215 results about "Residual noise" patented technology

The invention discloses a weak light image enhancement method fusing noise adaptive diffusion and illumination perception, and the method comprises the steps: firstly designing a reflection-illumination decomposition module RID based on a Retinex theory, and extracting a reflectivity component and an illumination component of an input weak light image; then designing an ADRD (adaptive diffusion reflection denoising) module, performing denoising enhancement on the reflectivity component, and outputting an enhanced reflectivity image; introducing a noise estimation network, and dynamically adjusting the de-noising amplitude of the diffusion model according to the noise intensity of the region in the reflectivity graph; designing an illumination perception enhancement module IEM, fusing an attention mechanism and a local enhancement block, enhancing the illumination image, and outputting an enhanced illumination component; and building a complete neural network framework, carrying out joint training on the provided module, finally testing the stored neural network model, and outputting an enhanced image. According to the method, the problems of detail loss, noise residue and uneven illumination of the enhanced image in the prior art are solved.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention provides a self-adaptive collaborative acoustic environment active treatment system and method. The system comprises a multi-dimensional environment sensing network module, an intelligent voiceprint recognition and sound field prediction module, an active noise control module, a central intelligent collaborative regulation, diagnosis and self-learning module and a passive acoustic intervention module. The multi-dimensional environment sensing network module collects multi-dimensional sensing data in real time; the intelligent voiceprint recognition and sound field prediction module outputs a voiceprint recognition result and a noise source space coordinate and predicts a sound field evolution trend; the active noise control module outputs a residual noise signal; the central intelligent cooperative regulation, diagnosis and self-learning module outputs an active and passive cooperative control instruction set; the passive acoustic intervention module controls broadband noise to block or change its propagation path. According to the invention, through a dual-channel self-learning mechanism of the deviation diagnosis capability, continuous evolution of system performance can be realized according to a deviation autonomous optimization acoustic model and a control strategy, and accurate, efficient and prospective active treatment is carried out on a complex noise environment.

The invention provides an ICESat-2 turbid water area sounding correction and water depth inversion method based on optical characteristics of a water body. The method comprises the following steps: S1, obtaining photon data and an optical remote sensing image of a near-shore turbid water research area, and carrying out preprocessing operation on the data; s2, performing spectral sensitivity analysis by acquiring an ICESat-2 photon error value and combining optical characteristics of a water body; s3, constructing an ICESat-2 turbid water area sounding compensation model based on water optical characteristics, and selecting the model preferentially according to evaluation indexes; s4, synthesizing a multi-temporal image, and realizing model adaptive adjustment of different space weight distribution based on the spectral sensitivity difference of the turbid water area; s5, constructing an ICESat-2 and remote sensing image joint inversion water depth model based on turbidity partitioning; s6, spatial interpolation is carried out on a water depth inversion result, residual noise and adjacent pixel differences possibly occurring in the image are suppressed, and continuous water depth is obtained; according to the invention, large-range water depth inversion without field actual measurement work can be realized.

The invention discloses a multi-sampling and cross-domain matching-combined roadside multi-laser radar point cloud splicing method, which comprises the following steps of: firstly, inputting roadside point cloud data at the same moment and at different visual angles, and adaptively adjusting a sampling proportion by analyzing point cloud density and noise level so as to enhance effective information and filter noise; then, geometric and shape features are extracted based on neural networks such as DGCNN and Point Transform, and preliminary cross-domain feature matching and coarse registration are carried out through algorithms such as GeoTransform, so that an initial transformation matrix is obtained; and carrying out fine point pair matching and rigid body transformation optimization in combination with RANSAC and ICP algorithms to realize high-precision point cloud registration. And finally, finishing point cloud merging by using the final transformation matrix, removing residual noise through bilateral filtering, and outputting a spliced high-quality point cloud image. According to the process, the splicing precision and robustness of the heterogenous laser radar point cloud are effectively improved.

