436 results about "Noise component" patented technology

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

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 invention belongs to the technical field of space optical remote sensing, and relates to a high-resolution remote sensing image semantic segmentation method based on a diffusion model. The specific process is as follows: first-stage training: labeling the type of each pixel of a high-resolution remote sensing image to generate a label image, and training an auto-encoder comprising an encoder and a decoder by using the label image; in the second stage of training, on the basis of the auto-encoder, a conditional diffusion model is loaded and trained, the conditional diffusion model comprises a noise injection module, a conditional encoding module and a de-noising U-Net, and parameters of the auto-encoder are fixed during training; wherein the noise injection module adds noise to hidden variables output by the encoder, the conditional encoding module is used for performing multi-scale feature extraction on the high-resolution image, and the de-noised U-Net performs image component and noise component prediction and image reconstruction under the guidance of the multi-scale features; the decoder is used for decoding the reconstructed image; and image semantic segmentation: carrying out semantic segmentation on the high-resolution remote sensing image by using the trained network.

The invention discloses a fan blade diagnosis method and system based on voiceprint perception, and relates to the technical field of intelligent monitoring of wind power equipment, and the method comprises the steps: carrying out frequency domain compensation processing, stripping environment wind noise components, and generating a pure voiceprint signal based on a wind flow characteristic parameter set; based on the pure voiceprint signal, identifying a sensitive acoustic frequency band of the fan blade, exciting a sound wave phase synergistic effect in the sensitive acoustic frequency band, and generating an enhanced voiceprint signal; quantifying the internal damage depth of the fan blade material according to the enhanced voiceprint signal, and calculating a sound energy flow disorder degree index; according to the sound energy flow disorder degree index, the blade health state of the fan is judged through a multi-dimensional acoustic characteristic state space mapping mechanism, and a three-dimensional health assessment report is generated; through a physical level wind noise stripping technology, a transfer function matrix is generated based on vortex interference modeling and sound wave-airflow phase offset correction, frequency domain energy reweighted decoupling is realized, and environmental wind noise is accurately stripped in a strong turbulence environment.

The invention discloses a low-dose CT image denoising method and system based on a conditional diffusion model, and the method comprises the steps: constructing a denoising network model, only employing a standard-dose CT image in a training process, and simulating a generation process of the standard-dose CT image through the training of the denoising network model. And abundant image priori knowledge is constructed. Meanwhile, the de-noising network model uses a U-Net encoder-decoder structure, a multi-scale cross axis attention mechanism is added in a decoder and an encoder, important image details are reserved in the de-noising process, irrelevant noise components are removed at the same time, and smooth areas and continuous boundaries of the image can be recovered. In addition, the frequency compensation block in the jump connection can compensate the insufficiency of medium-high frequency signals in the training process, so that the original structure and texture information of the image are recovered.

The invention discloses a high-reliability planetary gearbox signal denoising method, system, medium and equipment, and the method comprises the steps: obtaining original vibration signals of a nuclear power circulating pump planetary gearbox in different health states, and carrying out the detrending and demean preprocessing of the signals; performing modal decomposition on the original vibration signal by adopting an empirical mode decomposition (EMD) algorithm, an ensemble empirical mode decomposition (EEMD) algorithm and a variational mode decomposition (VMD) algorithm to obtain a plurality of different modal components; iteratively optimizing a hyper-parameter value in the variational mode decomposition algorithm VMD by adopting a sparrow search algorithm SSA so as to realize the self-adaptive decomposition of the variational mode decomposition algorithm VMD on the signal; and carrying out modal decomposition on the gearbox vibration signal by adopting a variational modal decomposition algorithm VMD after iterative optimization, removing noise components in modal components, and reconstructing the signal to realize gearbox vibration signal denoising.

