226 results about "Noise immunity" patented technology

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention relates to a microphone array sound source positioning method and system based on cross-correlation-beam forming closed-loop optimization, and belongs to the technical field of sound source positioning. The method comprises the following steps: collecting multichannel sound signals through a microphone array and preprocessing the multichannel sound signals to extract time-frequency features and suppress noise interference; time delay information among the microphones is estimated by adopting a generalized cross-correlation phase transformation algorithm, and an optimization strategy is introduced to improve estimation stability and anti-interference performance; enhancing the target sound source signal in combination with a minimum variance undistorted response beam forming algorithm and an adaptive Kalman filtering mechanism; constructing a closed-loop feedback optimization mechanism based on the beam output signal to realize feedback adjustment; and adopting a hybrid network architecture, taking the beam output signal amplitude spectrum as input, and outputting the frequency spectrum or mask of the obtained target sound source signal. The method has the advantages of high calculation efficiency, high positioning precision and strong anti-interference capability, and is suitable for real-time acoustic signal processing in a complex environment.

The invention discloses an HPLCamp (High Performance Liquid Chromatography) based on deep learning. The invention discloses an HRF dual-mode communication adaptive coding modulation and anti-noise method. The method comprises the following steps: acquiring an optical radio frequency signal amplitude-phase change rate and synchronously sampling and normalizing; calculating a node amplitude-phase residual error to generate a nonlinear mapping coefficient; monitoring coherent change to solve a drift trend, adjusting a modulation coding optimization scheme, compensating distortion and outputting an anti-noise result. According to the method, the instantaneous amplitude and phase of the optical radio frequency dual-mode signal are extracted, a multi-dimensional amplitude-phase characteristic matrix is formed in combination with time domain synchronization and a normalization template, differential residual modeling and nonlinear mapping coefficient calculation are carried out between impedance nodes, and dynamic compensation of amplitude-phase mismatch and envelope offset is achieved. A drift trend quantity is generated based on coherent offset parameter differentiation, feedback is provided for modulation format and coding strategy optimization, amplitude equalization and phase correction are completed, the signal synchronization degree and amplitude-phase consistency are improved, and the steady-state response and anti-disturbance performance of a transmission link are enhanced.

The invention belongs to the field of power system stability analysis, and discloses a response data and model fused subsynchronous oscillation parameter identification method for a wind power plant grid-connected system. Firstly, the current of a grid-connected line is monitored, the energy is calculated, and an early warning is given out when the energy is continuously increased along with time. Then, self-adaptive VMD decomposition is carried out on the early warning signal, and the system state is preliminarily determined; then, proper band-pass filter parameters are set according to the decomposition result, and filtering operation is carried out on the signals. And solving a complete SSO current component from the filtering signal according to a derived mathematical relationship between related electrical quantities and parameters of the series compensation power transmission line, and further obtaining an oscillation frequency and a damping factor. And finally, carrying out a simulation test based on series compensation capacitor grid-connected data of the direct-driven wind power plant. The oscillation identification method provided by the invention has strong noise immunity and robustness, and can realize accurate and rapid identification of the dominant SSO mode.

The invention provides a nonlinear amplitude-phase error correction method and device suitable for broadband FMCW radar imaging. The method comprises the following steps: establishing a frequency modulated continuous wave radar difference frequency signal model containing a nonlinear error; correcting the amplitude error by adopting an envelope normalization correction method; estimating the high-order polynomial phase error by adopting a method of combining a phase unwrapping recursive least square method and an OShea optimization strategy to obtain a high-order polynomial phase parameter; estimating the periodic multi-sine phase error by adopting a method of combining a MUSIC algorithm and a trust region algorithm to obtain a periodic multi-sine phase parameter; and based on estimation results of the high-order polynomial phase parameters and the periodic multi-sine phase parameters, phase error correction is carried out by adopting a time domain resampling method. The device comprises a nonvolatile memory in which the executable code is stored and a processor, and is used for implementing the method. According to the technical scheme of the invention, the problem that the existing method is generally insufficient in noise immunity can be solved.

