165 results about "S transform" patented technology

Beamforming method that allows a high speed and high accuracy beamforming with no approximate interpolations. This beamforming method includes step (a) that generates reception signals by receiving waves arrival from a measurement object; and step (b) that performs a beamforming with respect to the reception signals generated by step (a); and step (b) including without performing wavenumber matching including approximate interpolation processings with respect to the reception signals, and the reception signals are Fourier's transformed in the axial direction and the calculated Fourier's transform is multiplied to a complex exponential function expressed using a wavenumber of the wave and a carrier frequency to perform wavenumber matching in the lateral direction and further, the product is Fourier's transformed in the lateral direction and the calculated result is multiplied to a complex exponential function, from which an effect of the lateral wavenumber matching is removed, to perform wavenumber matching in the axial direction, by which an image signal is generated.

The invention discloses an intelligent fault classification and location method for an ultra-high voltage direct current transmission line, and belongs to the technical field of relay protection of power systems. The method comprises the following steps of: classifying fault data by using a neural network by adopting a layered and distributed neural network model; distinguishing fault types; sending the classified data into different neural networks respectively for performing fault location; when the direct current transmission line has a fault and a sampling frequency is 10 kHz, selecting a discrete line mode voltage signal which has the sampling sequence length of 100 after the fault and performing S-transform, wherein a transform result is a complex time-frequency matrix of 51*100; solving the modulus of each element in the complex matrix to obtain transient energy distribution of the line mode voltage at all frequencies; selecting first five energy spectrums as sample properties; selecting a transfer function and a learning rule; setting proper neural network parameters for constructing a BP network model; and performing fault classification and fault location. A large number of simulation results show that the method has a good effect.

The invention relates to a method for the intelligent analysis of transient power quality disturbance based on networking, which comprises the following steps of: combining disturbance signals of a plurality of monitoring points in an electric network, and performing disturbance detection to determine the starting time and the ending time of the transient power quality disturbance by using form non-sampling wavelet analysis; performing disturbance identification to perform feature extraction and encoding on power quality disturbance signals by combining window Fourier transform and S transform, and comparing the features of the power quality disturbance signals with a binary threshold matrix to identify the type of the disturbance; and during disturbance positioning, switching disturbance to a capacitor, taking the disturbance energy and the power spectrum of the disturbance signals as characteristics, combining a support vector machine to determine the switching position of the capacitor, and for voltage sag disturbance, judging the change polarity of current components at the time of voltage sag to position the relative position of a disturbance source so as to position the disturbance source. The method uses transient signals of a plurality of the monitoring points in the electric network, provides a comprehensive intelligent analysis result of the transient power quality disturbance, and can provide comprehensive and accurate data support for evaluating and controlling the problems of the transient power quality disturbance.

The invention relates to a seismic wave absorption and attenuation compensation method, and belongs to the geophysical exploration data processing technology. The method comprises the following steps: selecting one of VSP (vertical seismic profiling) initial data as reference data, carrying out generalized S-transform on the reference data, selecting sampling points as reference points, and recording the time and time-frequency spectrums of the sampling points; carrying out generalized S-transform on data of each sampling point, and dividing the time-frequency spectrum of corresponding frequency in the time-frequency spectrums of the reference points by the time-frequency spectrum of each sampling point so as to obtain an absorption and attenuation curve; determining a natural logarithm to figure out a phase compensation operator according to a formula of frequency difference of absorption and attenuation signals in a time frequency domain; and carrying out absorption and attenuation compensation and phase compensation in the time frequency domain to obtain a fine VSP wave field profile. The method has the advantages that noise can be eliminated during the process of fitting the absorption and attenuation curve; generalized S-inverse transform without energy loss is realized; signal-to-noise ratio and resolution of the data are improved after compensation; log resolution control is realized; the reference points are selected flexibly; and algorithm is simple and efficient.

