243 results about "Continuous wavelet" patented technology

This invention pertains to the extraction of the slowness dispersion characteristics of acoustic waves received by an array of two or more sensors by the application of a continuous wavelet transform on the received array waveforms (data). This produces a time-frequency map of the data for each sensor that facilitates the separation of the propagating components thereon. Two different methods are described to achieve the dispersion extraction by exploiting the time frequency localization of the propagating mode and the continuity of the dispersion curve as a function of frequency. The first method uses some features on the modulus map such as the peak to determine the time locus of the energy of each mode as a function of frequency. The second method uses a new modified Radon transform applied to the coefficients of the time frequency representation of the waveform traces received by the aforementioned sensors. Both methods are appropriate for automated extraction of the dispersion estimates from the data without the need for expert user input or supervision

The invention discloses a wavelet transformation and deep learning-based rolling bearing weak fault diagnosis method. The method comprises the following steps: a rolling bearing vibration signal is obtained, and the acquired vibration signal is subjected to continuous wavelet transformation to obtain a time-frequency diagram; autocorrelation operation is performed on wavelet coefficients corresponding to each frequency on the time-frequency diagram to filter out noise interference and extract periodic fault components; the Hilbert transformation is used to perform envelope demodulation to obtain a fault characteristic frequency; a processed time-frequency diagram is input as a feature diagram, and categories of early faults are determined by training a deep learning classification model. Theoretical and experimental results show that fault categories can be determined accurately at an early stage when weak fault of a rolling bearing occur based on an improved wavelet time-frequency diagram used as the classification model for input training, right determination results can be given when the method is applied to different bearings, high training speed can be realized, and high actual application value can be achieved.

The invention discloses a fault single end positioning method of a power transmission line based on traveling wave multi-scale information, which comprises the following steps of: selecting different phases as reference phases according to different fault types and carrying out phase-mode transformation on a fault current traveling wave signal of the power transmission line to obtain a fault current traveling wave mode signal for fault positioning; carrying out continuous wavelet transformation on the mode signal, extracting modulus maximums of wave heads of first and second traveling waves on each wavelet transformation coefficient and corresponding moments of the modulus maximums and judging a section where the fault occurs and determining frequency components of the wave heads of the first and the second traveling waves for positioning according to wavelet transformation modulus maximums to obtain mode wave speeds of the two traveling waves with different frequency components and arrival moments of the wave heads; and finally comprehensively calculating the fault distance of the power transmission line in combination with the mode wave speeds and the arrival moments of the two traveling waves. In the fault single end positioning method of the power transmission line, the frequency components, the mode wave speeds and the arrival moments of the wave heads of the traveling waves are accurately determined by utilizing the multi-scale information of the traveling waves so as to accurately calculating the fault distance of the power transmission line.

The invention relates to a method for selecting single-phase ground fault lines of power distribution networks on the basis of convolutional neural networks. The method includes acquiring zero-sequence voltages of busbars and zero-sequence current signals of various feed lines; carrying out continuous wavelet transformation on the various zero-sequence current signals according to set decomposition scales; acquiring time-scale wavelet coefficient gray-level graphs; identifying fault feed lines by the aid of trained convolutional neural network algorithms. The method for selecting the single-phase ground fault lines of the power distribution networks on the basis of the convolutional neural networks has the advantage that the fault feed lines and the non-fault feed lines can be accurately identified by the aid of the method under various fault working conditions when single-phase ground faults occur.

The invention relates to a self-adaptive failure diagnosis method of a rotary mechanical component based on continuous wavelet transformation. The method comprises the following steps of: 1, performing zero-mean preprocessing on an acquired discrete initial vibration signal to obtain a preprocessed signal from which a direct-current component is eliminated; 2, performing continuous wavelet transformation on the preprocessed signal obtained in the step 1 to obtain a wavelet coefficient which corresponds to each scale parameter; 3, calculating the kurtosis of the wavelet coefficient which corresponds to each scale parameter in the step 2 respectively; 4, searching for a wavelet scale parameter which corresponds to large kurtosis in the wavelet scale parameter obtained in the step 3 with a self-adaptive algorithm to configure an optimal analysis signal; and 5, performing envelope demodulation on the optimal analysis signal obtained in the step 4 to obtain an envelope signal. The method has the beneficial effects that: the accuracy of failure diagnosis is increased, and the method is particularly suitable for failure diagnosis of mechanical parts with impact damages.

