15 results about "Wavelet filter" patented technology

The application discloses a kind of based on adaptive wavelet decomposition's rotating machinery fault analysis method and system, wherein, method includes the vibration signal of the rotating machinery to be analyzed is collected and data preprocessing is carried out, obtains data sample;Depth feature extraction processing is carried out to the data sample by feature extraction module, and depth feature is obtained;The depth feature is input to adaptive wavelet decomposition model, and target decomposition feature is output and obtained;The target decomposition feature is subjected to fault type discrimination processing, and obtains fault analysis result.The adaptive wavelet decomposition model is carried out in the embodiment of the application wavelet filter and the automatic learning of wavelet decomposition route, and then separates fault component and interference component in rotating machinery fault analysis task, effectively improves the anti-noise performance of method, and can be widely applied in intelligent monitoring technical field.

The invention discloses a GPS (Global Positioning System) and inertial navigation fused high-precision navigation method, aiming at the problems that a GPS signal is easily shielded and interfered and an inertial navigation error is accumulated along with time, high-precision navigation is realized through data preprocessing, a self-adaptive fusion algorithm, GPS signal interruption processing and fusion result optimization. In the data preprocessing, multi-path effect detection and correction are performed on GPS data, and zero offset error compensation is performed on inertial navigation data; the adaptive fusion algorithm constructs a model based on a fuzzy logic system, and updates a fusion weight in real time; when a GPS signal is interrupted, map matching is adopted to assist navigation, and the prediction model is utilized to compensate an inertial navigation error; and a fusion result is subjected to wavelet filtering and smoothing processing, and the accuracy is ensured by using consistency check. The method can effectively overcome the limitation of independent use of GPS and inertial navigation, realizes high-precision, continuous and reliable navigation in a complex environment, and improves the adaptability and stability of a navigation system.

The invention provides an easily-confused emotion recognition method based on multi-path feature extraction and gating fusion. The method can be applied to scenes such as human-computer interaction, emotion calculation and psychological health auxiliary diagnosis. According to the method, a feature path is constructed for each group of emotions aiming at three groups of easily-confused emotions of happiness-angry, fear-surprising and sadness-neutral, each path adopts different wavelet parameter sets to generate a multi-scale wavelet filtering kernel, and multi-path time-frequency features are extracted from the same voice signal. And inputting each path output into a gating attention mechanism module, calculating a path weight and performing weighted summation to obtain a fusion feature for easily confused emotion discrimination, and inputting the fusion feature into a classifier to output an emotion category. According to the method, a wavelet filtering kernel group emphasizing different emotion categories is constructed, a multi-path feature extraction structure is generated, and fusion is performed by using a gating mechanism, so that the model can realize easy-to-confusion emotion recognition according to feature contributions of different paths, and the easy-to-confusion emotion recognition accuracy of the model is improved.

The invention belongs to the field of fault diagnosis, and particularly relates to a bearing single-source-domain generalization fault diagnosis method based on wavelet adaptive causal decoupling, and the method comprises the steps: carrying out the time domain decomposition and adaptive feature extraction of a source domain sample based on a training set and a test set, and obtaining a global feature; in the second convolutional neural network, introducing a causal decoupling strategy based on a binary mask, and displaying and decoupling the extracted global features into causal features and non-causal features; and performing second convolutional neural network training, establishing a fault classifier, and determining causal purification loss based on the fault classifier. In the feature extraction stage, multi-scale time-frequency decomposition of mechanical vibration signals is achieved by adaptively optimizing wavelet filter parameters, low-frequency stable information and high-frequency impact features can be effectively separated, the stability and discrimination of feature expression can still be kept even in a strong-noise and multi-component coupling environment, and the method is suitable for large-scale popularization and application. Therefore, the accuracy and robustness of fault feature extraction are remarkably improved.

The invention relates to the technical field of crossing of artificial intelligence and tobacco processing, discloses a cut tobacco feeding moisture regulation and control method and system based on LightGBM, and aims to solve the problems of moisture signal distortion and low regulation and control precision caused by dust interference. The method comprises the following steps: acquiring dust concentration and noise-containing moisture signals of a production line in real time; dynamically generating an optimal wavelet filtering parameter through a nonlinear mapping model based on the dust concentration; performing adaptive wavelet filtering on the moisture signal to reconstruct a pure signal; inputting the pure signal into a pre-trained LightGBM model to predict a moisture value; and generating a regulation and control instruction according to the deviation between the predicted value and the target value to realize closed-loop control. The system comprises a working condition sensing module, a signal acquisition module, a filtering parameter dynamic generation module, an adaptive filtering module, a moisture prediction module and a regulation and control instruction generation module. Through decoupling integration of dynamic filtering and an existing LightGBM model, on the premise that an original model is not changed, the moisture regulation and control precision and the response speed are remarkably improved, and the engineering transformation cost is reduced.

