622 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 time series data anti-noise anomaly detection method and system based on dynamic decomposition, and the method comprises the steps: obtaining a to-be-detected time series, inputting an anomaly detection model, and carrying out the dynamic decomposition of the time series; based on a channel-time mixed attention mechanism, obtaining channel feature representation of a trend component and time feature representation of a seasonal component; and obtaining a reconstruction sequence according to the channel feature representation and the time feature representation, calculating a difference value of each corresponding time step in the reconstruction sequence and the time sequence, and comparing and marking the difference value with an abnormal threshold value to obtain a marked abnormal detection sequence so as to complete sequence abnormal detection. According to the method, the time sequence needing to be detected is subjected to dynamic trend seasonal decomposition through learnable one-dimensional convolution and discrete Fourier transform of the anomaly detection model, the potential time pattern of the time sequence can be effectively extracted, the adaptability of the model to complex trends and periodic changes is improved, and the accuracy of abnormal data recognition is improved.

The invention relates to a reconfigurable battery energy storage system grid-connected and off-grid seamless switching control method and device. The method comprises the following steps of: sensing a real-time power grid state, pre-judging a system operation mode, and acquiring a millisecond-level panoramic information flow of the power grid state through a high-speed sampling voltage sensor, a high-speed sampling frequency sensor, a high-speed sampling phase sensor and a high-speed sampling communication interface. And pre-judging the stability and disturbance trend of the power grid through a deep learning algorithm, executing a power distribution and voltage support algorithm in grid-connected and off-grid modes, and calculating an optimal switching opportunity point and a system reconstruction target topology scheme. Meanwhile, the harmonic distortion rate and the voltage sag depth are calculated in real time through discrete Fourier transform, so that a mode pre-judgment mechanism is automatically triggered. After switching, key parameters such as voltage, frequency and power are automatically corrected through real-time sampling and observation, and zero-sensing switching is achieved. The method can effectively improve the response speed and stability of the battery energy storage system when the power grid fails, and guarantees the safe operation of the power grid.

The invention belongs to the field of edge computing acceleration, and relates to a fast Fourier transform and neural network reasoning collaborative computing acceleration method, which comprises the following steps of: deploying a computing method which is based on butterfly computing merging and a tensor mapping strategy and is mixed with DFT (Discrete Fourier Transform) and FFT (Fast Fourier Transform) in an operator deployment level; on the interface integration level, a bus interface of a register access path in a tensor accelerator control path is subjected to lightweight reconstruction, so that the bus interface is adaptive to an edge computing platform; in an operation level, a user-defined instruction is introduced into a tensor calculation unit, and an accelerator is enabled to independently complete a whole-process task of FFT signal processing and NN intelligent identification. According to the method, an operation instruction set is expanded on hardware, and delay overhead caused by returning a large amount of intermediate data to a processor is avoided, so that efficient execution of FFT and neural network tasks on unified hardware is guaranteed, and multiple requirements of high real-time performance, high precision and low power consumption are met.

The invention discloses a photovoltaic grid-connected broadband oscillation suppression system based on network construction type energy storage, and belongs to the technical field of photovoltaic grid connection. The invention aims to solve the problem of broadband oscillation in photovoltaic grid connection. Comprising the following steps: acquiring voltage and current of a public grid-connected point and energy storage voltage and current by adopting a data acquisition module, and preprocessing; a data processing module is adopted to carry out autoregressive empirical wavelet transform on voltage and current of a common grid-connected point to obtain an oscillation signal, and then interpolation discrete Fourier transform of a Hanning window is adopted to obtain modal information; filtering a fundamental component of the energy storage voltage based on the modal information by adopting an adaptive impedance remodeling module, and then filtering to obtain a filtered energy storage voltage harmonic component; self-adaptive impedance remodeling is carried out to obtain an energy storage voltage harmonic component after amplitude and phase adjustment; and inputting the harmonic component of the energy storage voltage after amplitude and phase adjustment as a voltage feedback signal to a PWM control link of the inverter, thereby realizing broadband oscillation suppression at the public grid-connected point. The method is used for photovoltaic grid-connected oscillation suppression.