The invention relates to a method of a boundary element utilizing polar wave S transform energy ratio to determine ultra high-voltage (UHV) direct current (DC) transmission line faults, belonging to the technical field of power system relay protection. The method is as follows: a starting element is started when a DC line breaks down, the polar wave voltages of the positive and negative lines are calculated according to dipolar DC voltages and DC currents measured at a protective installation part; a discrete polar wave voltage signal the sampling sequence length of which is 200 after faults is subjected to S transform, the transform result is a 101*200 time-frequency complex matrix, and mode operation is carried out on each element in the complex matrix. High-frequency component and low-frequency component of the polar wave voltage are extracted according to the obtained modular matrix, and then the ratio of the high-frequency energy to the low-frequency energy of the polar wave voltage is figured out; and whether the fault is interior or exterior is distinguished according to the value of the ratio of the high-frequency energy to the low-frequency energy. A large number of simulation results show that the invention has good effect.

The present invention relates to a method and system for segmenting texture of multi-dimensional data indicative of a characteristic of an object. Received multi-dimensional data are transformed into second multi-dimensional data within a Stockwell domain based upon a polar S-transform of the multi-dimensional data. Principal component analysis is then applied to the second multi-dimensional data for generating texture data characterizing texture around each data point of the multi-dimensional data. Using a classification process the data points of the multi-dimensional data are partitioned into clusters based on the texture data. Finally, a texture map is produced based on the partitioned data points. The present invention provides image texture segmentation based on the polar S-transform having substantially reduced redundancy while keeping maximal data variation.

The invention discloses a flexible direct-current power distribution network monopole fault line selection and section positioning method and system. The method comprises the steps that positive and negative currents of each feeder line of a direct-current power distribution line are collected and filtered; current sudden change data in a data window is extracted, S-transform is carried out on thedata to obtain a complex time-frequency S matrix of current signals; a characteristic frequency band is calculated according to line parameters; STCFB and W are calculated according to the complex time-frequency S matrix; a monopole fault line selection criterion and a section positioning criterion of the direct-current power distribution feeder lines are established, and fault feeder line identification is carried out on the basis of current sudden change Pearson correlation coefficients according to the line selection criterion; if the line is a faulty feeder line, the transient energy sumsof positive current and negative current sudden change characteristic frequency bands are compared for fault pole judgment; if the line is a faulty polar line, fault feeder line identification is carried out by using current sudden change correlation between the left side and the right side of each feeder line section. The method is not affected by factors such as transition resistance, data asynchronization and fault positions, and is rapid, sensitive and reliable.

Color measurements are generated from transform-based device profiles for use in a measurement-based color management system. The color measurements is generated by providing device-dependent color samples to a transform-based color management module and converting them to the profile connection space with the device's transform-based profile. Color measurements are then generated from the transform-based color management module by applying an identity profile in place of a destination device profile. In addition, measurement data can be extracted directly from either the tone reproduction curve / matrix or n-dimensional lookup table transforms contained within a transform-based device profile.

The invention relates to a power distribution network outgoing feeder fault route selecting method by S transform amplitude detection, namely, when the power distribution network generates single-phase ground fault, the bus zero sequence voltage before and after the fault can be performed for S transform to find out the moment corresponding to a module maximal value on the S transform highest frequency component position for accurately calibrating the fault moment; the each line zero sequence current before and after the fault moment can be performed for S transform to obtain the S transform complex matrix of each line zero sequence current; the amplitudes of highest frequency component at fault moment in the each line zero sequence current S transform complex matrix can be worked out, and the amplitude difference can be worked out; the integrative amplitude difference of each line zero sequence current can be worked out; the fault route selecting can be implemented by comparing the size of the integrative amplitude difference. The invention can avoid the route selecting failure caused by CT saturation and phase alternation, the invention implements the route selecting by using high-frequency components, without being influenced by arc-suppression coil compensation degree. The accurate route selecting under different fault conditions comprising small fault angle can be implemented.

The invention provides a fault diagnosis method of a variable-speed rotation machine based on time-frequency spectrum segmentation. The method includes following steps: step 1, obtaining normalized time-frequency spectrums of signals through multi-resolution generalized S transform, and generating multi-resolution binarized time frequency spectrums; step 2, combining the binarized time-frequency spectrums with all resolutions, and obtaining an optimal binarized time-frequency spectrum; step 3, segmenting the optimal binarized time-frequency spectrum into a plurality of connected domains, and performing information annotation on each connected domain; step 4, extracting an optimal expression atom of each connected domain, forming an optimal atom set, and calculating the expression of a fault signal in the optimal atom set; and step 5, calculating the appearance time and amplitude of an impact theory, and realizing fault diagnosis of variable-sped mechanical equipment through informationcomparison. According to the method, most strong background noises can be filtered, and fault diagnosis of impact type faults including cracking, pitting corrosion or spalling etc. of the variable-speed rotation machine can be realized.