The invention discloses an audio data processing method and device and electronic equipment, and relates to the field of audio processing. The method comprises the following steps: acquiring original multichannel audio data through a microphone array; generating a main beam output signal pointing to the target sound source direction and at least one sidelobe suppression output signal; extracting voiceprint feature vectors; comparing the voiceprint feature vector of the current frame with the center vector of the existing voiceprint class cluster to obtain a voiceprint clustering result; if it is determined that the first voiceprint class cluster and the second voiceprint class cluster exist, extracting a first audio frame belonging to the first voiceprint class cluster and a second audio frame belonging to the second voiceprint class cluster, and performing audio reconstruction to obtain a first voice stream and a second voice stream; and carrying out residual noise elimination on the sidelobe suppression output signal to obtain a first isolated audio stream and a second isolated audio stream. By implementing the technical scheme provided by the invention, the audio processing quality is improved.

The embodiment of the invention discloses a data noise reduction method and device for multi-level magnetotelluric data and a medium, and relates to the technical field of noise reduction processing, and the method comprises the steps: collecting an original magnetotelluric data sequence, determining first noise reduction data, carrying out the variational mode decomposition of the first noise reduction data, and determining second noise reduction data; determining a first signal-to-noise ratio corresponding to the second noise reduction data, and when the first signal-to-noise ratio is smaller than a preset signal-to-noise ratio reference value, performing dynamic fusion on the residual signal to determine a fused residual signal; performing noise reduction processing on the fused residual signal, determining fused residual noise reduction data, performing residual recovery based on the fused residual noise reduction data, and determining current noise reduction data after residual recovery; when the second signal-to-noise ratio corresponding to the current noise reduction data is not smaller than the signal-to-noise ratio reference value, the current noise reduction data is output, the signal recovery requirement of weak effective information in the magnetotelluric data can be met, the signal fidelity is enhanced, and the deep noise processing and weak signal protection capabilities are improved.

The invention discloses an industrial robot motor fault early warning method, and relates to the technical field of motor testing and state monitoring, and the method comprises the steps: introducing a kurtosis-based real-time statistical feature to adaptively adjust the noise amplitude and the decomposition frequency, enabling noise-added empirical mode decomposition to dynamically reconfigure parameters along with the working condition of a motor, and enabling the noise-added empirical mode decomposition to be more accurate. Stable intrinsic mode function components can still be obtained when the impact load frequently changes; a screening coefficient formed by three types of sub-indexes of correlation, an energy ratio and impact sensitivity is utilized to realize automatic elimination of noise dominant components, and the problems of modal aliasing and residual noise are effectively relieved; on this basis, target components are reconstructed and fractal box dimensions are extracted, so that the features still have strong distinguishing ability in the face of transient abrupt change and multi-scale roughness, and the sensitivity to early weak faults is enhanced; and vibration signal dynamic time warping with current as a time reference is further combined with multi-sensor feature fusion.

The utility model relates to the field of fuel gas pressure regulation, in particular to a double-layer noise reduction valve cage and a regulating valve, which comprises an inner-layer valve cage, a plurality of first noise reduction holes, a second-layer valve cage and a third-layer valve cage, the outer-layer valve cage is arranged outside the inner-layer valve cage in a sleeving manner and fixedly connected with the inner-layer valve cage, and a plurality of second silencing holes which are arranged according to a second rule are formed in the outer-layer valve cage; and the inner-layer valve cage and the outer-layer valve cage are coaxially arranged. According to the utility model, the inner-layer valve cage is used for preliminarily dispersing pressure waves and noise of fluid through the plurality of first silencing holes arranged according to the first rule, the outer-layer valve cage is used for further absorbing and scattering residual noise generated by the inner-layer fluid through the second silencing holes, and the inner-layer valve cage and the outer-layer valve cage are coaxially arranged; the additional flow resistance generated when fluid penetrates through different layers can be reduced, and meanwhile the continuity of a flow channel is guaranteed. Under the conditions of high pressure difference and large flow, noise of the regulating valve is effectively reduced, it is ensured that the noise level does not exceed 85 decibels, and the requirements of specific application occasions are met.

