26 results about "Separation matrix" patented technology

The invention belongs to the technical field of environment monitoring, and discloses an ecological environment anomaly detection and early warning method based on artificial intelligence. The emission characteristic fingerprint database is constructed according to enterprise production process characteristics by acquiring multi-point pollutant concentration, meteorological parameters and enterprise space distribution data in a park. By analyzing the space-time diffusion trajectory of pollutants, identifying abnormal areas and calculating reverse traceability parameters, a space-time superposition inversion model of multi-source emission is constructed. A multi-enterprise contribution degree separation matrix is generated based on fingerprint matching degree weighted calculation, and accurate separation of emission contributions of all enterprises in the mixed pollution field is achieved. By monitoring pollutant concentration fluctuation characteristics in real time, contribution degree weight distribution is dynamically adjusted, and the accuracy of a monitoring result is ensured. And accurately determining a responsibility subject with excessive emission and generating an early warning signal. According to the invention, accurate identification and responsibility determination of the pollution source can be realized in a complex multi-source emission environment, and the accuracy and fairness of industrial park environment supervision are effectively improved.

The invention discloses a graph signal blind source separation method in a white noise environment, and belongs to the technical field of signal processing. Aiming at the problem that the traditional blind source separation performance is reduced due to additive white noise, the invention provides a joint optimization scheme combining blind compression nonlinear noise suppression and image smoothing regularization. Firstly, a blind compression function is applied to a noisy observation image signal for preprocessing; secondly, carrying out mean value removal and whitening processing on the compressed signal; then, constructing a graph Laplacian matrix as a smoothing operator, establishing a joint diagonalization objective function fusing graph autocorrelation, a FastICA item and a graph smoothing regular item, and adopting a Givens rotation algorithm to iteratively optimize and solve a separation matrix; and finally, reconstructing a source signal through multiplication of the separation matrix and the whitening data. According to the method, noise interference is effectively suppressed through blind compression, signal structure consistency is maintained by using graph smoothing prior, and separation robustness, precision and convergence stability in a strong noise environment are remarkably improved.

The invention discloses a double-constraint second-order blind identification bridge dynamic deflection signal noise reduction method, which comprises the following steps: acquiring dynamic deflection observation signals of bridge target points, and constructing an observation signal matrix by using the observation signals of at least three adjacent target points; carrying out centralization processing on the observation signal matrix, and carrying out whitening transformation to obtain a whitening signal with a covariance matrix as a unit matrix; selecting a group of non-zero time delay values, calculating a time delay covariance matrix of the whitening signal under each time delay, and performing joint approximate diagonalization on all the time delay covariance matrixes to obtain a separation matrix and an initial separation source signal; optimizing the separation matrix based on sparsity constraint to obtain a hybrid matrix; performing dominant frequency optimization on the initial separation source signal; and reconstructing based on the optimized hybrid matrix and the optimized main frequency source signal to obtain a de-noised bridge dynamic deflection signal. The hybrid matrix optimization based on the sparsity constraint enables the reconstruction result to be closer to the real bridge dynamic deformation.

A mixed electric signal disassembling method and system based on residential electricity load, characterized in that the method comprises the following steps: step 1, collecting the mixed electric signal of the residential electricity node in the power distribution network through the load monitoring device, and adding random noise to the mixed electric signal; step 2, extracting signal fluctuation trend information from the mixed electric signal with random noise added, and obtaining a pre-disassembled signal; step 3, determining whether the pre-disassembled signal meets the general index of alternating current signal, thereby obtaining a first disassembled signal; step 4, sequentially extracting a second disassembled signal and subsequent disassembled signals by using the methods in steps 2 and 3; step 5, extracting first and second characteristic values, and obtaining effective disassembled signals; step 6, using a characteristic separation matrix to segment each of the effective disassembled signals to generate a plurality of single load electric signals, thereby completing the disassembly of the mixed electric signal.

The application provides a few-channel switching direction finding method based on blind source separation, and belongs to the technical field of emergency search and rescue direction finding. The method comprises the following steps: constructing a corresponding original signal matrix of each array element switching, and performing pretreatment to obtain a corresponding standard signal matrix; performing blind source separation on each standard signal matrix to obtain a corresponding separation matrix and a separation signal matrix; constructing a corresponding array manifold matrix of each array element switching, and calculating all waveform correlation coefficients; using all waveform correlation coefficients, completing signal alignment of all array manifold matrices, and using a reference matrix to obtain a calibration array manifold matrix corresponding to each array element switching; and using all calibration array manifold matrices to perform direction of arrival estimation on each rescue target. The application can effectively estimate the noise of a few-channel direction finding system, and improve the direction finding precision, angle resolution capability and robustness of the few-channel direction finding system.

