1280 results about "Signal characteristic" patented technology

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

The invention relates to the technical field of low-altitude safety emergency, and discloses a low-altitude safety emergency system based on large model application. The system comprises a low-altitude situation awareness module which constructs a dynamic situation prediction model based on historical flight data, collects a low-altitude target track coordinate sequence, radar echoes and communication signal characteristics in real time, and outputs a theoretical security situation value. The multi-source data fusion analysis module performs three-dimensional difference analysis on theoretical and actually measured situation values, which relates to time domain trend deviation, space domain coverage offset and event sequence matching degree, and generates a target-level difference feature matrix; and the risk propagation path deduction module inputs the matrix into a geographic topology analysis network, generates a probability thermal distribution diagram of an abnormal risk propagation path in combination with airspace node communication parameters and a target position, and locates a risk influence area. And the intelligent disposal strategy generation module configures parameters according to the graph, starts high-frequency monitoring for a high-probability area, and performs communication disturbance test on an adjacent target.

The invention relates to the technical field of circuit boards, and discloses a circuit board interlayer connection detection method, device and equipment, and the method comprises the steps: carrying out the electrical characteristic parameter measurement of a multilayer flexible circuit board, and obtaining the characteristic parameter data of the circuit board; designing and arranging a multi-test-point detection structure on the multi-layer flexible circuit board, and performing signal excitation to obtain test point position information; applying a differential excitation signal, and collecting a differential response waveform between the test points to obtain a time domain reflection signal feature; impedance spectrum conversion and parameter extraction are carried out on the time domain reflection signal features, and an interlayer connection impedance distribution diagram is obtained; and establishing an interlayer connection defect characteristic model aiming at nickel-based alloy wire fracture, interface cracking and liquid metal fiber felt delamination in various bending states by utilizing the interlayer connection impedance distribution diagram, so that accurate classification of defect types and severity is realized, and interlayer connection faults and surface welding quality problems can be distinguished; and the misjudgment rate is greatly reduced.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention relates to the technical field of submarine engineering and information processing, in particular to a submarine cable positioning method and system, and the method comprises the steps: synchronously collecting magnetic field, structured light and underwater acoustic signal data, and fusing inertial navigation information to construct a unified space-time reference system; precise registration of heterogeneous data is realized by adopting sliding time window screening, multi-modal noise reduction and rigid body transformation; constructing a graph structure model for expressing cable space-signal features based on the fused data, and reasoning cable path topology by using a graph convolutional network; reconstructing a cable center line through three-dimensional visualization, embedding the cable center line into submarine topography, and realizing risk area dynamic clustering by combining signal integrity score and burial depth estimation; historical time sequence data are fused, a long short-term memory network is adopted to predict cable drift, detectable attenuation and burial depth change trends, risk levels are divided based on prediction results, and spatial distribution early warning and disposal suggestions are output. According to the invention, the cable positioning precision and the risk assessment intelligent level in a complex seabed environment are improved.

A fault identification system and method. The system comprises an edge device and a server. The edge device can extract, from an original vibration signal, signal characteristics reflecting an abnormal vibration state of a device under test and report the signal characteristics to the server; and the server can identify a fault state of the device under test on the basis of the signal characteristics. The data volume of the signal characteristics reported in the process is small, thereby reducing the bandwidth requirement; and the signal characteristics can be used for accurate fault identification, thereby improving the accuracy of fault identification.

The invention provides a wireless signal detection and positioning method and system, and relates to the technical field of wireless communication, and the method comprises the steps: collecting signal characteristic parameters through a plurality of monitoring devices, constructing a three-dimensional time-frequency space matrix, carrying out envelope modulation to construct a primary function, carrying out iterative projection to obtain a reconstructed signal, and extracting a characteristic fingerprint through a high-order statistical moment; constructing a topological graph according to geographic distribution to calculate information entropy, and performing dynamic clustering fusion; a scene type is identified and a location is estimated in combination with historical data. The wireless signal detection accuracy and the positioning precision can be improved, and the method adapts to a complex electromagnetic environment.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The invention provides a full-focusing ultrasonic imaging method for multi-modal damage of a composite material, and the method comprises the steps: calculating the correlation of the reflection intensity of a sound wave signal at different moments and spatial positions through employing an established quantitative corresponding relation, recognizing a close contact region and a loose separation region between a bonding layer and a substrate, and carrying out the recognition of the close contact region and the loose separation region. Judging the continuity distribution condition of the interface contact state; according to the continuous distribution condition of the interface contact state, key feature points of dynamic damage evolution are extracted, a time sequence analysis method is adopted to track the feature points to adapt to damage features of dynamic changes, and prediction data of the damage expansion trend are obtained; and according to the dynamic representation result of the interface state, fusing the obtained multi-period signal characteristic data, carrying out quantitative calculation on the area change of the debonding region, judging the residual distribution state of the bonding strength, and obtaining a comprehensive evaluation report of the damage degree of the bonding interface.