The invention discloses an active pen and touch screen interaction system resistant to common-mode interference and a signal demodulation method. The system comprises a signal receiving terminal and a signal transmitting terminal. The signal transmitting terminal modulates data to be transmitted by adopting a differential binary phase shift keying modulation method, and transmits a generated modulation signal to the signal receiving terminal; the analog front-end receiving module is coupled with the signal transmitting terminal, receives the modulation signal and outputs a digital square wave signal after fidelity extraction processing; and the digital demodulation processing module performs differential coherent digital demodulation on the digital square wave signal based on the prior characteristics of the modulation signal, inhibits residual noise and recovers data to be sent. The analog front end focuses strong common-mode noise suppression and analog filtering, the digital demodulation algorithm performs secondary digital purification by using signal time sequence characteristics, and the two constructs an efficient anti-interference pipeline. Meanwhile, the regular square wave signals can be directly processed through differential coherent demodulation, and the requirements for performance and power consumption of a rear-end processor are lowered.

The invention discloses a shallow sea area water depth inversion method based on a satellite-borne laser radar, and particularly relates to the technical field of single-photon water depth information extraction. The denoising method comprises the following steps: S1, data preprocessing: reading data, carrying out visual presentation, dividing photons into offshore photons, sea surface photons and undersea photons, and intercepting an effective photon region; s2, coarse denoising: denoising the area intercepted in the step S1 by adopting a DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm; s3, fine denoising: screening effective signals of sea surface photons by adopting a Gaussian fitting algorithm, removing sea photons, separating subsea photons from the sea surface photons, performing density analysis on the subsea photons, extracting a density peak point, and constructing a density trend curve of the subsea photons by adopting a cubic spline interpolation method, eliminating residual noise deviating from normal distribution according to the trend curve; and S4, performing water depth inversion: performing refraction correction on the subsea photons subjected to fine denoising, fusing the corrected subsea photons and the sea surface photons subjected to fine denoising, and generating a water depth distribution diagram of the shallow sea area.

The invention relates to the technical field of electronic data processing, and discloses a diesel engine operation state real-time monitoring method and system, and the method comprises the steps: obtaining a vibration signal of a measurement point on the surface of an engine body as an original signal; modal decomposition is carried out to obtain a final intrinsic mode function, and the decomposition process comprises the steps that multiple times of lumped operation are executed, in each time of lumped operation, an auxiliary noise signal is added into a to-be-decomposed original signal, then the screening iteration process is executed repeatedly, and a set of temporary intrinsic mode functions are obtained through separation in sequence; performing weighted average on the temporary intrinsic mode functions of the same order obtained in all the lumped operations to obtain a final intrinsic mode function of the order; hilbert transform is carried out on the intrinsic mode function, joint distribution of instantaneous energy and instantaneous frequency of the intrinsic mode function is constructed, and statistical and geometric morphological features are extracted to form feature vectors; according to the method, modal aliasing can be inhibited, noise residues are eliminated, and the accuracy of a running state monitoring result is improved.

The invention provides a mobile traffic prediction method and device based on a diffusion model, and relates to the technical field of mobile traffic prediction.The method comprises the steps that on the basis of obtained historical mobile traffic, noise mobile traffic and dynamic characteristics of the historical mobile traffic are determined, and the noise mobile traffic is the mobile traffic added with noise; the noise priori estimation unit based on the diffusion model is used for carrying out noise estimation on the noise movement flow and the dynamic characteristics to obtain noise priori, and carrying out noise prediction on the noise movement flow based on a denoising network of the diffusion model to obtain residual noise; and performing flow prediction according to the noise prior, the residual noise and the noise moving flow to obtain future moving flow. According to the invention, the accuracy of mobile traffic prediction can be improved.

