434 results about "Discrete Fourier transform" patented technology

The invention relates to the technical field of geotechnical mechanics and engineering, and particularly discloses a soft soil foundation settlement monitoring method and system based on multi-field multi-source information, and the method comprises the steps: collecting multi-source data of a target soft soil area; performing constitutive parameter inversion, data standardization and discrete Fourier transform frequency domain conversion on the data to obtain a standardized frequency domain multi-field multi-source data set; based on the data set, a soft soil constitutive parameter library and a soil mechanics physical equation, constructing an FD-PINN frequency domain physical information neural network, embedding the physical equation as a prior constraint, and training the model by adopting an alternating optimization strategy; inputting the real-time frequency domain data flow into the model, and outputting the current settlement amount, the settlement rate and the multi-physical field frequency domain distribution; and in combination with a pre-established large scale model test result, through IDFT inverse transformation, a time-space domain settlement field is reconstructed, multi-stage early warning is triggered, a targeted reinforcement scheme is recommended, the soft soil foundation settlement monitoring precision and the engineering practicability are remarkably improved, and the method is suitable for construction, operation and maintenance of infrastructures such as high-speed rails and highways.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

According to the Mama network solenoid valve fault diagnosis method based on the frequency domain characteristics, the problem of solenoid valve system fault diagnosis can be solved. According to the method, a pneumatic solenoid valve fault data set is constructed by collecting operation signals such as voltage and current, and multi-scale frequency domain features of the signals are extracted by adopting Wavelet Packet Transform (WPT) and Discrete Fourier Transform (DFT). WPT-DFT preprocessing features are embedded into an improved channel-space joint attention mechanism module, and the improved channel-space joint attention mechanism module is combined with a Mamba network with extremely high sequence modeling capability to construct an end-to-end fault diagnosis model. Compared with a traditional convolutional neural network, the method can more effectively capture deep dynamic features in a time sequence and highlight a key frequency region, and experimental results show that the method has higher diagnosis precision and generalization ability and is suitable for intelligent detection of the electromagnetic valve under complex working conditions.

The invention discloses a gas meter power supply abnormity monitoring system and method based on a signal acquisition technology, and relates to the technical field of gas meter power supplies, the system comprises a multi-dimensional sensing module, a dynamic adaptive module, a multi-dimensional fusion analysis module, an intelligent judgment module and a verification execution module; according to the invention, the multi-dimensional fusion analysis module accurately calculates key parameters such as a power supply ripple coefficient and load transient response time in a corresponding mode, the electromagnetic interference intensity is calculated through professional algorithms such as discrete Fourier transform in combination with an environment electric field and a magnetic field sequence, a threshold value is dynamically adjusted based on a working mode, and a comprehensive score is calculated through multi-parameter deviation weighted fusion. The power supply state is deeply and comprehensively evaluated; the intelligent judgment module scientifically divides anomaly levels according to comprehensive scores, quantifies and distinguishes real anomaly and environmental interference by means of an environmental interference contribution degree formula, solves the problem that traditional monitoring cannot accurately recognize power anomaly under complex working conditions, and improves anomaly judgment accuracy.

Methods, systems, and devices for wireless communications are described. A transmitter may modulate a set of bits into an on-off keying (OOK) sample sequence for wireless transmission to a receiver in a set of frequency resources. The transmitter may apply a transform (e.g., a discrete Fourier transform (DFT)) to the OOK sample sequence to generate a frequency domain representation of the OOK sample sequence. In some cases. the transmitter may, using an orthogonal frequency division multiplexing (OFDM) waveform generator, generate an OFDM waveform based on mapping the frequency domain representation of the OOK sample sequence to a set of resource elements of the set of frequency resources. In some cases. the transmitter may transmit the OFDM waveform to the receiver via the set of frequency resources.

