215 results about "Wavelet basis functions" patented technology

The invention discloses a method for denoising acoustic testing data of porcelain insulator vibration based on wavelet decomposition threshold denoising. The method comprises the following steps: using a vibration acoustic method to detect a porcelain insulator to acquire a porcelain insulator vibration response signal containing noise; selecting a suitable wavelet basis function for the porcelain insulator vibration response signal containing noise and then carrying out multi-resolution wavelet decomposition, and transforming the vibration response from a time domain to a wavelet domain; rationally selecting a threshold function and a threshold, and machining a wavelet coefficient corresponding to the noise according to the threshold function; carrying out wavelet reconstruction, transforming the vibration response after denoising treatment to the time domain from the wavelet domain; storing the de-noised vibration response and sorting an acoustic testing result of the porcelain insulator vibration. According to the method, the noise in the acoustic testing data of the porcelain insulator vibration can be effectively filtered, so that sorting accuracy is improved.

Through sensor of stress wave, the invention picks up signal of stress wave of malfunction in early stage. The invention mainly solves issues of data acquisition for stress wave, noise suppression, feature extraction of signal, and fault recognition and position. Based on signal character, wavelet analysis selects suitable wavelet basis function, and carries decomposition of fault signal in multiple scales. Reconstructing waveform and spectrogram from each decomposition in different scale picks up minute character so as to determine type of fault and put forward scheme of treatment. Thus, the invention reduces economic loss to minimum.

The invention discloses a rotary machine fault detection method of dual-tree complex wavelet transformation with adjustable quality factors. The rotary machine fault detection method of dual-tree complex wavelet transformation with the adjustable quality factors comprises the steps of (1) building a reasonable sampling parameter set, building dual-tree complex wavelet base functions with different quality factors, (2) using each built dual-tree complex wavelet base function to carry out time-scale analysis on a vibration response signal of a rotary machine, calculating kurtosis information entropy of wavelet coefficients of each layer with participation of each dual-tree complex wavelet base function, selecting a dual-tree complex wavelet base function corresponding to the maximum feature kurtosis information entropy as the dual-tree complex wavelet base function which is in optimal matching with an impact component of the vibration signal, and (3) analyzing the vibration signal through the optimal dual-tree complex wavelet base function, and carrying out fault diagnosis. According to the rotary machine fault detection method of dual-tree complex wavelet transformation with the adjustable quality factors, the dual-tree complex wavelet base functions with any frequency-band focusing performance and time-domain oscillation performance can be built, the base function with the optimal matching performance can be selected in a self-adaptation mode, and accurate detection of periodicity impact type fault features and information of the impact period of a rotary machine device can be achieved.

The invention relates to a remotely sensed image small object super-resolution rebuilding method, which provides a remotely sensed image small object super-resolution rebuilding model; the space resolution factor adds 1.5 ploidy of the initial picture and effect depresses the extraneous wave; the linear material and the empirical distribution estimate the atmosphere disturb image parameter H; the redundancy wavelet distribution adopts mirror-image wavelet base function and uses the cross cut correlation to achieve morphology wavelet non-linear wavelet encoding and depresses the high frequency immediately noise; it dynamically evens the effect of high frequency noise signal and high frequency detail signal. It is applied in satellite image military target identifying, small target detecting and earth source remotely sensed image measuring.

The invention provides a potential field abnormal separation method based on a wavelet spectral analysis. The method, which is implemented on a computer, comprises the following steps that: gridding is carried out on a potential field abnormal signal observed by an irregular grid so as to form a potential field abnormal signal of a regular grid; according to a purpose of potential field separation abnormity and a wavelet basis characteristic, a wave basis function is determined; and a wavelet multiple dimensioned analysis is carried out on the potential field abnormal signal of the regular grid so as to obtain potential field abnormal signals under different dimensions; radial power spectrums of the potential field abnormal signals that are obtained under different dimensions are respectively calculated; and according to the radial power spectrums of the potential field abnormal signals obtained under the different dimensions, corresponding filed source body depths are respectively estimated. By employing the provided method in the invention, quantitative evaluation can be carried out on field source depths corresponding to separated potential field abnormal signals.

The invention aims at providing a target tracking method based on multi-scale dimensional decomposition, and the target tracking method comprises the following steps of selecting a wavelet-base function to decompose a target angle or track measurement data onto a scale, predicting and filtering the measurement data on a low-frequency subspace of each scale by utilizing extended kalman filtering (EKF) algorithm to obtain a rough tracking result of the target on different scales, and further eliminating the influence of noise and wildvalue by utilizing the wavelet threshold algorithm on the high-frequency subspace of different scales; and converging the tracking data on different scales through the wavelet reconstruction algorithm to obtain the precise tracking data of the target. The target tracking method can effectively, accurately, reliably and stably track the target in different complicated environment, the multi-scale EKF algorithm is realized by utilizing field programmable gate array (FPGA) parallel processing structure, the wavelet decomposition and rebuilding, the EKF algorithm of different scales and the wavelet threshold denoising are simultaneously implemented, and the real-time performance of the target tracking is ensured.