Provided in the present invention are an abnormal-sound detection method and system based on diffusion denoising for an air conditioner outdoor unit. The method comprises: acquiring normal operation sound data of an air conditioner outdoor unit and interference sound data in an environment where air conditioner detection is performed; pre-processing the sound data to obtain respective time-frequency features; training a Gaussian mixture model on the basis of the time-frequency feature of the interference sound data, obtaining the data distribution thereof to perform random sampling, and superimposing, onto the time-frequency feature of normal sound data of an air conditioner, sound data sampled from the data distribution, so as to obtain fifth sound data and a corresponding sampling step label; training a neural network model by means of the normal operation sound data of the air conditioner outdoor unit and the fifth sound data, so as to obtain a trained anti-interference abnormal-sound detection model for an air conditioner outdoor unit; and acquiring operation sound data to be subjected to detection of the air conditioner, pre-processing same, and then inputting same into the trained abnormal-sound detection model for an air conditioner, so as to determine whether an operation sound of the air conditioner is abnormal. The present application improves the anti-interference performance of a detection algorithm and the air conditioner outdoor unit detection efficiency, thereby reducing the labor cost.

The invention provides a state detection and health diagnosis method and system of an SOFC system, and relates to the technical field of solid oxide battery state monitoring, and the method comprises the steps: obtaining the operation parameters of an SOFC, and optimizing the data consistency and anti-noise capability based on the fusion algorithm of dynamic Bayesian reasoning and the conditional probability distribution of the operation state of the SOFC; frequency domain features are extracted through fast Fourier transform, dimension reduction is carried out in combination with improved principal component analysis, key variables are reserved, and feature expression is optimized for SOFC high-temperature vibration features; predicting SOH (state of health) and RUL (residual life) of the SOFC based on a random forest, a generative adversarial network and an online updating mechanism; and outputting a diagnosis result and generating a dynamic maintenance scheduling plan based on the RUL prediction, and optimizing the system life by minimizing the downtime and the cost. Through integration of multi-source data fusion, frequency domain feature extraction, AI diagnosis and data enhancement and predictive maintenance, limitations in precision, efficiency and service life optimization in the prior art are overcome.

The invention discloses a bearing fault noise immunity diagnosis method based on IPDSCS and Swin Transformer, and the method comprises the steps: collecting vibration signals of a bearing under different working conditions, and obtaining a plurality of groups of original vibration signal data; performing wavelet transform on the original vibration signal data, and converting the original vibration signal data into a time-frequency image; constructing a feature extraction model based on an improved inverted pyramid depth separable convolution sequence, and extracting the time-frequency image to obtain a multi-dimensional feature vector; using the feature vector as input data, performing model training based on a Swin Transform deep learning network, and establishing a bearing fault diagnosis model; and inputting a to-be-diagnosed bearing vibration signal into the bearing fault diagnosis model, and outputting a corresponding fault category. According to the method, the noise immunity of signal processing is improved through wavelet transform denoising and an IPDSCS feature extraction method; a Swin Transform network is adopted to carry out multi-scale modeling on the multi-dimensional features, so that the accuracy and robustness of fault diagnosis are remarkably improved; and high-precision diagnosis of bearing faults is realized.

The invention belongs to the technical field of wireless communication and artificial intelligence, and discloses a multi-mode semantic communication method and device oriented to multiple tasks, which avoid low-layer signal coding transmission of original data through extraction and transmission of semantic features, realize end-to-end optimization and channel adaptive robust transmission of semantic level communication, and improve the communication efficiency. The bandwidth occupation is obviously reduced; and the anti-noise performance is improved. Semantic transmission and task execution of multi-source data such as images, texts and voices under a unified framework are achieved through a shared neural network encoder and a channel interface, the robustness of tasks is kept under the complex channel condition, and cross-domain semantic expression among modes such as vision, texts and voices is achieved through the framework. Radio frequency data are sent and received in a real hardware platform, physical combination of an algorithm and a radio frequency link is achieved, and implementation of deep semantic communication in an actual wireless link shows that the method has engineering deployability and expansibility.

The invention discloses a signal control system for a mining locomotive, and aims to solve the problems of weak interference resistance and low safety of a traditional system. The system comprises a signal acquisition module, a dynamic frequency coding processing module, an intelligent decision-making module, an execution module, a communication module and a power supply module. The signal acquisition module acquires the position, speed and environmental parameters of the locomotive through an infrared position, speed and temperature and humidity sensor; the dynamic frequency coding module monitors the signal-to-noise ratio of a sub-band every 10 ms, when the signal-to-noise ratio of the sub-band is lower than 15 dB, the sub-band is automatically switched to a high-signal-to-noise-ratio sub-band larger than or equal to 20 dB, an encryption mapping table is generated for synchronous updating, and the equipment deals with sudden interference through an emergency band. The intelligent decision-making module generates a control instruction, and speed adjustment and parking are achieved through the execution module. An explosion-proof cable and a low-frequency through-the-earth technology are combined for communication, and a power supply is of an explosion-proof type and has multiple protections. The system improves the anti-interference performance and safety of signal transmission, adapts to the mine environment, and supports the intelligent operation of mine transportation.

