47 results about "Complex wavelet transform" patented technology

The invention discloses a vacuum arc-extinguishing chamber contact surface condition evaluation method and system, and belongs to the technical field of vacuum circuit breakers. According to the method, short-time Fourier transform noise reduction is improved by adopting an adaptive window length, signals are enhanced in combination with dual-tree complex wavelet transform, and data are dynamically normalized by improving K-means clustering; according to the constructed lightweight CRNN model, residual connection and an SE module are introduced into a convolution module, and a bidirectional quasi-recurrent neural network is adopted by a circulation module to capture time sequence features; the mixed loss function is combined with an AdamW optimizer, and the convergence stability is improved through L2 regularization; and dynamically evaluating the ablation degree of the contact based on a fuzzy logic algorithm. According to the method, through time-frequency feature fusion and lightweight network design, high-precision classification of direct-current pre-breakdown waveforms is realized, the problems of degradation, over-fitting and slow convergence of a traditional model are solved, and an intelligent solution is provided for vacuum arc-extinguishing chamber insulation performance evaluation.

The present disclosure relates to digital watermarking systems that may use visual masks to optimize watermark embedding in digital imagery. A visual mask provides guidance for adjusting digital watermark signal strength, enabling improved trade-offs between watermark imperceptibility and robustness. Multiple embodiments generate visual masks including: (1) a Perceptual Modeling Candidate approach using contrast masking and texture classification based on standard deviation mapping; (2) a wavelet-based approach using Dual-Tree Complex Wavelet Transform for translation-invariant frequency analysis; (3) artificial intelligence approaches employing convolutional neural networks trained to optimize embedding strength while minimizing perceptual distance metrics such as LPIPS; and (4) LPIPS threshold masking that determines optimal embedding strengths by testing multiple candidate strengths. Visual masks enable content-adaptive digital watermarking that places stronger signals in textured regions while maintaining imperceptibility in flat regions, improving visibility-robustness performance compared to uniform embedding approaches.

The invention belongs to the technical field of computer vision and computer graphics, and discloses a multi-view three-dimensional reconstruction method and device based on cross-domain feature fusion and a medium, and the method comprises the steps: carrying out the cross-feature-domain coding of an initial feature token generated by a multi-view image, according to the coding, characteristics are decomposed into low-frequency components and high-frequency components through dual-tree complex wavelet transform, amplitude modulation is carried out on the high-frequency components to enhance details, and space-frequency fusion characteristics are generated; variance embedding weighted combination is carried out on the multi-view space-frequency fusion features, the combination generates a weight by calculating the variance of each feature token, adaptive weighted clustering is carried out based on the weight, and fused multi-view features are generated; and performing three-source attention decoding on the fused features, and performing cross-domain attention calculation and up-sampling by taking static embedding as query, taking the space-frequency fused features as keys and taking the fused multi-view features as values, thereby finally generating a three-dimensional voxel reconstruction result of the target object.

The invention provides a millimeter-wave radar personnel perception method based on a time-frequency domain and a deep CNN, and relates to the technical field of radar signal processing, and the method comprises the steps: carrying out the preprocessing, spectrogram conversion and enhancement of a millimeter-wave radar echo signal; spatial features are extracted by using depth separable convolution of a residual structure, time-frequency features are extracted by combining dual-tree complex wavelet transform and attention-enhanced cavity convolution, motion features are extracted through optical flow estimation and three-dimensional convolution, and the three features are adaptively fused; the method comprises the following steps: constructing a dynamic spatio-temporal reasoning network, obtaining key spatio-temporal features by using a recurrent neural tensor network, a non-local neural network and deformable convolution, extracting multi-scale features and performing adversarial feature alignment, and completing personnel target classification through a dynamic routing mechanism based on a capsule network. According to the method, clutter interference can be effectively suppressed, space, time frequency and motion information is fully utilized, accurate perception of personnel targets is realized, and the classification accuracy is improved.