This invention relates to a hyperspectral image demixing method based on a graph wavelet transform convolutional network, belonging to the field of hyperspectral image demixing technology. The method includes: using a Mexican Hat graph wavelet filter to perform frequency domain decomposition on the undirected graph of the original hyperspectral image, obtaining low-frequency and high-frequency components, which are then input into a parallel dual-branch feature encoder network for feature learning, extraction, and adaptive recalibration. Enhanced low-frequency and high-frequency features are obtained and fused element-wise to obtain multi-frequency comprehensive features, which are then input into an endmember constraint and reconstruction module for abundance estimation. This yields a reconstructed hyperspectral image, which is compared with the original hyperspectral image. A multi-dimensional joint loss function is constructed and optimized to obtain a hyperspectral demixing model for hyperspectral image demixing. This method aims to solve the technical problem of existing technologies' inability to effectively distinguish the mixing characteristics of high-frequency boundary regions and low-frequency homogeneous regions, leading to boundary blurring and spectral distortion.

The invention discloses a method for accurately calculating passive discharge time, and the method comprises the steps: S1, carrying out the wavelet filtering processing, obtaining a time sequence tk and a voltage sequence vk of a passive discharge curve of a passive discharge resistor collected by an oscilloscope, carrying out the wavelet filtering of the voltage sequence vk, and obtaining a voltage sequence vfk after the wavelet filtering; s2, determining a passive discharge starting point, and obtaining a time point tstart corresponding to the starting point; s3, determining a passive discharge termination point and a time point tend corresponding to the termination point; and S4, calculating the passive discharge time. According to the method for accurately calculating the passive discharge time, the defects that in the prior art, manual point taking errors are large, and the accuracy of actually measured passive discharge time cannot be guaranteed are overcome.

The invention relates to the technical field of multi-source acoustic signal separation, in particular to a multi-source acoustic signal separation method based on Doppler wavelet filtering, and the method comprises the following steps: 1, collecting a trackside mixed acoustic signal: arranging a single microphone at a vertical distance d from a track, and collecting a mixed acoustic signal s (t) containing at least two sound sources; 2, calculating pseudo time-frequency distribution: performing improved Doppler wavelet transform (IDT) on s (t) to obtain pseudo time-frequency distribution FTFD: t0 is a time center, f0 is a characteristic frequency, and D (tau) is a dynamic distance between a sound source and a microphone; 3, sound source parameters are extracted, wherein time centers t01, t02,..., t0n and corresponding characteristic frequencies f0j of all sound sources are extracted from FTFD; and step 4, constructing a Doppler wavelet filter: aiming at the target sound source i. The invention provides a multi-source signal separation technology based on pseudo time-frequency distribution and Doppler wavelet filtering, can realize rail-side multi-source acoustic signal separation, and has a better application prospect in wayside acoustic fault diagnosis.

The application relates to the technical field of artificial intelligence and tobacco processing, and discloses a tobacco cut filler moisture control method and system based on LightGBM, which aims to solve the problems of distorted moisture signal and low control precision caused by dust interference. The method comprises the following steps: acquiring the dust concentration of a production line and a moisture signal with noise in real time; generating optimal wavelet filtering parameters through a nonlinear mapping model based on the dust concentration; performing adaptive wavelet filtering on the moisture signal to reconstruct a pure signal; inputting the pure signal into a pre-trained LightGBM model to predict the moisture value; generating a control instruction according to the deviation between the predicted value and a target value to realize closed-loop control. The system comprises six modules, namely, working condition sensing, signal acquisition, dynamic filtering parameter generation, adaptive filtering, moisture prediction and control instruction generation. The application realizes decoupling integration of dynamic filtering and the existing LightGBM model, significantly improves the moisture control precision and response speed without changing the original model, and reduces the engineering transformation cost.

This invention discloses a method, apparatus, and computer-readable storage medium for generating defect samples, relating to the field of deep learning technology. The method includes the following steps: inputting a preset defect image and a preset target image into a trained convolutional neural network to obtain defect image samples; the convolutional neural network includes a wavelet filtering module, a downsampling layer, and an upsampling layer connected in sequence, wherein the wavelet filtering module is used to perform preset wavelet filtering processing on the input image; wherein the downsampling layer includes at least one downsampling module and at least one wavelet filtering module alternately arranged; the upsampling layer includes at least one upsampling module and at least one wavelet filtering module alternately arranged; the number of downsampling modules in the downsampling layer is equal to and corresponds to the number of upsampling modules in the upsampling layer. This invention provides effective defect samples for training defect detection models.