The invention discloses a multichannel synchronous real-time digitalized photoacoustic imaging device and method. The multichannel synchronous real-time digitalized photoacoustic imaging device comprises a laser, a multi-element ultrasonic detector, a front amplification circuit and a data imaging processing unit, wherein the data imaging processing unit comprises a plurality of field programmable gate array (FPGA) acquisition cards, a clock synchronous board card, a programmable communication interface (PCI) extensions for instrumentation express (PXIe) backboard and a central processing unit (CPU) board card, and can be used for realizing acquisition processing for at least 32 paths of synchronous photoacoustic signals. The multichannel synchronous real-time digitalized photoacoustic imaging method comprises the following steps that: the multi-element ultrasonic detector receives the photoacoustic signals produced by laser irradiation on a biological tissue, amplifies the photoacoustic signals and respectively transmits the signals to the FPGA acquisition cards; and each FPGA acquisition card performs preprocessing, deconvolution, continuous wavelet transformation and photoacoustic attenuation compensation on the signals, then transmits the signals to another FPGA acquisition card in a peer-to-peer (P2P) manner, performs imaging processing by adopting a 2-D (two-dimensional) filtration back-projection algorithm, and finally transmits the structure to an upper computer. The method is finished on an FPGA processor; according to a modular design, the expansion of the large-scale detector is simplified; and the device and the method are favorable for clinical application of photoacoustic imaging systems.

The invention discloses an ultrasonic detecting method of defects in a thick-wall composite tubular structure. The ultrasonic detecting method includes the steps that original signal data are obtained, final signal data are obtained with signal processing methods such as noise reduction, fast Fourier transforming and continuous wavelet transforming, and a wavelet-coefficient-amplitude-time graph is generated; according to the generated wavelet-coefficient-amplitude-time graph, the positions and the sizes of inside damage of a composite material layer and the positions and the sizes of the unsticking portions of the boundary between the composite material layer and a metal layer are extracted, and distribution of the thickness of the metal layer is calculated. The ultrasonic detecting method can be used for detecting the multiple defects of the tubular structure composed of composite materials and metal, damage positioning and size assessing are carried out through the corresponding signal processing technology, and quantitative analysis of the whole structure performance can be achieved; the ultrasonic detecting method is high in accuracy, free of reference signals, simple, easy to operate and capable of being used for field detecting.

The invention discloses a rotary machine instantaneous rotation speed estimation method based on vibration signal synchronous compression transformation. The rotary machine instantaneous rotation speed estimation method comprises the following steps of 1 obtaining a vibration signal in the rotary machine operation process; 2 conducting frequency shift treatment on the measured vibration signal, 3 conducting synchronous compression continuous wavelet transformation on the vibration signal subjected to the frequency shift treatment to obtain the time-frequency distribution of the vibration signal subjected to the frequency shift treatment, 4 utilizing a Viterbi algorithm to extract first-order instantaneous frequency components of the vibration signal subjected to the frequency shift treatment from the obtain time-frequency distribution, and 5 utilizing the extracted first-order instantaneous frequency to recover calculation so as to obtain the rotary machine instantaneous rotation speed. The rotary machine instantaneous rotation speed estimation method adopts a frequency shift algorithm and the synchronous compression continuous wavelet transformation to process the signal, achieves accurate estimation of the instantaneous frequency of the vibration signal, utilizes the Viterbi algorithm to achieve accurate extraction of the instantaneous frequency and can accurately extract the instantaneous rotation speed of a rotary machine which cannot directly measure the instantaneous rotation speed through the vibration signal.