According to the intelligent frequency spectrum prediction system and method with the dynamic anti-interference function, through the lightweight AI prediction model deployed in the embedded terminal, prospective accurate prediction of the future frequency spectrum hole probability is achieved, and the time delay defect of traditional passive detection is overcome; through an interference identification and classification module, fine identification of an interference source is realized, and a basis is provided for a differential anti-interference strategy; dynamic global optimization of communication parameters is realized through fusion of a utility function decision mechanism of a prediction result, interference information and historical quality and GFSK / QPSK adaptive modulation switching, and optimal balance is obtained between noise immunity and spectrum efficiency; through maintenance of the pre-switching channel pool and a microsecond-level rapid switching mechanism, challenges brought by strong interference burst and high-speed moving scenes are effectively dealt with, and continuity and high reliability of communication are ensured.

The invention discloses an underdetermined blind source separation method based on improved single source point detection, and the method comprises the steps: firstly, carrying out the initial single source point extraction of a mixed signal through employing the cosine angle criterion of a real part and an imaginary part of a time-frequency matrix column vector; then, abnormal points are removed according to whether the linear relation between the single source points and the abnormal points exists or not, the influence of the abnormal points on single source point detection is reduced, and therefore the estimation precision of a hybrid matrix is improved; then, clustering is carried out on the obtained single source points by using a K-means algorithm, so that an estimated hybrid matrix is obtained; and finally, recovering a source signal by using a least square method. The method provided by the invention not only considers the absolute direction of the real part and the imaginary part in the single source point, but also considers the linear relation between different single source points to remove the abnormal points, so that the estimation precision of the hybrid matrix can be improved, the noise immunity of the method is enhanced, and finally, a high-quality recovery signal is obtained.

The present invention is directed towards an apparatus and method to detect electromagnetic radiation or signals emitted by plasma comprising a sensor unit, the sensing unit comprising a first sensor for detecting electromagnetic radiation or signals; an electromagnetic barrier wherein the electromagnetic barrier is configured to surround the first sensor to prevent ambient electromagnetic radiation reaching the first sensor; a first port through which electromagnetic radiation can pass, the port configured for coupling to a port of a plasma chamber such that electromagnetic radiation or signals emitted from the plasma can reach the first sensor; and a first output coupled with a balun and a dual coaxial system whereby a signal sensed is grounded and a phase reversed version of the signal is grounded. A signal acquisition system (SAS) for detecting electromagnetic radiation emitted by a plasma is also described.

The invention relates to the technical field of computers, and discloses a power battery SOH prediction method based on big data. The method comprises the following steps: acquiring voltage, current and temperature time sequence data in a battery charging process; intercepting a constant current charging segment and generating a two-dimensional time sequence spectrogram through continuous wavelet transform; constructing a multi-task attention deep convolutional network, performing decoupling quantization on three attenuation mechanisms of lithium ion precipitation, active substance loss and internal resistance increase, and outputting quantization vectors of the attenuation mechanisms; and finally, calculating an SOH value through a comprehensive evaluation model with physical significance. According to the method, the unification of end-to-end self-learning, strong noise immunity and high interpretability is realized, and the SOH prediction precision and practicability are improved.

Described is a touch-sensitive apparatus for sensing the presence of an object relative to a touch-sensitive surface using capacitance measurements. The apparatus includes an electrode array comprising at least one electrode, the electrode array providing the touch-sensitive surface; a signal generating module for generating a time-varying drive signal to be applied to the electrode array; a differential amplifier comprising: a first input configured to receive a time-varying sensor signal from an electrode of the electrode array, the sensor signal indicative of a capacitive coupling associated with the at least one electrode; a second input configured to receive a time-varying reference signal; a first output configured to output a first output signal from the differential amplifier based on a difference between the time-varying sensor signal and the time-varying reference signal; and control circuitry configured to determine the presence of an object, wherein the apparatus further comprises a subtractor for subtracting a signal from the first output signal from the differential amplifier to generate a subtracted signal, wherein the control circuitry is configured to receive a signal based on the subtracted signal and to determine the presence of an object based on the received signal. Further disclosed is a method for sensing the presence of an object relative to a touch-sensitive surface using capacitance measurements of a touch sensitive apparatus.