The present invention relates to a method and system for processing time-varying three-component signals to determine polarization dependent features therefrom. Relation data indicative of a predetermined expression relating ellipse elements describing an ellipse in three dimensional space to the components of the three-component signal are provided. Received signal data indicative of a three-component signal are processed for transforming each component of the three-component signal into time-frequency domain providing second signal data. Data indicative of polarization dependent features are then determined using the relation data and the second signal data. The method and system for processing time-varying three-component signals according to the invention are highly beneficial in numerous applications for evaluating three-component signal data such as seismology.

The invention provides a power quality disturbance identification method using time-domain compression multiresolution fast S-transform feature extraction. The power quality disturbance identificationmethod is applied to the analysis and research of power quality disturbance signals and is characterized by including the steps of simulating power quality disturbance signals, performing multi-resolution fast S-transform processing on the disturbance signals, on the premise of determining features, calculating the required reserved information for a fast Fourier transform inverse transform result for each major frequency point in the fast S-transform process, constructing an intermediate matrix, and extracting valid features from the intermediate matrix to construct a feature vector for classifier construction. The power quality disturbance identification method has the advantages of being scientific and reasonable, having strong applicability and a good effect, and being capable of completing the identification of complex power quality disturbance signals.

The invention relates to an S-transform based transmission fault traveling wave heat positioning method and belongs to the technical field of relay protection of a power system. The method comprises the following steps: carrying out data window truncation of a fault traveling wave signal and S-transform of a small quantity of key data, with amplitude-time-frequency information of the traveling wave signal expressed by an S-transform modular matrix; basically filtering influence of noise and initially determining the position of a head of the traveling wave according the different performance of the head of the traveling wave and the noise in different frequency range; intensively inspecting an amplitude-time graph at a point of the highest frequency in which a point of maximum amplitude represents a point of the most intensive vibration of the head of the traveling wave, namely a point of the arrival time of the head of the traveling wave; and marking polarity information of the head of the traveling wave according to the characteristics of an original signal near the point of the arrival time of the head of the traveling wave. Principle analysis and simulation data and engineering data verification show that the method can accurately indicate the arrival time of the head of the fault traveling wave and is advantageous to the prior method.

For the single-phase grounding fault and instant fault of a power distribution network including a photovoltaic microgrid, the invention discloses a photovoltaic microgrid fault isolation method based on a fault state. The method includes the following steps of (1) partitioning the microgrid; (2) isolating the fault sections; (3) cutting off a single-phase grounding fault wire; and (4) recovering the power supply of fault microgrid partitions. The microgrid is reasonably partitioned, the fault sections are isolated through a boundary switch, and the power-off range is reduced. By the full use of the anti-islanding protection function of a photovoltaic inverter, a fault line is selected through S transform after the photovoltaic power supply and the power grid are isolated, so that the reliability of relay protection and the accuracy of line selection are improved, and the problem that the power distribution network including the photovoltaic power supply is unreliable in relay protection due to the bidirectional flow of power is solved. The photovoltaic microgrid fault isolation method has better protection effects on the single-phase grounding fault and other instant faults in the power distribution network.

The invention relates to a method for determining a stratum stack quality factor by direct scanning of prestack time migration. By modeling methods for defining an equivalent stack quality factor, developing and utilizing prestack reflection seismic data and scanning and inversely demonstrating the stratum stack quality factor, a series of constant-Q and inverse-Q filtering is selected and combined with the prestack time migration to high-efficiently compensate ground reflection data and acquire a common reflection point set; generalized S transform is used for accurately solving an instantaneous frequency spectrum of a stack channel; the minimum average derivative after a logarithmic spectrum is used as an automatic selection standard of a correct stack Q value, so rapid and automatic inverse demonstration and analysis of a stratum stack Q value is realized; and a method for inversely demonstrating and analyzing the stack Q value of the common reflection point set based on viscous compensation migration is established. The method is applied to reflection seismic data processing in seismic exploration, is used for providing important input parameters for high-resolution seismic imaging, and has important application value in exploration of deep concealed oil and gas and mineral resources in China.