The invention discloses a guided wave self-adaptive enhanced time reversal imaging method and system for vehicle structure damage and a computer program product, and belongs to the technical field of aircraft structure health monitoring. The envelope of the damage scattering signal is calculated through Shannon continuous complex wavelet transform and Hilbert transform, and m wave bands with the maximum peak value are selected as potential damage scattering signal wave bands; the damage factor of each potential wave band is calculated, the wave band with the maximum damage factor is a real damage scattering wave band, other signals are set to be zero, and therefore guided wave self-adaption enhancement is achieved, and the signal-to-noise ratio is increased; on the basis of extraction of a damage scattering wave band, a time reversal focusing imaging method is adopted, imaging results generated when all the sensors serve as excitation are superposed, and the point with the maximum pixel value is the damage position. The implementation process is accurate and efficient, and damage positioning can be achieved through the piezoelectric sensor monitoring network on the main load-bearing structure.

The invention provides a millimeter-wave radar personnel perception method based on a time-frequency domain and a deep CNN, and relates to the technical field of radar signal processing, and the method comprises the steps: carrying out the preprocessing, spectrogram conversion and enhancement of a millimeter-wave radar echo signal; spatial features are extracted by using depth separable convolution of a residual structure, time-frequency features are extracted by combining dual-tree complex wavelet transform and attention-enhanced cavity convolution, motion features are extracted through optical flow estimation and three-dimensional convolution, and the three features are adaptively fused; the method comprises the following steps: constructing a dynamic spatio-temporal reasoning network, obtaining key spatio-temporal features by using a recurrent neural tensor network, a non-local neural network and deformable convolution, extracting multi-scale features and performing adversarial feature alignment, and completing personnel target classification through a dynamic routing mechanism based on a capsule network. According to the method, clutter interference can be effectively suppressed, space, time frequency and motion information is fully utilized, accurate perception of personnel targets is realized, and the classification accuracy is improved.

The application provides a millimeter wave radar personnel sensing method based on a time-frequency domain and a deep CNN, relates to the technical field of radar signal processing, and comprises preprocessing, spectrum conversion and enhancement of a millimeter wave radar echo signal; spatial features are extracted by using a deep separable convolution with a residual structure, time-frequency features are extracted by combining a dual-tree complex wavelet transform and an attention-enhanced hollow convolution, and motion features are extracted by optical flow estimation and three-dimensional convolution, and the three are adaptively fused; a dynamic space-time reasoning network is constructed, key space-time features are obtained by using a recurrent neural tensor network, a non-local neural network and a deformable convolution, multi-scale features are extracted and aligned in an adversarial manner, and a dynamic routing mechanism based on a capsule network is used to complete personnel target classification. The application can effectively suppress clutter interference, fully utilize spatial, time-frequency and motion information, realize accurate sensing of personnel targets, and improve classification accuracy.

The present invention discloses a method and system for evaluating the surface condition of contacts of a vacuum interrupter, which belongs to the technical field of vacuum circuit breakers. The method uses adaptive window length to improve short-time Fourier transform for noise reduction, combines dual-tree complex wavelet transform to enhance signals, and dynamically normalizes data through improved K-means clustering; a lightweight CRNN model is constructed, the convolution module introduces residual connection and SE module, and the recurrent module uses a bidirectional quasi-recurrent neural network to capture time series features; the hybrid loss function is combined with the AdamW optimizer to improve the convergence stability through L2 regularization; and the degree of contact ablation is dynamically evaluated based on a fuzzy logic algorithm. The method achieves high-precision classification of DC pre-breakdown waveforms through time-frequency feature fusion and lightweight network design, solves the problems of traditional model degradation, overfitting and slow convergence, and provides an intelligent solution for the insulation performance evaluation of vacuum interrupters.