The invention provides a wind power prediction method based on a frequency domain space-time diagram convolutional network. The method comprises the following steps: firstly, respectively constructing a spatial proximity weight matrix and a similarity matrix based on a fan position and historical feature data, and adaptively fusing the spatial proximity weight matrix and the similarity matrix into an adjacent matrix; performing high-dimensional embedding on the normalized features, and converting a time domain signal to a graph frequency domain by using graph Fourier transform GFT; designing a prediction and reconstruction double-branch network, extracting key frequency features through discrete Fourier transform (DFT), causal convolution and channel attention, and performing inverse transformation reduction; and finally, constructing a joint loss function of prediction and reconstruction loss weighting, and realizing end-to-end training through back propagation and an Adam optimizer. According to the method, spatial topology and time-frequency dynamic characteristics are fused, prediction and reconstruction advantages are combined, and the precision and robustness of wind power prediction are improved.

The invention provides a project state summarizing method and device, and belongs to the technical field of DFT (Discrete Fourier Transform), and the project state summarizing method comprises the following steps: distributing a plurality of test tasks to a plurality of predefined categories according to the corresponding test steps according to the mapping relation between the test steps and the predefined categories; and based on the task state of the test task included in each predefined category, determining the category state of the corresponding predefined category. Through a predefined mapping relation, original and scattered test tasks and states thereof are aggregated into classified and macroscopic class states.

The invention discloses a mode switching method, device and equipment of an energy storage converter and a storage medium, and relates to the technical field of power grid control, and the method comprises the steps: obtaining a multi-mode control grid-connected model of the energy storage converter, and the grid-connected model comprises a grid-following type control model and a grid-constructing type control model; based on the grid-connected model, injecting a high-frequency small signal into a power grid through an energy storage converter, and processing the high-frequency small signal by adopting a discrete Fourier transform algorithm to obtain grid-connected point voltage and current response; obtaining a power grid impedance parameter according to the grid-connected point voltage and current response; calculating an operation short-circuit ratio according to the power grid impedance parameter in combination with the actual output power of the energy storage converter and the power grid voltage amplitude; according to the operation short-circuit ratio and the short-circuit ratio threshold value, mode switching is carried out on the following network type control and the construction network type control, and the method can improve the accuracy of mode switching of the energy storage converter.

The invention discloses a hyperspectral image classification method based on frequency domain denoising and element gradient correction, and the method comprises the following steps: carrying out the preprocessing of all hyperspectral image data, and dividing an overall training sample set formed by the processed hyperspectral images into a training set and a verification set; constructing a sample weighting model based on frequency domain denoising and element gradient correction; in the training process, a parameterization frequency spectrum gating sensing transformation module is utilized to map features to a frequency domain through discrete Fourier transform, a learnable frequency spectrum response function is utilized to adaptively suppress spectrum jitter noise, and finally pure features are reconstructed. And automatically constructing a high-confidence pseudo-clean verification set based on a Gaussian mixture model and time domain consistency. According to the method, a time domain momentum updating mechanism is introduced, the variance of statistical estimation is effectively smoothed, random interference caused by training fluctuation is resisted, and the accuracy of pseudo clean set construction and the convergence stability of overall model training are further improved.

The invention relates to the technical field of permanent magnet synchronous motors, and discloses a permanent magnet synchronous motor turn-to-turn short circuit fault detection method and device, and the method comprises the steps: determining a discrete Fourier transform coefficient according to a carrier wave ratio; sampling the motor stator current for multiple times, and calculating a fundamental wave amplitude corresponding to the motor stator current by using discrete Fourier transform; and judging whether a turn-to-turn short circuit fault occurs or not according to the fundamental wave amplitude. According to the invention, fault detection is realized only through sampling and signal processing (for example, determining a DFT coefficient based on a carrier wave ratio and calculating a fundamental wave amplitude and an unbalance degree) on the stator current in an existing motor control system, and hardware equipment such as a resistance network and the like is not required to be additionally installed like a method based on a neutral-point voltage zero-sequence component, so that the hardware cost is prevented from being increased, and the fault detection efficiency is improved. And the system structure complexity and the subsequent maintenance difficulty are reduced, and the target of improving the reliability of the equipment is consistent.