The invention relates to a single-phase permanent fault recognition method for an extra-high voltage AC transmission line using S transform energy. The method comprises the following steps: after fault occurs to an AC line, selecting a disperse voltage signal of which the recovery voltage sampling sequence length is 1,800 after the fault, and performing S transform to obtain a 901*1,800 time frequency complex matrix; and operating modulus of each element in the complex matrix to obtain the energy value under each frequency point. As too many frequency ranges are not good for recognition, every 9 rows from the 2nd row to the 901st row to obtain a 101*1 matrix, the 901 frequency ranges are integrated into 100 frequency ranges, and the frequency of each frequency range is 50 Hz. The single-phase permanent fault is judged according to the size of the first frequency range, namely the 50 Hz frequency range. A large number of simulation results indicate that the effect of the method is good.

The invention discloses an estimation method of a multi-component non-stationary signal instantaneous frequency based on focusing S-transform, and mainly solves the problems that the multi-component non-stationary signal instantaneous frequency with high precision cannot be realized because the energy aggregation is not high and the time frequency resolution ratio cannot be flexible to adjust and cannot realize high precision. The estimation method comprises the realizing steps: 1, inputting a to-be-analyzed non-stationary signal; 2, optimizing a control parameter of a window function in the focusing S-transform according to an aggregation criterion; 3, calculating the optimized focusing S-transform and acquiring a signal time frequency distribution energy diagram; 4, carrying out binarization processing on the time frequency distribution energy diagram of the non-stationary signal; and 5, obtaining the instantaneous frequency estimation on a signal according to a binaryzation time frequency diagram after the binarization processing. According to the method provided by the invention, the self-adaption adjustment of the control parameter of the window function can be realized by improving the S-transform window function, and the time frequency distribution energy aggregation and the instantaneous frequency estimation accuracy can be improved, therefore the method can be used for radar interception, communication countermeasure, voice identification, and medical brain wave signal analysis.

The invention discloses a seismic spectrum imaging method based on deconvolution generalized S transform, which comprises the steps that a generalized S transform spectrum is acquired by performing two-dimensional convolution on Wigner distribution of original signals and a Gaussian window, a transform spectrum is acquired by performing generalized S transform on seismic data, and time-frequency distribution of the original signal can be acquired through deconvolution when the generalized S transform spectrum and the Wigner distribution of a window function are known. The seismic spectrum imaging method combines advantages of generalized S transform and Wigner-Ville distribution, generation of a cross term of the Wigner-Ville distribution is effectively suppressed through a generalized S transform window, and the generalized S transform spectrum is enabled to acquire high time-frequency aggregation at the same time; and deconvolution generalized S transform can adaptively adjust an analysis window along with variations of a frequency component, is applicable to time-frequency analysis for unstable seismic data, and can acquire high time-frequency resolution; and the seismic spectrum imaging method is applied to detecting the oil-gas possibility of a reservoir, thereby being conducive to improving the reservoir prediction accuracy.

The invention provides an extra-high voltage direct current transmission line boundary element forming method based on a support vector machine (SVM). The method comprises the following steps: when the direct current line has fault, computing the anode line polar wave voltage and the cathode line polar wave voltage according to the direct current voltage and direct current of the two poles measured in the relay location of the line; selecting the discrete polar wave voltage signal of the fault pole, with sampling sequence length of 200, after fault to undergo S transform to obtain a 101*200 time-frequency complex matrix and computing the modules of elements in the complex matrix; computing the energy values corresponding to the first 20 frequencies according to the modular matrix obtainedthrough S transform, sticking tag values to the energy values as the characteristic quantity and inputting the characteristic quantity to the SVM to be trained; and distinguishing the area fault fromthe out-area fault according to the training result of the SVM. Lots of simulation results show the method has good effect.