The invention discloses a color three-dimensional image zero-watermark processing method based on eight-element matrix factorization, which comprises the following steps of: firstly, cutting square invariant feature region images at the centers of left and right viewpoints of a three-dimensional image, and carrying out non-subsampled dual-tree complex wavelet transform on the invariant feature region images in different channels; averaging the four low-frequency sub-bands obtained by each channel image to obtain a low-frequency mean sub-band, then performing block scrambling on the low-frequency mean sub-band from each channel, selecting a part of sub-blocks to construct an octuple matrix, and performing real number field octuple singular value decomposition on the octuple matrix; obtaining a maximum singular value matrix and then binarizing the maximum singular value matrix to obtain a robust binary feature matrix; and finally, carrying out XOR on the binary watermark which is scrambled and encrypted by the four-wing chaotic system and the cyclic code and the robust binary feature matrix to obtain a zero-watermark image. Before watermark authentication, a KAZE matching algorithm is used to correct left and right viewpoints of a to-be-authenticated stereo image. The method has relatively high robustness on common non-geometric attacks and geometric attacks.

The present invention discloses a method for actually measuring the attenuation coefficient of broadband laser ultrasonic Lamb waves in metal thin plates, belonging to the field of nondestructive testing. Data acquisition stage: Use a nanosecond laser to set a point source excitation on the surface of an aluminum alloy thin plate, and then use a scanning laser Doppler vibrometer (SLDV) to measure the wave field and collect linear wave field data. Data processing stage: Use the time-frequency analysis of continuous complex wavelet transform to obtain the time-frequency spectrum of the one-dimensional wave field signal. Determine the effective analysis range, including the time upper limit and the frequency upper limit. Extract the amplitude data represented by the intercept line. Attenuation characteristic analysis stage: Extract the peaks of the amplitude data at multiple distance points to obtain discrete points of the amplitude change with distance at a specific frequency. The present invention provides a method for actually measuring the attenuation characteristics of broadband laser ultrasonic Lamb waves. The solution of the attenuation coefficient provides an important theoretical reference for the calculation of the effective detection range and the selection of the filtering frequency of damage scattered waves.

The application discloses a virtual viewpoint image quality evaluation method and system based on double-tree complex wavelet transform. The method comprises the following steps: using an algorithm to perform feature matching on a reference image; performing multi-scale double-tree complex wavelet transform on the matched reference image and a virtual viewpoint image for multiple times to obtain a first wavelet subband set corresponding to the matched reference image and a second wavelet subband set corresponding to the virtual viewpoint image; calculating gradient amplitude similarity between third-level wavelet subbands of the first wavelet subband set and the second wavelet subband set, and using the gradient amplitude similarity to calculate a texture distortion score of the virtual viewpoint image; calculating structural similarity between fifth-level wavelet subbands of the first wavelet subband set and the second wavelet subband set, and using the structural similarity to calculate a structure distortion score of the virtual viewpoint image; and calculating a quality score of the virtual viewpoint image according to the texture distortion score and the structure distortion score. The application can accurately evaluate the quality of the virtual viewpoint image.

The invention discloses a common transformer side single-phase intelligent electric meter electric quantity acquisition accuracy improving system, and relates to the technical field of electric variable measurement. According to the system, sampling rate adaptive switching is realized through the working condition modal identification module and the dynamic sampling control module, full-band power accurate metering is carried out by using complex wavelet transform, online calibration and optimization are carried out by means of the parameter self-learning module, the accuracy and adaptability of an electric meter in a complex power grid environment are comprehensively improved, and the accuracy and adaptability of the electric meter in the complex power grid environment are improved. The problems that the fixed sampling rate of a traditional intelligent electric meter is not matched with the dynamic working condition, the harmonic and inter-harmonic power metering is lacked, and the working condition self-adaptive capacity is lacked are effectively solved, the electric meter achieves self-adaptive balance of precision and efficiency in a complex power grid environment, full-band accurate metering of fundamental wave, harmonic wave and inter-harmonic power is completed, and the electric energy utilization rate is improved. And the metering accuracy and stability of the electric meter in long-term operation are ensured through a self-learning mechanism.