The invention discloses a voice interaction method and system based on an intelligent doll, and relates to the technical field of intelligent acoustic interaction, and the method comprises the steps: carrying out the time-frequency analysis processing of a vibration feature matrix, and generating a modal parameter set of vocal cord vibration through biomechanical modeling in combination with the human neck tissue density features; inputting the modal parameter set of vocal cord vibration into the vibration-acoustic model to generate a sound source excitation field, performing environmental noise compensation on the sound source excitation field by using the acoustic characteristic matrix, and generating a time domain pure voice signal through an acoustic wave equation; and collecting real-time position coordinates of the user, calculating and driving a piezoelectric loudspeaker array of the intelligent doll to adjust the phase according to the frequency spectrum characteristics of the time domain pure voice signal, forming a directional focusing sound field, recording user physiological feedback, and generating a user physiological feedback matrix. According to the method, the vocal cord displacement modal function vector is solved through the nonlinear integral and augmented Lagrange algorithm, so that the acquisition precision and the anti-noise capability of the voice signal are improved from the source.

The invention discloses an artificial intelligence recognition device based on big data in the technical field of outfield explosion parameter testing equipment, and the device comprises a multi-modal data collection module which is used for collecting explosion infrared light, images, heat, sound and vibration signals; the photoelectric clock synchronization module is used for realizing space-time alignment of multi-modal signals; the noise immunity module is used for performing transient feature enhancement and background noise suppression on the signal; the intelligent signal generation module is used for generating a trigger signal based on multi-modal combined logic and intelligently optimizing a threshold parameter; the intelligent signal triggering module outputs a standard triggering timestamp to the detection instrument; the above modules achieve synchronization through multi-mode fusion, an intelligent learning model and hardware acceleration, and the trigger delay and the false trigger rate are reduced. The intelligent triggering system which is ultrahigh in precision, strong in anti-interference and self-adaptive is achieved, and the strict requirements for reliability, real-time performance and environmental adaptability of a photoelectric parameter identification triggering mechanism in the explosion test and safety monitoring field are met.

The invention discloses a digital measurement and control circuit of a resonant accelerometer based on an orthogonal phase-locked loop. Comprising an FPGA core board, an ADC / DAC board card and a gauge outfit analog interface board, and the high-precision resonant accelerometer is driven and detected through digital closed-loop control. The implementation method comprises the following steps: after capacitance / voltage conversion, modulation and demodulation and filtering, a resonator displacement signal is acquired by a high-precision ADC and input into an FPGA, a driving signal generated by the FPGA through an orthogonal phase-locked loop and an automatic gain control module is output through a DAC, and the driving signal is applied to a resonator driving end after shunt superposition of a direct current quantity. The digital quadrature phase-locked loop is adopted to replace a traditional phase-locked loop, the contradiction between dynamic response and noise suppression is solved, and the digital quadrature phase-locked loop has the advantages of being high in phase control precision, wide in capture bandwidth, high in anti-interference performance and excellent in compatibility.

The invention relates to the technical field of power quality identification, and discloses a power quality disturbance identification method and device, equipment and a storage medium, and the method comprises the steps: obtaining a power quality disturbance signal; performing feature extraction on the power quality disturbance signal by using a preset one-dimensional convolutional neural network and a one-dimensional residual neural network to obtain a target time feature vector of the power quality disturbance signal; performing feature fusion processing on the target time feature vector by using a preset multi-head attention mechanism to obtain a fused signal feature; and classifying the fused signal features by using a preset classifier to obtain a final classification result used for indicating the disturbance type of the power quality disturbance signal. According to the method, various feature information can be integrated, the influence of noise on a classification result is effectively reduced, the classification performance and generalization ability of a classifier are improved, better performance is achieved in the aspects of classification accuracy and noise immunity, disturbance recognition accuracy is improved, and effective support is provided for electric energy fault diagnosis in an electric power system.

The invention belongs to the field of signal processing, particularly relates to a signal processing method, system and equipment based on an MIMO radar, and aims to solve the problem of limited detection precision caused by insufficient utilization of multi-channel signals and simple fusion strategy in the prior art. The method comprises the following steps: obtaining a target multi-channel echo signal, carrying out distance and angle processing, extracting phase change by using phase unwrapping, carrying out two-stage weighted fusion based on slope entropies of a receiving channel and a transmitting channel in sequence, obtaining a micro-motion signal, carrying out variational mode decomposition and frequency band matching screening, and reconstructing a final output signal. According to the method, the problem of insufficient utilization of multi-channel information is effectively overcome, and the signal noise immunity and the micro-motion feature fidelity are remarkably improved through a double fusion strategy, so that the detection and recognition precision of a weak target is improved.