The invention provides a distribution network type lightning arrester leakage current optimization measurement method. The method comprises the steps of signal acquisition, preliminary signal denoising and preprocessing, wavelet transform signal decomposition, high-frequency noise component elimination and processing, input convolutional neural network CNN descrambling, integrated learning model optimization, output of descrambled leakage current signals, performance evaluation and result feedback. Through a multi-level processing method combining wavelet transform and a convolutional neural network, high-frequency noise in a weak leakage current signal is effectively removed, effective characteristics of the signal are reserved in the denoising process, and compared with a traditional method depending on a single signal processing technology, signal descrambling can be more accurate and effective, and the signal descrambling efficiency is improved. According to the wavelet transform signal decomposition method, the signal can be decomposed into components with different frequencies, high-frequency noise components are effectively eliminated, low-frequency and intermediate-frequency effective signals are reserved, then the convolutional neural network further improves the definition and reliability of the signal through depth feature extraction, and accurate detection of the leakage current signal is ensured.

The invention relates to the technical field of signal processing, and discloses a power equipment partial discharge signal denoising method and system, and the method comprises the steps: S1, generating an exponential decay type pulse signal simulating partial discharge, and superposing white noise and narrow-band interference into the exponential decay type pulse signal to generate a noisy signal; s2, decomposing the noisy signal into a plurality of intrinsic mode components through an ensemble empirical mode decomposition method, calculating a correlation coefficient and a kurtosis index of each intrinsic mode component, and identifying a noise component and a signal component based on the correlation coefficient and the kurtosis index; s3, wavelet threshold denoising is carried out on the noise components, and a wavelet threshold is dynamically adjusted for each noise component through a particle swarm optimization algorithm; and S4, reconstructing the de-noised noise component and the reserved signal component into a final de-noised signal, and evaluating the de-noising performance based on the signal-to-noise ratio, the root mean square error, the peak signal-to-noise ratio and the correlation coefficient.

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

The invention relates to the field of electrical measurement and testing, in particular to a fault monitoring method and early warning system for a bottom loading and unloading crane pipe, and the method comprises the steps: obtaining and preprocessing the current, voltage and vibration sequence of a servo motor, and obtaining a corresponding IMF component and an energy spectrum; and in combination with historical normal operation data and to-be-monitored data, the importance degree of the IMF component is evaluated, and the time domain anomaly degree is obtained based on the standard deviation of the sliding window variance and the DTW distance. Odd harmonic information is extracted from the energy spectrum, and the time domain anomaly degree is combined to obtain the comprehensive anomaly degree. And obtaining a fault degree through weighted integration, and inputting the fault degree into a control system to judge a fault. According to the method, the energy spectrum difference of the IMF components, the first-order difference sequence difference and the time-frequency domain abnormal degree are comprehensively analyzed, the operation state of the servo motor is comprehensively evaluated, key fault information loss and single time domain feature monitoring limitation caused by elimination of high noise components in a traditional method are avoided, and the accuracy and sensitivity of fault monitoring are remarkably improved.

The invention discloses an acceleration method, device and equipment for partial discharge detection of a switch cabinet and a storage medium, and relates to the technical field of power equipment fault detection, partial discharge signals are collected in real time through a multi-parameter sensor, multi-source signal alignment is realized by combining a hardware trigger mechanism and timestamp synchronization, noise components are separated through multi-scale wavelet transform, and the partial discharge detection of the switch cabinet is realized. The method comprises the following steps: extracting time-frequency joint features through discrete wavelet transform, reducing dimensionality by adopting a principal component analysis method, taking low-dimensional feature vectors as input limited by a Gaussian mixture model (GMM), taking feature vectors limited by the GMM as input of an SVM classifier, outputting a classification result, and performing online judgment according to the classification result. When the output is partial discharge, an alarm prompt is triggered through the interface module; and when the output is normal, real-time monitoring is continued. According to the invention, signal processing and classification are accelerated by using an FPGA parallel processing architecture, and real-time early warning of power equipment faults is realized.