The invention discloses a multi-probe on-line real-time film thickness measuring method and a multi-probe on-line real-time film thickness measuring device. In combination with the multi-wavelength analysis capability and multi-channel parallel signal characteristics of the spectrum detection unit, different plane positions are adopted to correspond to different wavelengths or central wavelengths, the transmissivity of each wavelength is obtained, and the thickness of the corresponding position is obtained respectively; the precision and the accuracy of obtaining the thickness by a single wavelength are improved through a multi-mode cooperative signal analysis algorithm, so that the thicknesses of a plurality of different positions of the same thin film are accurately obtained at the same time through one spectrum detection unit, or the thicknesses of a plurality of different thin films are obtained; according to the invention, the problems of large absorption, transparent or non-transparent substrate compatibility, quick response, multi-branch and on-line real-time measurement of the film are successfully solved; the device and the method can be integrated in film growth equipment to carry out rapid and high-precision measurement on the film, so that closed-loop control on film production is realized, the iteration speed is increased, the yield of high-quality film production is greatly increased, and the film production cost is saved.

The disclosure generally relates to a method, system and apparatus for processing audio through a neural network contained in a hearing device. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The invention relates to the cross technical field of signal processing and artificial intelligence, in particular to a multi-source noise removal method and system based on DAE, and the method comprises the steps: 1, carrying out the data collection and feature extraction of multi-source noise and pure signals; step 2, constructing a de-noising recognition knowledge base based on feature analysis; step 3, constructing a deep denoising auto-encoder model based on a knowledge base; step 4, hierarchical training and optimization guided by a mixed loss function of the deep denoising model; step 5, denoising processing of a target signal and output evaluation based on a discrimination model; according to the invention, noise data in multiple fields such as electromagnetism, remote sensing and biological signals are integrated, a dynamic mixing strategy and a data enhancement technology are adopted, a training set which highly simulates a real environment is constructed, and a unique cross-scene adaptation module can perform adaptive adjustment according to signal characteristics of different application scenes; the problem that a traditional method is poor in scene adaptability is solved.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The GNSS signal is processed to estimate signal characteristics. Methods and apparatus are provided for processing GNSS signals received at a receiver. The method includes obtaining a sample of a GNSS signal. The samples include a carrier signal modulated by a spreading code. The method includes generating codeless samples based on the obtained samples. These codeless samples contain carrier signals, but spreading codes have been erased. The method may include storing a sequence of codeless samples, the sequence having an associated duration. Further, the method includes analyzing the sequence of codeless samples to estimate one or more signal characteristics over a duration of the sequence. The signal characteristic includes a carrier phase of the carrier signal.

The disclosure generally relates to a method, system and apparatus for processing audio through a neural network contained in a hearing device. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The invention discloses a high-precision positioning method and system based on a multi-band single Beidou signal, and the method comprises the steps: receiving a multi-band signal from a single Beidou satellite, the multi-band signal comprises three bands B1I, B1C and B2a, extracting a pseudo-range measurement value and a carrier phase measurement value of the signal of each band, and calculating the pseudo-range measurement value and the carrier phase measurement value of the signal of each band according to the pseudo-range measurement value and the carrier phase measurement value; constructing an observation value model containing multi-path errors and ionosphere delay; performing wavelet transform decomposition on the multi-band observation value sequence to obtain signal characteristics on different frequency scales, and constructing a multi-path-ionosphere coupling separation discriminator using pseudo-range and carrier phase observation value characteristic difference and a frequency dependency relationship based on a wavelet decomposition result; the error dominant type is determined according to the size of the discriminant value output by the separation discriminator, the observation value is corrected by applying a corresponding error correction strategy, the corrected observation value is used for position calculation, and a high-precision positioning result is obtained.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

A systema and method for atmospheric anomaly detection is disclosed. The method may include performing a time synching between a network of ground-based nodes and a plurality of satellites of a constellation. The method may include directing a transmission of signals between the network and the plurality of satellites, where the signals are configured to be aimed along a plurality of paths through an atmospheric space between the network and the satellites. The method may also include receiving and aggregating signal data corresponding to the signals via at least one of the network or the satellites, determining signal characteristics based on the signal data, identifying one or more atmospheric anomalies based on the signal characteristics, and adjusting a flight plan based on the one or more atmospheric anomalies.