The invention belongs to the technical field of optical measurement, and particularly relates to a high-stability synthetic wavelength phase demodulation method. The method comprises the following steps: based on three-wavelength synthesis phase preprocessing and branch correction, expanding an unambiguous range to a millimeter level by calculating a secondary synthesis wavelength; in the staged processing strategy, a rough measurement height contour is generated by using a corrected synthetic phase, and then efficient candidate height screening is realized by combining integer period traversal and local microentry error function optimization; and finally, abnormal solutions are eliminated through multi-wavelength consistency inspection, and the noise immunity is ensured. The method does not need to depend on complex hardware, large-range (dozens of microns) high-precision (sub-nano-scale) measurement is achieved through pure-method design, the surface contour detection requirement of a large-size workpiece is met, the single-pixel processing time is shortened to the microsecond level, real-time measurement is supported, and the method is suitable for large-size surface appearance detection in the fields of aerospace, precision manufacturing and the like.

The invention discloses a mechanical equipment residual life prediction method based on multiple expert models, and belongs to the technical field of mechanical equipment. Comprising the steps of receiving an original mechanical vibration signal, performing preprocessing, constructing a residual life prediction model of the mechanical equipment, training the residual life prediction model of the mechanical equipment based on a preset data set, inputting a mechanical vibration signal to be detected into the trained residual life prediction model of the mechanical equipment, and performing fault category prediction of the mechanical equipment. According to the method, the accuracy, generalization and practicability of residual life prediction of mechanical equipment are remarkably improved, and through multi-scale time-frequency domain feature fusion and a double attention mechanism, the identification capability and noise immunity of the model to a complex fault mode are enhanced; the domain adaptive routing network realizes cross-device efficient knowledge migration, greatly improves the prediction performance on unseen devices, has high precision and high real-time performance, and provides reliable technical support for industrial predictive maintenance.

The invention discloses a multi-scale fracture detection method based on high-precision spectral decomposition, and the method comprises the steps: S10, inputting a seismic data cube, and carrying out the super-resolution spectral decomposition; s20, carrying out adaptive multi-scale frequency band division; s30, according to the divided multi-scale frequency bands, PDV feature extraction is carried out on each frequency band, and multi-scale PDV feature body construction is completed; s40, carrying out dual-channel 3DCNN fusion, and fusing the features of each frequency band; and S50, obtaining a fracture probability body through space attention weighting. According to the method, through high-precision time-frequency spectrum decomposition, multi-scale depth feature fusion and phase enhancement fracture response, the resolution limit is broken through, and the noise immunity is improved.

The invention belongs to the field of electric energy quality analysis of an electric power system, and provides an electric energy quality disturbance classification method for a tunneling roadway power supply system in order to solve the problem that various electric energy quality disturbances cannot be accurately identified and classified in a tunneling roadway at present. According to the method, the time domain information and the frequency domain information of the electric energy quality disturbance signals are fused, the model has high noise immunity, high training speed and good classification accuracy, the electric energy quality disturbance signal data collected in real time are classified, and therefore the electric energy quality disturbance of the coal mine driving working face power supply system is rapidly and accurately classified.

The invention discloses a model-free predictive current control method based on an improved observer, and belongs to the technical field of permanent magnet synchronous motor control. The control method comprises the following steps: firstly, by analyzing the frequency characteristic of ESO, selecting an extended state observer order for obtaining balance between the anti-interference capability and the noise suppression capability; secondly, designing a model-free predictive current loop controller, and calculating the observer gain through a bandwidth method; then, a judgment mechanism of a current loop controller is constructed, the mechanism judges whether the system is in a steady state or not at the moment according to the current deviation value in the current state, and therefore the observer order with the better control effect is selected. According to the model-free predictive current control method based on the improved observer, the anti-interference performance of the system and the noise suppression capability of the current are remarkably improved, it is guaranteed that the improved observer has rapid tracking response and excellent steady-state performance, and the robustness of the control system is enhanced.

