202 results about "Noise component" patented technology

A leakage sound signal denoising method based on a combination of optimized VMD and an improved wavelet threshold, for use in solving the problem in existing noise processing methods of low identification accuracy in processing leakage sound signals of water supply pipe networks. The present invention comprises: acquiring leakage sound signals of a real water supply pipe network, and analyzing noise components and ranges of the leakage sound signals; on the basis of a goshawk optimization algorithm, performing parameter optimization on the number K of decomposition modes and a penalty factor α of VMD to obtain optimal parameters, and using the optimal parameters to construct a variational model; using the variational model to decompose the leakage sound signals to obtain a plurality of intrinsic mode components; using a correlation coefficient method to screen the plurality of intrinsic mode components to obtain high-frequency components and low-frequency components; performing wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; and reconstructing the low-frequency components and the denoised high-frequency components to obtain denoised leakage sound signals. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the identification accuracy is improved.

The embodiment of the invention discloses a malignant load identification method, device and equipment, a medium and a program product, and relates to the technical field of power load monitoring. The method comprises the following steps: performing modal decomposition on an original power utilization sequential sequence to obtain a plurality of intrinsic mode components, and reconstructing intrinsic mode components which do not belong to noise components to obtain a target power utilization sequential sequence; performing feature extraction on the target power consumption time sequence to obtain target power consumption features, and inputting the target power consumption features into a pre-trained malignant load identification model for identification to obtain an identification result; the malignant load identification model is obtained by updating model parameters of a weak learner based on a natural gradient descent method and performing training optimization. The lightweight malignant load learning model obtained through training in the scheme can be deployed and operated on the intelligent electric meter, high-quality input features are obtained through multi-mode decomposition and reconstruction, the accuracy of malignant load recognition is improved, and accurate recognition of the malignant load based on the lightweight model is achieved.

This disclosure provides methods, devices, and systems for audio signal processing. The present implementations more specifically relate to speech enhancement techniques for separating microphone signals into speech, echo, and noise signals. In some aspects, a speech enhancement system may include a delay estimator and an acoustic echo and noise (AEN) decoupling filter. The delay estimator receives a microphone signal via a microphone and a far-end audio signal for output via a speaker and estimates a reference audio signal based on a delay between the microphone signal and the far-end audio signal. In some aspects, the AEN decoupling filter may determine a speech mask, an echo mask, and a noise mask based on the microphone signal and the reference audio signal and may suppress an echo component and a noise component of the microphone signal based on the determined set of masks.

The invention discloses a coal mine tunneling roadway advanced geological exploration and ground stress inversion method, particularly relates to the technical field of seismic signal processing in geophysical exploration, and is used for solving the problems of geological exploration signal distortion and insufficient stress inversion precision caused by strong noise interference of tunneling equipment in the prior art. Rock mass vibration signals are collected through an optical fiber sensor arranged on a roadway wall, equipment noise characteristics are identified through spectral analysis, a quantitative mapping relation between working condition parameters and noise characteristics is established, a noise reference time period is determined according to the quantitative mapping relation, a spatial coherence characteristic template is constructed, high-coherence noise components are inhibited through template comparison, and the noise reference time period is determined according to the spatial coherence characteristic template. Finally, the geological structure and stress field distribution in front of the working face are inverted based on the purified seismic wave signals, a stress concentration area and a disaster risk area are identified, the signal-to-noise ratio of the seismic signals in the strong noise environment and the reliability of the geological inversion result are effectively improved, and accurate technical support is provided for safe mining of a coal mine.

The invention relates to the technical field of power system modeling and simulation, in particular to a load model identification error analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining the prediction output and the measurement output of a preset load model, and building a target function based on the error between the prediction output and the measurement output; decomposing the measurement output to obtain an actual output and a noise component of a preset load model; and based on the objective function and the actual output, establishing a linear model between the identification error of the preset load model and the noise component through a first-order approximation method, and performing linear regression according to the linear model to obtain an analysis result of the identification error. Therefore, by constructing the theoretical model of the load model identification error, the problems that the identification error is difficult to predict, the data processing strategy selection lacks theoretical guidance and the like in related technologies are solved, and a theoretical basis is provided for selecting an optimal load modeling data processing strategy.