The invention relates to the technical field of ultrasonic signal processing, in particular to a wavelet and MAD adaptive threshold combined laser ultrasonic signal denoising method. The method comprises the following steps: firstly, preprocessing an acquired trigger channel signal and an ultrasonic channel signal, determining a signal starting point through differential positioning, and intercepting an effective signal segment; carrying out multilayer wavelet decomposition on the effective signal segment to obtain a wavelet coefficient of each layer; extracting a detail coefficient of the highest decomposition layer, and adaptively estimating a noise standard deviation based on a median absolute deviation criterion; calculating an adaptive threshold according to the noise standard deviation and the signal length, and processing each layer of wavelet coefficient by adopting a hard threshold function; and finally, carrying out wavelet inverse transformation reconstruction to obtain a denoised signal. According to the method, prior noise information is not needed, the noise level can be adaptively estimated, the optimal threshold value can be determined, the signal features are reserved while noise is effectively suppressed, and the signal-to-noise ratio is remarkably improved.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention discloses a non-convex contiguous group sparse variational filtering hyper-parameter adaptive calculation method, which comprises the following steps of: selecting a reference signal according to ultrasonic signal characteristics, and determining the size of a group sparse group through a specific formula in combination with a sampling frequency and a probe frequency; after interference removal is carried out on the ultrasonic signal, a scattering noise standard deviation is calculated based on the reference signal, and a regularization parameter is obtained in combination with a formula; calculating a penalty factor; when the detection sound path distance is less than 1000mm, adopting global filtering; when the sound path distance is detected to be larger than 1000 mm, local filtering is adopted, a punishment intensity reference value is estimated based on the noise level, the reference value is corrected in combination with the signal-to-noise ratio, and then a formula is substituted to calculate a punishment factor coefficient and a final punishment factor; in the iteration process, the relative variable quantity of the cost function is used as a convergence criterion, iteration is stopped when the variable quantity is smaller than a preset threshold value, and efficient and stable filtering is ensured; the method completely meets the engineering application requirements of industrial ultrasonic detection.

The invention discloses an oil-immersed transformer active defense method and system based on pulse current characteristics. The acquisition device is used for acquiring output signals of high-frequency pulse current sensors mounted at a high-voltage bushing end screen grounding position, a high-voltage bushing position and an iron core grounding position to obtain digital pulse current signals; whether active defense is started or not is judged according to whether the instantaneous value of the pulse current signal at the iron core grounding position reaches a protection starting threshold or not; judging whether the current pulse cluster is a discharge pulse cluster with abnormal internal discharge or not based on the three groups of pulse current signal characteristics; and judging whether protection acts or not according to the number of abnormal discharge pulse clusters in a set period. According to the method, serious partial discharge in the large oil-immersed transformer can be identified, protection measures are actively taken, and the situation that serious discharge defects are further developed into arc breakdown faults is effectively avoided.

The invention discloses an intelligent acupuncture optimization method, system and device based on acupoint electric signal feedback, and relates to the field of electric data processing, the method comprises the following steps: obtaining associated acupoints of a target diagnosis and treatment mode and treatment parameters corresponding to the associated acupoints; controlling an electric needle to apply electric stimulation to associated acupuncture points according to the treatment parameters; after the electrical stimulation is applied to the associated acupuncture points, acquiring an electrical stimulation feedback signal in the target diagnosis and treatment mode, and determining a signal characteristic parameter according to the electrical stimulation feedback signal; the current treatment state is evaluated according to the signal characteristic parameters, and treatment parameters are adjusted according to the evaluation result. The electrical stimulation feedback signals of intelligent acupuncture in different diagnosis and treatment modes are analyzed in real time, the treatment state is evaluated, the treatment parameters of the acupuncture diagnosis and treatment equipment are correspondingly adjusted, real-time feedback and dynamic optimization of acupuncture treatment are achieved, and the accuracy and the curative effect of acupuncture treatment are improved.

The invention is applicable to the technical field of magnetic field measurement, and provides a current sensor magnetic field measurement method based on a tunneling magnetoresistance effect, which comprises the following steps of: analyzing a real-time output signal through a machine learning algorithm based on a mapping relation model of a parasitic capacitance parameter and a signal characteristic of a tunneling magnetoresistance sensor circuit; determining an actual value of a parasitic capacitance parameter; controlling a negative capacitance generation circuit and a numerical control resistance network to adjust an equivalent negative capacitance value and a resistance value according to the actual value of the parasitic capacitance parameter, and generating a matched compensation parameter set; feature extraction is carried out on the compensated signal, and an effective magnetic field component is obtained through multi-resolution time domain decomposition and phase consistency detection; calculating the magnetic field intensity and the change gradient based on the effective magnetic field component, and determining the effective working frequency range of the sensor according to the signal attenuation characteristic; and dynamically adjusting a compensation strategy according to a comparison result of the effective working frequency range and a preset target frequency band, and reducing measurement errors.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention belongs to the technical field of sewage treatment and membrane separation, and provides an in-situ nondestructive membrane pollution detection system and method based on an ultrasonic transmission signal, and the system comprises an ultrasonic excitation module which is used for generating a sine wave excitation signal which penetrates through a membrane body to excite the ultrasonic transmission signal for pollution detection; the signal processing module is used for continuously collecting and preprocessing ultrasonic transmission signals of the clean membrane body and the membrane bodies with different pollution degrees, extracting ultrasonic transmission signal characteristics under different pollution degrees, and establishing a pollution thickness-signal characteristic mapping model; wherein the ultrasonic transmission signal features comprise a time-frequency domain energy gravity center feature and a frequency attenuation spectrum slope feature; and the pollution detection module is used for judging the pollution stage of the membrane body to be detected based on the pollution thickness-signal feature mapping model, and completing in-situ nondestructive membrane pollution detection. According to the technical scheme, the sludge pollution state in the ceramic membrane and flat membrane filtering process can be monitored in real time, and early warning and pollution dynamic tracking are achieved.