17 results about "Wavelet basis functions" patented technology

The present application relates to the technical field of composite material structure monitoring, in particular to a composite material structure damage identification method based on acoustic emission wavelet packet energy characteristics, which comprises: S1, obtaining acoustic emission signal data of the composite material and performing pretreatment; S2, using a wavelet base function and a discrete wavelet inverse transform to perform wavelet packet decomposition on the denoised acoustic emission signal data of the composite material; S3, obtaining the processed acoustic emission signal data of the composite material, and extracting the characteristic energy of each acoustic emission signal data; S4, using a clustering method to perform clustering analysis and completing the composite material structure damage identification. The present application uses acoustic emission signals as the basis for analysis, uses wavelet transform and k-means clustering to accurately realize the structure damage identification and judgment of the composite material, can realize dynamic and continuous monitoring, is not affected by the complexity of the structure, has wide applicability, and is suitable for various composite materials.

The present application relates to the technical field of data analysis, and more particularly, to a solid-state battery performance test method and system based on data analysis, comprising: obtaining voltage time series data of a solid-state battery, and removing a direct current component to obtain a fluctuating voltage sequence; based on a ratio of a second-order difference to a first-order difference of the fluctuating voltage sequence and a signal-to-noise ratio, calculating a local transient response index at each time, which is used to represent a local steepness of voltage fluctuation. The present application aims at the problem that solid-state battery micro-short circuit waveforms are diverse and easily covered by noise, and through constructing a local transient response index, the steepness of voltage fluctuation is quantified in real time, and a small deformation wavelet basis function highly matched with the current signal form is dynamically generated, and a feature value calculation method with a form proximity penalty term is introduced, so that precise matching and enhancement of weak short circuit signals are realized, non-fault interference is effectively eliminated, and the signal-to-noise ratio and accuracy of detection are significantly improved.

The present application relates to the technical field of electric vehicle reducer fatigue analysis, and particularly relates to a method for preparing a frequency domain of a gear fatigue load spectrum of an electric vehicle reducer based on CCWOA, comprising: S1: constructing a bending stress load spectrum of a reducer gear of an electric vehicle; S2: generating an optimal wavelet base function based on discrete wavelet parameter optimization of an energy leakage criterion; S3: performing threshold optimization based on a CCWOA algorithm to obtain an optimal threshold; the CCWOA algorithm introduces a Logistic-Tent chaotic mapping mechanism and a cosine iteration strategy in the WOA algorithm; S4: performing discrete wavelet transform on the bending stress load spectrum of the reducer gear of the electric vehicle based on the optimal wavelet base function and performing load spectrum frequency domain coding through the optimal threshold; and S5: realizing fatigue analysis of the electric vehicle reducer based on a gear fatigue acceleration load spectrum of the electric vehicle reducer. The present application realizes high-precision compression of the reducer gear load spectrum and equivalent retention of fatigue damage, and provides a reliable scheme for fatigue analysis of the electric vehicle reducer.

This invention relates to a wavelet threshold-based method and system for denoising cold test vibrations, comprising the following steps: constructing a vibration parameter database based on the acquired diesel engine cold test vibration signal; performing wavelet decomposition on the vibration parameters in the database according to the selected wavelet basis function and decomposition level to obtain wavelet coefficients; performing wavelet reconstruction on the vibration parameters based on a threshold function to obtain denoised cold test vibration parameters, specifically: shrinking the wavelet coefficients within the threshold range to zero, while keeping the wavelet coefficients outside the threshold unchanged; and processing the acquired diesel engine cold test vibration signal using the denoised cold test vibration parameters to obtain a denoised cold test vibration signal. The threshold function is used to solve the oscillation and distortion problems existing in the denoising process, thereby improving the denoising effect of the cold test vibration signal.

The application discloses a circuit breaker feature extraction method and device based on a wavelet threshold, equipment and a medium, and relates to the field of power data analysis.The method comprises the following steps: determining a wavelet base function according to the data characteristics of time-series current data of an intelligent circuit breaker to be analyzed; performing wavelet threshold denoising on the time-series current data under a plurality of preset decomposition layers respectively according to the wavelet base function, so as to obtain a plurality of groups of denoised current data; wherein, the wavelet threshold denoising is performed based on a wavelet threshold function with an adjustable threshold processing amplitude adjustment factor; determining the correlation degrees of the time-series current data and each group of denoised current data respectively according to a preset correlation degree screening function, and screening a plurality of groups of denoised current data according to the correlation degrees to obtain optimal denoised data; and extracting error features of the intelligent circuit breaker according to the time-series current data and the optimal denoised data. Through the implementation of the application, the accuracy of feature extraction of the data of the intelligent circuit breaker can be improved.