The invention relates to the technical field of electro-oculogram signal processing, and discloses a medical-grade electro-oculogram signal noise reduction filtering processing method and system, and the method comprises the steps: obtaining a to-be-processed electro-oculogram signal and a synchronous electroencephalogram signal, and marking a target signal segment; separating an electro-oculogram noise component and an electro-oculogram effective component by an electro-oculogram separation algorithm based on mutual information combination; performing decomposition and weighted fusion on the electro-oculogram noise component by adopting a double-coefficient fusion mode denoising model to generate a preliminary denoising signal; a special high-pass convolution noise reduction model for the EEG channel is used for filtering, and low-frequency noise is suppressed; signals are detected according to medical-grade parameter requirements, and if the signals do not reach the standard, the signals are returned for reprocessing; and checking the channel consistency of the qualified signal, and integrating to form a final signal. The system comprises six units. The method and the system can accurately separate the noise, guarantee the signal quality, solve the problems of inaccurate noise processing and poor signal stability in the prior art, and meet the high-quality requirement of clinical medical treatment on the electro-oculogram signal.

The invention discloses a sound signal processing method and device, equipment and a storage medium, and belongs to the technical field of audio processing. According to the invention, sound signal noise reduction with noise suppression and target signal reservation is realized. The method comprises the following steps: after acquiring a sound signal collected in a running state of mechanical equipment, firstly performing time-frequency analysis on the sound signal; then, a noise determination threshold is automatically determined based on the logarithmic magnitude spectrum of the sound signal, and noise estimation is performed based on the determined noise determination threshold. According to the scheme, a completely data-driven parameter selection mechanism is realized, and manual parameter or threshold setting is not needed, so that the automation degree is improved, the unreliability of manual parameter or threshold setting is avoided, and the accuracy and robustness of noise estimation are enhanced. In addition, the noise-reduced sound signal does not comprise noise components, so that the accuracy and reliability of subsequent operation state recognition and fault diagnosis of the mechanical equipment are ensured.

The invention relates to a belt weigher weighing precision compensation method based on multi-sensor data fusion. According to the method, an original weighing signal and an electromagnetic interference signal of the belt weigher in a running state and a baseline noise signal in a no-load state are synchronously acquired, a feature vector is extracted based on the electromagnetic interference signal, and an intermodulation noise component in the weighing signal is predicted by using a nonlinear system identification model in combination with the baseline noise signal. Then, the noise component is subtracted from the original weighing signal through an adaptive cancellation algorithm to obtain a local compensation weighing signal, and finally, a final weighing signal is generated based on a plurality of local compensation weighing signals through a signal-to-noise ratio weighted fusion algorithm, so that intermodulation noise caused by electromagnetic interference generated by the variable frequency driver is effectively suppressed. The weighing precision and the anti-interference capability of the belt weigher are improved.

In accordance with implementations of the subject matter described herein, a solution for speech enhancement is proposed. In this solution, a target time-frequency representation at least indicating intensities of an input audio signal at different frequencies over time is obtained. The input audio signal comprises a speech component and a noise component. Frequency correlation information and time correlation information of the input audio signal is determined based on the target time-frequency representation. A target feature representation is generated based on the frequency correlation information, the time correlation information, and the target time-frequency representation. The target feature representation is for distinguishing the speech component and the noise component. An output audio signal is generated based on the target feature representation and the target time-frequency representation. The speech component is enhanced relative to the noise component in the output audio signal. In this way, the performance of speech enhancement can be improved.

This application belongs to the field of coherent diffraction imaging technology, specifically disclosing an ultra-low dose coherent diffraction imaging method and apparatus. Based on a blind source separation strategy, this application uses principal component analysis to process the acquired diffraction signals, performing noise separation and updating in reciprocal space. This effectively separates mixed noise energy into noise components, thus avoiding crosstalk to the reconstruction process and significantly improving the convergence stability and robustness of coherent diffraction imaging when reconstructing diffraction signals with extremely low signal-to-noise ratios under ultra-low exposure doses. Simultaneously, this application constructs noise components separately for each scanning position and correlates noise components at different positions through low-dimensional spatial projection, achieving non-stationary noise separation. This enables more effective handling of random noise caused by the low quantum efficiency of ultra-short band detectors, thus maintaining extremely high noise robustness and reconstruction accuracy even under ultra-low exposure doses, achieving an effective improvement in resolution.