The invention provides a blind noise reduction method for MEMS multi-sensor self-contrast learning, which belongs to the technical field of MEMS, and comprises the following steps: receiving observation data of MEMS multi-sensors, and constructing a whitening matrix to obtain whitening data; initializing a separation matrix by using a random unit vector, solving an optimal separation vector, and outputting a normalized signal source and a noise source after orthogonalization processing; enabling a single observation signal, a signal source and a noise source to share encoder weight extraction features, and inputting a decoder to reconstruct a signal; and calculating the total loss, and outputting a high-precision noise reduction signal when the total loss is minimum. According to the blind noise reduction method for MEMS multi-sensor self-contrast learning, the difficulty of non-Gaussian modeling for unknown environment interference in a traditional method is solved, and the problem of amplitude uncertainty of signal blind separation under the state that a physical quantity to be measured does not have prior information is solved.

The application discloses a kind of sensor fault detection method and device of structural health monitoring system, it is related to structural health monitoring technical field, comprising: obtaining the nonlinear structure monitoring data of sensor system collected by structural health monitoring system, nonlinear structure monitoring data is handled using improved geometric PNL hybrid model, obtain linear to-be-separated mixed signal, using FastICA model to process to-be-separated mixed signal, obtain multiple independent elements, determine the separation matrix of improved geometric post-nonlinear independent component analysis model according to multiple independent elements, using the processing of improved geometric post-nonlinear independent component analysis to real-time collection nonlinear structure monitoring data, determine whether sensor system exists fault and the sensor of fault occurrence.The method can still complete linearization processing to sensor mixed signal under the condition that prior knowledge is unknown, compared with simple linear ICA analysis algorithm, it is more suitable for complex nonlinear structure.

This invention belongs to the field of environmental monitoring technology. It discloses an artificial intelligence-based method for detecting and warning of ecological and environmental anomalies. By acquiring data on pollutant concentrations, meteorological parameters, and spatial distribution of enterprises at multiple locations within an industrial park, an emission characteristic fingerprint database is constructed based on the production process characteristics of each enterprise. By analyzing the spatiotemporal diffusion trajectory of pollutants, abnormal areas are identified, and reverse source tracing parameters are calculated to construct a spatiotemporal superposition inversion model for multi-source emissions. A multi-enterprise contribution separation matrix is ​​generated based on fingerprint matching degree weighted calculation, achieving accurate separation of emission contributions from each enterprise in a mixed pollution field. By monitoring pollutant concentration fluctuation characteristics in real time and dynamically adjusting the contribution weight allocation, the accuracy of monitoring results is ensured. The responsible parties for excessive emissions are accurately identified, and early warning signals are generated. This invention can achieve accurate identification of pollution sources and determination of responsibility in complex multi-source emission environments, effectively improving the accuracy and impartiality of environmental supervision in industrial parks.

Embodiments of the present application disclose a voice signal processing method and device and electronic equipment. The device comprises a dereverberation module and a blind source separation module; wherein: the dereverberation module is configured to utilize a prediction coefficient matrix G(n) of the n th moment to perform dereverberation processing on a delay signal group Y(n-D) corresponding to an input signal y(n) of the n th moment, to obtain a dereverberation actual signal x(n) of the n th moment; the blind source separation module is configured to utilize a separation matrix W(n) of the n th moment to process the dereverberation actual signal x(n) of the n th moment, to obtain a separation actual signal z(n) of the n th moment; wherein the device further comprises a calculation module configured to calculate the prediction coefficient matrix G(n) of the n th moment by the following method, comprising: obtaining a separation matrix W(n-1) of the (n-1) th moment; obtaining the prediction coefficient matrix G(n) of the n th moment according to the separation matrix W(n-1) of the (n-1) th moment and a prediction coefficient matrix G(n-1) of the (n-1) th moment.

