262 results about "Time frequency domain" patented technology

Embodiments of the present application are related to a method and apparatus of supporting physical uplink control channel (PUCCH) repetition. According an embodiment of the present application, an exemplary method includes: receiving a first signaling indicating a first repetition number for a PUCCH resource; determining a second repetition number equal to or smaller than the first repetition number based on the first repetition number; determining time-frequency domain resources for each repetition of the PUCCH resource; and transmitting the PUCCH resource with the second repetition number based on the determined time-frequency domain resources.

This invention discloses a method and system for monitoring abnormal data in power grid dispatching. The method includes: embedding multidimensional perturbation codes containing first-type (time-domain delay) and second-type (frequency-domain oscillation) features into dispatching commands; target devices generating physical ripples with specific time-domain fingerprint features in response to dispatching commands, and dynamically selecting a set of responding devices based on node impedance matrices and sensitivity coefficients; target devices and the set of responding devices collaboratively triggering responses with specific frequency-domain fingerprint features, and achieving spatial multiplexing verification through a time synchronization protocol; extracting time / frequency-domain fingerprint features from feedback data, and performing spatiotemporal consistency verification using a dynamic transfer function model and multivariate mutual information entropy. Furthermore, by periodically injecting false interference data, the survival verification of the monitoring system status is achieved. This invention can effectively monitor system failures and identify covert data injection attacks, significantly improving the security defense capabilities of power grid dispatching.

This invention provides a method and system for predicting the state of power equipment based on online testing adaptation, comprising: collecting power equipment state data in real time through sensors and forming test samples; filtering a set of adapted historical samples from a historical sample memory bank that meet preset conditions in terms of similarity to the test samples in the latent space through a transferable historical sample selection module, wherein the historical sample memory bank stores historical power equipment state data; performing time-frequency domain hybrid data augmentation on the test samples and the adapted historical sample set through a transferable online augmentation module to generate an augmented sample set; inputting the augmented sample set into a pre-trained power equipment state prediction model for batch training, dynamically adjusting the model parameters to adapt to the distribution shift; and fusing the output of the dual-stream predictor of the power equipment state prediction model to generate the power equipment state prediction result for the next time period. This invention can perform power equipment state prediction.

This invention discloses a method for estimating LFM pulse signal parameters under aliasing conditions, applied in the field of radar technology. Addressing the problem of aliasing in the time-frequency domain of direct-arrival wave signals from multiple external radiation sources with similar parameters, this invention utilizes fractional Fourier transform to accumulate energy in the direct-arrival wave signal. Based on the analytical expression of the peak-to-peak linear frequency modulated (LFM) pulse signal obtained after accumulation and its fractional Fourier transform, a multi-channel signal is constructed. Finally, using the constructed multi-channel signal, the LFM pulse signal is de-aliased in the fractional Fourier transform domain through blind source separation, ultimately improving the signal-to-noise ratio and separating different pulse signals with similar parameters, thereby achieving accurate parameter estimation. The process of this invention can be implemented using the fast fractional Fourier transform method, which is beneficial for engineering implementation.

The present application relates to radar signal processing technical field, specifically provide a kind of staggered pulse repetition interval time-frequency domain dual-polarized radar detection variable estimation method, comprising: in frequency domain, the estimation of echo signal-to-noise ratio, reflectivity factor, differential reflectivity of horizontal / vertical polarization based on power spectral density;And the estimation of average Doppler velocity and velocity spectrum width;In time domain, after ground clutter filtering based on odd-even pulse, respectively, odd-even pulse correlation parameter estimation is carried out, so that two detection variables of dual-polarized radar correlation coefficient and differential propagation phase shift of odd-even pulse are obtained respectively, then odd-even combination can obtain the estimation result of accurate correlation coefficient and differential propagation phase shift.The present application realizes the accurate estimation of dual-polarized radar multi-class detection variable, ensures that estimation result is not disturbed by ground clutter interference, simultaneously adapts single pulse repetition interval and staggered pulse repetition interval two working modes, improves the reliability and applicability of radar detection.