The application discloses a kind of power system low-frequency oscillation characteristic signal extraction methods, comprising: the electrical variation data of acquisition each generator outlet is preprocessed;Through complementary ensemble empirical mode decomposition, variation data is decomposed, and noise component is filtered out;SPA smoothing algorithm is used to process the electrical variation signal after denoising;Prony method is used to decompose the signal after smoothing, and the amplitude and phase of each frequency component are extracted;The application has more excellent noise suppression capability and signal fidelity, can effectively solve the problem of modal aliasing, can adaptively identify and filter out various noise components, and shows good adaptability to noise interference under different operating conditions of power system;SPA smoothing algorithm reduces the error introduced by complementary ensemble empirical mode decomposition while effectively retaining the key features of low-frequency oscillation;Combined with Prony method, the characteristic parameters of the oscillation signal can be accurately extracted in a strong noise environment, providing a reliable data basis for low-frequency oscillation source identification.

This invention belongs to the technical field of environmental multi-parameter monitoring methods, and particularly relates to an environmental multi-parameter monitoring method and system for optimizing and denoising island and reef data. Based on prior knowledge of island and reef environmental physics, an unsupervised pseudo-label generation system is constructed. A multi-parameter numerical simulation model is built based on tidal cycles, temperature-salinity-density coupling, and the physicochemical equilibrium law of dissolved oxygen-temperature-pH. Environmental time-series characteristics of measured noise data are input to generate clean simulation reference data. A self-supervised learning framework is used to pre-train the basic denoising model with the clean simulation data as a weakly supervised signal. A physics-driven multi-parameter coupling and noise decoupling mechanism is constructed, transforming the physicochemical coupling relationships of temperature, salinity, pH, and dissolved oxygen into hard constraints for the model and establishing a parameter coupling correlation matrix. Through feature decoupling branches, environmental coupling change components and noise components are separated, distinguishing between parameter coupling pseudo-noise and real noise from sensor drift and transmission interference. Denoising bias of a single parameter is corrected through coupling constraints.

The invention discloses a data set denoising processing method based on an empirical mode decomposition algorithm, and the method comprises the following steps: S1, data selection: obtaining an operation monitoring data set of a proton exchange membrane fuel cell, the data set at least comprising an output voltage signal; s2, degradation index determination: performing correlation analysis on the data set, and screening out an output voltage signal which is strongly and negatively correlated with the service life of the proton exchange membrane fuel cell as a performance degradation core index; s3, data sampling simplification: sampling the output voltage signals in the step S2 at equal time intervals to obtain a simplified voltage time sequence; s4, empirical mode adaptive decomposition: performing empirical mode decomposition on the simplified voltage time sequence to obtain a plurality of intrinsic mode function components and a residual component; s5, noise component elimination: eliminating high-frequency noise components from the plurality of intrinsic mode function components; and S6, signal reconstruction: superposing the residual intrinsic mode function component after elimination with the residual component, and reconstructing to obtain a denoised voltage degradation signal. According to the EMD-based denoising processing method, accurate extraction of real degradation features is realized through data screening, simplification, adaptive decomposition and noise elimination.

The invention discloses an active noise control method based on multichannel adaptive filtering, and relates to the technical field of automobile noise control and signal processing. The invention discloses an active noise control method based on multi-channel adaptive filtering. The method comprises the following steps: acquiring original noise signals at different positions in a vehicle through a reference microphone; performing multi-band band-pass filtering on the acquired noise signal to extract noise components of a target frequency band; inputting the filtered reference signal into a multi-channel adaptive filtering controller to generate an anti-noise signal; estimating a secondary path to generate an actual anti-noise signal based on the transmission path of the loudspeaker and the error microphone; through a feedback signal collected by an error microphone, closed-loop iteration is carried out to update and control a filter coefficient, and real-time offset and dynamic convergence of a noise signal are realized; through vector superposition of an original noise signal and an actual anti-noise signal, efficient and real-time suppression of multi-channel and broadband noise is realized. The method has more excellent multi-target noise reduction performance and robustness.

