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43 results about "FastICA" patented technology
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FastICA is an efficient and popular algorithm for independent component analysis invented by Aapo Hyvärinen at Helsinki University of Technology. Like most ICA algorithms, FastICA seeks an orthogonal rotation of prewhitened data, through a fixed-point iteration scheme, that maximizes a measure of non-Gaussianity of the rotated components. Non-gaussianity serves as a proxy for statistical independence, which is a very strong condition and requires infinite data to verify. FastICA can also be alternatively derived as an approximative Newton iteration.
Rolling bearing fault feature extraction method based on independent component analysis and cepstrum theory
InactiveCN103471848AEfficient separationEasy to separateMachine bearings testingVibration accelerationFrequency spectrum
The invention provides a rolling bearing fault feature extraction method based on an independent component analysis and cepstrum theory. The rolling bearing fault feature extraction method comprises the steps of acquiring a vibration acceleration testing signal of a rolling bearing by using an acceleration sensor; decoupling and separating the vibration acceleration testing signal by using FastICA based on negentropy maximization; selecting a separated signal capable of representing fault feather information to the maximum extent; carrying out cepstrum analysis on the selected separated signal, and drawing a cepstrum chart; observing whether the cepstrum chart has a fault feature frequency or an obvious peak value at a frequency multiplication position, and furthermore, judging whether the rolling bearing has a fault. By using the rolling bearing fault feature extraction method, the feature information of a fault signal of the rolling bearing can be effectively recognized from a complex sideband signal, a periodical fault component in a sideband can be conveniently extracted, the fault information is remarkably enhanced, the fault diagnosis precision is greatly improved, the fault diagnosis time period is shortened, and the spectral analysis difficulty is simplified; in addition, the rolling bearing fault feature extraction method is easy to realize and good in real-time property.
Owner:HARBIN ENG UNIV
Mechanical vibration fault characteristic time domain blind extraction method
ActiveCN104198187AReduce the impactSolve the problem of order uncertaintyMachine bearings testingSpecial data processing applicationsSignal subspaceMechanical equipment
The invention relates to a mechanical vibration fault characteristic time domain blind extraction method, and belongs to the technical field of mechanical equipment status monitor and fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method includes: firstly, expanding a vibration observation signal into a high dimension signal subspace; then, obtaining a low dimension signal; afterwards, performing FastICA independent component analysis, calculating normalization kurtosis of all independent components, figuring out a component signal corresponding to the minimum normalization kurtosis, and using an orthogonal matching pursuit algorithm to reconstitute periodic signals; subsequently, removing the reconstituted periodic signal from each independent component, and then using an improved KL distance algorithm to calculate a distance matrix among the independent components after the periodic signals are removed from the independent components, and performing dynamic particle swarm clustering so as to obtain an estimation signal; finally, analyzing an envelope demodulation spectrum of the estimation signal, and performing fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method is suitable for processing a long convolution data problem, can effectively reduce influences from periodic ingredients on a blind separation result, and simultaneously can solve blind separation result order uncertainty problems, and finally achieves bearing fault characteristic extraction.
Owner:KUNMING UNIV OF SCI & TECH
Detection method of harmonic and inter-harmonic based on single-channel FastICA (Fast Independent Component Analysis)
The invention discloses a detection method of harmonic and inter-harmonic based on single-channel FastICA (Fast Independent Component Analysis). The detection method comprises the following steps of separating the harmonic or the inter-harmonic in an original voltage signal or an original current signal, and respectively carrying out fast Fourier transformation on each harmonic or inter-harmonic so as to obtain parameters, such as the frequency, amplitude and phase. Owing to application of a circulation translation method and a principle component analysis method, the harmonic or the inter-harmonic with prior amplitude is easier to detect, the detection method still keeps good accuracy in the larger noise, the problems that the harmonic or the inter-harmonic with the small amplitude is difficult to detect and is sensitive to the noise in the conventional detection method are solved, and a novel detection method is provided for detection of the harmonic or the inter-harmonic in power quality of a power system.
Owner:STATE GRID CORP OF CHINA +1
Pipeline positioning method
ActiveCN108954020AReduce the effects of residual noiseImprove computing efficiencyPipeline systemsFastICADecomposition
The invention discloses a pipeline positioning method. The pipeline positioning method comprises the steps that original signals are subjected to CEEMD; then noise-containing signals which are formedafter decomposition is accomplished are subjected to noise removal; cross-correlation calculation is conducted on the signals, and invalid redundancy components are removed according to the cross-correlation degree; and finally, the signals which are subjected to decomposition and noise removal are rebuilt, single leakage source signals are isolated through FastICA, and leakage points are positioned by use of a time difference positioning formula. The experiment result shows that the pipeline positioning method is suitable for detection and positioning of multi-point leakage of a metal pipeline, can improve the accuracy of pipeline leakage positioning to a large extent, and provides the powerful theoretical basis and the practical experience for practical engineering application.