The application discloses a kind of big data driven wind power assembly repeated quality intelligent determination method and system.Method includes setting sensor, automation equipment and environmental monitoring instrument in wind power assembly station, obtains multidimensional original data through edge computing node, realizes data alignment with precision time protocol, is converted into unified structured data stream and carries out denoising, and outputs the assembly signal data after denoising;Establish data infrastructure layer including distributed storage and containerization arrangement;Assembly signal data is processed, and time domain, frequency domain and time-frequency domain features are extracted, combined with environmental and equipment state parameters to construct assembly quality feature vector, process using quality determination model, calculate Mahalanobis distance and compare with preset threshold value, process drift is judged by Mann-Kendall test.The application can effectively identify the repeatability quality defects and process drift in wind power assembly, improve assembly quality consistency.

The present application relates to the technical field of noise diagnosis, in particular to a method and device for identifying abnormal sound source applied to a vehicle transmission system, the method comprising: acquiring abnormal sound noise data, and confirming an abnormal sound generation period according to the abnormal sound noise data; acquiring transmission system vibration data, and performing time-frequency domain analysis on the transmission system vibration data and the abnormal sound noise data according to the abnormal sound generation period to obtain abnormal sound time domain data; the transmission system vibration data is obtained by vibration detection of the vehicle transmission system at a previous time; performing cross-correlation identification of the abnormal sound source according to the abnormal sound time domain data to obtain an abnormal sound source identification result; the abnormal sound source identification result is used to represent that the abnormal sound source is a gearbox and / or a drive axle. By considering the state of strong coupling connection between the gearbox and the drive axle in the vehicle transmission system, cross-correlation analysis is performed on the abnormal sound time domain data to identify the abnormal sound source, thereby improving the accuracy of identifying the abnormal sound source of the vehicle identification system.

The application provides a baseband radio frequency signal data feedback processing method based on vector modulation, which first receives a baseband signal data set transmitted by a target radio frequency link in a preset frequency band, which contains multiple signal frame original modulation parameters and corresponding radio frequency feedback signal waveforms, then calculates feedback error indicators composed of phase offset, amplitude distortion and spectral leakage factor according to the radio frequency feedback signal waveforms, quantizes the modulation error, analyzes the feedback signal waveforms in the time-frequency domain, extracts error feature vectors, inputs the pre-trained model for cross-frame correlation learning, generates a dynamic adjustment parameter sequence containing time sequence dependence, and finally constructs a closed-loop feedback control link based on the dynamic adjustment parameter sequence and the original modulation parameters, so as to realize accurate processing and optimized control of the baseband radio frequency signal and effectively improve the signal transmission performance.

This invention discloses a method and device for detecting the load at the charging port of an electric vehicle charging pile, relating to the field of charging safety technology. The method includes: acquiring an electrical signal from the charging port; extracting a clean load response signal using a quantum noise-resistant processor based on Majorana fermion operators; inputting the clean load response signal into a pulse neural network autoencoder to generate a neural pulse sequence through a pulse firing mechanism; performing time-frequency domain transformation on the neural pulse sequence to reconstruct a complex impedance tensor, and solving the eigenvalue sequence of the complex impedance tensor; embedding the eigenvalue sequence into a five-dimensional anti-de Sitter space boundary based on the AdS / CFT duality principle, and generating a three-dimensional holographic tomographic map by solving the Einstein field equations relating the volume to the boundary; this invention achieves topological separation of noise and load signal through a Majorana zero-mode waveguide channel, solving the problem of insufficient high-frequency noise suppression in dynamic load scenarios.

The present application relates to the technical field of unmanned aerial vehicle photoelectric load vibration control, and particularly relates to a multi-frequency band vibration compensation control method for an unmanned aerial vehicle airborne photoelectric platform based on a disturbance observer, comprising: obtaining unmanned aerial vehicle task phase information, synchronously collecting piezoelectric sensing signals and fiber grating sensing signals, performing time-frequency domain feature fusion and generating a composite feature description in combination with the task phase information; performing mode classification on the current disturbance based on the composite feature description and selecting a corresponding compensation algorithm, predicting the disturbance trend according to the task phase information and activating the compensation algorithm in an event-triggered manner by scheduling computing resources; obtaining an optical axis residual error and a deviation between an actual disturbance and a predicted disturbance, modifying time-frequency domain feature fusion parameters and mode classification discrimination boundaries based on the optical axis residual error, and modifying feature library parameters of the task phase information and a trigger threshold of event-triggered scheduling based on the deviation. The present application realizes adaptive and accurate suppression of wide-frequency band disturbances and improves long-term operation reliability.