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.

The invention provides a power grid frequency detection method, system and device and a medium, and the method comprises the steps: carrying out the narrowband signal decomposition of an obtained input voltage signal of a power grid through a null-space tracking algorithm, and obtaining a fundamental main mode component of the input voltage signal; performing low-pass processing on the fundamental wave main mode component to obtain a fundamental wave signal of the fundamental wave main mode component; performing discrete Fourier transform on the fundamental wave signal to obtain phase information of the fundamental wave signal; according to the phase information of the fundamental wave signal, calculating phase deviation information of the fundamental wave signal in pre-selected adjacent cyclic waves to obtain frequency estimation information of the power grid; according to the invention, narrow-band decomposition is carried out on an input voltage signal through a null-space tracking algorithm, so that a fundamental main mode can be accurately extracted; and phase deviation information can be determined based on discrete Fourier transform, so that when the interference frequency of the power grid is close to the fundamental frequency, high separation precision can be kept, and the accuracy of power grid frequency estimation can be improved.

The invention relates to a power load prediction system based on time sequence self-supervised representation learning, and the system comprises a data preprocessing module, an expansion time convolution network, a trend representation decoupler, a seasonal representation decoupler, a feature converter, and a predictor. The expansion time convolution network performs dynamic adaptive grouping on different variables in the same data processing unit, establishes an intra-group variable relationship by sharing a convolution kernel weight and stacking a plurality of expansion time convolution layers, and establishes an inter-group variable relationship through subsequent multi-group representation splicing operation and single-layer expansion time convolution; the trend representation decoupler is used for separating trend representation Z (T) from feature representation extracted from the expansion time convolution network based on a plurality of parallel one-dimensional causal convolution blocks with different scales; the seasonal representation decoupler adopts discrete Fourier transform to separate seasonal representation Z (S) from the feature representation extracted from the expansion time convolutional network; the feature converter maps a combined feature vector of trend representation and seasonal representation back to an original space from a potential space by stacking a plurality of deconvolution layers; and the predictor maps the feature representation of the original space into a power load prediction result by using a linear projection layer, and the adaptability, robustness and generalization ability of the model for modeling complex power load time series data can be improved.

Embodiments of the present disclosure relate to methods and devices for wake-up signals. A method implemented at a network device comprises forming an OOK subsequence with multiple OOK symbols that comprise at least one OOK ON-symbol and at least one OOK OFF-symbol. The multiple OOK symbols are associated with one OFDM symbol. The method further comprises mapping the OOK subsequence into multiple pre-Discrete Fourier Transform (pre-DFT) subsequences respectively to form a pre-DFT sequence, and performing transform precoding on the pre-DFT sequence. The method further comprises mapping output of the transform precoding into frequency resources allocated to a Wake-up Signal (WUS) and generating the WUS, and transmitting to a terminal device the WUS.

The invention discloses an electrochemical impedance spectroscopy detection method and system and related equipment. The electrochemical impedance spectroscopy detection system comprises: a battery pack comprising a plurality of cells; the battery cell management module is connected with the plurality of battery cells and is used for acquiring response voltage of the battery pack and performing discrete Fourier transform on the excitation current and the response voltage to obtain an electrochemical impedance spectrum of the battery pack under the target excitation frequency; the battery management module is connected with the excitation source module and used for sending the target excitation frequency to the excitation source module; and the excitation source module is used for generating excitation current according to the target excitation frequency sent by the battery management module.

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 relates to the technical field of video recognition and analysis, and discloses a video recognition and analysis method based on physical characteristics. Video frame pixels are mapped to a two-dimensional coordinate system with the upper left corner as an original point, and mirror image expansion and median filtering are carried out on a gray level image; constructing a binary image based on a gray threshold value, and analyzing and extracting a target region by using a four-neighborhood connected domain; using neighborhood search and polar angle sorting to close the tracking contour, and generating equidistant re-sampling points based on Euclidean distance and an interpolation method; the curvature of the re-sampling points is estimated through a three-point difference algorithm, and zero denominator is avoided through numerical protection; performing discrete Fourier transform on the curvature sequence to extract a frequency spectrum, and normalizing an amplitude to form a standardized feature vector; and finally, inter-frame similarity is calculated based on the feature vector, and the most similar frame is automatically retrieved. By processing unified data standards in stages, edge noise is suppressed, sampling uniformity is ensured, and feature stability and cross-frame comparability are improved.