Owner:CHANGZHOU UNIV
VMD and FastICA combined rolling bearing fault diagnosis method
ActiveCN108444709ASuppress modal aliasingClear and accurate extractionMachine bearings testingCharacter and pattern recognitionDiagnosis methodsDecomposition
The invention relates to a VMD and FastICA combined rolling bearing fault diagnosis method comprising the steps that the original vibration signals of a rolling bearing are acquired by using the present data acquisition equipment; VMD decomposition is performed on the acquired original vibration signals of the rolling bearing; the original vibration signals are decomposed into k modal components through VMD decomposition, and three continuous modal components act as a sequence combination to perform FastICA analysis so as to obtain reconstruction fault signals; and Hilbert transform is performed on the reconstruction fault signals to obtain the envelope spectrum of the reconstruction fault signals, the characteristic frequency, the modulation characteristic frequency and the side band under each fault characteristic are extracted from the obtained envelope spectrum and thus occurrence of the fault in the rolling bearing and the fault type are judged according to the extracted characteristic frequency component.
Owner:北京科信机电技术研究所有限公司
High-precision denoising method for a local ultrasonic array signal
ActiveCN109583350AImprove de-drying effectHigh precisionCharacter and pattern recognitionTesting using acoustic measurementsFastICAAlgorithm
The invention discloses a high-precision denoising method for a local ultrasonic array signal, which comprises the following steps of: separating a multi-channel array signal into statistical independent components by using a fast independent component Fast ICA, carrying out autocorrelation analysis on the independent components, and preliminarily eliminating the influence of noise; Further denoising the signal of each channel by using an ensemble empirical mode decomposition (EEMD) algorithm; And recombining the signals subjected to fine denoising to obtain high-precision denoised signals. According to the method, the defects that traditional EMD decomposition can only denoise a single sensor and a single channel signal, and FastICA can only roughly denoise an array signal are overcome, too large direction finding and positioning errors caused by interference of on-site noise signals are avoided, and therefore the ultrasonic array signal denoising effect and the direction finding andpositioning precision are improved.
Owner:JIANGSU FRONTIER ELECTRIC TECH +3
Mixed-noise-resisting blind image source separating method based on feedback mechanism
The invention relates to a mixed-noise-resisting blind image source separating method based on a feedback mechanism. The method comprises the steps of utilizing wavelet transform to enable mixed signals to be sparse, carrying out linear cluster on sparse wavelet coefficients to estimate a hybrid matrix of a system, carrying out primary separation according to mixed images, then respectively calculating mean values of separating branches, taking out the most value to be outputted, utilizing a 0-setting feedback method to remove signals of the branch out of original mixed signals, carrying out next blind separation on the residual mixed signals in the method, and repeatedly carrying out the process until only a noise branch is left, namely completely separating all mixed signals. The method can effectively and blindly separate image mixing where Gaussian white noise participates. Compared with classis FastICA, the method can achieve higher separation accuracy.
Owner:BEIJING NORMAL UNIVERSITY
Live video monitoring method and system, storage medium and electronic device
InactiveCN107465657AReduce noiseMaximize Perfect RecognitionSpeech recognitionTransmissionFastICAVideo monitoring
The invention discloses a live video monitoring method and system, a storage medium and an electronic device, and relates to the field of live video monitoring. The method comprises the following steps: a server analyzes a live video stream to obtain a voice data signal, and the server performs modal decomposition on the voice data through the CEEMDAN to obtain an n-order IMF component; the server separates the n-order IMF component through the FastICA to obtain m reconstructed voice signals, wherein m is not smaller than 1 and is not greater than n-2; the server identifies all reconstructed voice signals, judges whether a reconstructed voice signal containing poor information exists, if so, performs abnormality mark on the current direct broadcasting room; and otherwise, performs normal marking on the current direct broadcasting room. By adoption of the live video monitoring method and system, the noise of the voice data signals can be greatly reduced so as to maximally and perfectly identify the live broadcast voice data, greatly improve the monitoring precision and provide forceful guarantee for the spread of bad information on the Internet.