The invention relates to a weak object detection method on the basis of generalized S transformation, which pertains to the field of image processing, in particular to a weak object detection method by applying the two-dimensional generalized S transformation of an image. The method carries out two-dimensional generalized S transformation to the original image I (x', y') at first to obtain a generalized S transformation result S (x, y, kx, ky); then detection is carried out to a weak object in the image according to the generalized S transformation result S (x, y, kx, ky); by fixing the value of spatial frequency kx or ky, a four-dimensional S transformation result S (x, y, kx, ky) is reduced into three-dimensional data; and the combination of frequency component power of a vertical section image (y, ky) or (x, kx) corresponding to each value selected along x or y direction is compared with a threshold value so as to determine the positions of row and line of the weak object in the original image. The method carries out generalized S transformation to the original image; on basis of the generalized S transformation result, by dimension reduction and visualization processing, the information of the S transformation domain is utilized to detect the weak object, thus being capable of effectively overcoming the influences by obstruction factors such as noise, flooding wave, cloud, and the like, in the spatial domain.

The invention discloses a high-precision synchronous extraction generalized S transform time frequency analysis method. The method comprises the steps of firstly performing generalized S transform ona signal to obtain a generalized S transform value, thereby obtaining a time frequency spectrum of the generalized S transform; based on the generalized S transform value, calculating an instantaneousfrequency, obtaining a synchronous extraction operator (SEO) according to the instantaneous frequency, extracting the time frequency spectrum of the generalized S transform by utilizing the SEO, onlyreserving energy near a time frequency ridge line in a time frequency plane, removing all other divergent energy, and obtaining a synchronous extraction generalized S transform value; and finally converting the synchronous extraction generalized S transform value into a time frequency spectrum of synchronous extraction generalized S transform. The size of a window function can be flexibly adjusted according to the actual need; the method has higher time frequency decomposition precision and relatively high flexibility; and the time and frequency resolution of the signal is greatly increased.

The present invention relates to a method and system for processing time series signal data or image signal data indicative of a characteristic of an object. Received signal data are transformed into second signal data within a Stockwell domain based upon a sparse approximation of a S-transform of the signal data. The second signal data are then processed within the Stockwell domain to extract features therefrom. The processing includes determination of local spectra at predetermined locations; determination of voices at predetermined frequencies; and filtering of the signal data using a filter function in dependence upon frequency and time or space. The signal processing method and system according to the invention enables signal processing based on the S-transform using a desktop computer or workstation.

The present invention provides a stratum oil-gas possibility prediction method with a high time resolution. By utilizing improved generalized S transform, a Gauss window function with a time window width in directly proportional change with frequency f is employed, the time window is narrow at a low frequency band, the high time resolution is obtained, and seismic wave low frequency absorption attenuation gradient is calculated; at the same time, by using generalized S transform, a Gauss window function with a time window width in inversely proportional change with the frequency f is employed, the time window is narrow at a high frequency band, the high time resolution is obtained, high frequency absorption attenuation gradient is calculated, and the low frequency absorption attenuation gradient and the high frequency absorption attenuation gradient are utilized comprehensively. The method has better time resolution at a low frequency band, an influence of high frequency interference is avoided, at the same time, the method has better time resolution at a high frequency band, an influence of low frequency interference is avoided, oil and gas prediction time resolution is raised greatly, an algorithm is simple, and computational efficiency is high.

The invention provides a sparse feature extraction method for a fault signal of a rotating machine. The method includes the steps of constructing a fault overcomplete atom library according to a faultgeneration mechanism of an impact fault of the rotating machine; calculating a multi-resolution time-frequency spectrum of a to-be-analyzed signal through multi-resolution generalized S-transform resolution and normalization processing according to a mathematical model of a to-be-observed signal and the fault overcomplete atom library; calculating a maximum value of the normalized multi-resolution time-frequency spectrum, and calculating an atom set capable of best performing sparse representation on the to-be-observed signal by combination with an orthogonal matching tracking algorithm; andaccording to the fault sparse representation signal and fault feature frequency of a device and rotating speed information, determining a fault type, to achieve a rapid fault diagnosis of the mechanical device. The sparse feature extraction method has the advantages that the complexity of the atomic search and the search time are greatly reduced, the efficiency of sparse decomposition is improved,and the efficiency of fault diagnosis of impact faults such as crack, pitting or spalling is improved.