The application discloses a curtain wall system connecting node stress state recognition method based on deep learning, and the method comprises the following steps: collecting original stress time series data and synchronous environment temperature data of a curtain wall connecting node to form a sample set; based on dynamic time clustering, the original stress time series data of each sample is segmented and divided, and an adaptive feature mapping function combining segmented information and a wavelet base function is used for feature mapping, and then dimension reduction is performed through principal component analysis to obtain a dimension reduction feature vector; a deep learning recognition network is constructed, and a double supervision loss function containing a weighted time series focal loss and an attention consistency regularization term is used to train the deep learning recognition network; and the obtained enhanced feature sequence and external physical features are input into the trained deep learning recognition network to output a stress state category of a node to be recognized. The application realizes automatic and engineering deployable transformation from original multi-source monitoring data to a clear state grade.

A transformer short-circuit impedance three-phase synchronous testing method and system, comprising: synchronously collecting different signals on the high-voltage side of the transformer; dynamically adjusting the center frequency and bandwidth parameters of the Morlet wavelet base function based on the real-time frequency; respectively performing wavelet decomposition on the different signals based on the adjusted Morlet wavelet base function to obtain the complex wavelet coefficient matrix corresponding to the different signals; purifying the complex wavelet coefficient matrix of each signal; selecting an effective calculation interval based on the maximum ridge line area of the modulus matrix of the purified different signals, and solving the optimal estimation value of the short-circuit impedance through the least square method according to the elements of the purified complex wavelet coefficient matrix of the different signals in the effective calculation interval; and correcting the calculated short-circuit impedance value through temperature and frequency. The present application ensures the strict alignment of three-phase data and effectively supports the fault diagnosis based on three-phase unbalance degree.

This invention provides a method, system, and storage medium for wavelet basis extraction of digital images, belonging to the field of digital image data processing technology. The method includes: acquiring the PSF field, image tilt field, and amplitude sampling sequence covering a target image region, and establishing the spatial correspondence among the three; for each desired location within the target image region, extracting the PSF signal from the PSF field, extracting the image slope value, and constructing a sampling path along the slope direction in the PSF signal based on the slope value; performing amplitude superposition processing on the PSF signal along the slope direction to generate the wavelet basis function corresponding to the desired location; performing a one-dimensional convolution operation on the wavelet basis function and the amplitude sampling sequence along the vertical direction to generate a single-channel composite image corresponding to the desired location, and arranging all single-channel composite images according to their spatial positions to form the final image composite profile. This invention achieves an accurate method for wavelet basis extraction and digital image synthesis in digital images.

This invention discloses a parameter tuning method and system for an online transient voltage stability assessment system for new energy power grids, comprising: acquiring and constructing a set of typical instability scenarios as the basic data environment for parameter tuning; constructing a multi-objective optimization function based on the set of typical instability scenarios, with transient voltage stability assessment accuracy and index discrimination as joint optimization objectives; determining the optimal values ​​of weight coefficients α and β for calculating the transient voltage stability index WMTVSI based on the multi-objective optimization function; wherein the transient voltage stability index WMTVSI is calculated based on wavelet time-frequency energy characteristics; determining the optimal combination of wavelet basis functions and decomposition levels for discrete wavelet transform; and performing parameter tuning based on the optimal values ​​of weight coefficients α and β, the optimal wavelet basis functions, and the number of decomposition levels, so that the online transient voltage stability assessment system for new energy power grids calculates the transient voltage stability index WMTVSI based on the optimal values ​​of weight coefficients α and β, the optimal wavelet basis functions, and the number of decomposition levels.

The application discloses a wavelet base function selection method and device for identifying seismic velocity pulse, and the method comprises the following steps: performing integral calculation on seismic record data, and calculating an energy concentrated interval of an original velocity time history; iteratively analyzing the original velocity time history by using different wavelet base functions in sequence, and obtaining time history distribution data and the energy concentrated interval corresponding to each wavelet base function; performing down-sampling on the energy concentrated interval corresponding to each wavelet base function, and obtaining the down-sampled interval of each wavelet base function; calculating a first correlation coefficient of the down-sampled interval of each wavelet base function and the energy concentrated interval of the original velocity time history; calculating a second correlation coefficient of the velocity pulse time history identified by each wavelet base function and the original velocity time history; and selecting the wavelet base function by using the size order of the first correlation coefficient and the second correlation coefficient. The application can improve the fidelity of velocity pulse identification.

The present application belongs to the technical field of radio frequency fingerprint identification, and discloses a radio frequency fingerprint identification method and system based on an adaptive wavelet threshold denoising algorithm.The method comprises the following steps: receiving a radio frequency signal; training the received radio frequency signal, including: performing standardization processing on the received radio frequency signal; extracting a wavelet base function and a wavelet threshold matched with the radio frequency signal; performing denoising processing on the radio frequency signal after standardization processing based on the wavelet base function and the wavelet threshold; adding the radio frequency signal before denoising processing and the radio frequency signal after denoising processing to serve as the input of a classification network to obtain the prediction probability of each category to which the radio frequency signal belongs; updating the network parameters according to the prediction probability of each category to which the radio frequency signal belongs; and repeating the training of the received radio frequency signal until the network converges.Thus, on the basis of solving the time-consuming problem of manually selecting a wavelet base in a general wavelet threshold denoising algorithm, the performance of a subsequent identification network in a low signal-to-noise ratio environment is improved.