Owner:WUHAN DOUYU NETWORK TECH CO LTD
Line selection method based on FastICA for low current grounding system in single-phase grounding fault
The invention relates to the technical field of grounding fault identification, in particular to a line selection method based on a FastICA for a low current grounding system in a single-phase grounding fault. According to the method, the FastICA method is utilized to process zero-sequence current of the grounding fault, three observation channels are arranged to separate fault steady-state components, transient-state components and noise signals from observation signals, the line fault characteristics are obvious after processing, and accurate line selection can be conducted on a fault line according to the energy of line separation variables. Because the noise signals are separated at the same time, the method is reliable, high in anti-interference capability and capable of not being affected by interference signals; meanwhile, for different initial phase angles of the fault, satisfactory line selection results can all be obtained by means of the method. By using the method to decompose and analyze the zero-sequence current of the fault, the cancellation or compensation among the components of the signals can be avoided, so that the fault characteristics are obvious to achieve the accurate line selection, and the method is widely applicable to the low current grounding system.
Owner:GUANGXI UNIV
Supervised facial recognition system and method
A computer executed method for supervised facial recognition comprising the operations of preprocessing, feature extraction and recognition. Preprocessing may comprise dividing received face images into several subimages, converting the different face image (or subimage) dimensions into a common dimension and / or converting the datatypes of all of the face images (or subimages) into an appropriate datatype. In feature extraction, 2D DMWT is used to extract information from the face images. Application of the 2D DMWT may be followed by FastICA. FastICA, or, in cases where FastICA is not used, 2D DMWT, may be followed by application of the l2-norm and / or eigendecomposition to obtain discriminating and independent features. The resulting independent features are fed into the recognition phase, which may use a neural network, to identify an unknown face image.
Owner:UNIV OF CENT FLORIDA RES FOUND INC
Seismic multi-attribute fusion method based on Shearlet-fastICA
InactiveCN106997060AEliminate redundancyTargeted optimizationSeismic signal processingFastICASeismic attribute
The invention discloses a seismic multi-attribute fusion method based on Shearlet-fastICA. The method is characterized by comprising the following steps: S1, preprocessing seismic attribute, then carrying out shearlet transformation, and determining initial low-frequency subband coefficients and initial high-frequency subband coefficients of different seismic attribute data in a spatial domain; S2, respectively mapping the initial low-frequency subband coefficients and the initial high-frequency subband coefficients from the spatial domain to an ICA domain by using an ICA primary function; S3, carrying out fusion in the ICA domain to determine fused low-frequency subband coefficients and fused high-frequency subband coefficients; and S4, mapping the fused low-frequency subband coefficients and the fused high-frequency subband coefficients back to the spatial domain from the ICA domain and carrying out Shearlet reconstitution to obtain seismic attribute fusion results. According to the seismic multi-attribute fusion method based on Shearlet-fastICA, the mutual redundancy is eliminated by the fused attribute data; the relevant characteristics of the geological mass can be more clearly and obviously described; the powerful technical support is supplied to subsequent reservoir prediction and construction interpretation.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)
Single-channel gearbox multi-fault separation dual-core micro-processing system
ActiveCN109916625AReduce noiseReduce the impact of redundant signalsMachine part testingTime delaysDual core
The invention relates to a single-channel gearbox multi-fault separation dual-core micro-processing system and belongs to the gearbox fault diagnosis technology and signal processing technology field.The system of the invention is the dual-core micro-processing system of DSP and ARM. A DSP core includes a phase space reconstruction parameter estimation module, a phase space reconstruction module,a reconstruction parameter adjustment module, a reconstruction signal separation module, and a Fourier transform module. An ARM core includes a time frequency domain spectrogram drawing module. A time delay reconstruction method is used to expand, which avoids human intervention of a reconstructed signal taking experience as a judgment standard. A principal component analysis method is used to reduce influences of a noise and redundancy in a gearbox on the reconstructed signal. A limited support sample kernel function and FastICA fusion algorithm is used to the estimate a source signal probability density function, and then a nonlinear function according with a statistical characteristic of a source signal is obtained. And finally the signal is effectively separated. In the invention, theinfluence of improper selection of reconstruction parameters and a separated nonlinear function is greatly reduced on the aspect of gearbox multi-fault separation.