To provide a technique capable of emphasizing a sound signal with high estimation accuracy even if there is a state where the number of allocated time frames is small.SOLUTION: A sound signal enhancement device for acquiring a signal in which a target sound is enhanced from an observation signal in which the target sound and noise are mixed includes a separation matrix application unit 3 for applying a plurality of separation matrices to the observation signal to acquire a plurality of estimation signals, and a switch unit 4 for acquiring the signal in which the target sound is enhanced by using any of the plurality of estimation signals. The state regularization weight is further used when the plurality of separation matrices are updated using the observation signals, and the state regularization weight is used to update the switch weight used by the switch.SELECTED DRAWING: Figure 1

The invention discloses an anti-interference radar signal extraction method and device and electronic equipment, and the method comprises the steps: carrying out the whitening processing of a to-be-processed radar signal through a zero-phase component analysis whitening algorithm, and obtaining a to-be-separated radar signal after the whitening processing. An initial separation matrix is obtained by performing iterative optimization processing on a preset target separation function for multiple times. And performing fine adjustment on the initial separation matrix according to a second-order blind source separation algorithm to obtain a target separation matrix. Performing signal separation processing on the radar signal and the interference signal according to the target separation matrix to obtain an initial radar signal, and performing denoising processing on the initial radar signal according to a target denoising algorithm to obtain a radar signal, the target denoising algorithm being any one of a wavelet transform denoising algorithm and an improved stationary wavelet transform denoising algorithm. The problems of high calculation complexity and low efficiency in the prior art are avoided, and the stability and accuracy of radar signal extraction are improved.

The invention discloses a blind source separation transfer learning monitoring method, a power grid system, a blind source separation transfer learning monitoring device and a storage medium, and the method comprises the steps: obtaining base current data to construct a training data set, firstly controlling each circuit breaker to be conducted for steady-state operation, sequentially controlling the on-off action of each circuit breaker to obtain current waveform base signals collected by a current transformer in each on-off process to construct a training data set; obtaining an initial separation matrix by using the training data set and the branch data set, and establishing a blind source separation model; iteratively executing a transfer learning step for multiple times: acquiring a real-time main circuit current; obtaining an estimated current waveform signal of each execution branch based on a blind source separation model according to the real-time main circuit current; obtaining the on-off action of each execution branch, and analyzing the variation of the separation matrix; iteratively calculating a target separation matrix, and updating a blind source separation model at the next moment by using the target separation matrix; according to the design, the monitoring structure is simplified, the operation cost is reduced, and the diagnosis accuracy is optimized.

The application discloses a kind of multi-channel data synchronous acquisition and optimization processing method, it is related to data processing technical field, comprising: through synchronous trigger signal control multi-channel sensor synchronous acquisition data, the multi-channel data collected is preprocessed, and multi-channel data matrix is generated;Initial separation matrix is constructed, based on mutual information value dynamic adjustment learning rate, iteration optimization separation matrix is carried out through negative entropy maximization criterion, realize the preliminary separation of multi-channel mixed signal, and the source signal of target frequency band is screened;Modal decomposition is carried out to target source signal, extracts extreme point and fits envelope line, iteration is screened modal component that satisfies preset condition, and effective modal component set is obtained;Modal component is classified as cross-channel common mode or channel exclusive mode based on cosine similarity, and according to signal-to-noise ratio, weight is distributed and weighted fusion, after evaluating the quality of fusion signal, output result, the signal-to-noise ratio and stability of fusion signal are significantly improved.

To separately collect a sound field radiated from a sound source in an area and a sound field coming from the outside of the area by using a distributed spherical microphone array.SOLUTION: A separation matrix calculation part 10 of a sound collection device 1 calculates a separation matrix in which position information and size are reflected and which is expressed by a spherical harmonic function on the basis of preset distributed spherical microphone array position information. The frequency analysis unit 11 obtains a time-frequency spectrum by performing Fourier transform on a sound collection signal from the distributed spherical microphone array M in a time domain. A spherical harmonic spectrum vector calculation part 12 calculates a spherical harmonic spectrum vector having a spherical harmonic spectrum of a sound field radiated from a sound source S in an area and a spherical harmonic spectrum of a sound field due to an interference wave coming from the outside of the area as elements on the basis of a separation matrix and a time frequency spectrum of a sound collection signal.SELECTED DRAWING: Figure 3

A convolution blind source separation method for identifying a gas turbine related noise source is disclosed, in which an observation signal of a gas turbine noise is collected, and a corresponding time-frequency domain complex value signal X is obtained by performing a synchronous compression transformation on the observation signal; an instantaneous blind source separation is performed on the time-frequency domain complex value signal X at each frequency point, a complex value normalized boundary target function is established, and an iteration optimization is performed at each frequency point based on a sub-gradient optimization method, a complex value separation matrix and a separation signal are obtained by performing a plurality of blind extraction estimations, a power spectrum spectral density distance method is used to perform a permutation calibration on the separation signal, an amplitude calibration is performed using a minimum distortion algorithm to obtain a time-frequency domain separation signal, and the time-frequency domain separation signal is restored to a time domain by using a synchronous compression inverse transformation to obtain a reconstructed time domain separation signal.