The invention discloses a data full-link dynamic anti-interference optimization transmission method. The method comprises the following steps: S1, constructing a data transmission system architecture comprising a sending end, a transmission link and a receiving end; the sending end detects environment disturbance characteristics in real time, and the receiving end collects receiving signals containing background noise and link distortion; s2, designing a self-adaptive waveform coding strategy at a sending end; s3, performing active channel compensation on a transmission link; channel characteristics are extracted, and channel interference is counteracted in real time through pre-distortion waveform adjustment; s4, deploying an intelligent noise reduction decoding algorithm at a receiving end; a depth feature separation network is constructed, and link residual interference is filtered out by analyzing the time-frequency domain difference between a target signal and a noise component. Through a three-level anti-interference mechanism of adaptive coding of the sending end, active compensation of a transmission link and intelligent noise reduction of the receiving end, the problem that the transmission reliability of signals is reduced due to environment disturbance, channel distortion and noise pollution in a complex environment is solved.

The invention relates to the technical field of sensing data processing, and discloses a sensing data chip method based on multi-source information fusion and dynamic parameter adjustment. The method comprises the following steps: collecting original data streams of a plurality of heterogeneous sensors, and generating synchronized sensing data through timestamp alignment and format standardization processing; extracting an environment noise component, and dynamically suppressing noise by using an adaptive filtering algorithm to obtain de-noised sensing data; performing multi-dimensional feature decomposition on the de-noised data, calculating time domain, frequency domain and space domain feature vectors, and generating a multi-dimensional feature set; dynamically adjusting a feature weight distribution strategy in combination with a current system load state and a multi-dimensional feature set historical distribution rule, and generating an optimized fusion weight matrix; and carrying out weighted aggregation on the multi-dimensional feature set and the optimized fusion weight matrix, and outputting a multi-source fusion feature vector. According to the method, the problems of data synchronization, noise suppression and feature fusion suitability in multi-source heterogeneous sensing data processing can be solved, and the processing requirements of different scenes are met.

The invention relates to the technical field of sound reception, and discloses a system for multi-directional sound reception and noise reduction of a smart glasses microphone array, and the system comprises at least five microphones which are disposed at different positions of a smart glasses frame, and the first microphone is a unidirectional microphone, is located below the glasses frame, and is used for picking up the voice of a wearer; the second microphone and the third microphone are bi-directional microphones, are located right in front of the glasses frame and are used for picking up voice signals right in front of the wearer. The directivity weight of the microphone array is adjusted in real time through the dynamic optimization module, and the pickup effect of the target voice signal is enhanced preferentially. The voice enhancement module is matched to extract a target voice signal by using a Bayesian inference method, so that noise components in a mixed signal can be effectively separated, voice distortion is reduced, the definition and fidelity of the voice signal are remarkably improved, and the voice has better intelligibility in various complex noise environments.

The invention discloses a wind turbine generator blade vibration mode feature recognition method, which mainly comprises the following steps of: arranging three-axis piezoelectric acceleration sensors at equal intervals from a blade root to a maximum chord length position, and adaptively selecting an optimal channel as a target input vibration signal according to envelope dispersion and a period proportion; introducing a vibration signal denoising method based on regenerative phase shift sine-assisted empirical mode decomposition, and constructing a Fisher ratio-based multi-dimensional fusion index to remove a noise component; estimating a system order range according to a singular entropy jump value theory, and designing modal similarity and a confidence index to accurately estimate a real order of a blade system; introducing three types of constraints of structure maintenance, modal sparsity and energy smoothness to jointly optimize a low-rank approximation strategy so as to realize optimal reconstruction of the Hankel matrix; constructing a fitness function selected by a clustering center by combining the point set density of the sample and Euclidean distance information, and optimizing a modal extraction result by adopting inter-class dispersivity and an intra-class sample number; according to the method, the environmental noise can be effectively removed, the system order can be accurately determined, and finally the modal parameters of the system can be accurately identified.

