• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.573-579
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• 2001

Wavelet thresholding is a method for he reduction of noise in image. Wavelet coefficients of image are correlated in local characterization. Thee correlations also appear in he original pixel representation of the image, and they do not follow from the characterizations of the wavelet transform. In this paper, we compare noise-free distributions of Bayes approach to improve the classical threshold algorithm.

• 한국음향학회지
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• 제15권1호
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• pp.87-92
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• 1996

수중에서 발생하는 천이 신호는 강한 비정재성을 갖고 다양한 천이 신호원이 함께 존재하기 때문에 분석 및 식별에 어려움이 있다. 본 논문에서는 디지털 신호처리 기법을 천이 신호의 분석에 적용하여 특징벡타를 추출하는 기법에 대하여 논하고 기존의 고전적인 방법보다 더 좋은 인식률을 얻을 수 있는 wavelet 변환을 이용한 특징벡타 추출 방법을 제안한다. 모의실험을 통하여 제안된 방법이 고전적이 방법보다 더 적은 특징 벡타 수로도 좋은 성능을 보임을 확인한다. 특히, Daubechies 계수를 필터계수로 하는 PR-QMF wavelet 변환을 이용한 특징벡타 추출 방법은 구현방법이 용이하고 잡음 환경 하에서도 우수한 성능을 보인다.

• Smart Structures and Systems
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• 제15권1호
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• pp.119-133
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• 2015

This paper proposed a structural time-varying damage detection method by using synchrosqueezing wavelet transform. The instantaneous frequencies of a structure with time-varying damage are first extracted using the synchrosqueezing wavelet transform. Since the proposed synchrosqueezing wavelet transform is invertible, thus each individual component can be reconstructed and the modal participation factor ratio can be extracted based on the amplitude of the analytical signals of the reconstructed individual components. Then, the new time-varying damage index is defined based on the extracted instantaneous frequencies and modal participation factor ratio. Both free and forced vibrations of a classical Duffing nonlinear system and a simply supported beam structure with abrupt and linear time-varying damage are simulated. The proposed synchrosqueezing wavelet transform method can successfully extract the instantaneous frequencies of the damaged structures under free vibration or vibration due to earthquake excitation. The results also show that the defined time-varying damage index can effectively track structural time-varying damage.

• 한국전기전자재료학회논문지
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• 제14권1호
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• 전기학회논문지P
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• 제60권2호
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• pp.76-81
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• 2011

Power system frequency is the main index of power quality indicating an abnormal state and disturbances of systems. The nominal frequency is deviated by sudden change in generation and load or faults. Power system is used as frequency relay to detection for off-nominal frequency operation and connecting a generator to an electrical system, and V/F relay to detection for an over-excitation condition. Under these circumstances, power system should maintain the nominal frequency. And frequency and frequency deviation should accurately measure and quickly estimate by frequency measurement device. The well-known classical method, frequency estimation technique based on the DFT, could be produce the gain error in accuracy. To meet the requirements for high accuracy, recently Wavelet transforms and analysis are receiving new attention. The Wavelet analysis is possible to calculate the time-frequency analysis which is easy to obtain frequency information of signals. However, it is difficult to apply in real-time implementation because of heavy computation burdens. Nowadays, the computational methods using the Wavelet function and transformation techniques have been searched on these fields. In this paper, we apply the Recursive Discrete Wavelet Transform (RDWT) for the frequency estimation. In order to evaluate performance of the proposed technique, the user-defined arbitrary waveforms are used.