System and method for adaptively optimizing digital-to-analog converter input range values for improved signal-to-noise ratio for analog-in-memory computing (AIMC) systems. The method includes a step of tuning the input ranges of the digital-to-analog (DAC) converters of a “tile”, comprised of Processing Elements (PEs) in a crossbar arrangement, to minimize the matrix-vector-multiplication (MVM) error under the presence of some residual noise term that is applied to the output of the MVM. Alternatively, the method includes tuning the input ranges of the DAC converters to minimize the accuracy of a predefined task, e.g., a classification task, under the presence of some residual noise term applied to the output of each matrix-vector-multiplication. The DAC input value range is optimized with respect to a metric that depends on a residual noise source present in AIMC systems. In an embodiment, the system minimizes the error introduced by the residual noise and the quantization.

The utility model discloses a noise reduction device in a cabin and a vehicle, at least one target position sensor is arranged in the cabin, and the device comprises a microphone assembly arranged on the cabin and used for collecting original noise signals; the sensor assemblies are arranged at the positions of at least one target seat and used for collecting residual noise signals at the corresponding target positions, and the sensor assemblies correspond to the target seats one to one; the controller is connected with the microphone assembly and each sensor assembly, and is used for outputting an electric signal based on the original noise signal and the residual noise signal acquired by the sensor assembly; the loudspeaker assemblies are arranged at the positions of at least one target seat, the loudspeaker assemblies are connected with the controller and used for outputting anti-phase sound waves based on the electric signals, and the loudspeaker assemblies correspond to the target seats in a one-to-one mode. Therefore, the device realizes active noise reduction in the cabin based on the original noise signal and the residual noise signal obtained at the target position, and the riding comfort of passengers in the cabin is improved.

The invention discloses an active and passive noise reduction silencer, and relates to the technical field of noise reduction silencers, the active and passive noise reduction silencer comprises a noise reduction pipe, and a honeycomb turbulent flow noise reduction assembly, a spiral injection speed reduction assembly and a kinetic energy turbine conversion assembly are arranged in an inner cavity of the noise reduction pipe in the airflow flowing direction; efficient noise reduction and stable operation are realized through three processes of multi-stage speed reduction, active and passive cooperative noise reduction and kinetic energy conversion; through conical expansion of an inlet section pipeline, rectification and backflow interference of a honeycomb turbulent flow noise reduction assembly, and extension paths of a porous sound absorption confluence module and a spiral flow guide turning module of a spiral injection speed reduction assembly, multi-stage stepped speed reduction is formed, air flow kinetic energy and impact force are gradually reduced, and noise is further reduced; the porous sound absorption confluence module reduces airflow vibration noise through shunting and multiple sound absorption, the kinetic energy turbine conversion assembly converts airflow kinetic energy into mechanical kinetic energy, residual noise is accurately counteracted in cooperation with active noise reduction of the tail end of the noise reduction section pipeline, and the noise reduction effect is greatly improved through active and passive combination.

The invention discloses a stop-band residual compensation method and device of a digital filter, a storage medium and a computer program product, and relates to the technical field of audio signal processing, and the method comprises the steps: collecting an original audio signal and a digital filtering signal; the digital filtering signal and the original audio signal are synchronously input into a pre-constructed stop-band residual noise dynamic compensation model to be processed, a smooth transition signal is obtained, and the stop-band residual noise dynamic compensation model takes a hardware filtering signal output by a hardware simulation filter as a target label; and superposing the smooth transition signal and the digital filtering signal according to a preset superposition proportion to obtain a calibration signal. The stop-band residual noise dynamic compensation model learns smooth transition characteristics when the hardware simulation filter reproduces parameter adjustment, and the flexibility of the digital filter is considered, so that the response speed when the digital filter adjusts the cut-off frequency in real time at high frequency is improved, and the stop-band residual energy at the moment of cut-off frequency adjustment is inhibited.