The invention relates to the technical field of voltage regulation cabinet monitoring, and particularly provides a medium and low voltage voltage regulation cabinet intelligent monitoring method based on big data and Internet of Things, and the method comprises the following steps: installing a sensor in voltage regulation cabinet equipment, and collecting key parameters, such as pressure, temperature, flow, current and the like, in the operation process of the equipment in real time; transmitting the data to a cloud platform through a wireless network; and the cloud platform performs deep analysis on the acquired data based on big data storage, processing and analysis technologies, and performs equipment operation health scoring, state evaluation and fault prediction by utilizing innovative design and fusing a vector random forest (SRA) of a support vector machine and a random forest algorithm. According to the algorithm, fine classification capability, noise immunity and feature evaluation are fused, the equipment operation health state can be accurately scored, and faults can be effectively predicted. The innovation point lies in that the advantages of the two algorithms are complemented by using a vector fusion method, efficient feature extraction and decision fusion are realized in a multi-dimensional feature space, and the complex monitoring requirements of big data and the Internet of Things are met. According to the invention, a data acquisition module of a high-precision sensor and a data transmission module of wireless communication are deployed in the gas medium-pressure equipment, and a big data storage and processing technology, a machine learning algorithm and an intelligent early warning mechanism are combined, so that real-time monitoring, fault prediction and maintenance optimization of the operation state of the equipment are realized; therefore, the safety, the reliability and the operation and maintenance efficiency of the gas equipment are improved.

The invention relates to the technical field of audio signal processing. The invention provides a method, system and device for predicting pulmonary nodules through sound and a storage medium. The method comprises the following steps: in a quiet test environment, directionally collecting a sound signal of a patient through an audio sensor; wherein the sound signals comprise breath sounds, cough sounds, vowels and sentences; performing noise reduction processing on the collected sound signals to obtain a noise reduction data set; extracting multi-dimensional voiceprint features from the noise reduction data set, and constructing a sound feature matrix; and performing multi-modal fusion analysis on the sound feature matrix and clinical data of the patient, inputting the sound feature matrix and the clinical data into a sound prediction model optimized by a focus loss function, and outputting a pulmonary nodule risk prediction result and a visual diagnosis report. The problems that the misdiagnosis and missed diagnosis rate is high, the radiation risk is large and the equipment dependence is strong when the pulmonary nodules are examined by the existing iconography, and the characteristic analysis is single, the noise immunity is poor and the screening accuracy is insufficient in the sound detection technology are solved.

A high-noise-immunity and low-delay level shift circuit for a GaN half-bridge drive chip, relating to the technical field of power management in integrated circuits. By additionally providing a path from a floating power rail VDDH to the ground, the level shift circuit simulates the noise effect caused by a sudden change in the voltage of the floating power rail in the level shift circuit. The level shift circuit has a good noise immunity capability and a simple structure.

The invention relates to the technical field of transformer sound wave detection, in particular to a noise processing method for transformer hot-spot temperature ultrasonic detection, which comprises the following steps of: multiplying a pseudo-random sequence coded signal by an electric signal of a transmitting end of an ultrasonic sensor to generate a modulation signal with a pseudo-random binary coded sequence; performing time domain windowing processing on the collected ultrasonic signals to obtain a spectrogram; performing dynamic frequency domain segmentation based on the spectrogram; performing clustering analysis on the low-frequency sub-bands, distinguishing steady-state vibration noise and transient interference, and establishing a corresponding noise feature library; performing wavelet threshold denoising on each windowed signal, and retaining a high-frequency ultrasonic signal; and performing cross-correlation operation on the reconstructed and spliced complete signal and the pseudo-random sequence coded signal, and extracting a cross-correlation function peak value. By combining the pseudo-random sequence coding signal with the ultrasonic signal, effective coding of the ultrasonic signal is realized, the noise immunity is enhanced, and the noise interference problem of the transformer in the ultrasonic detection process is effectively solved.

The invention discloses a level shift circuit for subnanosecond transmission delay of a GaN gate driving chip, and relates to the technical field of gallium nitride half-bridge gate driving chips. Based on a traditional level shifting circuit, two auxiliary capacitors are additionally arranged in two pulse control branch nodes of a level shifting main circuit, charge and discharge logic control is achieved through a capacitor control circuit, and the capacitors are actively charged in the signal transmission stage to accelerate node voltage establishment by means of the time sequence characteristic of narrow pulse signals; and in the noise interference stage, rapid discharge is carried out to compensate the voltage drop caused by common-mode noise, and collaborative optimization of the anti-noise capability and the signal transmission efficiency is realized. And efficient and stable operation of the GaN power device under a high-frequency working condition is ensured.