The invention relates to the technical field of heart sound signal processing, and particularly discloses a heart sound signal respiration interference suppression method and system based on multi-source sensing fusion, and the method comprises the steps: constructing a multi-source synchronous signal set through synchronously collecting heart sound signals and respiration monitoring signals; separating a heart sound principal component, a breathing interference component and a noise component by utilizing an adaptive modal decomposition and multi-dimensional feature clustering technology; constructing a time-frequency domain interference template through multi-scale cross-correlation analysis and phase registration by taking a respiration monitoring signal as a reference; based on the template, adopting time-varying gain control and an iterative optimization strategy to realize accurate suppression of respiratory interference, and generating a pure heart sound signal; and through multi-resolution feature enhancement and multi-index quantitative evaluation, generating a purity report containing a signal quality grade and a clinical applicability scheme.

The invention provides a backdoor attack defense method and system for a deep neural network model, and the method comprises the steps: obtaining a test data sample set matched with the deep neural network model through the classification of an original data set, dynamically adding a noise component to the test data sample set, and removing at least part of backdoor disturbance. Noise is mixed into the test data to change the original data structure of the rear door disturbance; adding features based on noise components of the test data sample set, performing denoising repair processing, testing the deep neural network model by using the test data sample set, restoring an original data structure of the test data through denoising repair, and eliminating back door disturbance mixed in the original data structure at the same time; judging whether the deep neural network model works normally or not based on the test result information, adjusting the noise component adding state of the test data sample set again in combination with the dynamic adding record of the previous noise component, eliminating the back door disturbance of the test data, and improving the test accuracy under the condition of maintaining the original performance of the deep neural network model. And the defense robustness of the backdoor attack is improved.

This invention discloses a method and system for denoising blasting vibration signals. It decomposes the original noisy blasting vibration signal using CEEMDAN, and classifies each intrinsic mode function (IMF) into effective vibration and noise using a correlation coefficient threshold, reconstructing the primary and secondary IMF components. Further, it applies wavelet packet threshold denoising at multiple decomposition levels to the primary and secondary IMF components, combining these levels to obtain the optimal decomposition levels for the primary and secondary IMF components. Based on these optimal decomposition levels, it denoises the primary and secondary IMF components, reconstructing the denoised primary and secondary IMF components into a high-precision blasting vibration denoised signal. This effectively reduces noise components in the blasting vibration signal while preserving the effective features of the original signal to the maximum extent.

The application provides a ground penetrating radar-based underground ore body positioning and identification method and system, and relates to the technical field of ore body detection.The method comprises the following steps: inputting a corrected multi-dimensional point cloud feature set into a pre-trained closed-loop convolutional neural network, optimizing network parameters through transfer learning, and outputting a category confidence of an ore body target and a spatial position probability distribution point cloud; based on the spatial position probability distribution point cloud, combining electromagnetic wave double-path travel time calculation rules and Cagniard resistivity measurement results, and generating an ore body depth and horizontal positioning point cloud through a reverse projection algorithm; and performing multi-scale two-dimensional empirical mode decomposition on the reverse projection positioning point cloud, filtering noise components to extract a high-confidence target point cloud.The application improves the accuracy and stability of ore body identification.

