• 한국생산제조학회지
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• 제8권1호
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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.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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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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• 제3A권4호
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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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• 제6권5호
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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.

• 정보처리학회논문지B
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• 제11B권2호
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• pp.177-186
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• 2004

비디오 압축에 사용되는 움직임 예측은 많은 계산과정을 요구하기 때문에 전체적인 부호기 복잡도를 높이는 단점을 지닌다. 이러한 부호기의 복잡도를 줄이기 위해 3D-WT과 같은 움직임 예측을 사용하지 않는 연구들이 소개되고 있다. 하지만, 기존의 3D-WT 기법들은 부호화를 위한 과도한 메모리 요구사항과 복호를 위한 수신 측의 지연시간이 가장 큰 단점으로 지적되었다. 본 논문에서는 수정된 Haar wavelet filter와 개선된 부호화 알고리즘을 통해서 메모리 사용량과 재생을 위한 지연시간을 최소로 하는 확장 가능한 3D-WT 기법인 FS(Fast playable and Scalable) 3D-WT를 소개한다. 3D-WT 중 가장 개선된 형태인 3D-V 기법과의 실험 결과 3D-V와 거의 비슷한 계산 처리 시간으로 높은 압축률과 수신 측에서의 짧은 지연시간을 보였다.

• 전자공학회논문지B
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• 제32B권11호
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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.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2000년도 학술발표대회 논문집 제19권 2호
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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.

• 전기학회논문지P
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• 제64권1호
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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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• 제24권7호
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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.

• 한국컴퓨터정보학회논문지
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• 제29권10호
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• pp.21-34
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• 2024

MRI(Magnetic Resonance Imaging) 영상과 CT(Computed Tomography) 영상과 같은 의료영상에서 잡음제거는 의료영상 시스템의 성능에 중요한 영향을 미친다. 최근 영상처리 기술에 딥러닝(Deep Learning)의 도입으로 잡음제거 방법들의 성능이 향상되고 있다. 그러나 영상영역에서 디테일을 보존하면서 잡음만을 제거하는 것은 한계가 있다. 본 논문에서는 웨이블렛 변환 기반 CNN(Convolutional Neural Network) 모형, 즉 WT-DnCNN(Wavelet Transform-Denoising Convolutional Neural Network) 모형을 통해 잡음제거 성능을 높이고자 한다. 이는 잡음 영상에 웨이블렛 변환을 사용하여 주파수 대역별로 구분하여 일차적으로 잡음을 제거하고, 해당 주파수 대역에서 기존 DnCNN 모형을 적용하여 최종적으로 잡음을 제거하고자 한다. 본 논문에서 제안된 WT-DnCNN 모형의 성능평가를 위해 다양한 잡음, 즉, 가우시안 잡음(Gaussian Noise), 포아송 잡음(Poisson Noise) 그리고 스펙클 잡음(Speckle Noise)에 의해 훼손된 MRI 영상과 CT 영상을 대상으로 실험하였다. 성능 실험 결과, WT-DnCNN 모형은 정성적 비교에서 전통적인 필터 즉, BM3D(Block-Matching and 3D Filtering) 필터뿐만 아니라 기존의 딥러닝 모형인 DnCNN, CDAE(Convolution Denoising AutoEncoder) 모형보다 우수하고, 정량적 비교에서 PSNR(Peak Signal-to-Noise Ratio) 과 SSIM(Structural Similarity Index Measure) 수치는 MRI 영상에서 각각 36~43과 0.93~0.98, CT 영상에서 각각 38~43과 0.95~0.98 정도로 우수한 결과를 보였다. 또한, 모형의 실행 속도 비교에서 DnCNN 모형은 BM3D 모형보다는 훨씬 적게 결렸으나 DnCNN 모형과의 비교에서는 웨이블렛 변환 추가로 인해 오래 걸림을 알 수 있었다.