The invention discloses a high-speed magnetic suspension track settlement monitoring method based on wavelet transform, which comprises the following steps of: extracting global phase information of a power curve in an existing high-speed magnetic suspension millimeter wave communication system in a train running process, extracting a local phase of a signal by adopting complex Morlet wavelet transform, and calculating the settlement of the high-speed magnetic suspension track; and a global mapping relation between the phase difference and the orbit settlement amount is established, space-frequency domain analysis is performed on signals through wavelet transform, and signal phase difference information is extracted in combination with analysis of a wavelet coefficient envelope and a ridge line, so that high-precision sensing and monitoring of the orbit settlement state and the evolution trend thereof are realized. According to the method, the bottlenecks of an existing track monitoring method in the aspects of complex operation, poor real-time performance, high cost and the like are broken through, the problems that a modern monitoring technology is poor in settlement adaptability, expensive in equipment and the like are solved, additional hardware equipment is not needed, the system construction, operation and maintenance cost is reduced, high-precision, low-cost and real-time monitoring is achieved, and the method is suitable for popularization and application. And a new technical path is provided for dynamic tracking of track settlement and deformation.

The application discloses a motor output state evaluation method and system, comprising collecting the stator current signal of a target motor, and performing zero-phase band-pass filtering pretreatment on the stator current signal of the target motor to obtain a pretreated current signal. The application firstly retains the phase characteristics of the original signal through zero-phase band-pass filtering pretreatment, then accurately extracts an instantaneous frequency signal through complex wavelet transform, constructs a frequency-adaptive comb notch filter set relying on the motor working specification, filters out the operation signal related to the normal operation of the motor in the instantaneous frequency signal, so as to capture the residual frequency fluctuation caused by the dynamic balance fault, and then obtains an enhanced imbalance confidence index by combining the instantaneous spectrum kurtosis and the fractal dimension calculation, thereby improving the accuracy and reliability of the dynamic balance fault evaluation, and more accurately identifying the early dynamic balance abnormality of the motor, providing a reliable basis for the preventive maintenance of the motor, and effectively guaranteeing the operation safety of the transmission system.

The application discloses an infrared image non-uniformity prediction and correction method based on wavelet transform, which comprises the following steps: adopting double-density dual-tree complex wavelet transform to perform multi-scale decomposition on an input infrared image; in a high-frequency subband, detecting a blind element position based on a local variance statistical method; in a low-frequency subband, constructing an autoregressive model, predicting non-uniformity of a background region, and performing accurate processing on a prediction result; combining the prediction results of the high-frequency subband and the low-frequency subband to generate a pre-correction coefficient; and adopting the pre-correction coefficient to perform correction processing on an original infrared image to obtain a corrected image. Through the combination of an adaptive fusion strategy, space-time consistency constraint and dynamic gain and bias updating technology, the problems of non-uniformity noise, balance between details and background, device drift compensation and consistency in a dynamic scene in infrared image processing are solved, and the accuracy and stability of infrared image processing are significantly improved.

The present invention relates to a ground-penetrating radar signal denoising method that combines two-dimensional VMD with DT-CWT. The method first performs one-dimensional VMD on the original two-dimensional ground-penetrating radar noisy data in the time dimension, removes the intrinsic modal components corresponding to the noise according to corresponding judgment criteria, and then reconstructs the data. The reconstructed data is then subjected to one-dimensional VMD in the spatial dimension, and the intrinsic modal components corresponding to the noise are removed and then reconstructed. The reconstructed data after the two-dimensional VMD denoising process is subjected to a discrete dual-tree complex wavelet transform, and a corresponding threshold formula is selected for threshold denoising. The data is then reconstructed by inverse dual-tree complex wavelet transform to obtain the denoised data. This method takes into account the noise distribution of the data in the spatial dimension, performs two-dimensional VMD denoising on the original noisy data, and effectively improves the data signal-to-noise ratio. At the same time, to further improve the data signal-to-noise ratio, a dual-tree complex wavelet transform that can achieve precise reconstruction is used to perform secondary denoising on the data.

The invention relates to the technical field of wind turbine generator monitoring, and discloses a fault diagnosis method and device for a fan gear box, electronic equipment and a storage medium, and the method comprises the steps: obtaining a vibration signal of the fan gear box; calculating a power spectrum of the vibration signal; determining a first type of fault characteristic frequency and a second type of fault characteristic frequency respectively according to a first region and a second region in the power spectrum; performing multi-scale complex wavelet transform on the vibration signal and demodulating envelope to obtain a multi-scale complex wavelet envelope spectrum; analyzing the multi-scale complex wavelet envelope spectrum to obtain a third type of fault characteristic frequency; and performing fault diagnosis on the fan gearbox according to at least one of the first type of fault characteristic frequency, the second type of fault characteristic frequency and the third type of fault characteristic frequency to obtain a fault diagnosis result. Through the above mode, the vibration signals are analyzed in different modes, so that the vibration signals can be comprehensively analyzed, and a more complete fault characteristic frequency is obtained.