Disclosed is an electronic device. The electronic device includes: a communication unit configured to perform communication with a server device storing an artificial intelligence (AI) model that performs computation in an encrypted state; a memory configured to store a plurality of images; a processor configured to divide each of the plurality of images into a plurality of divided images and list all the divided images in a form of a one-dimensional vector to perform slot encoding, and configured to obtain CinS-encoded data in a form of a frequency signal by performing discrete Fourier transform (DFT) on slot-encoded data in units of slots corresponding to each image and transmit a homomorphic ciphertext obtained by performing homomorphic encryption on the CinS-encoded data to the server device through the communication unit; and a display.

The invention discloses a three-dimensional visual transformer substation equipment local abnormal discharge positioning method and a three-dimensional visual transformer substation equipment local abnormal discharge positioning system, and relates to the technical field of discharge positioning. Performing Hanning window processing and discrete Fourier transform to obtain a power spectrum, defining a working band, calculating a power spectrum mean value and a cross-channel spectrum value to determine a free spectrum interval, and generating a comb line center set; calculating a parasitic leakage index, and dividing the broadband energy by the index to obtain equalized broadband energy; based on this, a partial discharge space coordinate is calculated by combining sensor installation parameters, and finally, a device three-dimensional model is associated to realize three-dimensional visualization of a positioning result; according to the scheme, the interference of parasitic resonance on positioning is eliminated, the comparability of amplitude and distance attenuation is recovered, the positioning accuracy is improved, three-dimensional visualization is convenient for operation and maintenance personnel to quickly identify a discharge part, and the safe operation of substation equipment is ensured.

The invention discloses a deep and far sea wind power time-varying impedance model grid connection analysis method based on a time domain residual error method, relates to the technical field of power system modeling simulation, and can efficiently and accurately analyze deep and far sea wind power grid connection. The method comprises the following steps: establishing a differential algebraic equation set containing high-capacity submarine cable impedance and a plurality of nonlinear links, and constructing a recursive coefficient matrix; dividing a preset global time domain into a plurality of time domain subintervals, and solving the recursion coefficient matrix in each time domain subinterval by referring to a universal analytic solution represented by a power series explicit operator to generate a global time domain explicit solution corresponding to the preset global time domain; performing discrete Fourier transform on the global time domain explicit solution to obtain a time-frequency domain spectrum result; and constructing a time-varying impedance model according to the differential algebraic equation, setting the time-varying impedance model by adopting a time-frequency domain spectrum result, and analyzing the deep and far sea wind power grid-connected control system by utilizing the set time-varying impedance model.

The invention relates to the technical field of image processing, in particular to an OCR intelligent recognition method for a chronic disease management platform, and the method comprises the steps: carrying out the preprocessing of a document image collected at the front end of the chronic disease management platform, inputting the preprocessed document image into a convolutional neural network model, and carrying out the feature extraction, and obtaining an output signal graph; converting the output signal diagram into a spectrogram through discrete Fourier transform, and performing frequency band analysis on the spectrogram to determine a character effective area from the output signal diagram; according to the integrity of the character effective area and the expression coefficient that the character effective area is integrally presented as characters, adjusting the output signal graph to obtain an updated signal graph; and inputting the updated signal diagram into an OCR engine for character recognition to obtain recognized text information, performing post-processing verification on the recognized text information, and storing the recognized text information into a database of a chronic disease management platform. Therefore, the reliability and accuracy of OCR recognition of the medical record image are improved.

The invention relates to the technical field of optical fiber sensing, in particular to an OTDR event analysis method and system, electronic equipment and a storage medium. The method comprises the following steps: carrying out de-noising processing on an OTDR original curve to obtain an OTDR curve; acquiring a local curve segment corresponding to each sampling point in the OTDR curve, calculating a local slope value and acquiring a jitter degree; setting a compensation value according to the jitter degree, and constructing a dynamic threshold interval corresponding to the local curve segment based on the compensation value and a preset optical fiber loss value; when the local slope value exceeds the dynamic threshold interval, determining a suspected event starting point from the local curve segment; in the OTDR curve, obtaining a suspected event curve segment by combining the suspected event starting point with a preset length; and performing discrete Fourier transform and normalization processing on the suspected event curve segment to obtain a corresponding frequency domain normalization sequence and obtain corresponding statistical characteristics, and inputting the statistical characteristics into a pre-trained support vector machine classifier to obtain an event analysis result. The method and the device are used for improving the accuracy of OTDR event analysis.