This invention relates to a sensing-integrated waveform design method based on OFDM time-frequency domain phase coding, belonging to the field of wireless communication technology. In this method, the transmitting end generates a complementary Gray sequence and a pseudo-random sequence. These two sequences are then divided into blocks in the frequency domain according to their indices and phase-coded to generate a sensing waveform. A communication signal is generated through an OFDM system. The frame header of the sensing waveform is aligned with the start position of the SSB of the communication signal to generate a sensing-integrated waveform. The time slot of the transmitted communication signal is used; this waveform is the result of multiplying the communication waveform and the sensing waveform in the time domain; otherwise, the waveform is the sensing waveform. The receiving end shares the synchronization signal block and the sensing waveform. The SSB portion of the bandwidth signal is extracted from the received echo signal to estimate the time-frequency, and the echo signal is compensated. The sensing waveform is then separated from the compensated signal to reconstruct the communication signal. The waveform generated by this invention can effectively eliminate spurious peaks in the OFDM waveform, improving the sensing accuracy of the waveform without sacrificing communication performance.

The application discloses a residual life prediction method based on small sample enhancement and interpretable migration contribution, and relates to the technical field of predictive maintenance of rotating machinery. The method comprises the following steps: inputting a source domain time sequence into a preset data enhancement network for data enhancement to obtain a plurality of sub-source domain time sequences; determining a plurality of sub-source domain feature vectors and a predicted life of a rotating equipment based on time-frequency domain features corresponding to the plurality of sub-source domain time sequences, an interpretable estimation network and a life prediction network; determining a target domain feature vector based on time-frequency domain features corresponding to a target domain time sequence and the interpretable estimation network; constructing a life prediction loss and a maximum mean difference loss between the plurality of sub-source domain time sequences and the target domain time sequence according to the predicted life, the plurality of sub-source domain feature vectors and the target domain feature vector, and performing iterative training. The application can solve the problem of inconsistent distribution between a source domain and a target domain in migration learning.

This invention discloses an acoustic detection method for unmanned aerial vehicles (UAVs) based on dual-domain harmonic enhancement features, belonging to the field of acoustic signal processing and target detection and recognition. The method first performs time-frequency analysis on the acoustic signal to obtain a reference power spectrum; then, it performs two-way feature enhancement in parallel: in the time-frequency domain, a floor-scaled soft comb mask is constructed based on the estimated fundamental frequency, and selective gain is implemented to highlight harmonic ridges; in the cepstral domain, a harmonic structure robust to fundamental frequency drift is extracted through adaptive decomposition and energy ratio-based gain modulation. The two enhanced features are respectively input into a lightweight dual-branch network using rectangular convolutional kernels and depthwise separable convolutions for feature extraction. After fusion, the detection and recognition results are output through a classifier. This invention does not rely on blind source separation and can stably enhance UAV harmonic features under conditions of low signal-to-noise ratio, frequency drift, and multi-source aliasing, while also possessing advantages such as high computational efficiency and suitability for edge deployment.

This invention provides a low-pressure SiC MOSFET lifetime prediction method that integrates wavelet singular entropy and deep learning. By conducting a full-cycle aging test on a sample SiC MOSFET, the voltage drop signal between the drain and source terminals of the SiC MOSFET in the on-state is obtained. Wavelet decomposition is then performed to effectively separate interference signals from valid signals. Furthermore, the feature signal is extracted from the matrix formed by the coefficients after wavelet decomposition, fully reflecting the complexity and cumulative degradation amplitude of the voltage drop signal in the time-frequency domain. This feature signal is then used to train a deep learning model, effectively ensuring the accuracy of the remaining lifetime prediction of the SiC MOSFET under test and providing accurate data support for the stability of the SiC MOSFET system.

The application discloses a strategy for extracting fault features of electronic circuit systems, named as SSEST strategy, which is used for perceiving global information and paying attention to notable local information, and mining important local information means realizing expression of intermittent fault features of electronic circuit systems, specifically, first, S transformation is performed on a circuit output time sequence signal to acquire time-frequency domain features, then a squeeze and excitation network attention module is used to distribute channel weights, subsequently, input into a Swin Transformer framework, and pay attention to local information related to faults from global signals, and deep mining is performed on fault features, and two electronic circuits are taken as experimental circuits, the proposed diagnostic strategy realizes rapid and high-precision diagnosis, and shows that the proposed multiple attention mechanism is efficient for feature mining of intermittent faults of electronic circuit systems.

