Processing Fault Data Method in Load Forecasting Based on Wavelet Analysis

The reliability and reality of load historical data is the foundation of load forecasting.But,the impact load in running power system,and the disturb data in collecting load data through the SCADA may cause much fault data in load historical data. Focusing on solving this problem, a method through adjusting amplitade of its wavele modulus maxima and processing the wavelet decomposed detail signal by soft threshold based on wavelet analysis and singularity theory, then fault date can be eliminated,so that,the real historical imformation and regulation data can be gained by load forecasting.     

Detection Method for R-Wave of ECG Signal With Wavelet Transform

The R-wave of ECG signal represents the electrical activation of the ventricles, which initiates ventricle contraction, and the typical peak value singular signal, so the R-wave of ECG signal is localized precisely and analyzed accurately using the wavelet transform. The principium of the precise detection method for R-wave in ECG signal is researched. The special properties of Mexican hat wavelet in time-domain are analyzed, too. This wavelet has every order continuity, symmetry, exponential attenuation and one vanishing moment. For this reason, the mexican hat wavelet basis has the excellent localization and analyzing precision. Using the MIT/BIH (Massachusetts Institute of Technology / Boston's Beth Israel Hospital) Arrhythmia Database and the applications in clinic, the precise detection method can detect accurately and localize precisely to the R-wave in ECG signal in the serious noise signal. This method has the quite high locating precision (its error is not more than one sampling point and the points of the R-wave in ECG signal about 80 percent are localized precisely) and analyzing accuracy (no accumulative error). The real-time of the method is excellent, and the real-time detection to the R-wave of ECG signal can achieve using this method.     

Wavelet Application in Algorithms of Data Mining

Wavelets have been widely applied in such research areas as image processing, computer vision, network management and data mining. Wavelets have many favorable properties, which are hierarchical and multiresolution decomposition structure, linear time and space complexity of the transformations. The paper presents wavelet application on the algorithms of data mining, in cluding as clustering, classification, distributed data mining, similarity search, approximate query processing, visualization and so on. Finally, the paper concludes by discussing the impact of wavelets on data mining research and outlining potential future research directions and applications.     

VBR Coding Algorithm Based on Wavelet Transform

The introduction of the asynchronous transfer mode(ATM) concept has significantly influenced the coding theory.According to the characteristics of the ATM network,using the advantages based on the wavelet transform,how to apply the wavelet transform to plane separation and how to apply VBR coding to the layered data are discussed.The results of experiment are described respectively.     

Video Compression of Vector Coding based on Wavelet Transform

It is possible to get high compression rate for the interrelationship between neighboring frames of moving pictures. The kernel of research on video compression is that the efficient image compression algorithm is adopted while the interrelationship between neighboring frames and time consumption is concerned to improve the compression efficiency. It is significant that motion compensation is applied to video compression, nevertheless it is too time consuming. A vector coding method based on wavelet transform is presented to avoid searching procedure of motion compensation. Simulation shows that high compression rate is achieved with less time consumption.     

A Virtual Wavelet Transform Analyzer for the Signal Analysis Based on the Direct Algorithm

A virtual wavelet transform analyzer for the signal analysis based on the direct algorithm is introduced so that the discrete wavelet transform and continuous wavelet transform is maken to signal in the direct algorithm. The authors first introduce the direct algorithm of the WT, which is numerical algorithm obtained from the original formula of the wavelet transform by directly numericalizing. Then some conclusions are drawn on the direct algorithm. The examples are the sampling principle and technology for the wavelets, the limitation of the scale range of the wavelets and the measures to solve the edge phenomenal in the direct algorithm of the discrete wavelet transform, and some conclusions in the direct algorithm of the continuous wavelet transform. The virtual wavelet transform analyzer for the signal analysis based on the direct algorithm explored based on these studies and combined with virtual instrument technique can make the discrete wavelet transform and continuous wavelet transform to signal with any basic wavelet. It can be applied in studying the property of any basic wavelet and learning the theory on the wavelet transform, and also in making some engineering signal analysis. In the end, the authors give some typical examples for the application of the virtual analyzer. These examples show that the analyzer can be applied in many situations.     

Application of Wavelet Transform to De-noising Method

The wavelet transform is a new subject developed quickly in the past ten years. Compared with the Fourier transform, the wavelet transform is a part of time-frequency transform. The most important character is that it can be used to transform a signal into basic units at different scales and location, each unit represents a component of original signal difference from others. The wavelet transform has been proven to be a powerful and efficient tool for processing signal due to this character. This paper introduces the de-noising principles of the wavelet transform. It is proved to be an effective method by the simulating analysis.     