The present invention relates to the field of power integrated circuits, and disclosed is a level shift circuit for improving the anti-noise capability of a high-voltage gate driver chip. When a high-voltage domain power rail rises rapidly, a feedback control module detects that common-mode noise is present in a signal transmission path, and outputs a feedback signal to control the load impedance of a level shift branch to be reduced, thereby reducing the amplitude and time of impact of the noise, and filtering out residual noise by a rear-stage filter circuit can ensure that the noise would not cause logic function disorder of a high-voltage domain circuit. Moreover, the time of impact of the noise can be greatly shortened due to the action of a dynamic load structure and the feedback control module, and the noise can be thoroughly filtered out by the rear-stage filter circuit. Therefore, the allowed minimum pulse width of an input signal can be effectively reduced, and a signal transmission delay can be greatly reduced.

System and method for adaptively optimizing digital-to-analog converter input range values for improved signal-to-noise ratio for analog-in-memory computing (AIMC) systems. The method includes a step of tuning the input ranges of the digital-to-analog (DAC) converters of a "tile", comprised of Processing Elements (PEs) in a crossbar arrangement, to minimize the matrix-vector- multiplication (MVM) error under the presence of some residual noise term that is applied to the output of the MVM. Alternatively, the method includes tuning the input ranges of the DAC converters to minimize the accuracy of a predefined task, e.g., a classification task, under the presence of some residual noise term applied to the output of each matrix-vector-multiplication. The DAC input value range is optimized with respect to a metric that depends on a residual noise source present in AIMC systems. In an embodiment, the system minimizes the error introduced by the residual noise and the quantization.

The invention provides a variational Bayesian adaptive iterative Kalman filtering positioning method and system based on LiDAR and IMU in complex weather, aiming at non-Gaussian measurement noise caused by residual noisy points after LiDAR point cloud denoising in complex weather conditions and limitation of a fixed noise covariance matrix in traditional Kalman filtering. Belongs to the technical field of automatic driving. According to the method, LiDAR feature points and IMU original data are directly fused instead of loosely coupled pose fusion, so that the robustness of state estimation is improved; in order to overcome the problem of negative definite or non-positive definite possibly occurring in a classical adaptive filtering method and meet the requirements of adaptive capacity to time-varying noise, high positioning precision and the like, a variational Bayesian method is adopted to approximate noise point distribution. The method is suitable for the automatic driving technology, the positioning precision under the complex weather condition is remarkably improved, and important technical support is provided for achieving the positioning technology under the complex weather condition.

The invention discloses a transformer substation equipment defect image generation method and system based on two-stage denoising, belongs to the technical field of computer vision and image generation, and solves the problem that in the prior art, the geometry and contour of equipment cannot be still kept in a steady state under the condition that an inference end does not depend on a control chart. The method comprises the following steps: acquiring normal samples and defect samples of a substation scene target defect category, extracting structure priori in the normal samples, and selecting a diffusion base model at the same time; in the first stage, a diffusion model base and a lorabase model are fused to generate latent variables with residual noise, and in the second stage, a diffusion base model, the lorabase model and a loradeject model are fused to continue decoding the latent variables with the residual noise to generate defect images; and based on the training set B and a defect image training discrimination model, discriminating the defect image, and screening generated defect data which cannot be distinguished from real distribution, namely generating a final substation equipment defect image. The method is used for generating the transformer substation equipment defect image with two-stage denoising.