The invention discloses an intelligent power grid equipment fault monitoring system and method based on the Internet of Things, and relates to the technical field of the Internet of Things. Discrete Fourier transform and a Fisher information processing method are adopted to fuse multi-source data, meteorological interference is corrected, and accurate collection of electrical quantity and quantification of environmental influence are achieved; and setting a fault monitoring sliding window and a harmonic analysis time interval, and obtaining harmonic data of different time scales. Extracting harmonic features through slope calculation, and identifying voltage abnormal features; historical data is processed according to a time sequence prediction algorithm to construct a prediction set, voltage abnormity is judged based on a historical data threshold value, intelligent early warning of the voltage state is achieved, the abnormity capturing precision is improved, and when the system detects the voltage abnormity, information of strain section equipment sending out a fault early warning signal is obtained and transmitted to an OTA upgrading terminal. And enabling the OTA upgrading terminal to carry out state identification and data interaction on the upgrading process of the intelligent power grid equipment based on the equipment information.

The invention discloses an active bird shooting method and system based on voiceprint recognition and automatic localization, and belongs to the technical field of intelligent bird voiceprint collection and recognition, and the method comprises the steps: collecting bird sounds through a voiceprint collection device, and carrying out the discrete Fourier transform of the collected bird sounds; inputting the processed bird chirp data into a voiceprint recognition neural network model based on deep learning to obtain a high-dimensional feature vector; comparing the high-dimensional feature vector with each piece of bird voiceprint feature data in a bird voiceprint feature library to obtain bird species corresponding to the voiceprint feature data; according to the obtained closest bird species, based on a positioning sensor, determining the positions of the birds by using a multi-point cross positioning technology; according to the determined positions of the birds, the camera adjusts the shooting angle and the focal length to shoot the birds. According to the method, manual intervention is not needed, so that efficient and accurate bird monitoring is realized, and the monitoring efficiency and accuracy are improved.

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.

Methods, systems, and devices for wireless communications are described. In some examples, a transmitting device associated with one or more non-coherent demodulation reference signal ports may transmit data including a phase change estimation reference signal with multiple reference signal groups. In some examples, multiple different phase change estimation reference signal ports may be code-division multiplexed to transmit the phase change estimation reference signal in the same set of reference signal groups using a different orthogonal cover code for each phase change estimation reference signal port. In some other examples, the different phase change estimation reference signal ports may be used for transmitting the phase change estimation reference signal in the different time-division multiplexed sets of reference signal groups. In such cases, by assigning two phase change estimation reference signal ports to two TDM sets, the two phase change estimation reference signal ports may be time-division multiplexed.

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.

A method is provided that is performed by a device comprising a digital baseband of a communication transmitter. The method includes setting (1102) a first portion of a plurality of elements in a time variant filter to zero to obtain a truncated time variant filter; and generating (1104) an approximate discrete Fourier transform spread-orthogonal frequency division multiplexing, DFTS-OFDM, waveform based on filtering data symbols in time domain with the truncated time variant filter.

The invention relates to the technical field of power quality control, and discloses an inverter harmonic suppression cooperative control method, device and equipment and a storage medium, and the method comprises the steps: employing sliding window recursive discrete Fourier transform to decompose output voltage harmonic waves in real time; generating a reverse compensation voltage for the detected harmonic component; dynamically adjusting the sampling frequency according to the load current change rate; and superposing a high-frequency jitter signal in the modulated wave. Therefore, the problem of balance among harmonic suppression efficiency, dynamic response speed and system loss of a traditional inverter is solved. The dynamic switching frequency is adaptive, and various switching frequencies can be switched to replace traditional fixed frequency modulation based on the load current change rate; harmonic compensation is adopted, PR, namely SMC double closed-loop control is matched, low-harmonic high-quality waveform output is achieved, and meanwhile the anti-interference performance of the system is improved.