The invention relates to a transient electromagnetic signal denoising method based on self-adaptive fuzzy optimization singular spectrum analysis (SSA), which aims at denoising transient electromagnetic signals, and comprises the following specific steps of: 1, decomposing a noisy signal into a plurality of modes through a complementary ensemble empirical mode decomposition (CEEMD) method; and according to the sample entropy, classifying the modals into a signal-dominant category and a noise-dominant category. 2, taking a signal fuzzy entropy (FE) as a target function of each modal optimization, obtaining a strict boundary of each modal SSA reconstruction order by using a particle swarm optimization (PSO) algorithm, and taking a singular value in the strict boundary as a signal component; thirdly, solving loose boundaries of each modal reconstruction order through a singular value spectrum and a singular value difference spectrum, and generating a fuzzy interval by combining the loose boundaries with the strict boundaries; and 4, obtaining the membership degree of the singular value belonging to the signal or noise component in the fuzzy interval through a weighted fuzzy noise clustering algorithm (WFNC), and carrying out signal component reconstruction by taking the membership degree as a weight. And finally, adaptive reconstruction of each mode is carried out on the signal components by using an SSA method, and a de-noised signal is obtained through mode summation. According to the method, a low-signal-to-noise-ratio adaptive singular spectrum analysis denoising method is provided, the problem of wrong singular value selection in the reconstruction process is avoided, and the signal-to-noise ratio is effectively improved.

The invention provides a water pump intelligent monitoring control system and an application method thereof. In the method, a control system firstly extracts a mechanical noise component corresponding to a real-time rotating speed from initial baseline data by utilizing the relevance between the rotating speed and mechanical noise, and then constructs a baseline noise template to obtain mechanical noise interference during normal operation of a water pump. And the control system subtracts the baseline noise template from the original acoustic vibration signal to obtain a residual signal containing impurity impact information. And then the control system carries out continuous wavelet transform on the residual signal, and a generated time-frequency energy distribution diagram can identify the time node and frequency characteristics of the impact event so as to realize positioning of the impact event. And finally, the control system calculates the impact event rate and the average impact energy value in a preset statistical time window, and quantifies the progressive accumulation process of blockage. According to the method, the technical problem that early blockage cannot be identified by fixed threshold alarm is relieved, and the timeliness of fault prediction is improved.

The invention discloses a noise component calculation method and device of a hemispherical resonator gyroscope. The method comprises the following steps: acquiring a test result of the zero-bias stability of the hemispherical resonator gyroscope; carrying out ALLAN variance analysis on the test result of the zero bias stability, and determining a quantization noise coefficient, an angle random walk coefficient, a zero bias instability coefficient, a rate random walk coefficient and a rate slope noise coefficient; and calculating and determining a quantization noise component, an angle random walk noise component, a zero offset instability noise component, a rate random walk noise component and a rate slope noise component according to the quantization noise coefficient, the angle random walk coefficient, the zero offset instability coefficient, the rate random walk coefficient and the rate slope noise coefficient. According to the method, contributions of different noise components in the zero offset error source of the hemispherical resonator gyroscope can be obtained, and the long-term stability and the measurement precision of the hemispherical resonator gyroscope are improved.

The invention discloses an active noise reduction method, device and equipment for a take-off and landing point of a hovercar and a program product. The method comprises the steps that real-time noise signals and real-time environment parameters around a take-off and landing platform and real-time attitude parameters of the hovercar are acquired; performing propagation attenuation compensation on the real-time noise signal to obtain a corrected noise signal; matching in the model noise model library to obtain a corresponding rotor noise reference spectrum, an airflow noise reference spectrum and a power system noise reference spectrum; performing feature analysis on the corrected noise signal to obtain a rotor noise component, an airflow noise component and a power system noise component; predicting the change trend of the noise component through an adaptive filtering algorithm to obtain a predicted noise signal, and predicting the next airspace position of the hovercar according to the real-time attitude parameters; and generating an offset sound wave signal according to the predicted noise signal, and transmitting the offset sound wave signal to a next airspace position. The method realizes active noise reduction of the take-off and landing point of the hovercar, and can be applied to the technical field of hovercars.