This invention proposes a method for multi-target recognition, tracking, and vibration extraction of bridge cable-stayed bridge videos based on unmanned aerial vehicles (UAVs). The method includes: Step 1: Constructing a refined tilted and slender target detection model for bridge cable-stayed bridges based on the YOLOv11 model; Step 2: Proposing a multi-target tracking algorithm that integrates the tilted and slender target detection model and the StrongSORT algorithm; Step 3: Improving the displacement extraction method by combining SIFT / ORB feature point matching and sub-pixel refinement techniques; Step 4: Designing a UAV motion correction algorithm based on variational mode decomposition and time-frequency domain joint screening; Step 5: Constructing a joint working mode analysis algorithm that combines natural excitation technology and random subspace recognition algorithm. This method achieves high-precision extraction of cable-stayed bridge vibration signals and identification of cable-stayed bridge modal parameters, providing technical support for health monitoring of long-span cable-stayed bridges.

This invention relates to a method and system for locating coaxial counter-rotating sound sources in rotorcraft, belonging to the technical field of sound source identification methods. It includes: processing the sound pressure signal of the moving sound source using continuous wavelet transform to obtain time-frequency domain variation results; performing Doppler effect correction based on experimental conditions; estimating the spatial location and relative intensity of the noise source using a cross-spectral matrix and weight vector; and performing phase-locked analysis of the rotor noise source intensity using a phase-locked averaging method. This invention differs from traditional time-domain delay superposition algorithms and frequency-domain cross-correlation spectrum methods. It can image unsteady sound sources using time-frequency domain characteristics, solving the acoustic imaging problem of counter-rotating propeller noise and possessing potential applications in the rotorcraft industry.

This invention discloses a zero-time and time-frequency feature fusion arc detection method for preventing and controlling electricity meter burnout. The method includes: S1, synchronously acquiring current and voltage signals from the electricity meter circuit and extracting a detection window containing two consecutive power frequency cycles; S2, calculating the zero-time-voltage joint feature coefficient A1, the time-frequency domain similarity comprehensive feature coefficient A2, and the harmonic content coefficient A3 for a specific frequency band based on the current and voltage signals within the detection window; S3, performing a fusion decision on the feature vector [A1, A2, A3] calculated for the current detection window to obtain the arc detection result. This invention reduces the dimensionality of features through a fusion and decision mechanism, thereby reducing redundancy and computational complexity, achieving a highly reliable, low-computational-load arc detection method, thus effectively supporting the prevention and control of electricity meter burnout.

The application discloses a kind of based on branch current's latch detection method, device and latch protection circuit.The method includes obtaining the branch current from a protection branch;Branch current is converted into time-frequency domain signal;Time-frequency domain signal is structured as gray scale image;Extract the texture feature of gray scale image, whether the corresponding branch occurs latch and micro-latch is judged using pre-trained machine learning model.The method of this embodiment, directly for branch current detection, detection point quantity is exponentially decreased, improve the utilization of PCB board, also reduce the single board power consumption.In addition, the branch current of the application is structured as gray scale image, extracts the current texture feature in image, and is judged by pre-set machine learning model, the accuracy of identifying latch and micro-latch is high, supports the detection of various types of devices, so as to effectively protect the single board circuit in orbit operation, prevent it from being damaged due to latch or micro-latch.

This invention discloses a rapid evaluation method for the enhancement factor of the dynamic coefficient by the high-frequency component of a strong impact load, belonging to the field of structural dynamic response analysis technology. This method performs time-frequency domain energy accumulation analysis on the strong impact load signal, extracts the concentrated frequency of load energy and its proportion in the total energy, and then, based on this, combines the dynamic amplification factor under the triangular time history assumption to quantitatively calculate the enhancement factor of the dynamic coefficient by the high-frequency component of the strong impact load. This method is further extended to arbitrary frequency components and the entire frequency band to obtain the comprehensive enhancement result of the dynamic amplification factor by the high-frequency effect of the strong impact load. Compared with traditional dynamic analysis methods based solely on idealized time history assumptions, this invention can introduce the real time-frequency characteristics of strong impact loads while maintaining computational efficiency, effectively compensating for the insufficient consideration of high-frequency excitation effects. It is applicable to rapid dynamic response evaluation in engineering fields such as aerospace structures, high-frequency components, and precision equipment.

