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This paper gives a method which bases on the wavelet decomposition and the neural network to predict the short-time load. Using wavelet transform, the load sequence is decomposed into sub-sequences on different scales, then using appropriate artificial neural network models the sub-sequences of forecasting date are predicted. Finally, by means of restructuring from the sub-sequences, the final forecasting...
This paper deals with analysis of power signals using complex wavelet transform. In the first step power signals containing sag, swell, harmonic, sag-harmonic, swell harmonic, transient and spike were generated using Matlab. Various features like energy, kurtosis, entropy, skewness etc. were extracted using `db4' and complex wavelet decomposition up to 11 levels. Next, an extensive database of these...
A THz image edge detection approach based on wavelet and neural network is proposed in this paper. First, the source image is decomposed by wavelet, the edges in the low-frequency sub-image are detected using neural network method and the edges in the high-frequency sub-images are detected using wavelet transform method on the coarsest level of the wavelet decomposition, the two edge images are fused...
A new approach for image classification based on the color information, shape and texture is presented. In this work, we use the three RGB bands of a color image in RGB model to extract the describing features. All the images in image database are divided into 6 parts. We use the Daubechies 4 wavelet transform and first order color moments to obtain the necessary information from each part of the...
Clusters of microcalcifications in mammograms are an important early sign of breast cancer in women. In this paper an approach is proposed to develop a computer-aided diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs. The proposed method has been implemented in three...
We have developed a modular analog circuit fault- diagnostic system based on neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. Our proposed system has the ability to identify faulty components or modules in an analog circuit by analyzing its impulse response. In this approach, the circuit is divided into modules, which, in turn, are...
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