The invention relates to a traffic fusion data acquisition and processing method and a data acquisition system. The method comprises the following steps: acquiring multi-dimensional multi-path signal data acquired by an intelligent lamp post integration system; carrying out data preprocessing on the collected original data; for the preprocessed data, an orthogonal rational wavelet group filter bank is matched with a sensor array and a network to carry out multipath separation on multipath signal data to form multi-dimensional wavelet filter array data; and performing data fusion on the separated multi-path signal data, and performing optimization in the range of the whole network by adopting a wavelet probabilistic neural network model to obtain a numerical value closest to a real value. Compared with the prior art, the method has the advantages that the problem of multi-path separation is solved, multi-dimensional multi-path signals are fused and optimized in the range of the whole network, data accuracy is improved, and urban managers are effectively helped to detect traffic conditions and deal with possible problems.

The invention relates to the technical field of welding seam detection, in particular to a steel structure welding seam defect identification method and system based on a visual function, and the method comprises the following steps: S1, obtaining an assembly type steel structure welding seam image, and identifying a welding seam defect area which comprises fine crack features through a CCD camera acquisition module; s2, extracting initial noise pixel points in the defect area, and performing division processing on each initial noise pixel point according to an AI noise adaptive filtering strategy to obtain a plurality of initial noise pixel areas; the initial noise pixel region comprises a plurality of initial noise pixel points for determining the size of a filtering window; according to the method, accurate suppression of complex field noise is realized through an AI-driven noise adaptive weighted wavelet filtering strategy, accurate identification of fine crack features in the assembly type steel structure welding seam image is realized in combination with the U-Net deep segmentation network, and the assembly type steel structure welding seam defect identification precision and robustness are improved.

The present application belongs to the field of power distribution network management, and relates to a power distribution network harmonic optimization method and system based on electrochemical energy storage, comprising: collecting initial harmonic data and preprocessing the initial harmonic data to obtain processed harmonic data; the harmonic data includes power grid current, energy storage system parameters, PCS power and power grid voltage; the preprocessing includes sequentially performing wavelet filtering processing and direct current component filtering processing on the initial harmonic data; based on the processed harmonic data, fundamental wave parameters are calculated; the fundamental wave parameters include fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power; based on the fundamental wave parameters, fundamental wave components are calculated, and based on the fundamental wave components, a compensation current target value is determined; the fundamental wave components include fundamental wave active component, fundamental wave reactive component and harmonic component; based on the compensation current target value, control instructions are generated through model predictive control and fast repetitive control coordination; the overall power quality and the reliability of operation of the power distribution network are improved.

The application discloses an ECG signal adaptive threshold wavelet filtering method and device, which is applied to ECG signal measurement, and adjusts the wavelet threshold of each layer of wavelet filtering from an initial value by a set step length, and calculates a self-correlation function according to a fast algorithm of the self-correlation function. After each filtering, the self-correlation value of the filtering result is recorded, and when the self-correlation value reaches a maximum value, the corresponding wavelet threshold is recorded as an optimal wavelet threshold. The self-correlation function is calculated from the sequence correlation of the signal itself after denoising according to the correlation and self-correlation characteristics of adjacent time instants of the ECG signal. Finally, the optimal wavelet threshold is used for adaptive filtering of the ECG signal. The application selects the optimal wavelet threshold of the wavelet filtering through the calculation result of the self-correlation function, can evaluate the filtering effect of the wavelet filtering method in the case that there is no clean and noise-free reference signal, and further optimizes the wavelet threshold parameter, and can better retain useful information and obtain a higher signal-to-noise ratio.

The application discloses a kind of railway roadbed security situation identification method and system, it is related to railway roadbed operation and maintenance field, wherein the method comprises: obtaining M area real-time radar information, and according to wavelet filtering algorithm, M area real-time radar information is denoised, and M area denoised radar information is obtained;Build roadbed disease identification model;Based on roadbed disease identification model, respectively, M area denoised radar information is identified, and M roadbed area disease identification result is obtained;Based on the first weather forecast data and the first forecast railway operation data, respectively, M roadbed area disease identification result is carried out development trend prediction, and M roadbed area disease prediction result is obtained, and target roadbed disease survey report is generated in combination with M roadbed area disease identification result.The technical problem that the disease identification accuracy of railway roadbed in prior art is low, comprehensive is poor, leading to the technical problem that the disease survey effect of railway roadbed is not good.