The present application relates to the technical field of industrial pipe network leakage detection, and discloses a printing and dyeing industry water pipe network leakage rapid identification and positioning system, comprising: a sensing module for collecting multi-dimensional data of the printing and dyeing workshop water pipe network; a processing and construction module for obtaining vibration conduction coefficients and pseudo-signal characteristic ranges; an extraction module for blind source separation of the pre-processed pressure-acoustic wave mixed signal; a verification module for verifying the extracted features in combination with a preset threshold, separation confidence and working condition data; and a calculation output module for calculating leakage point coordinates based on the propagation time difference of the real leakage signal. Through multi-dimensional data preprocessing and the construction of a multi-body dynamics model containing pipe network aging factors, the blind source separation of the pressure-acoustic wave mixed signal is realized by constraining the separation matrix with the pseudo-signal characteristic range, thereby realizing the efficient separation of the pseudo-signal and the real leakage signal generated by the dynamic deformation of the printing and dyeing workshop pipe network.

The invention discloses a multi-channel data synchronous acquisition and optimization processing method, and relates to the technical field of data processing, and the method comprises the steps: controlling a multi-channel sensor to synchronously acquire data through a synchronous trigger signal, carrying out the preprocessing of the acquired multi-channel data, and generating a multi-channel data matrix; constructing an initial separation matrix, dynamically adjusting a learning rate based on a mutual information value, iteratively optimizing the separation matrix through a negentropy maximization criterion, realizing initial separation of multi-channel mixed signals, and screening source signals of a target frequency band; performing modal decomposition on the target source signal, extracting an extreme point and fitting an envelope line, and iteratively screening modal components meeting a preset condition to obtain an effective modal component set; the modal components are classified into cross-channel common modals or channel exclusive modals based on cosine similarity, weights are distributed according to the signal-to-noise ratio and weighted fusion is carried out, a result is output after the quality of the fusion signal is evaluated, and the signal-to-noise ratio and stability of the fusion signal are remarkably improved.

The present disclosure provides a sound source separation method and device, and relates to the technical field of intelligent vehicles, which comprises the following steps: performing sound source separation on a plurality of sound collection signals in a previous observation window according to a separation matrix corresponding to a previous round of sound source separation to obtain a plurality of separation estimation signals corresponding to the previous round of sound source separation, and obtaining a speech existence probability corresponding to the separation estimation signals; updating the separation matrix corresponding to the previous round of sound source separation according to the speech existence probability; and performing sound source separation on a plurality of sound collection signals in a current observation window according to the updated separation matrix to obtain a plurality of separation estimation signals corresponding to the current round of sound source separation. The speech existence probability of the separation estimation signals obtained based on the previous round of sound source separation is used to update the separation matrix, so that the speech existence information of the sound collection signals in the previous observation window is added when the separation matrix is updated, and the separation coefficients of various sound sources are adjusted accordingly, thereby enhancing the separation effect of the sound sources.

The invention relates to a control method and device of an intelligent range hood, a storage medium and the intelligent range hood. Vibration sound signals acquired by a microphone array on the intelligent range hood are acquired, and multi-source vibration separation processing is performed on the vibration sound signals based on a pre-stored separation matrix; obtaining a thermally induced vibration signal generated when the LED lamp is turned on; according to the method, the thermally induced vibration signal is acquired, spectral analysis is performed on the thermally induced vibration signal, attenuation compensation is performed on the LED lamp based on a spectral analysis result, the light attenuation condition of the LED lamp can be detected on line in the working process of the range hood, and timely adaptive compensation is performed, so that better cooking illumination experience is provided.

The invention is suitable for the technical field of image processing, and provides a heart rate calculation method and device based on independent components and electronic equipment. Performing region-of-interest detection on the video stream to obtain a region-of-interest sequence; for each region-of-interest in the region-of-interest sequence, calculating a spatial average value of the region-of-interest in each channel; determining a time sequence signal corresponding to each channel according to the space average value of each region of interest in each channel; constructing a mixed signal matrix according to the time sequence signal corresponding to each channel; according to the mixed signal matrix and a preset objective function, the separation matrix to be iterated is iterated until one of the following iteration stopping conditions is met, and a target separation matrix is obtained; converting the mixed signal matrix into an independent component matrix according to the target separation matrix; and calculating the heart rate according to the independent component matrix. Through the method, the accuracy of the heart rate can be improved.

