signal analysis which is called "Time-Frequency Analysis" has been developed. Wavelet and Wigner Distribution are used to the method. Wavelet transform(WT) is applied to time-frequency analysis of waveforms obtained by an ultrasonic pulse-echo technique. The Gabor function is adopted as the analyzing wavelet. Wavelet analysis method is an attractive technique for evolution of material characterization evoluation. In this paper, the feasibility of suppression of ultrasonic background noise signal using WT has been presented. These results suggest that ultrasonic background noise ginal can be suppressed and enhanced even for SNR of 20.8 dB. This property of the WT is extremely useful for the detecting flaw echos embedded in background noise.und noise.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.347-354
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• 1996

This paper presents digital hearing aids signal processing system in WT(wavelet transform) domain. For implementation of hearing aids in WT domain, the gain in frequency domain is approximated in WT domain. We also present the gain selection algorithm to deal with the change of input signal power. Most transform methods produce blocking effect, and this effect degrades the convergence rate of feedback canceller. As a solution, we proposed wavelet transform bascd feedback canceller. To evaluate the performance, we compared it with LOT (lapped orthogonal transform) method in the frequency domain. This system has not shown the blocking effect, and improves convergence rate as compared with the LOT based feedback canceller.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.214-221
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• 2003

In view of the fact that gas insulated switchgear (GIS) is an important piece of equipment in a substation, it is highly desirable to continuously monitor the state of equipment by measuring the partial discharge (PD) activity in a GIS, as PD is a symptom of an insulation weakness/breakdown. However, since the PD signal is relatively weak and the external noise makes detection of the PD signal difficult, it therefore requires careful attention in its detection. In this paper, the algorithm for detection of PD in the GIS using the wavelet transform (WT) is proposed. The WT provides a direct quantitative measure of the spectral content and dynamic spectrum in the time-frequency domain. The most appropriate mother wavelet for this application is the Daubechies 4 (db4) wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, is very well suited to detecting high frequency signals of very short duration, such as those associated with the PD phenomenon. The proposed algorithm is based on utilizing the absolute sum value of coefficients, which are a combination of D1 (Detail 1) and D2 (Detail 2) in multiresolution signal decomposition (MSD) based on WT after noise elimination and normalization.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.177-186
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• 2004

It is a weak point of the motion estimation technique for video compression that the predicted video encoding algorithm requires higher-order computational complexity. To reduce the computational complexity of encoding algorithms, researchers introduced techniques such as 3D-WT that don't require motion prediction. One of the weakest points of previous 3D-WT studies is that they require too much memory for encoding and too long delay for decoding. In this paper, we propose a technique called `FS (Fast playable and Scalable) 3D-WT' This technique uses a modified Haar wavelet transform algorithm and employs improved encoding algorithm for lower memory and shorter delay requirement. We have executed some tests to compare performance of FS 3D-WT and 3D-V. FS 3D-WT has exhibited the same high compression rate and the same short processing delay as 3D-V has.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1383-1392
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• 1995

In this paper, the method to incorporate TCVQ(Trellis Copded Vector Quantizer) into the encoding of the wavelet trans formed(WT) image followed by a variable length coding(VLC) or an entropy coding(EC) is considered. By WT, an original image is separated into 10 bands with various resolutions and directional components. TCVQ used to compress these WT coefficients is a finite state machine that encodes the input source on the basis of the current input and the current state. Wavelet basis used in this paper is designed by orthogonal spline function. A modified set partitioning algorithm to Wang's is also presented. A simple modification to Wang's algorithm gives a highly time-efficient result. Proposed WT-TCVQ encoder shows a very competitive result, giving 37.46dB in PSNR at 1.002bpp when encoding 512$\times$512 LENA.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.197-200
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• 2000

This paper presents a time-based Pitch Determination Algorithm (PDA) for reliable estimation of pitch Period (PP) in speech signal. In proposed method, we use the Dyadic Wavelet Transform (DyWT), which detects the presence of Glottal Closure Instants (GCI) and uses the information to determine the pitch period. And, the proposed method also uses the periodicity property of DyWT to detect unsteady GCI. To evaluate the performance of the proposed methods, that of other PDAs based on DyWT are compared with what this paper proposed. The effectiveness of the proposed method is tested with real speech signals containing a transition between voiced and the unvoiced interval where the energy of voiced signal is unsteady. The result shows that the proposed method provides a good performance in estimating the both the unsteady GCI positions as well as the steady parts.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.1
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• pp.35-40
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• 2015

Generators are the most complex and expensive single element in a power system. The generator protection relays should to minimize damage during fault states and must be designed for maximum reliability. A conventional CDR(Current Differential Relaying) technique based on DFT(Discrete Fourier Transform) filter have the disadvantages that the time information can lead to loss in the process of converting the signal from the time domain to the frequency domain. A WT(Wavelet transform) and WT analysis is known that it is possible with the local analysis of the fault and transient signal. In this paper, to overcome the defects in the DFT process, an application of WT for fault detection of generator is presented. This paper describes an selection of mother Wavelet to detect faults of generator. Using collected data from the fault simulation with ATPdraw, we analyzed the several mother Wavelet through the course of MLD(multi-level decomposition) using MATLAB software. Finally, it can be seen that the proposed technique using detail coefficient of Daubechies level 2 which can be fault discriminant of generator.

• Bulletin of the Korean Chemical Society
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• v.24 no.7
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• pp.971-974
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• 2003

By utilizing singular value decomposition (SVD) and shift averaged Harr wavelet transform (WT) with a set of Daubechies wavelet coefficients (1/2, -1/2), a method that can simultaneously eliminate an unwanted large solvent peak and noise peaks from NMR data has been developed. Noise elimination was accomplished by shift-averaging the time domain NMR data after a large solvent peak was suppressed by SVD. The algorithms took advantage of the WT, giving excellent results for the noise elimination in the Gaussian type NMR spectral lines of NMR data pretreated with SVD, providing superb results in the adjustment of phase and magnitude of the spectrum. SVD and shift averaged Haar wavelet methods were quantitatively evaluated in terms of threshold values and signal to noise (S/N) ratio values.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.21-34
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• 2024

In medical images such as MRI(Magnetic Resonance Imaging) and CT(Computed Tomography) images, noise removal has a significant impact on the performance of medical imaging systems. Recently, the introduction of deep learning in image processing technology has improved the performance of noise removal methods. However, there is a limit to removing only noise while preserving details in the image domain. In this paper, we propose a wavelet transform-based CNN(Convolutional Neural Network) model, namely the WT-DnCNN(Wavelet Transform-Denoising Convolutional Neural Network) model, to improve noise removal performance. This model first removes noise by dividing the noisy image into frequency bands using wavelet transform, and then applies the existing DnCNN model to the corresponding frequency bands to finally remove noise. In order to evaluate the performance of the WT-DnCNN model proposed in this paper, experiments were conducted on MRI and CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. The performance experiment results show that the WT-DnCNN model is superior to the traditional filter, i.e., the BM3D(Block-Matching and 3D Filtering) filter, as well as the existing deep learning models, DnCNN and CDAE(Convolution Denoising AutoEncoder) model in qualitative comparison, and in quantitative comparison, the PSNR(Peak Signal-to-Noise Ratio) and SSIM(Structural Similarity Index Measure) values were 36~43 and 0.93~0.98 for MRI images and 38~43 and 0.95~0.98 for CT images, respectively. In addition, in the comparison of the execution speed of the models, the DnCNN model was much less than the BM3D model, but it took a long time due to the addition of the wavelet transform in the comparison with the DnCNN model.