This invention discloses a ghost wave suppression method for OBS data based on horizontally extended complex wavelet transform. The method transforms land and water detection data into the complex wavelet domain using horizontally extended dual-tree complex wavelet transform. Then, the envelope energy ratio and phase ratio of the land and water detection data are calculated in different frequency bands of the complex wavelet domain to obtain the land detection amplitude and phase correction operators for each frequency band. These operators are applied to the land detection data to obtain calibrated land detection data. Subsequently, the land and water detection data are subtracted to obtain downlink wave data. Simultaneously, the above data undergoes an inverse complex wavelet transform to obtain a time-domain ghost wave model. Finally, the original land detection data and the ghost wave model are subtracted using least-squares matched subtraction to obtain the final uplink wave data. This invention, through fine calibration in the horizontally extended complex wavelet domain, can more effectively eliminate amplitude and phase differences in land and water detection data, thereby achieving higher-precision uplink and downlink wave field separation and ghost wave suppression.

The application discloses a system for improving the accuracy of power collection of a single-phase intelligent electric meter on the public power supply side, and relates to the technical field of electric variable measurement. The system realizes adaptive switching of a sampling rate through a working condition mode identification module and a dynamic sampling control module, performs accurate power measurement of a full frequency band by using complex wavelet transform, and is online calibrated and optimized with the help of a parameter self-learning module, thereby comprehensively improving the accuracy and adaptability of the electric meter in a complex power grid environment, effectively solving the problems of mismatching of a fixed sampling rate of a traditional intelligent electric meter with dynamic working conditions, missing of harmonic and interharmonic power measurement, and lack of working condition adaptability, enabling the electric meter to realize adaptive balance of precision and efficiency in a complex power grid environment, completing full-band accurate measurement of fundamental wave, harmonic and interharmonic power, and guaranteeing the measurement accuracy and stability of the electric meter in long-term operation through a self-learning mechanism.

The application belongs to the technical field of digital watermark embedding and relates to an adaptive digital watermark embedding method, device and readable storage medium. An image is subjected to double-tree complex wavelet transform decomposition to obtain a low-frequency approximation subband and a high-frequency complex coefficient subband. The subband variance, subband entropy and gradient energy of each high-frequency complex coefficient subband are calculated to obtain the joint score of each high-frequency complex coefficient subband. The amplitude graph of the high-frequency complex coefficient subband with the highest joint score is subjected to non-overlapping block division to obtain a plurality of candidate blocks. The block variance, block gradient intensity and gradient direction consistency of each candidate block are calculated to obtain the block score of each candidate block. The embedding block set is screened based on the block score, the embedding strength of each embedding block is calculated, the amplitude of all pixel positions in each embedding block is subjected to multiplicative embedding, the complex coefficient of the target embedding subband corresponding to each embedding block is reconstructed, and the watermark image is reconstructed based on the reconstructed target embedding subband, the low-frequency approximation subband and the high-frequency complex coefficient subband.

The application belongs to the technical field of computer vision and computer graphics, and discloses a multi-view three-dimensional reconstruction method based on cross-domain feature fusion, equipment and medium, the method comprises the following steps: cross-feature domain coding is carried out on the initial feature token generated by multi-angle images, the coding decomposes the feature into low-frequency and high-frequency components through dual-tree complex wavelet transform, and the amplitude modulation is carried out on the high-frequency component to enhance the details, and the space-frequency fusion feature is generated; the space-frequency fusion features of the multi-angle are weighted and merged through variance embedding, the weight is generated by calculating the variance of each feature token, and adaptive weighted clustering is carried out based on the weight to generate the fused multi-angle feature; three-source attention decoding is carried out on the fused feature, the static embedding is taken as the query, the space-frequency fusion feature is taken as the key, and the fused multi-angle feature is taken as the value, cross-domain attention calculation and up-sampling are carried out, and finally the three-dimensional voxel reconstruction result of the target object is generated.