An autoscaling method and apparatus in a Kubernetes cluster, which method and apparatus are applied to the technical field of computers. The method comprises: in historical monitoring data, using an interpolation method to impute missing data between adjacent data points to obtain preprocessed sequence data; performing a fast discrete Fourier transform on the preprocessed sequence data to obtain a spectrogram of original data; analyzing the spectrogram, and identifying candidate periods in the spectrogram; using a preset correlation coefficient calculation function to verify the candidate periods to obtain a dominant period; on the basis of the dominant period and operation data in a cluster, performing peak prediction; and when a predicted peak exceeds a preset autoscaling threshold, triggering a scale-up or scale-down operation corresponding to a system. By means of the present disclosure, the requirement for resources in a period of time in the future can be predicted, and scaling up or scaling down can be performed, so that a more active and accurate resource allocation policy is realized, and the stability of a service is ensured.

The invention relates to the technical field of optical detection, and particularly provides a white light interference signal demodulation method and device, a medium and a computer program product, and the method comprises the steps: processing interference light intensity signal data which are non-uniformly distributed on a wave number domain through non-uniform fast Fourier transform, and obtaining the frequency spectrum information of the interference light intensity signal data, determining a first optical path difference; analyzing the measurement resolution of the non-uniform fast Fourier transform, and if the measurement resolution is lower than a preset threshold value, constructing a to-be-measured optical path difference sequence within a preset accurate measurement range by taking the first optical path difference as a center; calculating the frequency spectrum amplitude of each to-be-measured optical path difference in the to-be-measured optical path difference sequence through non-uniform discrete Fourier transform, and obtaining the amplitude spectrum of the to-be-measured optical path difference sequence; and determining a second optical path difference according to the spectrum peak of the amplitude spectrum. According to the invention, through NUFFT coarse positioning and NUDFT local refinement, demodulation precision and efficiency are effectively balanced.

The invention discloses a settlement degree measuring method and system for bridge design, and belongs to the field of bridge engineering. Geological profile data in a bridge site range is collected, a settlement influence function is constructed based on geological parameters, and a bridge pier load matrix is extracted in combination with bridge design load working conditions; multiplying the settlement influence function by the load matrix to obtain a design settlement initial value of each pier; further calculating a settlement difference value between adjacent piers, constructing a bridge floor elevation deviation function, performing discrete Fourier transform on the bridge floor elevation deviation function, and extracting a dominant frequency characteristic value to judge whether a high-frequency settlement fluctuation behavior exists or not; if the dominant frequency exceeds a preset threshold value, marking the corresponding pier as a settlement sensitive area, and outputting a load adjustment parameter and a foundation elevation correction suggestion; the method can realize prospective identification and parameterized regulation and control of the non-uniform settlement trend in a bridge design stage, has the advantages of high structural adaptability, high prediction precision and the like, and is suitable for bridge foundation optimization design in a complex geological environment.

The invention discloses a hardware acceleration method for a Fourier neural operator. The hardware acceleration method comprises the following steps: setting an FFT module and a DFT / NN module which are independent from each other on an FNO hardware accelerator; performing fast Fourier transform (FFT) calculation on the batch input data by using an FFT module; a DFT / NN module is utilized to execute discrete Fourier transform (DFT) calculation based on a Bluestein algorithm, and access mode parallel random access of twiddle factors is reconstructed into parallel rule access; in an FNO hardware accelerator, an FFT module or a DFT / NN module is adaptively selected as a unit for executing calculation based on static parameter analysis of input dimensions, transformation points and frequency cut-off output points, and corresponding operator fusion is executed based on a predefined operator fusion mode. The adaptive capacity of the FNO to different transformation points and dimension structures is enhanced, and effective unification of computing performance, hardware efficiency and application flexibility is achieved.