The invention discloses a three-dimensional measurement method and system based on single grating projection. The three-dimensional measurement method comprises the steps that a physical grating is projected to an object to be tested in an out-of-focus manner so as to form a sinusoidal fringe pattern, and a deformed fringe pattern of the object to be tested is collected; the deformed fringe pattern is processed by using an S-transform method to obtain the principal value phase Phi(x, y); unwrapping is performed based on a wrapping elimination method to obtain the absolute phase phi(x, y), and a three-dimensional measurement system for telecentric imaging is calibrated so as to build an imaging model, and three-dimensional coordinate information of the tested object is calculated. The three-dimensional measurement system is used for executing the method. The three-dimensional measurement method is based on the single fringe pattern, zero padding is performed on both ends of the principal value phase after the principal value phase is obtained according to the S-transform method so as to acquire up-sampling in the frequency domain, the frequency spectrum of the phase is shifted to the original position according to frequency shift characteristics of Fourier transform, a phase component of carriers is eliminated, the height-phase mapping relationship is fitted by adopting the telecentric imaging model, the three-dimensional measurement system is calibrated, the three-dimensional coordinate information of the tested object is obtained, the steps are simple, and the measurement speed is higher.

The invention discloses a method for identifying discharge types of partial discharge signals of electric power equipment. The method comprises the steps of: S1, acquiring a partial discharge time domain waveform of a known type, and establishing a partial discharge type fingerprint database; S2, using a wavelet packet to perform denoising preprocessing on the partial discharge signals; S3, performing S transform on the partial discharge signals to obtain a time-frequency matrix A; S4, performing spectrum kurtosis decomposition on the partial discharge signals, and extracting a time-frequencymatrix B; S5, conducting singular value decomposition on the time-frequency matrix B, selecting an appropriate number of singular values, and normalizing the singular value as an eigenvector; S6, regarding an eigenvector of a training sample as an input of a probabilistic neural network optimized by adopting a genetic algorithm, and training to obtain the optimal probabilistic neural network structure; S7, and inputting a test sample eigenvector into the optimal probability neural network structure to obtain a partial discharge signal type. The method disclosed by the invention has the advantages of high identification speed and high identification precision.

The invention discloses a time frequency decomposition and reconstruction method of generalized S transform signals for synchronous extrusion. Firstly, the four parameter generalized S transform is conducted on a signal, the variation trend of a basic wavelet function is adjusted by the adjustment of the basic wavelet amplitude, energy attenuation rate, energy delay time and basic wavelet video rate; secondly, the modulus of the result of the four parameter generalized S transform is obtained, and the energy of each time-frequency point is obtained, thus the time-frequency spectrum is obtained; thirdly, the instantaneous frequency is obtained by the four parameter generalized S transform of the signal; fourthly, the frequency set after the generalized S transform is set as the central frequency set, each time frequency point corresponding to the instantaneous frequency close to the interval of the central frequency is extruded to the central frequency point, and the generalized S transform of synchronous extrusion is obtained; finally, the inverse transformation formula of generalized S transform for synchronous extrusion is deduced. The generalized S transform of synchronous extrusion has the advantages of synchronous extrusion transform and generalized S transform, and is a time-frequency decomposition and reconstruction method for high precision signals.

The invention provides a digital discrete signal hopping sliding discrete Fourier transform method, namely an SDFT (Modulated Hopping DFT, mHDFT) transform method for solving the problem of potential instability and accumulated errors of HDFT transform of an HDFT (hexagon discrete Fourier transform) transform method in practical engineering application. The method utilizes DFT circulating frequency domain shifting characteristics and modified UVT transform for effectively removing twiddle factors in a feedback loop of an HDFT resonator, accurately fixing HDFT poles on a unit circle and obtaining a DFT transform result of a frequency point k at n time, thereby effectively reducing the cumulative errors as well as ensuring the constant stability of the HDFT.

The invention relates to a method of quickly and accurately diagnosing the fault of a converter in a flexible DC power transmission system. Different faults of a voltage source converter in a flexible DC power transmission system are simulated, and DC bus voltage waveforms under different faults are extracted as fault feature signals. The fault feature signals are transformed to a frequency domain through Fourier transform, a fault feature matrix is constructed based on the local time-frequency analysis ability of S transform and the nonlinear system fault classification ability of a support vector machine, a classification algorithm is optimized using a genetic optimization algorithm, and thus, accurate classification of the fault types of the voltage source converter is realized. Compared with previous fault diagnosis methods, the method of the invention can save more calculation time and ensure higher fault diagnosis rate.