The invention relates to a winter wheat area estimation method based on remote-sensing time series data, which is based on the remote-sensing time series data, and comprises the steps that remote-sensing vegetation index time series data is converted into a wavelet coefficient spectrum by continuous wavelet conversion; wavelet coefficient binary images representing annual vegetation change characteristics of pixels in a study region are established on the basis; the wavelet coefficient binary images of a plurality of known winter wheat sampling points are superposed; standard wavelet coefficient binary images of winter wheat in the study region are generated; the wavelet coefficient binary images of the pixels in the study region and the standard wavelet coefficient binary images of the winter wheat are superposed one by one; winter wheat identification criteria are established after statistic superposition; the pixels are subjected to winter wheat identification one by one; and finally a winter wheat planting area of the whole study region is obtained by summarizing and computing. The method can effectively solve the problems that annual variance amplitude of the original vegetation index is inconsistent due to various factors, and has the advantages of high anti-noise capacity, good classification accuracy, wide application scope and the like.

The invention relates to a method for inverting vegetation parameters by remote sensing based on reflection spectrum wavelet transform. The method comprises the following steps of: 1) acquiring the vegetation parameters and the original spectrum thereof under different conditions, and performing spectrum transform on the original spectrum; 2) performing continuous wavelet transform on the original spectrum by using different wavelet functions, and generating wavelet coefficients with different frequencies; 3) performing stepwise regression by taking different scales of wavelet coefficients as independent variables and taking the vegetation parameters as dependent variables, selecting spectrum wave bands needed by the inversion of the vegetation parameters, constructing a model of quantitative inversion of the vegetation parameters, and calculating R2 of the model; and 4) comparing modeling R2 of the constructed model according to different wavelet decomposition scales, and determining the model with the maximum modeling R2 as the optimal model. By the method, the hyperspectral remote sensing inversion precision of the vegetation parameters can be obviously improved, and the remote sensing inversion precision of biochemical parameters can be improved preferably. The method has wide parameter applicability, is applicable to leaf or canopy reflection spectrum, and is applicable to satellite remote sensing hyperspectral data.

Slowness dispersion characteristics of multiple possibly interfering signals in broadband acoustic waves as received by an array of two or more sensors are extracted without using a physical model. The problem of dispersion extraction is mapped to the problem of reconstructing signals having a sparse representation in an appropriately chosen over-complete dictionary of basis elements. A sparsity penalized signal reconstruction algorithm is described where the sparsity constraints are implemented by imposing a l1 norm type penalty. The candidate modes that are extracted are consolidated by means of a clustering algorithm to extract phase and group slowness estimates at a number of frequencies which are then used to reconstruct the desired dispersion curves. These estimates can be further refined by building time domain propagators when signals are known to be time compact, such as by using the continuous wavelet transform.

The invention relates to a storage tank bottom plate corrosion noncontact ultrasonic detection method based on a dynamic wavelet fingerprint technology. The method is characterized by comprising the following steps: selecting a steel plate which is smaller than a water trough from a detected test piece; immersing the detected steel plate into the transparent glass water trough, and fixing an immersion-type non-focusing probe on a movable arm of a four-axis linkage scanning platform; determining a scanning step in an X direction and a Y direction; analyzing a group of acquired echo signals by virtue of a computer, and drawing an original time domain chart of waveforms of the echo signals; intercepting a section of the time domain chart as an effective detection zone; resolving an intercepted time domain signal by utilizing wavelet transformation of the dynamic wavelet fingerprint technology; conducting continuous wavelet transformation on a signal after denoising treatment to obtain a wavelet coefficient matrix; conducting normalization processing on the acquired matrix; simultaneously writing an acquired imaging time point and a location coordinate of the time point recorded during the scanning into the matrix to obtain a C scanning image. According to the method, corrosion defects on a storage tank bottom plate can be detected under the situation that a storage tank does not need to be cleaned, so that the detection efficiency can be improved, and the detection cost can be reduced.

The embodiment of the invention discloses a fault detection method and fault detection apparatus used for a shafting device, relating to the automatic control technical field, and can detect and discriminate minimal fault forms, and increase fault prediction accuracy. The method comprises: obtaining operation signals of a shafting device in a ship when the ship sails smoothly, the operation signals comprising displacement, speed and acceleration signals of the bearing, axle tube and coupler in the shafting device in vertical and horizontal directions; performing frequency domain main energy extraction on the operation signals, generating reconstructed signals, performing continuous wavelet decomposition on the reconstructed signals, and generating a wavelet grey-scale map based on the result of continuous wavelet decomposition; performing feature extraction on the wavelet grey-scale map, removing redundancy feature vectors with relevance, and generating a fault characteristic vector set according to kept characteristic vectors; and determining whether the shafting device has faults in dependence on the fault characteristic vector set. The fault detection method and fault detection apparatus are suitable for shafting device fault detection.