The application discloses a kind of sand belt abrasion prediction method and system of fusion acoustic signal and acoustic emission signal.Method includes: installing acoustic signal sensor and acoustic emission sensor and collecting corresponding signal, time domain analysis and frequency domain analysis are carried out to two kinds of signals, and the time domain characteristics and frequency domain characteristics of signal are respectively extracted;Using LSTM neural network fusion the time-frequency domain characteristics of the two kinds of signals, establish the nonlinear model of sand belt abrasion in the process of complex blade robot grinding;Using state estimation technique in probability statistics method, predict the probability of sand belt stable abrasion and severe abrasion occurring at next time;According to the abrasion probability, feedback adjustment and control the process parameters in grinding process, prevent tool excessive abrasion from causing blade to appear burn and other quality decline phenomenon.The abrasion state of sand belt in the process of processing is monitored in real time and accurately, timely feedback regulation, prevent complex blade from being damaged due to severe abrasion of sand belt in the process of robot processing.

The application discloses a kind of intelligent high-low voltage electrical line fault locating method and system.The method comprises: constructing multi-modal disturbance observer based on power transmission physical equation, state space modeling and residual analysis are carried out on the electrical parameters of monitoring point, and low-intensity initial fault characteristic pulse packet is extracted;Using super-resolution anti-interference processing based on variational bayes Kalman filter and low-rank matrix approximation, time-frequency domain joint noise reduction and waveform reconstruction are carried out on fault traveling wave signal.The application realizes end-to-end collaborative optimization of each technical link by constructing comprehensive loss function across modules linkage, solves the problems of existing technology, such as lagging start point of early fault detection, information fault in signal processing and artificial intelligence model, and lack of cross-time closed-loop experience solidification ability of fault diagnosis model, significantly improves fault locating precision, anti-interference ability and system adaptive evolution ability.

This invention provides a method and system for spectral inversion calculation of chirped gratings in lithium niobate thin films. The method includes: sequentially performing time-frequency domain transformation, window truncation, and time-shifting on a preset ideal target response of the grating to obtain a physically realizable target response; using the physically realizable target response as the initial complex reflection response, performing layer-by-layer recursive calculation on the grating using a layer-by-layer peeling algorithm to obtain the complex coupling coefficients corresponding to each layer of the grating, and extracting the coupling coefficient distribution and phase distribution of the grating; obtaining the structural parameters of the chirped Bragg grating based on the coupling coefficient distribution and phase distribution; constructing a physical model of the grating based on the structural parameters, and performing simulation calculations on the physical model to obtain the spectral response. This invention, based on the traditional grating design process, introduces a layer-by-layer peeling algorithm for inversion calculation of the grating structure, inversely obtaining the coupling coefficients and phase distribution of the grating, as well as the corresponding grating structural parameters, and constructing a corresponding physical model, exhibiting strong target-oriented characteristics.

A communication method and device are used to reduce the transmission overhead based on OTFS. The method comprises the following steps: a first device obtains a first data signal in a delay-Doppler domain and a pilot signal in the delay-Doppler domain; the first device maps the pilot signal in the delay-Doppler domain and the first data signal in the delay-Doppler domain to a time-frequency domain to obtain a pilot signal in the time-frequency domain and a first data signal in the time-frequency domain; and the first device transmits a time domain signal, the time domain signal being obtained according to a first time-frequency signal, the first time-frequency signal comprising the pilot signal in the time-frequency domain and the first data signal in the time-frequency domain, wherein the pilot signal in the time-frequency domain in the first time-frequency signal and the first data signal in the time-frequency domain in the first time-frequency signal are orthogonal.