This invention discloses a brain-computer interface (BCI) teaching demonstration system and control method, belonging to the field of BCI and artificial intelligence education technology. The system includes: a multi-channel EEG headband for synchronously acquiring EEG signals and head posture data; an edge AI hub with a built-in NPU accelerator for dynamically gating and adaptively filtering EEG signals based on head posture data, generating a two-dimensional time-frequency graph through complex Morlet wavelet transform, and using a lightweight RepEEG-Net neural network with structural reparameterization and quantization processing to analyze user intentions in real time, while simultaneously generating visualized teaching data; and a microcontroller execution chassis equipped with a real-time operating system (RTOS) for controlling the actions of the execution mechanism through multi-priority task scheduling. This invention achieves low-latency, highly interference-resistant brain-controlled demonstrations by deploying lightweight intelligent algorithms at the edge, and visualizes the algorithm processing process, significantly improving the real-time performance, robustness, and intuitiveness of the teaching demonstration.

The invention belongs to the technical field of computer vision detection, and provides a wind power blade dynamic vision detection imaging quality control method and system, and the method comprises the steps: an image quality evaluation step: initializing parameters and obtaining a blade surface image, calculating the optimal definition and the optimal information entropy, carrying out the optimization to obtain an optimal parameter combination, and obtaining a high-quality image; and an image definition evaluation step: performing dual-tree complex wavelet transform on the high-quality image, calculating logarithmic energy layer by layer according to scale factors, and adding the logarithmic energy to obtain an image definition value. According to the method, the optimal imaging parameter is automatically obtained through an algorithm, the optimal quality image is ensured to be obtained, and the problem that the image quality is uncontrollable in wind power blade detection in a complex environment is solved; a dual-tree complex wavelet transform method is introduced, so that the accuracy of definition evaluation is further improved on the basis of ensuring the operation speed of an image definition evaluation algorithm; by establishing an image quality evaluation and composite optimization algorithm, imaging parameters such as optimal focal length, focusing and exposure are determined, and an optimal image is obtained.

The present invention discloses an OBN data denoising method and related device. The method comprises: performing a dual-tree complex wavelet transform on the P component data and the Z component data respectively; matching the amplitude spectrum of the Z component data according to the maximum amplitude of the amplitude spectrum of the P component data to obtain the amplitude spectrum amplitude value of the Z component data that matches the P component; calculating the amplitude spectrum of the Z component data after amplitude matching; calculating the amplitude matching coefficient according to the amplitude spectrum of the Z component data after amplitude matching; calculating the complex wavelet coefficient of the Z component data after amplitude matching according to the amplitude matching coefficient; performing a dual-tree complex wavelet inverse transform on the complex wavelet coefficient of the Z component data after amplitude matching to obtain the denoised Z component data. The present invention can significantly suppress Vz noise in the Z component data, and the signal-to-noise ratio of the denoised Z component gather data and stacked profile is significantly improved.

The invention discloses a data enhancement method based on dual-tree complex wavelet transform, and mainly solves the problems that a non-stationary signal data enhancement method in the prior art lacks multi-scale analysis capability, cannot fully retain local key features and is insufficient in model generalization capability. According to the scheme, the method comprises the following steps: 1) preprocessing an original signal to enable the original signal to meet the input requirement of dual-tree complex wavelet transform (DTCWT); 2) performing multi-scale decomposition on the signal by adopting DTCWT, and separating a low-frequency approximate component and a high-frequency detail component; 3) selecting a random zero sequence to replace the RZSR or a random noise sequence to replace the RNSR according to an enhancement requirement; 4) generating a random sequence, replacing high-frequency detail components, and meanwhile, retaining low-frequency components; and 5) reconstructing the signal through the inverse DTCWT, generating an enhanced signal, and outputting an enhanced signal. The method can effectively enhance the diversity of data, retains the global characteristics and local detail characteristics of signals, and can be used in the technical field of wireless communication.