The invention discloses a log domain analog wavelet transform circuit for detecting an electrocardiogram signal QRS wave, comprising a forming circuit of differential input current signals and a current mirror circuit, four parallel log domain differential voltage filter circuits, four log differential compression circuits and four exponential differential expanded circuits. Each input of the log domain differential voltage filter circuits is connected with the forming circuit of differential input current signals and the current mirror circuit through the log differential compression circuits. Each output of the log domain differential voltage filter circuits is connected with an input of the exponential differential expanded circuits. The log domain analog wavelet transform circuit has the advantages of being integrated in a piece of analog integrated circuit, real-time realizing continuous wavelet transform under the characteristic dimension of the electrocardiogram signals with low power consumption, satisfying application requirements of an implanted artificial cardiac pacemaker for low power consumption and small size.

The invention discloses a nonlinear ultrasonic mixing method for testing in the structural fatigue crack direction, and belongs to the field of non-destructive testing. The method comprises the following steps: firstly, according to a tested object and a nonlinear ultrasonic mixing harmonic condition, determining the testing parameters such as the mode, frequency, incident angle and the like of two fundamental frequency waves; performing the nonlinear ultrasonic mixing testing of the structural fatigue crack; performing continuous wavelet transformation on testing signals in each annular receiving point, extracting the amplitudes of mixing waves, and performing the mixing sound field directivity analysis; finally, calculating the fatigue crack direction according to the change rule of thereflecting mixing wave propagation direction along with the fatigue crack direction.

The invention relates to a fault range finding method for a power transmission circuit by use of current travelling waves and voltage travelling waves of two ends of the circuit, and belongs to the technical field of fault range finding of electric power systems. When a power supply circuit fails, current travelling waves and voltage travelling waves of the circuit are acquired from two measurement points of the circuit. Reflection wave head recognition is performed on the current travelling waves and the voltage travelling waves of the two ends of the circuit through continuous wavelet transformation and four groups of fault distance data is acquired according to initial travelling wave heads and emission wave heads. Composite integration is performed on the four groups of the data by use of the global geometrical average optimal method, so fault range finding of the power transmission circuit is achieved. According to the invention, accuracy of fault range finding of the power transmission circuit can be effectively improved and the method is quite highly reliable.

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 method for comprehensively recognizing sedimentation hiatal surfaces in an ultra-thick seam. The sedimentation hiatal surfaces in the ultra-thick seam are comprehensively recognized according to five parameters acquired through coal petrography and coal quality analysis, microelement analysis, organic matter stable carbon isotope analysis, one-dimensional continuous wavelet transformation of log data and Milankovitch cycle analysis of the log data; growth control factors of the sedimentation hiatal surfaces are explained from the origin essentially through recognition of Milankovitch cycles in the ultra-thick seam, the root cause of growth of the sedimentation hiatal surfaces is found out, and the accuracy and the credibility of the sedimentation hiatal surfaces are greatly improved. Besides, weight scoring is performed according to sensitivity of different recognition methods, the recognized sedimentation hiatal surfaces are correspondingly assigned, the scores obtained with all the methods are added up, and the reliability of the sedimentation hiatal surfaces is described quantitatively. The recognition method is convenient and flexible and has better practicability.

The invention finds a method capable of simultaneously finishing LC / MS or LC signal base line correction, peak position distinguish and peak position alignment. The invention mainly comprises the following steps: step 1, correcting a metabolic spectrum base line and distinguishing peak position, wherein the main process comprises: 1, within the range of 1-12 feet, taking the Mexican hat function as a mother wavelet, and carrying out continuous wavelet transform (CWT) of which the step length is 1 to each mass spectrum signal; 2, at each time point, detecting the maximum value of the wavelet transform of each mass spectrum signal on each scale to obtain a new signal of which the horizontal coordinate is time and the vertical coordinate is the maximum value of the wavelet transform; and step 2, aligning the peak position. A threshold value (the maximum deviation of the peak position of the same component in different samples) is given, the peak of the deviation in the wavelet transform signal in each sample within the threshold value is incorporated into the same peak, and the corresponding peak position in each sample is modified.