The application provides a neural snake venom poisoning discrimination data processing method and system based on neural electrical signals, and relates to the technical field of signal processing. The method comprises the following steps: converting the neural electrical signals of a subject into a digitized multi-channel time sequence matrix; obtaining a multi-channel time sequence signal through band-pass filtering, power interference suppression, hierarchical artifact removal, baseline correction and resampling; extracting time domain, frequency domain, time-frequency domain and synapse and neuromuscular transmission characteristics, wherein the characteristics include four train-of-four ratios, repetitive nerve stimulation response decrement rates, acetylcholine release frequencies and amplitude variation coefficients corresponding to acetylcholine release; standardizing the characteristics and performing dimensionality reduction by principal component analysis to obtain an optimized characteristic set; inputting a pre-trained poisoning discrimination model to output poisoning probability, confidence and key abnormal characteristics. The application solves the problem that the prior art lacks a specific processing flow and a full-process engineering scheme for neural snake venom poisoning, resulting in low discrimination accuracy and difficulty in meeting emergency clinical needs.

This invention discloses a multi-structure data-driven method and system for quantitative identification of wheel flats. It constructs a vehicle-track rigid-flexible coupling dynamic model, using synthetic flats as excitations for wheel out-of-roundness to obtain the dynamic response of the axle box under different working conditions. The vertical vibration acceleration of the axle box is processed in the time domain, frequency domain, and time-frequency domain to create sample sets with different structural forms. A multi-input convolutional neural network (MCNN) with appropriate structure and configuration parameters is constructed, using vehicle speed signals as network constraints. Sample sets of different structural forms and their combinations are fused with speed signals and input into the MCNN model for training. The accuracy and timeliness of the MCNN model for quantitative identification of wheel flats under different data structures are compared. Based on this invention, the optimal combination of sample input forms with the best recognition performance can be obtained. Furthermore, the resulting recognition model not only has good noise resistance but also exhibits better performance due to using speed as network constraint information, demonstrating overall superiority.

The present application relates to the technical field of fault detection, and relates to a relay contact fault identification method based on wavelet denoising and a support vector machine, comprising the following steps: S1, constructing a current signal acquisition and double-channel reconstruction circuit: a Rogowski coil is sleeved on an output loop of a relay contact, and a differential voltage signal is inducted and output by the Rogowski coil; S2, respectively adopting a first wavelet base and a second wavelet base to perform multi-scale wavelet decomposition, threshold denoising and wavelet reconstruction on a complete current digital sequence and a transient current digital sequence; S3, extracting time domain statistical features from the complete current digital sequence after denoising, extracting time-frequency energy features from the transient current digital sequence after denoising, and outputting a fault category of the relay contact after classification and decision by a support vector machine classifier. The present application overcomes the inherent contradiction that a single acquisition channel is difficult to simultaneously consider slow-changing contact components and transient disturbance components in terms of dynamic range and denoising strategy.

This application relates to the technical field of flying cars, and in particular to a method, device, and equipment for controlling the split actuators of flying cars. The method includes: acquiring the current signal of the split actuator of the flying car; performing a time-frequency domain transformation on the current signal to obtain multi-dimensional frequency domain features; matching the multi-dimensional frequency domain features with a preset state fingerprint database, and using a fuzzy inference algorithm to obtain the attitude offset angle between the flying car's flight body and the ground-based vehicle under the current road slope; generating a decoupling transformation matrix based on the attitude offset angle, which is used as the transformation matrix from the virtual master axis space to the dual motors; in a preset virtual master axis model, the target motion command in the virtual master axis model is fed forward and allocated through the decoupling transformation matrix to generate dual motor control commands, so that the dual motors drive the split actuators to move. This improves the control accuracy of the dual motors and achieves dynamic compensation of the attitude angle.

This application discloses an automated flexible assembly control method and system for an automotive tailgate electric strut. The method includes the following steps: during assembly at the first critical assembly station, synchronously collecting time-series sensor data throughout the entire process and generating a real-time process data stream; extracting real-time features from the data stream to generate a time-frequency domain hybrid feature parameter vector; inputting this vector into a process chain collaborative optimization module, which then generates a collaborative control instruction set for the second critical assembly station in real time; sending the instruction set to the second critical assembly station to control the actuator to complete the assembly; obtaining the final assembly performance test results, and storing the feature vector, instruction set, and test results in association for module parameter updates. The system includes corresponding functional units. This method, through real-time data feedforward collaboration and closed-loop learning, helps to achieve proactive adjustments across processes, thereby improving assembly consistency, adaptability, and production line flexibility.