A Context Model of Wavelet Coefficients for Image Compression

Through the combination of the idea of the linear prediction used in the GlicBawls coding scheme and the idea of the quantization taken in the CALIC coding scheme to reduce the number of contexts, a new context model of wavelet coefficients for image compression is proposed. Wavelet coefficients are encoded by the arithmetic encoder, with the contexts formed by quantizing linear prediction values. Experimental results show that the model achieves higher lossless compression rate of image than lossless SPIHT and lossless EBCOT used in JPEG2000. In addition, by exploiting the multiresolution property of wavelet, the model can compress the transformed image for progressive resolution and earn higher compression rate for each scale of the image than EBCOT.     

The Extended Kalman Filter Data Fusion Algorithm Based on Discrete Stationary Wavelet Transformation

It is difficult to establish precise mathematical model in extended Kalman filter data fusion algorithm for GPS/ DR integrated vehicle navigation system, an improved extended Kalman filter data fusion algorithm is put forward based on Discrete stationary wavelet transformation. The simulation results show that the algorithm is better than the extended Kalman filter data fusion algorithm, and is available for GPS/DR integrated vehicle navigation system.     

Systems Analysis Based on Continuous Wavelet Transform

The linear time invariant vibration system is analyzed by Continuous Wavelet Transform(CWT),the relationship of wavelet transform of output signal with the pulse response of system and input signal is put forward.As an exapmle,the wavelet transform of the output signal of system with single degree of freedom is calculated and compared with the direct wavelet transform result of the actual output signal,it shows that the two results are in complete agreement.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

Optimal Design of Plane Cascades Based on Wavelet Theory

Based on the optimal control theory and Lagrange interpolation an optimal design model of plane cascades of 3D blade in an arbitrary surface of revolution is established. Using wavelet theory to solve the model, and lots of numerical results are obtained. The results show that the wavelet method is more effective than the conventional methods.     

General Construct Method & Software of Wavelet Base's Filter

Wavelet analyses have been used for many fields deeply, especially , the wavelet transforms on compact support sets have been applied to signal dealing and image compression etc. However, the constructing of wavelet base is a hard work to do. In this paper, for N=2k the analysis structure of wavelet base on compact support sets are found successful. That is the general solutions structure of equations which fits to the wavelet base orthogonal conditions, Those formulas or algorithms make it very easily construct many filters of wavelet base, at the same time , Daubechies's filters and some other filters which are important in apllications have been tested correct; With the aid of our formulas , it is very easy to dynamically choose the wavelet bases.     

Color Image Compression Method Based on Incomplete Scanning Method

In view of color image characteristic, the paper improves the zero algorithm in the application of concrete algorithm, draws a new algorithm-incomplete scanning method, which is applied to color image compression. The authorsconvert color image from the RGB space into luminance-chrominance YUV space, then compress the converted image to encode/decode. Theoretical analysis and simulation experiment indicate that the compression ratio and encoding/decoding speed are increased, at the sase time the quality of restored image is guaranteed.     

Enhancing the Industrial Computerized Tomography by Wavelet Method

In industrial computerized tomography ( I C T ) , with the wavelet analysis method, we enhance the image of ICT. Comparing with the old image of ICT, this method can reduce the noise of the image and can filter the non white noise. This method can be applied in medical computerized tomography, too.     

Context Model Based on 3D Wavelet Coefficients

Based on by typical coding schemes such as EZW,SPIHT, EBCOT,3D-IEZW,3D-SPIHT and 3D-ESCOT,a context model(3D-OWNER) of wavelet coefficients is proposed,which makes use of partial order,self-similarity between subbands of wavelet coefficients and the correlation of neighboring wavelet coefficients.Experimental results show that the model can achieve higher PSNR than 3D-ESCOT.     

集成GASA混合学习策略的BP神经网 络在水稻虫害预测中的应用

为改进受多变量、时变和不确定因素影响的作物虫情预测的效率和准确性，以重庆市永川区为研究区域，将1996－2004年的气象和虫害数据分为独立的训练和检验数据集，以研究集成GASA混合学习策略的BP神经网络应用于水稻虫情预测中的可行性。与广泛应用的时间序列分析法和传统BP神经网络相比，集成混合学习策略的BP神经网络预测精度和拟合性能都有了很大提高，因而具有较好的应用前景。     

Speedy Recognition of Noise in Real Time Signal Based on Wavelet Transform

It's necessary to process position and gesture of rocket on real time within the flight of rocket. This work should base exact data. But much noise has been found in the data and the noise recognize is regarded as a focus. A speedy online arithmetic recognizing noise is proposed based on wavelet transform. The computing complexity measured by time of this arithmetic is a constant which is greatly reduces the works of calculation of wavelet transform. It can recognize the noise fast when the signal is gathered. The applications in these problems show that the effective arithmetic satisfies the needs of real time and can handle the real time data measured in other yields.