The application discloses a speech noise reduction method and system based on subspace filtering, a medium and equipment, and belongs to the field of software engineering and communication technology.The method is as follows: noisy speech is subjected to frame processing; autocorrelation coefficients and mel cepstrum coefficients of each frame are extracted, spliced to form a feature vector, and input into a noise estimation network to predict the noise autocorrelation coefficients of each frame; a subspace separation matrix is constructed according to the autocorrelation coefficients, noise autocorrelation coefficients and unit matrix of each frame, and eigenvalue decomposition is performed to obtain eigenvalues and a characteristic matrix; the frame data is transformed to a subspace by using the characteristic matrix to obtain subspace signals in each direction; a subspace gain matrix is calculated in combination with the eigenvalues, the subspace signals are filtered to obtain a noise reduction signal; the noise reduction signal is inversely transformed according to the characteristic matrix to obtain noise reduction speech frames, which are spliced to obtain noise reduction speech. Therefore, by implementing the application, the speech noise reduction effect can be improved, and the algorithm calculation amount and complexity can be reduced.

The invention discloses a gesture recognition method based on surface myoelectricity blind source separation, and relates to the technical field of interaction. The method mainly comprises the steps that original surface electromyogram signal data are acquired and preprocessed, and signal segments corresponding to gesture actions are generated; performing time delay channel expansion and splicing on the signal segments corresponding to the gesture actions, performing centralization processing to obtain centralized signal matrixes, and combining the centralized signal matrixes of the gesture segments to obtain an electromyographic signal group; performing ZCA whitening processing on the electromyographic signals corresponding to the gestures in the electromyographic signal group; according to the corresponding gesture labels, decomposition is carried out in sequence, so that separation vectors are obtained; carrying out SIL threshold value check on the decomposed whitening electromyographic signals, and reserving a separation vector and a peak value detection threshold value of a motion unit after de-weighting and a whitening matrix corresponding to a gesture; and independently decomposing each window by using a separation matrix and a whitening matrix which are trained offline to train a classifier, thereby realizing high-precision, real-time and robust gesture recognition.

This invention discloses a method for restoring degraded archival handwriting using cross-band information analysis. The method involves acquiring grayscale images of the archive to be restored in multiple narrowband bands and stacking them into a three-dimensional data cube. The spectral curves of each spatial location are extracted, and a sliding delay operator is used for convolution to obtain a mixed observation sequence. A discrete wavelet transform is applied to the mixed observation sequence, and an initial separation matrix is ​​constructed at each frequency point. This separation matrix is ​​then iteratively optimized by minimizing the trace of the auxiliary variable. After applying the separation matrix to the mixed observation sequence, an inverse wavelet transform is used to reconstruct the cross-band data cubes representing the front and back of the handwriting, as well as the paper background. Feature absorption bands are selected based on the product of variance and edge change intensity. A two-dimensional grayscale matrix is ​​extracted through linear interpolation of adjacent bands, and the restored handwriting image is output. This invention stably separates double-sided overlapping handwriting under nonlinear mixing conditions and eliminates false contours.

Described herein are methods for separating analytes by applying an electric field to one or more samples in a liquid or semi-liquid separation matrix shared by the one or more samples.

This invention provides a blind noise reduction method based on self-contrast learning for MEMS multi-sensor systems, belonging to the field of MEMS technology. The method includes receiving observation data from multiple MEMS sensors, constructing a whitening matrix to obtain whitened data; initializing a separation matrix with random unit vectors, solving for the optimal separation vector, and outputting normalized signal and noise sources after orthogonalization; allowing individual observation signals, signal sources, and noise sources to share encoder weights to extract features, which are then input into the decoder to reconstruct the signal; calculating the total loss, and outputting a high-precision denoised signal when the total loss is minimized. This invention employs the aforementioned blind noise reduction method based on self-contrast learning for MEMS multi-sensor systems, solving the dilemma of modeling the non-Gaussianity of interference in unknown environments in traditional methods, and addressing the amplitude uncertainty problem in blind signal separation when there is no prior information about the measured physical quantity.

The invention discloses a non-contact physiological signal extraction and look-ahead motion correction device, which comprises a signal separation module, a parameter calculation module, a real-time separation and filtering module and an adaptive threshold module, and is characterized in that the signal separation module obtains a feature vector and a separation matrix of a motion signal; the parameter calculation module sets a trigger threshold value of the motion signal; the real-time separation and filtering module performs operation on the multi-channel BPT signal, the feature vector and the separation matrix to obtain a real-time motion signal; and the self-adaptive threshold module responds to the change trend of the real-time motion signal and updates the trigger threshold. The invention further discloses the rapid compressor and a compression ratio adjusting method. According to the method, the prospective motion correction of the motion signal is realized, and the magnetic resonance image which is less influenced by motion artifacts can be quickly obtained.