The invention relates to an AMT signal denoising method and device based on self-adaptive multi-level U-Net. According to the method, noise segment classification is performed on a noisy AMT signal, a corresponding position mask of the clean AMT signal is recorded, and noise positioning information is provided for training of a denoising model, so that the training effect of the denoising model is improved, and loss of non-noise segments can be avoided; the method also uses two U-Net networks to form a U-Net cascade structure, the first network performs preliminary denoising, the second network processes fragment data output by the first network, generates a noise intensity probability by using a residual error, uses the noise intensity probability as a driving signal, distributes a corresponding weight value, and outputs the driving signal to the U-Net cascade structure. According to the method and the system, the second network is dynamically controlled to selectively absorb and fuse fragment data output by the first network, an area with remarkable residual noise can be adaptively concerned, complementation and optimization of a feature level are realized, and finally, the denoising effect of the denoising model on the noisy AMT signal is improved.

The invention provides an audio processing method, an audio processing device and a computer storage medium. The audio processing method is applied to the bathroom sound system. The audio processing method comprises the following steps: acquiring an original playing audio and a mixed audio signal acquired by a microphone; extracting a residual noise signal based on the original playing audio and the mixed audio signal; obtaining a dynamic gain based on the residual noise signal and a preset auditory masking threshold; and processing the original playing audio according to the dynamic gain, and obtaining and outputting a gain playing audio. Through the audio processing method, dynamic gain compensation can be realized for a bathroom scene, audio output can be dynamically adjusted according to environmental noise, intelligent noise reduction and adaptive adjustment are realized, and a bathroom sound box is ensured to provide rich and fine sound effect performance.

The invention discloses a secondary path online modeling and real-time ANC system observation method without additional white noise, and the method comprises the following steps: monitoring the stability of an ANC system through a real-time observer, and preventing divergence caused by the change of a secondary path or an overlarge step length; a secondary path online modeling method is designed, secondary path online precise modeling without adding white noise is realized by combining an offline modeling initial value and online self-adaptive updating, and residual noise rise and human ear discomfort caused by introduction of the white noise in a traditional online modeling method are avoided; secondary path coupling is eliminated by using a loudspeaker time-sharing sounding strategy, and the modeling calculation amount is reduced; and an intelligent ANC system is constructed, a secondary path model is dynamically updated, the step length is adaptively adjusted, and stable operation of the system is ensured. According to the invention, the stability and the noise reduction performance of the ANC system are improved, and the calculation complexity and the human ear interference risk are reduced at the same time.

The application provides a kind of multi-channel active noise control method, system, terminal and storage medium, and noise control method includes the following steps: establishing distributed wave domain ANC network model;The global cost function of wave domain ANC problem is split into local cost function and the form of the sum, and a distributed optimization problem is established;Multi-channel distributed wave domain ANC problem is solved using diffusion LMS algorithm, to obtain the estimation of loudspeaker weight;Secondary sound field is generated and residual noise signal is cancelled out.The application also provides a kind of multi-channel active noise control system, terminal and computer readable storage medium.The application uses diffusion strategy to obtain multi-channel distributed wave domain ANC algorithm, so that the calculation burden is dispersed among nodes, compared with centralized scheme, the application makes the expansibility of wave domain ANC system stronger, more robust, more suitable for large-scale application occasions.

The invention provides an air source heat pump with an active noise reduction function, and belongs to the technical field of air source heat pumps, the air source heat pump can carry an active noise reduction module, the active noise reduction module is used for reducing low-frequency noise generated in the operation process of the air source heat pump, and hardware of the active noise reduction module is composed of an MCU, a DSP and other peripheral circuits. The MCU is responsible for reading the frequency of a fan and a compressor of a heat pump through a Modbus protocol and obtaining the fundamental frequency of noise, the MEMS noise microphone is used for collecting original noise, and the MEMS error microphone is used for collecting residual noise; the audio codec is used for converting analog signals collected by the MEMS microphone into digital signals and then sending the digital signals to the DSP, the DSP processes the collected signals through an FxLMS algorithm, noise reduction signals are obtained through integration and calculation, the audio codec and the power amplifier drive the secondary loudspeaker to output noise reduction sound waves with the phase opposite to that of noise, and the noise reduction sound waves are sent to the power amplifier. Noise reduction is realized through superposition of the noise reduction sound waves and the noise sound waves.

