• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.102-108
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• 2007

The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This study introduces a technique to detect and identify faults in induction motors. Stator currents were measured and stored by time domain. The time domain is not suitable for representing current signals, so wavelet transform is used to convert the signal; onto frequency domain. The raw signals can not show the significant feature, therefore difference values are applied. The difference values were transformed by wavelet transform and the features are extracted from the transformed signals. The dynamic time warping method was used to identify the four fault types. This study describes the results of detecting fault using wavelet analysis.

• 전기학회논문지
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• 제60권12호
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• pp.2333-2338
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• 2011

In this paper, we develope a wavelet transform based time-frequency domain reflectometry (WTFDR) for the fault localization of underground power cable. The conventional TFDR (CTFDR) is more accurate than other reflectometries to localize the cable fault. However, the CTFDR has some weak points such as long computation time and hard implementation because of the nonlinearity of the Wigner-Ville distribution used in the CTFDR. To solve the problem, we use the complex wavelet transform (CWT) because the CWT has the linearity and the reference signal in the TFDR has a complex form. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for various fault types of the underground power cable.

• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.763-768
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• 2011

Image denoising is a lively research field. Today the researches are focus on the wavelet domain especially using wavelet threshold method. We proposed an adaptive threshold method which considering the characteristic of different sub-band, the method is adaptive to each sub-band. Experiment results show that the proposed method extracts white Gaussian noise from original signals in each step scale and eliminates the noise effectively. In addition, the method also preserves the detail information of the original image, obtaining superior quality image with higher peak signal to noise ratio(PSNR).

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.31-34
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• 2000

In this paper, we proposed multispectral image compression method using CIP (classified inter-channel prediction) and SVQ (selective vector quantization) in wavelet domain. First, multispectral image is wavelet transformed and classified into one of three classes considering reflection characteristics of the subband with the lowest resolution. Then, for a reference channel which has the highest correlation with other channels, the variable VQ is performed in the classified intra-channel to remove spatial redundancy. For other channels, the CIP is performed to remove spectral redundancy. Finally, the prediction error is reduced by performing SVQ. Experiments are carried out on a multispectral image. The results show that the proposed method reduce the bit rate at higher reconstructed image quality and improve the compression efficiency compared to conventional method.

• 전자공학회논문지
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• 제51권2호
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• pp.156-160
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• 2014

Thresholding in frequency domain is a simple and effective noise reduction technique. Determination of the threshold is critical to the image quality. The optimal threshold minimizing the Mean Square Error (MSE) is chosen adaptively in the wavelet domain; we utilize an equation of the MSE for the soft-thresholded signal and the histogram of wavelet coefficients of the original image and noisy image. The histogram of the original signal is estimated through the deconvolution assuming that the probability density functions (pdfs) of the original signal and the noise are statistically independent. The proposed method is quite general in that it does not assume any prior for the source pdf.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 C
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• pp.1679-1681
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• 2001

In this paper, As the wavelet transform has the properties of multi-resolution analysis and time-frequency domain localization, application of wavelet transform is used at partial discharge(PD) signal detected by electromagnetic wave detection method to extract PD signal's various frequency component and its time domain. therefore we can analyzed PD signal's time-frequency domain simultaneously. On the other hand, using wavelet transform denoising process, inclued noise signal in detected PD signal is well elimiated. we can propose the true shape of PD signal.

• Journal of Welding and Joining
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• 제17권6호
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• pp.100-107
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• 1999

In this paper, we deal with the speckle noise reduction and parameter estimation of ultrasonic NDT(non-destructive test) signals obtained during weld inspection of piping. The overall approach consists of three major steps, namely, speckle noise analysis, proposition of wavelet domain AR(autoregressive) model and flaw detection by proposed model parameter. The data are first processed whereby signals obtained using vertical and angle beam transducer. Correlation properties of speckle noise are then analyzed using multiresolution analysis in wavelet domain. The parameter estimation curve obtained using the proposed model is classified a flaw in weld region where is contaminated by severe speckle noise and also clear flaw signal is obtained through CA-CFAR threshold estimator that is a nonlinear post-processing method for removing the noise from reconstructed ultrasonic signal.

• 조명전기설비학회논문지
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• 제15권4호
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• pp.62-68
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• 2001

본 논문은 전기적 검출법에 의해 측정된 부분방전 신호에 대하여 웨이브렛 변환을 적용하여 각기 다른 주파수 성분을 동시에 추출하고 이들의 시간정보를 얻음으로써 시간과 주파수 영역에서 동시에 해석할 수 있도록 하였다. 그리고, 웨이브렛 변환의 노이즈 제거기법을 적용하여 부분방전 측정시에 포함되어 있는 노이즈를 제거하여 잡음제거 효과를 나타내었다.

• East Asian Economic Review
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• 제19권2호
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• pp.189-220
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• 2015

Multi-scale representations are effective in characterising the time-frequency characteristics of financial return series. They have the capability to reveal the properties not evident with typical time domain analysis. Given the aforesaid, this study derives crucial insights from multi scale analysis to investigate the co-movements between Indian and emerging Asian equity markets using wavelet correlation and wavelet coherence measures. It is reported that the Indian equity market is strongly integrated with Asian equity markets at lower frequency scales and relatively less blended at higher frequencies. On the other hand the results from cross correlations suggest that the lead-lag relationship becomes substantial as we turn to lower frequency scales and finally, wavelet coherence demonstrates that this correlation eventually grows strong in the interim of the crises period at lower frequency scales. Overall the findings are relevant and have strong policy and practical implications.

• 한국음향학회지
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• 제19권4호
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• pp.3-15
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• 2000

본 논문은 웨이블렛 영역에서 신호성분을 보존하면서 첨부된 잡음성분을 제거할 수 있는 새로운 잡음제거 필터를 제시한다. 적응적 웨이블렛 수축(AWS) 필터라 불리는 제안된 필터는 웨이블렛 제거기와 적응적 수축기의 두 개 연산기로 구성되어 있으며 각각의 연산기는 웨이블렛 계수의 국부적 통계성을 이용하여 적응적으로 추정되는 threshold에 의존하여 선택되는데 웨이블렛 제거기는 threshold보다 작은 웨이블렛 계수들을 0으로 대신하여 웨이블렛 영역에서 잡음을 제거하게 된다. 또한 적응적 수축기는 threshold보다 큰 계수들을 적응적으로 수축하여 신호성분을 보존하면서 잡음성분을 줄이게 된다. 실험 결과, 제안된 필터는 기존의 방법들보다 잡음을 제거하면서 신호성분을 보존하는데 더욱 효과적임을 보여준다.