The invention provides a full-spectrum water quality multi-parameter dynamic inversion method and a model construction method thereof, and belongs to the technical field of spectral analysis based on machine learning. By constructing a closed-loop optimization architecture of noise reduction, wavelength optimization, turbidity correction and adaptive modeling, high-precision detection of four water quality parameters of chemical oxygen demand, total organic carbon, total nitrogen and nitrate nitrogen is realized. According to the method, a variational mode decomposition and improved threshold translation invariant wavelet combined noise reduction algorithm is provided, a kurtosis-correlation coefficient dual index is adopted to screen noise components, a multi-translation average strategy is combined to suppress a pseudo-Gibbs phenomenon, and meanwhile, a global parameter collaborative tuning mechanism based on Bayesian optimization is designed; a cross-module hyper-parameter coupling space is established in the whole process of noise reduction, wavelength optimization, turbidity correction and modeling. Through a multi-dimensional feature enhancement mechanism, the model feature representation capability is improved by 35%, and an innovative solution is provided for multi-parameter online detection in a complex water quality scene.

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.

The invention relates to the technical field of speech enhancement, in particular to a time-frequency domain speech enhancement method based on KAN channel attention, which comprises the following steps: processing a speech data set to obtain frequency domain representation; extracting local features in the frequency domain representation through an encoder to obtain output features; the output features are input into a TF-Transform block, noise components are recognized and suppressed, and output components are obtained; inputting the output component into a KAN-based channel attention module, sequentially obtaining a channel feature map and a spatial feature map, and successively multiplying the output component by the channel feature map and the spatial feature map and introducing jump connection to obtain refined features; and obtaining a time domain voice signal based on refined feature recovery, and processing the recovered time domain voice signal through an amplitude decoder and a phase decoder to correspondingly obtain an amplitude spectrum and a phase spectrum. And through a KAN-based channel attention module, the denoising and reconstruction performance of sparse and sensitive high-frequency components in the voice is improved.

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 relates to the technical field of noise recognition, and discloses an intelligent recognition method and system for Bluetooth headset environment noise, and the method comprises the steps: collecting two paths of original audio of a Bluetooth headset, determining the dynamic evolution characteristics of environment noise components, carrying out the frequency domain cepstrum transformation of the environment noise components, and obtaining the frequency domain noise characteristics; carrying out attention fusion on the dynamic evolution features and the frequency domain noise features to obtain fused audio features, and constructing an association feature matrix of the fused audio features; constructing a noise scene candidate set of the Bluetooth headset, and performing noise type screening on the noise of the Bluetooth headset by using the associated feature matrix and the noise scene candidate set to obtain a noise type candidate list; and performing adversarial enhancement processing on the noise corresponding to each noise type in the noise type candidate list to obtain an enhanced noise set, extracting a dynamic characteristic spectrum of the enhanced noise set, and identifying the environmental noise of the Bluetooth headset. According to the invention, the recognition precision of the environment noise of the Bluetooth earphone can be improved.

The present invention discloses a method, device, storage medium and system for DC electric energy metering noise reduction. The method calculates the optimal quadratic penalty factor and modal decomposition number through an improved harmonic search intelligent optimization algorithm, realizes adaptive variational modal decomposition of the original sampling data sequence of DC voltage and the original sampling data sequence, and realizes reliable distinction and screening of the effective component and noise component of each decomposition mode by calculating the maximum mutual information coefficient between each decomposition modal sequence and the original sampling data sequence. The de-noised DC voltage sequence and the de-noised DC current sequence are reconstructed based on the effective components obtained by screening. The noise reduction method, device, storage medium and system improve the measurement accuracy of DC electric energy.

The invention discloses a robot inspection control method for textile equipment monitoring, and relates to the technical field of textile equipment monitoring, and the method comprises the following steps: a robot scans an environment along a preset path at a variable sampling rate through a mobile noise fingerprint collection method, synchronously collects working condition associated noise decomposition of textile equipment, and constructs a dynamic noise substrate; performing space-time-frequency domain constraint blind source separation by taking a dynamic noise base as a physical constraint and combining a sound vibration sensor, decoupling a background noise component, an equipment global vibration component and a residual signal, performing kurtosis detection on the residual signal, and performing preliminary screening on a fault signal by combining a preset abnormal triggering mechanism; according to the method, dynamic reconstruction of the noise base is achieved through multi-sensor cooperative working condition correlation modeling, the problems of noise time-varying characteristics and spatial isomerism are solved, an accurate reference is provided for subsequent noise suppression, the noise base serves as a physical anchor point for separation, the fault component separation purity is improved, and suspicious fault signals are effectively and preliminarily screened out.