This invention provides a complex-domain iterative parameter separation method for extracting pre-stack depth-domain seismic wavelets, named CISPE-PreStack, belonging to the field of energy and resource seismic exploration. This method extracts the combined scale factor, mean wavenumber, phase, and amplitude of the depth-domain wavelet basis functions, employing angle-separated optimization to achieve high-precision extraction of pre-stack depth-domain seismic wavelets. It obtains pre-stack depth-domain seismic wavelets with ideal shapes, effectively maintaining waveform consistency under multiple incident angles, significantly improving the matching degree between synthetic and actual seismic records in the depth domain, significantly reducing computational complexity, and avoiding getting trapped in local optima. The method is practical, robust, and scalable, effectively supporting higher-precision pre-stack depth seismic inversion and reservoir prediction.

The application relates to a self-construction method of a wavelet network approximation framework, which comprises the following steps: setting a nonlinear function, obtaining training data, and processing the nonlinear function by using a wavelet network approximation framework, which comprises the following steps: determining an initial wavelet space for approximating the nonlinear function by using an initial wavelet frequency estimator of the wavelet network approximation framework; and increasing wavelet base functions on the basis of the initial wavelet space to further approximate the nonlinear function based on a wavelet base function increasing mechanism of the wavelet network approximation framework. The scheme can reduce the calculation complexity and accelerate the neural network training speed.

The present application relates to a kind of based on pressure waveform analysis's indwelling catheter blockage early warning method and system, it is related to monitoring early warning technical field, including acquisition contains original pressure waveform data, instantaneous flow rate data and electrocardiogram synchronous signal data multisource monitoring data set;Original pressure waveform data is executed based on Daubechies wavelet base function four-layer multiscale decomposition processing, generates multiscale waveform component set;Combining patient basic information constructs catheter downstream vascular bed fluid mechanics impedance model, deduces and generates catheter downstream vascular bed impedance change index;Through sliding time window, dynamic baseline drift modeling is carried out, and adaptive catheter blockage risk benchmark threshold is set;Construct patient-specific risk factor graph structure, through recurrent neural network, time series prediction analysis is carried out to feature sequence, generates future risk evolution trend index;Generate three-level early warning signal.The present application has beneficial effect for significantly improving the sensitivity and specificity of catheter blockage early warning.

This invention relates to the field of hydropower system technology, specifically to a joint denoising method for weak fault signals of turbine runners based on improved wavelet thresholding and Kalman filtering. The method includes the following steps: acquiring acoustic signals from the turbine runner under normal, 3-crack, and 8-crack operating conditions; performing wavelet decomposition on the original signal, selecting the optimal wavelet basis function and decomposition level, and calculating the universal threshold and minima threshold; processing the wavelet coefficients using an improved threshold function and reconstructing the signal to obtain a preprocessed signal; inputting the preprocessed signal into a Kalman filter module, executing a prediction and update process to obtain the optimal estimate; and outputting a denoised signal, the denoising effect of which can be evaluated using various metrics. This invention optimizes the suppression effect of high-frequency random noise by improving the wavelet threshold function and combines it with Kalman filtering to remove low-frequency dynamic noise, achieving layered and precise denoising, adapting to different fault conditions, and effectively preserving weak fault characteristics.

This invention relates to the field of zero-sequence current detection technology, and discloses a zero-sequence current detection method and system based on current synthesis. The method includes: detecting three-phase current using phase current sensing coils to obtain a synthesized current value sequence; directly measuring the current using a zero-sequence current sensing coil to obtain a directly measured current value sequence; fusing the directly measured current value sequence and the synthesized current value sequence with confidence levels to obtain a fused current value sequence; denoising the fused current value sequence using a target wavelet basis function and a target decomposition level to obtain a denoised current value sequence; and extracting fault features from the denoised current value sequence to obtain zero-sequence current detection data. This invention can improve the accuracy and stability of zero-sequence current detection and enhance the ability to identify early or minor faults in power equipment.

This invention provides a method, system, device, and medium for detecting partial discharge in cables, relating to the field of cable partial discharge. The method includes: acquiring the original signal of the partial discharge pulse current of the target cable, and performing multi-scale layered threshold denoising processing on the original signal using wavelet basis functions to obtain a denoised signal to be processed; wherein frequency layers with frequencies below a preset frequency threshold are subjected to threshold shrinkage processing to suppress power frequency harmonics, and frequency layers with frequencies above or equal to the preset frequency threshold are subjected to bandpass filtering to filter out white noise; the signal to be processed is processed using an empirical mode decomposition algorithm to obtain intrinsic mode function components and construct a multi-dimensional feature set; the multi-dimensional feature set is input into a pre-trained classification model to obtain the detection result. This invention can effectively solve the problem of reduced discriminability of discharge pulses due to the superposition of power frequency harmonics and white noise in complex environments, and effectively improve the accuracy of partial discharge detection in target cables.