The invention relates to the technical field of deep convolutional neural networks, in particular to a cryoelectron microscope noise particle image detail reservation noise reduction method based on edge enhancement, which comprises the following steps: inputting a cryoelectron microscope noise particle image into a trained image noise reduction model, and outputting a noise-reduced cryoelectron microscope particle image; in the image noise reduction model, an initial feature map of a noise particle image of the cryoelectron microscope is extracted; inputting the initial feature map into a multi-scale feature extraction module to capture multi-scale structure characterization; inputting the multi-scale feature map into an edge enhancement module to detect and enhance macromolecule boundary features to generate an edge enhanced feature map; inputting the edge enhancement feature map into a detail retention module for processing and retaining structure details to generate a detail enhancement feature map, and performing convolution processing to generate a prediction noise component; and subtracting the predicted noise component from the cryo-electron microscope noise particle image to obtain a noise-reduced cryo-electron microscope particle image. According to the invention, the noise reduction quality of the noise particle image of the cryoelectron microscope can be improved.

The application provides a frequency switching-based continuous variable quantum key distribution anti-noise method and system, which comprises the following steps: a frequency switching step: acquiring a channel state, calculating a channel noise power spectrum, and finding a position with the lowest channel noise power, which is recorded as a window; and moving a transmission signal spectrum of a sending end to the window for transmission; an optimal filtering step: moving a received signal spectrum of a receiving end back to a baseband, and then filtering the received signal by using a low-pass filter to retain the transmission signal. The application combines the frequency switching idea with the filter, avoids the transmission signal from being submerged by the channel noise, is beneficial to reducing the noise components received by the receiving end of the continuous variable quantum key distribution system as much as possible, can effectively reduce the excessive noise in the system, and improves the system key rate.

The invention belongs to the technical field of aviation cable fault positioning, and particularly relates to a cable fault positioning method based on time-frequency domain analysis, which comprises the following steps: firstly, collecting a cable fault signal, adopting a GWO-VMD algorithm, taking a minimum sample entropy as a fitness function, and optimizing to find an optimal penalty factor and decomposition layer number, so as to obtain a fault positioning result; removing a high noise component obtained by VMD decomposition according to the current sample entropy, and obtaining a reflected signal after noise reduction; performing cross term suppression on the reflected signal after noise reduction by using smooth pseudo Wigner distribution to obtain discrete SPWVD; and performing blind area elimination on the discrete SPWVD based on a blind area elimination algorithm of hypothesis verification to obtain a fault distance. A grey wolf optimization algorithm is applied to a variational mode decomposition algorithm, and parameters of decomposition layers and penalty factors of the VMD are optimized, so that the problem of interference of noise on reflection signal extraction is solved; secondly, the smooth pseudo Wigner distribution is adopted to solve the problem of cross term interference among a plurality of linear components in the traditional WVD distribution, and the positioning precision is improved.

This application belongs to the field of visual compensation technology and provides a visual compensation method under starlight conditions. Through pre-sampling and grayscale variance statistics, it achieves the determination of starlight compensation intervals and the dynamic division of target and background regions in the imaging plane. It adopts a sampling method with different exposure time series for the target and background regions to obtain multiple frames of original sampled data and pixel integration time. Based on the pixel integration time, it constructs a spatially variable gain matrix and completes inter-frame registration and gain normalization processing to separate signal and noise components in the image. The signal component is used as a sparse sampling stream, and the pixel variance distribution of the noise component is used as a hyperparameter of the variational inference algorithm. Image reconstruction is completed through probability density gradient estimation, making the variational inference process match the actual noise distribution characteristics under starlight conditions, thereby improving the quality and reliability of visual imaging under starlight conditions.

This invention proposes a method, device, and storage medium for detecting the radiated noise floor of an earphone battery. The method includes: configuring a controlled switch device connected to the power supply circuit of the earphone battery under test to form a controlled discharge path; configuring a timing controller that outputs periodic switching control signals to the control terminal of the controlled switch device; controlling the controlled switch device to periodically switch on and off at predetermined time intervals through the switching control signals, thereby causing the earphone battery under test to intermittently discharge in the controlled discharge path; monitoring the electrical signals at both ends of the earphone speaker under test during the intermittent discharge of the earphone battery under test, wherein the earphone battery under test and the earphone speaker under test are arranged adjacent to each other in the earphone space; determining the radiated noise floor level of the earphone battery under test based on the noise component in the monitored electrical signal that is synchronized with the switching control signal, which greatly shortens the troubleshooting cycle of earphone problems and improves the efficiency of quality inspection.