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 provides an intelligent charging pile management system and method with a remote diagnosis function, and relates to the technical field of power control, and the method comprises the steps: carrying out the one-dimensional variational mode decomposition and multi-dimensional variational mode decomposition of load data and operation state signals of a charging pile, and obtaining a stationary component, a noise component and a state sub-mode signal set; determining load identification characteristics according to the stationary component, the external environment data and the noise component, and performing joint analysis processing on the state sub-mode signal set to obtain fault diagnosis characteristics of the charging pile; carrying out feature fusion on the load identification features and the fault diagnosis features to obtain a modal fusion matrix, and then carrying out joint diagnosis analysis on the modal fusion matrix to obtain a load prediction curve and a fault diagnosis result of the charging pile; according to the method and the device, the charging piles can be subjected to collaborative analysis on the basis of load prediction and fault diagnosis, so that the accuracy of remote scheduling control is improved.

This disclosure provides methods, devices, and systems for audio signal mixing. The present implementations more specifically relate to mixing audio signals from a microphone array by performing fixed beamforming to generate beams, reducing noise on the beams, and mixing the beams to generate a final audio signal for playback. In some aspects, an audio mixing system includes a fixed beamformer to generate beams from audio signals from a microphone array and noise reduction units (NRUs) to reduce a noise component of each audio beam. The system also includes logic to calculate a signal characteristic of each reduced noise audio beam to determine, based on the signal characteristics, the reduced noise audio beams that include a speech component. The logic also generates a gain for each audio beam based on the selection, with the gains used in beam mixing. In some aspects, the NRU includes a neural network noise reduction unit.

The invention discloses a de-noising system based on adaptive beam forming and ICA (independent component analysis). The de-noising system comprises a multi-channel sensor array, an adaptive beam forming unit, an independent component analysis unit and a cooperative control module, the multi-channel sensor array is used for collecting a mixed signal containing a target signal and noise; the adaptive beam forming unit suppresses noise in an interference direction through spatial filtering and outputs a preliminary enhanced signal; the independent component analysis unit is used for receiving the preliminary enhanced signal and separating residual independent noise components through a blind source separation algorithm; the cooperative control module dynamically adjusts a beam forming weight and an ICA separation parameter, and balances voice distortion and noise suppression effects; through cooperation of beam direction nulling and blind source separation, reverberation, multipath scattering and Gaussian / non-Gaussian mixed noise are effectively processed, the system is suitable for scene coverage speech enhancement, medical signal processing and radar anti-interference, and the denoising effect of the system is more excellent comprehensively.

The invention provides a radar signal adaptive noise reduction method, which belongs to the field of radar signal processing, and comprises the following steps: carrying out SGMD on a preprocessed radar signal to obtain a plurality of symplectic geometric mode components; the fractal dimension of each symplectic geometric mode component is calculated, a fractal dimension difference spectrum is constructed according to the fractal dimensions, the maximum value of the fractal dimension difference spectrum serves as the order of the noise signal, and a reconstruction signal is obtained based on components after the order of the noise signal; the invention further provides a noise reduction device. The invention also provides a radar signal modulation identification method and system. Fractal dimensions are adopted to screen signal components after SGMD decomposition, useful signal components and noise components are easier to separate in a fractal domain by calculating the fractal dimensions of all the components, fractal dimension difference spectrums of the components are constructed at the same time, the useful components are extracted and signals are reconstructed in a dynamic adjustment mode by means of the component order corresponding to the maximum value of the difference spectrums, and the fractal dimensions of all the components are calculated. Interference of noise on signals can be remarkably reduced, the self-adaptability of component screening is improved, and parameters do not need to be set.

A method for acoustic logging a wellbore includes making a plurality of directional sonic logging measurements while rotating an acoustic logging tool in a wellbore. A noise component of each of the measurements is compared with a threshold. Measurements for which the noise component is greater than the threshold are discarded. Measurements for which the noise component is less than the threshold are retained.

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.