The application discloses a structure interlayer displacement automatic measurement method and system based on visual data and belongs to the technical field of structure displacement measurement. The measurement method comprises the following steps: acquiring video data in the vibration process of a building based on a monitoring camera; processing the video data by using an improved kernel correlation filtering algorithm to extract building interlayer displacement information; verifying the extracted displacement information and outputting building interlayer displacement results. On the basis of a classical kernel correlation filtering target tracking method, the application utilizes image pyramid, robust feature extraction and discrete Fourier transform up-sampling technology to expand the tracking range, enhance the edge features and improve the tracking precision; the application fully utilizes the existing monitoring camera equipment to measure the interlayer displacement, does not need to additionally arrange sensors and reduces the cost budget.

The invention discloses a logistics conveying equipment fault prediction method and system based on DFT optimization, and belongs to the technical field of conveying equipment fault prediction, and the logistics conveying equipment fault prediction method based on DFT optimization comprises the following steps: obtaining an equipment operation signal; performing sparse DFT optimization and frequency spectrum denoising on the equipment operation signal to obtain a sparse denoised signal; carrying out DFT frequency domain feature extraction and multi-dimensional feature fusion on the sparse denoising signal to obtain a feature fusion vector; and inputting the feature fusion vector into a fault prediction model to obtain a fault prediction result. Through sparse DFT optimization, spectrum denoising, DFT frequency domain feature extraction and multi-dimensional feature fusion, the speed and accuracy of a fault prediction result are improved.

Reconfigurable digital logic circuits and methods of operation can be used to accelerate computation of Fast Fourier Transforms (FFT) and discrete Fourier transforms (DFT) for a range of sizes. In some embodiments, the same circuit can be reconfigurable to support FFT of any size 2k (for positive integer k) and DFT for of any size 2a*3b*5c for non-negative integers a, b, c.

The invention relates to an electricity consumption information data cleaning and filtering method and system, and the method comprises the steps: dividing collected electricity consumption data into an end point data set, a middle data set and a tail end data set according to properties, and carrying out the basic abnormal value removal and missing data filling; converting the time domain data of each data set into a frequency domain pattern capable of representing the fluctuation mode of the time domain data through discrete Fourier transform; performing correlation analysis from top to bottom based on a graph, screening an intermediate data set by utilizing the graph of the endpoint data set, and classifying and screening an end data set by utilizing the screened intermediate data set graph; performing block processing on the screened end data set, extracting core features of the end data set by separating an input effect, a load effect and associated components, and dividing the core features into weak, medium and strong categories according to the fluctuation intensity of the features; carrying out inverse transformation on each processed graphic expression, and outputting cleaned and filtered high-quality time domain power utilization data; the accuracy of data cleaning can be improved, and the data value can be deeply mined.

This invention discloses a multi-array direct positioning method based on two-dimensional discrete Fourier transform. First, the cross-covariance of the received signals from each observation station is calculated, and the automatically matched azimuth information of each observation station is obtained through two-dimensional discrete Fourier transform (2D-DFT). Then, a phase rotation matrix is ​​constructed to compensate for the azimuth information of each observation station, resulting in more accurate azimuth information. Finally, the target position is directly solved by combining the accurate information from all observation stations using the least squares approach. This multi-array direct positioning method based on two-dimensional discrete Fourier transform can effectively locate targets. Furthermore, this method eliminates the need for two-dimensional spectral peak search, significantly reducing computational complexity. At low signal-to-noise ratios, its positioning performance is close to that of the minimum variance distortionless response (MVDR) direct positioning algorithm and outperforms the traditional angle-of-arrival (AOA-K-Means) clustering two-step positioning method.