The invention relates to a pipe pressure anomaly diagnostic method based on combined filtering and dynamic thresholds. The pipe pressure anomaly diagnostic method includes the steps of obtaining pressure signals of liquid to the head end and the tail end of a pipe in real time, carrying out decomposition reconstruction through low-pass filtering and discrete wavelet filtering, obtaining pressure signals generated after combined filtering, carrying out segment treatment on the pressure signals, calculating the dynamic thresholds of the segmented pressure signals obtained after combined filtering, judging whether the pressure signals of the pipe are abnormal or not in real time through the dynamic thresholds, judging whether the pressure anomaly signals at the head end and the tail end of the pipe are pressure anomaly signals caused by a same source or not with the Pearson related coefficient method, carrying out continuous wavelet transformation on the pressure anomaly signals with the same source to obtain moments corresponding to the maximum value points of wavelet coefficients, and carrying out anomaly point positioning through the difference of the moments corresponding to the maximum value points. By means of the pipe pressure anomaly diagnostic method, the pipe anomaly diagnostic sensitivity is improved, the misjudgment rate is reduced, and pressure anomaly points are accurately detected and positioned in the shortest time.

A method for extracting data from an overall signal, generated by a plurality of impulses, by use of a computer using wavelet transform analysis of timing and strength of each of said plurality of impulses is disclosed. The method includes using a Discrete Wavelet Transform analysis or a Continuous Wavelet Transform analysis.

The invention discloses a seismic data time-frequency analysis method based on second-order extrusion wavelet transformation. Firstly, a second-order synchronous extrusion operator is introduced on the basis of continuous wavelet transformation, the transformation is rearranged in a time-scale domain according to wavelet transformation coefficients, accordingly energy diffusion is pressed, and analysis results higher in time-frequency resolution ratio can be obtained by adopting the method. Compared with existing synchronous extrusion transformation, the method is more suitable for analysis of seismic signals having stronger frequency modulation, thus the signals are applied to time-frequency analysis of actual seismic data, the boundary of an underground carbon and hydrogen reservoir can be more accurately defined, riverways, faults and other geological information can be more elaborately depicted, and accordingly further seismic data explanation and well position determination are promoted.

An improved method for detecting the presence of humans or animals concealed within in a vehicle uses a combination of the continuous wavelet transform and a ratio-based energy calculation to determine whether the motion detected using seismic sensors placed on the vehicle is due to the presence of a heartbeat within the vehicle or is the result of motion caused by external factors such as the wind. The method performs well in the presence of light to moderate ambient wind levels, producing far fewer false alarm indications. The new method significantly improves the range of ambient environmental conditions under which human presence detection systems can reliably operate.

The invention discloses a complex analytic optimal wavelet demodulation method which is characterized by comprising the following steps of: obtaining a vibration signal spectrogram, and capturing an effective frequency band centralized region; setting a shape factor sigma of a Morlet wavelet and an initial value of the initial frequency omega 0, thereby obtaining the wavelet factor Cs(b, a); obtaining continuous wavelet amplitude value spectrum information measure SH; improving the wavelet kurtosis; calculating the smooth index; calculating the optimal wavelet envelope spectrum; and detailing a modulation spectrum, and locating the fault of a detected target. According to the invention, not only are analysis wavelet parameters optimized and selected, but also the conventional wavelet kurtosis calculation method is improved, and the accuracy of screening the wavelet coefficients is improved; meanwhile, in order to solve the problem that the wavelet coefficient features decomposed by fault signals at the early stage are not obvious, the smooth index is additionally set, which plays better positioning and screening effects on the wavelet coefficients for the fault frequency band. The method not only can disclose weak fault features in complex signals in the multi-component signals accurately and effectively, and has high practical value.