The invention relates to the technical field of signal processing, and discloses a velocity vector sensing method and device, equipment, a storage medium and a product, and the method comprises the steps: carrying out the down-conversion and matched filtering of a pilot signal transmitted by a user side, obtaining a target discrete signal sequence, carrying out the fast Fourier transform of the target discrete signal sequence, and obtaining a target discrete signal sequence; and determining a signal frequency sequence based on a discrete Fourier transform result, determining a frequency increase rate and a Doppler offset according to the signal frequency sequence, obtaining a signal propagation delay according to the discrete Fourier transform result, and further determining a velocity vector sensing result of the user side. According to the invention, hardware complexity and power consumption are reduced through down-conversion and matched filtering processing, and the signal-to-noise ratio is improved; according to the method, fast Fourier transform and inverse fast Fourier transform are combined, a signal frequency sequence, signal propagation time delay, frequency increase rate and Doppler offset are extracted, the velocity vector of a user side is sensed comprehensively, and the limitation that only part of parameters can be sensed in the prior art is overcome.

This invention discloses a bioacoustic complexity index method with low sensitivity to noise, comprising: performing a short-time discrete Fourier transform on the field bioacoustic monitoring recording data during the analysis period to obtain its time-frequency power spectrum; estimating the narrowband noise average power of each frequency point using a set of noise time-frequency points without bioacoustic signals; performing power spectrum subtraction on each frequency point to obtain the noise-enhanced bioacoustic time-frequency power spectrum; subdividing the analysis bandwidth and analysis period into time-frequency domains according to a certain frequency step and time step, and calculating the time-frequency partition index value; and summing all the time-frequency partition index values ​​within the analysis bandwidth and analysis period to obtain the final bioacoustic index value, i.e., the noise-reduced bioacoustic complexity index based on the change in bioacoustic time-frequency power after noise enhancement. This invention significantly reduces the sensitivity of the index results to noise and significantly expands the spatiotemporal application range of similar acoustic index strategies.

The invention relates to a verification method and system for a mutual inductor calibrator based on accurate frequency tracking. The method comprises the following steps: sampling output signals of a standard mutual inductor and a mutual inductor to be tested to obtain original signals; sequentially performing high-pass and low-pass digital filtering on the original signal to obtain a filtered signal; performing accurate frequency calculation on the filtering signal by adopting a mode of combining zero crossing point detection and a similar triangle interpolation method, and if an absolute value of a difference value between a current fundamental frequency obtained by calculation and a fundamental frequency measured last time is greater than a set value, updating a hardware sampling interval until the absolute value is smaller than the set value; and discrete Fourier transform calculation is carried out on the filtering signals of the standard mutual inductor and the mutual inductor to be detected respectively, fundamental wave amplitudes and fundamental wave phases of the corresponding signals are obtained, and a ratio difference and an angular difference are calculated to serve as verification results of the mutual inductor calibrator. According to the invention, spectrum leakage and a fence effect in the fundamental wave extraction process can be eliminated, and the verification precision of the angular difference and the ratio error of the mutual inductor calibrator is 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.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit uplink signaling via a cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) waveform or a discrete Fourier transform (DFT) spread OFDM (DFT-s-OFDM) waveform. A configured resource muting pattern may be based on or may reuse a phase tracking reference signal (PTRS) frequency pattern. The resource muting pattern may mute every other resource element (RE) of an uplink symbol or uplink subband of subband full duplex (SBFD) symbols, and the transmitting UE may perform uplink rate matching (e.g., applying a zero power PTRS pattern and inserting the zero power REs after a DFT spread operation, oversampling after a DFT-s-OFDM procedure and doubling a size of an IFFT, or repeating an input sequence to a DFT and doubling a size of the IFFT, power boosting, updating a power control calculation, etc.).