The invention discloses a transformer noise prediction method based on a wavelet neural network and the wavelet technology. A neuronal hyperbolic tangent S-type excitation function of a hidden layer in the traditional BP (back propagation) neural network is replaced with a wavelet-based function, momentum factors are introduced when parameters of the neural system are adjusted, and accordingly, a prediction model is higher in convergence speed and higher in error precision. Vibration and noise digital signals are decomposed by means of the wavelet decomposition technology, wavelet low-frequency coefficients obtained are used as input-output pairs for the prediction model, the wavelet low-frequency coefficients obtained by prediction are reconstructed by means of the wavelet reconstruction technology after modeling, and predicted noise digital signals are obtained. The transformer noise prediction method based on the wavelet neural network and the wavelet technology has the advantages that fewer training samples are required, time of training neurons in the neural network is shortened, and the problem that poor prediction effect is caused by ambient high-frequency interference noise contained in actually-measured transformer noise data is further avoided.

The invention discloses a segment hidden crack identification method based on matching pursuit and wavelet transform, and a technology of combining orthogonal matching pursuit and wavelet transformation is adopted to process a shield tunnel lining hidden water-containing micro-crack geological radar detection signal, the influence of a strong impedance interface can be effectively weakened, and micro-weak reflection signals of a target detection object are enhanced, so that the purpose of accurately detecting and identifying the shield tunnel lining hidden quality defects is achieved. Firstly,according to the sparse representation theory, a surface strong reflection and abnormal strong reflection stripping method for layer and wavelet constraint matching pursuit is provided, and by combining a matching pursuit algorithm and a strong reflection forming mechanism, a sparse dictionary matched with the characteristics of the strong reflection signals is selected, and two-time matching decomposition is carried out on each signal, so that the micro-weak target reflected signals submerged in the strong reflection can be well displayed. Secondly, a wavelet basis function matched with thesignal and a proper wavelet transformation scale are selected, and the image profile is processed and enhanced again by adopting a continuous wavelet transform method, so that the hidden water-containing micro-crack signals are effectively highlighted.

The invention discloses a method for calculating the characteristic peak of a derivative detection spectrum through using a wavelet. The method comprises the following steps: respectively carrying out continuous wavelet transformation on spectra through using the first derivative and the fourth derivative of a Gaussian function as wavelet basis functions, extracting a maximum point position from the wavelet calculation fourth derivative spectrum of gaus4 to obtain the position of an original spectrum peak, and extracting extreme point positions from the wavelet calculating first derivative spectrum of gaus1 to obtain the inflection point positions of the original spectrum peak. Characteristic points are obtained through detecting extreme points, so the method is more convenient and accurate than zero-crossing point calculation.

The invention discloses a wavelet denoising method with variable threshold value, comprising five steps. The method comprises: Step 1, inputting the original image and adding the corresponding Gaussian noise; step 2, selecting a wavelet basis function and determining that number of layers O of wavelet decomposition: decomposing the noise image S to obtain a low-frequency coefficient A1 of the first lay, horizontal and vertical high-frequency coefficients H1 and V1, and diagonal high-frequency coefficient D1; decomposing A1 to obtain The second layer low frequency coefficient A2, the horizontaland vertical high frequency coefficients H2 and V2, and the diagonal high frequency coefficient D2; decomposing A2 to obtain The third layer low frequency coefficients A3, horizontal and vertical high frequency coefficients H3 and V3 and diagonal high frequency coefficients D3 ; decomposing sequentially until O layer; step 3, selecting the combined wavelet threshold and wavelet threshold functionwith straight line (shown in the description) as asymptotic line to process wavelet coefficients; step 4, carrying out wavelet reconstruction on the wavelet coefficients after threshold quantization;Step 5, outputting the denoised image. The invention can improve the precision of wavelet transform processing noise signal, effectively improve the de-noising effect of the image, and obtain high-quality de-noising image.

The invention discloses a method for rapidly predicting content of soil organic matters based on eleven spectrum wavelet coefficients. The method is characterized by comprising the following steps: (1) collecting a soil diffuse reflectance value (R) with the spectrum range of 400-2450nm at the wavelength interval of 1nm; (2) converting the soil diffuse reflectance value into a soil absorbance value through a formula of A=-log(R); (3) performing discrete wavelet transform on the soil absorbance value by selecting a sym7(Symlets7) wavelet base function, extracting scale-5 low-frequency wavelet coefficients, and obtaining 75 low-frequency wavelet coefficients; (4) compressing the 75 low-frequency wavelet coefficients by utilizing a successive projections algorithm, and extracting the 9th, 11th, 22nd, 27th, 35th, 39th, 48th, 61st, 64th, 65th and 67th characteristic wavelet coefficients, totaling eleven wavelet coefficients; and (5) substituting the eleven characteristic wavelet coefficients into a multiple linear regression calculation formula, and calculating to obtain the content of soil organic matters. The method can rapidly predict the content of soil organic matters and is suitable for development and utilization of portable test instruments.

The invention discloses a pulse signal classification method based on wavelet packet conversion and hidden markov models. The method includes the following steps that a db4 wavelet is adopted as a wavelet basis function of wavelet packet conversion, and the wavelet packet conversion is carried out on two kinds of collected pulse signals to obtain wavelet packet decomposition coefficients of various frequency bands; an optimal frequency band is selected according to a local area discriminant base algorithm; an optimal energy feature vector is selected by means of a Fisher criterion; one part of the two kinds of pulse signals is selected to serve as training signals, the other part of the two kinds of the pulse signals serves as testing signals, and the optimal feature vectors of the two kinds of signals are figured out according to the method; the optimal energy feature vector of the training signals serves as a continuous hidden markov observation vector to train two hidden markov models; the optimal energy feature vector of the testing signals is respectively input into the trained two models, the probability values P(O | lambada i) of the optimal energy feature vectors are worked out according to a forward-backward algorithm, the probability values are compared, and classification of the pulse signals is completed.