• ETRI Journal
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• 제28권1호
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• pp.51-58
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• 2006

In this paper, we propose a method of applying a lifting-based wavelet domain e-median filter (LBWDEMF) for image restoration. LBWDEMF helps in reducing the number of computations. An e-median filter is a type of modified median filter that processes each pixel of the output of a standard median filter in a binary manner, keeping the output of the median filter unchanged or replacing it with the original pixel value. Binary decision-making is controlled by comparing the absolute difference of the median filter output and the original image to a preset threshold. In addition, the advantage of LBWDEMF is that probabilities of encountering root images are spread over sub-band images, and therefore the e-median filter is unlikely to encounter root images at an early stage of iterations and generates a better result as iteration increases. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters, then applies an e-median filter in the wavelet domain, transforms the result into the spatial domain, and finally goes through one spatial domain e-median filter to produce the final restored image. Moreover, in order to validate the effectiveness of the proposed method we compare the result obtained using the proposed method to those using a spatial domain median filter (SDMF), spatial domain e-median filter (SDEMF), and wavelet thresholding method. Experimental results show that the proposed method is superior to SDMF, SDEMF, and wavelet thresholding in terms of image restoration.

• Structural Engineering and Mechanics
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• 제58권6호
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• pp.1087-1107
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• 2016

A method of damage detection based on the moving harmonic excitation and continuous wavelet transforms is presented. The applied excitation is used as a moving actuator and its frequency and speed parameters can be adjusted for an amplified response. The continuous wavelet transforms, CWT, is used for cracks detection based on the resulting amplified signal. It is demonstrated that this identification procedure is largely better than the classical ones based on eigenfrequencies or on the eigenmodes wavelet transformed. For vibration responses, free and forced vibration analyses of multi-cracked beams are investigated based on both analytical and numerical methodological approaches. Cracks are modeled through rotational springs whose compliances are evaluated using linear elastic fracture mechanics. Based on the obtained forced responses, multi-cracks positions are accurately identified and the CWT identification can be highly improved by adjusting the frequency and the speed excitation parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2702-2719
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• 2013

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

• 한국음향학회지
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• 제14권3호
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• pp.71-81
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• 1995

본 논문에서는 수중에서 발생되는 전이 신호의 자동 식별을 위하여 특징벡타를 추출하는 기법과 식별 알고리즘에 대하여 논한다. 특징벡타 추출기법으로 적은 계수로도 우수한 성능을 보이는 wavelet 변환을 사용한 방법을 제안하고 기종의 고전적인 방법들과 비교한다. 자동식별을 위해서는 MLP (Multilayer Perceptron), RBF (radial Basis Function), MLP-클래스 등 세 종류의 신경회로망을 사용하고, 성능 및 신뢰성을 높이기 위해서 두가지 특징벡타 및 세 식별기를 결합하는 방법을 사용한다. Traco의 표준 천이 데이터 집합 (standard transient data set) I과 모의 실험 데이터를 사용하여, 주어진 천이신호가 배경잡음에 비하여 충분히 에너지가 크고, 유한개의 소음원이 존재하며, 동시에 둘 이상의 소음원이 존재하지 않는다는 가정하에서 제안된 특징벡타 추출기법과 식별 알고리즘의 우수성을 확인한다.

• Journal of Power Electronics
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• 제11권5호
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• pp.759-766
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• 2011

This paper focuses on monitoring and predicting the short circuit faults of the rotor windings of large turbo-generator systems. For the purpose of increasing efficiency and decreasing maintenance cost, a method that combines the HHT (Hilbert Huang Transform) with a wavelet has been studied. This method is based on analyzing a classical Albright detecting coil. Due to the Empirical Mode Decomposition (EMD) and the Intrinsic Mode Functions (IMF) of the HHT the exact location of a short circuit of rotor windings may be given. However, a part of the useful information is eliminated by the unreasonable decomposing scale of the wavelet. Based on the thermodynamics modeling method, this study was illustrated with a 50MW turbo-generator system that is installed in Northern China. The analysis results, which have very good agreement with those of a previous study, show that the method of combining the HHT with a wavelet is an effective way to analyze and predict the short circuit faults of the rotor windings of large generators, such as supercritical turbo-generator systems and wind turbo-generator systems. This work can offer a useful reference for analyzing smart grids by improving the power quality of a distribution network that is supplied by a turbo-generator system.