This invention discloses a motor fault diagnosis method based on a large language model, belonging to the field of motor fault diagnosis. The method includes: first, collecting three-phase current data; then, using dual-tree complex wavelet transform to extract the amplitude and phase information of multi-layer complex wavelet coefficients as signal fault features, avoiding manual feature selection; next, constructing a multi-layer feature extraction and hierarchical weighted domain adversarial collaborative training framework that integrates grouping and Browsing optimization to extract multi-scale features and enhance domain invariance, thereby improving cross-condition generalization ability; finally, aligning the extracted signal embedding and text embedding across modalities through category-level comparative learning, and fine-tuning the large language model to recognize signal modes. This invention improves fault classification accuracy while enhancing model generalization and achieving textual semantic interpretation of diagnostic results.

A method of estimating a micro-seismic cloud related to a hydraulic fracturing operation. The method includes receiving a treating pressure signal generated during hydraulic fracture propagation, calculating a complex wavelet transform coefficient on the received treating pressure signal, calculating energy of the received treating pressure signal at multiple scales to create a signal-energy scalogram, normalizing the signal-energy scalogram to create a normalized signal-energy scalogram, and establishing a correlation between signatures of dynamic fracture propagation modes identified in the normalized signal-energy scalogram and corresponding fracture propagation mode influence on temporal development of a micro-seismic events cloud.

The application provides a PE pipeline multi-source signal denoising method based on a dual-tree complex wavelet transform, and belongs to the technical field of signal denoising, and the method comprises the following steps: S1, multi-source signal acquisition and pretreatment; S2, signal collaborative calibration; S3, dual-tree complex wavelet decomposition; S4, noise level estimation; S5, adaptive threshold denoising; S6, signal reconstruction; S7, effect evaluation and optimization. The application adopts a single pendulum wave signal as a benchmark signal, and assists in time synchronization and frequency calibration, thereby improving the accuracy of signal processing, effectively suppressing various noise interferences, and significantly improving the signal-to-noise ratio (SNR) of the denoised signal.

The invention provides an anti-counterfeiting image texture feature extraction method and system, and the method comprises the steps: carrying out the YCbCr color space conversion of an anti-counterfeiting image, carrying out the two-dimensional dual-tree complex wavelet transformation of Y, Cb and Cr components, and obtaining a multi-scale multi-direction high-frequency sub-band coefficient matrix; energy of each high-frequency sub-band is calculated, global texture complexity parameters are determined based on the energy, a dynamic selection threshold value is generated, and the high-frequency sub-bands with the energy exceeding the threshold value are effective texture sub-bands; for the effective texture sub-band, calculating information entropy, determining the size of a sub-block, dividing the sub-block, and reserving the sub-block of which the standard deviation is greater than the mean value of the standard deviation of the sub-block in the sub-band; the effective texture sub-band reserved sub-block coefficients are scanned and spliced into a one-dimensional feature vector according to a Hilbert curve, sliding windows with odd lengths are used for traversing, a bit 1 is generated when window center coefficients are larger than a left neighborhood coefficient mean value and a right neighborhood coefficient mean value at the same time, otherwise, a bit 0 is generated, and an anti-fake image texture feature hash sequence is generated through splicing.

The present application relates to the field of image processing technology, and discloses a method and system for detecting defects in plastic packaging materials in a radiation environment based on machine vision. The method comprises: collecting radiation-resistant images in a gamma-ray environment through a high-radiation-tolerant image sensor shielded by lead-tungsten alloy, and performing adaptive contrast enhancement. Defect features, including microcracks, bubbles, deformation, and color difference, are extracted using dual-tree complex wavelet transform and HSV color space. Multi-dimensional features are input into the AR‑DCNN network for multi-task learning to identify and score defect types. Based on the Weibull distribution and random forest algorithm, the radiation resistance life of the plastic packaging material is predicted, and the material degradation curve and safety threshold warning are obtained. The present application realizes the standardized processing of quantitative indicators of radiation damage and effectively improves the prediction accuracy.