Methods and systems are provided for analyzing a physiological signal by applying a continuous wavelet transform on the signal and comparing the wavelet transformation to a library of wavelet signatures corresponding to one or more physiological conditions and / or patient conditions. A pulse oximeter system may relate the wavelet transformation with one or more of the wavelet signatures based on filters and / or thresholds, and may determine that the wavelet transformation indicates that the patient of the physiological signal has a physiological condition indicated by the related wavelet signature. In some embodiments, the pulse oximeter system may use previous analyses in a neural network to update the library. Further, non-physiological components of the wavelet transformation may also be identified and removed.

The invention relates to an evaluation method of influence of an optical element surface waviness on a laser damage threshold and a method for obtaining optimal processing parameters of the element therefrom, in particular to a method for evaluating surface quality of an optical element and a method for obtaining the optimal processing parameters of the element. The evaluation method comprises the following steps of obtaining a morphology data matrix of an original processing surface; obtaining a relative laser damage threshold relevant to each characteristic frequency by utilizing a power spectrum density method, a two-dimensional continuous wavelet transformation method and a fourier modulus method; and selecting a minimum value as an evaluation result. In the method for obtaining the optimal processing parameters of the optical element, the optimal processing parameters are obtained by utilizing the evaluation method and comparing the relative laser damage thresholds of thr optical element obtained under various processing parameter conditions. The invention can be used for evaluating the quality of the optical element and can also be used for guiding the processing process of the optical element.

The invention discloses a method for identifying a mixture component based on similarity under a raman spectrum, and relates to the technical field of component analysis; the method comprises the following steps of collecting raman spectrums of a pure substance and a mixture to be identified, aiming at different characteristics of the pure substance and the to-be-identified mixture, a characteristic peak is searched for by adopting continuous wavelet transformation with different wavelet as a master wavelet function, so that accurate identification is realized; characteristic vector groups are extracted from the found characteristic peaks, so as to carry out similarity calculation on the pure substance and the to-be-identified mixture so as to preliminarily screen out the candidate pure substance; after the spectral data of the candidate pure substances are subjected to displacement correction, a final component recognition result is obtained by utilizing non-negative least squares; the whole process does not need manual participation, and can be rapidly and effectively applied to the identification of the mixture components, so that the precision ratio and the recall ratio of the mixture identification are improved to a certain extent, and the method has the characteristics of being rapid and lossless, simple in implementation process, accurate in identification result and the like.

The invention discloses a method for improving seismic data resolution. The method comprises the specific steps of: applying continuous wavelet transform (CWT) time series analysis to original seismic data; calculating harmonic information W<~>( tau, s) of wavelets at various scales through harmonic frequency analysis by utilizing a multiresolution feature of a continuous wavelet domain; transforming the harmonic information back into a time domain through inverse transformation; adding obtained time threshold information into the original seismic data, and expanding frequency width of the seismic data effectively. The method disclosed by the invention breaks through the limitation of the traditional convolution model in the method principle, does not need to estimate seismic wavelets anymore, does not need to assume that a stratum reflection coefficient is sparse, extracts harmonic components by utilizing dominant frequency band information, ensures that a frequency expanding result has a high signal-to-noise ratio, is implemented by adopting an FFT algorithm specifically, is high in calculation efficiency, and can expand the low frequency and high frequency simultaneously.

The invention provides a nuclear radiation detection method suitable for a complex radiation background. The nuclear radiation detection method suitable for the complex radiation background includes: combining a traditional measurement method with a modern digital signal processing technology, fully using information acquired by a nuclear radiation detector, and achieving quick and accurate judgment for nuclear radiation energy and strength. The nuclear radiation detection method suitable for the complex radiation background uses a high speed data acquisition card to acquire gamma pulse signals output from an HPGE (high purity germanium) detector, and inputs the gamma pulse signals into a data acquisition and processing unit after performing AD (analog-digital) conversion and smoothing on the gamma pulse signals. Then, a data processing unit converts a large number of the gamma pulse signals into gamma energy spectra. Finally, continuous wavelet transformation in wavelet analysis is adopted to convert the gamma energy spectra, and converts the gamma energy spectra to an energy-dimension plane, and then wavelet coefficients of the gamma energy spectra under different dimensions are obtained. In the end, quick and accurate positioning for a characteristic peak position is achieved and quick calculation for the peak area is achieved according to characteristics of gamma characteristic peak wavelet coefficients in the energy-dimension plane.