The invention relates to an underwater sonar signal enhancement method based on an RLS adaptive filter. The method comprises the following steps: S1, receiving an original sonar signal through an RLS adaptive filter module and executing real-time filtering processing; s2, acquiring underwater environment parameters by using an environment sensing module, and generating an environment feature vector; s3, dynamically adjusting a forgetting factor lambda and an initial covariance matrix P (0) of an RLS adaptive filter module through a parameter feedback adjustment module according to the environment feature vector; and S4, updating the weight of the RLS adaptive filter module by using the adjusted parameters, performing residual noise processing on the filtered signal, and outputting an enhanced signal. According to the invention, by introducing a feedback adjustment mechanism based on environmental perception data, dynamic optimization of key parameters of the RLS algorithm is realized, so that the signal enhancement capability and stability of an underwater communication system in a complex environment are improved.

The invention relates to the cross technical field of elevator fault diagnosis and signal processing, in particular to an elevator shaft breakage early fault detection signal denoising method. The method comprises the following steps: acquiring an original low and medium frequency discrete signal of elevator shaft body vibration, and filtering by adopting a four-order Butterworth band-pass filter to obtain a filtered signal; performing complementary set empirical mode decomposition on the filtering signal to obtain a plurality of IMF components and a residual component, and screening effective IMF components from all the IMF components; the center frequency of each effective IMF component is calculated, and Gaussian weight distribution is carried out on each effective IMF component based on the center frequency of each effective IMF component; performing signal reconstruction on all the effective IMF components and the residual components according to the weight of each effective IMF component to obtain a reconstructed signal; and smoothing residual noise in the reconstructed signal by adopting self-adaptive moving smoothing filtering to obtain a de-noised signal. According to the method, the 30-60Hz low and medium frequency characteristic signals generated in the elevator spindle microcrack propagation stage can be accurately extracted, so that fault judgment is facilitated.

The invention discloses an ocean low-power-consumption weak electromagnetic signal identification and acquisition method, which comprises the following steps of: removing noise and interference in an ocean weak electromagnetic signal through adaptive filtering, amplifying the preprocessed signal by using a low-power-consumption amplifier, and carrying out secondary filtering on the amplified signal through a digital filter to eliminate residual noise; wavelet transform and Fourier transform are adopted to extract frequency, amplitude and phase features of the signals to construct feature vectors, and the feature vectors are classified and identified based on a machine learning algorithm; the signal acquisition frequency is controlled through the low-power-consumption microcontroller, and the identified data is compressed and encrypted; the signals processed in the fourth step are transmitted to a remote terminal through a low-power-consumption layered transmission method based on an energy cost function; the method improves the recognition precision, reduces the energy consumption, enhances the practicability and response speed of the system, and is suitable for multiple fields of marine environment monitoring, underwater communication and the like.

The invention relates to the technical field of earphones, in particular to an active noise reduction earphone adjusting system, which comprises the following steps: S1, acquiring two-dimensional noise and state, acquiring environmental noise through an external microphone, acquiring auditory meatus residual noise through an internal microphone, and synchronously detecting a wearing state through an infrared sensor; s2, a signal processing module receives the noise data collected in the S1, noise features are analyzed through a fast Fourier transform algorithm, main frequency components in the environmental noise are recognized, and the environmental noise is obtained through a full-closed-loop technical scheme including two-dimensional collection, intelligent analysis, execution in different frequency bands, closed-loop feedback and personalized optimization. The problem that an existing active noise reduction earphone cannot dynamically adapt to complex and changeable environmental noise is solved, and the problem that the noise reduction effect fluctuates obviously when the same earphone is worn by different users due to the fact that an existing system does not conduct dynamic calibration according to the individual difference is solved.