The invention discloses a grid-connected converter harmonic current control method and system based on fixed frequency prediction, and relates to the technical field of grid-connected control. The method comprises the following steps: establishing a fixed-frequency predictive control model according to a topological structure of the grid-connected converter, and performing current predictive control based on a cost function of the fixed-frequency predictive control model; total harmonic waves are extracted based on sliding discrete Fourier transform, and a state prediction equation of the total harmonic waves is constructed; a self-adaptive variable weight coefficient is introduced into the state prediction equation of the total harmonic, and state delay compensation calculation is carried out; and optimizing the cost function according to a state delay compensation calculation result. According to the method, the quality of electric energy output by the grid-connected converter is improved, and meanwhile, the transient response speed of traditional predictive control is balanced.

The application discloses an active direct wave suppression method, device, computer and storage medium, which filters a received signal by using a time domain blocking matrix, suppresses an interference frequency component, and forms a new sampling sequence; and performs a discrete Fourier transform on the new sampling sequence generated after filtering to obtain a power spectrum signal after interference suppression. According to the active direct wave suppression method, device, computer and storage medium, the uniformity of a single-frequency signal in time domain sampling is utilized, alignment on a time domain waveform is realized through phase compensation, and a difference cancellation process is performed on a reference signal and an original signal, so that the frequency component is cancelled, and static reverberation is suppressed. The method is convenient to calculate, has good single-frequency direct wave interference and reverberation suppression capability, and can be used for active target detection in a single-frequency long pulse signal transmission mode, and the effective distance is improved.

Described herein are systems, methods, and instrumentalities associated with wireless communications using single carrier waveforms. A wireless transmit / receive unit (WTRU) as described herein may receive configuration information that may indicate a control channel element (CCE) and a plurality of resource groups associated with the CCE. Each resource group may include a respective time resource element (TRE) and a respective frequency resource group (FRG), wherein each respective TRE may include a non-integer number of time units, and each respective FRG may include multiple frequency units. The WTRU may determine that the CCE may be associated with a downlink control channel transmission, and further determine allocation pattern information associated with the resource groups in a search space. The WTRU may receive the downlink control channel transmission based on the determined allocation pattern information and receive a downlink shared channel transmission based on information in the downlink control channel transmission.

The application discloses a signal adversarial sample detection method based on Fourier spectrum, and belongs to the technical field of signal security of machine learning, and comprises the following steps: preparing a benign sample data set and an adversarial sample data set, respectively extracting Fourier spectrum features of the benign sample data set and the adversarial sample data set and respectively performing feature fusion, composing a feature data set and dividing a feature training set and a feature test set; constructing a detection network, performing binary classification training on the detection network by using the feature training set, and obtaining an optimal detection network model; and inputting the feature test set into the optimal detection network model and outputting a detection result. The data is processed by using discrete Fourier transform, the features of the benign sample and the adversarial sample are respectively extracted and fused, the method process is effectively shortened, and a complex full-connection network is used for classification, so that the features of the electromagnetic signal are more fitted, and the detection precision can be effectively improved.

Provided are an apparatus for encoding an audio signal and a method of an operation thereof. An audio signal encoding method includes obtaining quantized linear prediction (LP) coefficients by performing a linear predictive coding (LPC) analysis and quantization on an input audio signal, generating a reference signal by applying discrete Fourier transform (DFT) to the input audio signal, obtaining LP residual coefficients from the reference signal, scaling magnitudes of the LP residual coefficients using the quantized LP coefficients and the reference signal, and quantizing phases of the LP residual coefficients and the scaled magnitudes of the LP residual coefficients.

A terminal according to one aspect of the present disclosure includes a receiving section that receives downlink control information (DCI), and a control section that ignores, when a physical uplink shared channel (PUSCH) waveform can be dynamically switched and a discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-s-OFDM) waveform is indicated for the PUSCH, at least one of a demodulation reference signal (DMRS) sequence initialization field and a phase tracking reference signal (PTRS)-DMRS related field of the DCI. According to one aspect of the present disclosure, it is possible to appropriately switch a waveform.