Owner:CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Rotating Machinery Fault Diagnosis Method Based on Fastica-Spectral Kurtosis-Envelope Spectrum Analysis
InactiveCN103575523BEfficient separationEliminate the effects of fault feature extractionMachine part testingFastICAVibration acceleration
The invention provides a rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis. The method includes the steps that (1), an acceleration sensor is used for obtaining vibration acceleration test signals of a rotating machine; (2), the test signals are separated in a decoupling mode by the adoption of a Fast ICA method with negentropy maximized; (3), the spectrum kurtosis of the separated signals is calculated, and the separated signals capable of representing fault information best are screened out; (4), Hilbert envelope spectrum analysis is performed on the selected separated signals; (5), the frequency corresponding to the peak value of an envelope spectrum is compared with the fault character frequency of a bearing, so that a concrete fault is diagnosed. The effectiveness of the method is verified well by the inner ring fault diagnosis of a rolling bearing of the rotating machine, the fault information is obviously increased, the precision of the fault diagnosis is greatly improved, the fault diagnosis method is easy to achieve and good in real-time performance, and it is illustrated that the rotating machine fault diagnosis method has good application prospects.
Owner:HARBIN ENG UNIV
Rain sound signal noise reduction processing method and system
PendingCN110263876AOptimize the rain sound separation subsystemMeet separation needsCharacter and pattern recognitionICT adaptationIndependent component analysis algorithmEnvironmental sounds
The invention discloses a rain sound signal noise reduction processing method which comprises the steps that real-time sound signals during rainfall are collected, and the real-time sound signals at least comprise the real-time rain sound signals; and a rain-sound separation subsystem is used for removing a real-time first ambient sound signal and a real-time second ambient sound signal from the mixed sound signal, and a rain-sound signal which does not contain the ambient sound signal is seperated from the real-time sound signal, wherein the ambient sound signal comprises a first ambient sound signal and a second ambient sound signal. According to the method, a fixed point algorithm (Fast ICA) algorithm in an independent component analysis (ICA) algorithm is adopted; the rain-sound separation subsystem is established, separation matrix parameters are optimized, real-time sound signals during rainfall are imported into the rain-sound separation subsystem, the environmental sound signals are removed, relatively pure rain-sound signals are exported, and the rainfall level is judged according to the frequency spectrum of the exported rain-sound signals.
Owner:NANJING UNIV OF INFORMATION SCI & TECH
Electric power system low-frequency oscillation mode identification method based on FastICA and TLS-ESPRIT
ActiveCN112688324AAccurate and comprehensive responseEasy to keepVoltage-current phase angleInformation technology support systemFastICANoise
The invention discloses an electric power system low-frequency oscillation mode identification method based on FastICA and TLS-ESPRIT. The method comprises the following steps: obtaining a low-frequency oscillation signal to carry out zero mean and whitening processing; constructing a separation matrix and calculating an inverse matrix of the separation matrix; performing iterative loop, and updating a separation matrix; after convergence is judged, using the signal recovered by FastICA as a new dominant signal for sampling; carrying out TLS-ESPRIT analysis on the new dominant signal, and calculating the frequency, attenuation factor, amplitude and phase of each oscillation mode. According to the method, the original characteristics of the signal can be well reserved under the condition of noise interference, and the signal-to-noise ratio is improved; and moreover, the method is accurate in identification and higher in precision, can reflect the characteristics of the low-frequency oscillation signal more accurately and comprehensively, and has a good application prospect in the design of a low-frequency oscillation early-warning and damping controller.
Owner:FUJIAN UNIV OF TECH
Electroencephalogram signal preprocessing method based on self-adapting noise cancellation system
InactiveCN108836321ARealize component automatic identificationSolve component automatic identificationDiagnostic recording/measuringSensorsElectricityFastICA
The invention relates to an electroencephalogram signal preprocessing method based on a self-adapting noise cancellation system. In the method, fast independent component analysis (FastICA) and discrete wavelet transform (DWT) are used for estimating electronystagmogram signals in original electroencephalogram signals, wherein estimated electronystagmogram signals are used as reference electronystagmogram input signals of a self-adapting noise cancellation system (ANC); and a self-adapting filter and a weighting coefficient updating module are used for automatically removing electronystagmogram artifacts in the electroencephalogram signals, so that the automatic electronystagmogram signal component recognition, automatic parameter updating, and automatic electronystagmogram signal removalare achieved.
Owner:XUZHOU NORMAL UNIVERSITY
Rotary machine coupling fault diagnosis method based on SCA and FastICA
PendingCN112082793AEfficient extractionImprove signal-to-noise ratioStructural/machines measurementLow noiseNoise reduction algorithm
The invention relates to the technical field of rotary machine fault diagnosis, and concretely relatesto a rotary machine coupling fault diagnosis method based on SCA and FastICA. The method specifically comprises the following steps: 1, acquiring a rotor vibration signal of a coupling fault of the rotary machine through a plurality of sensors; 2, introducing a synchronous accumulation average noise reduction algorithm, and performing noise reduction on the acquired vibration signal in combination with signal equalization and smoothing processing; 3, separating the denoised signals by using aFastICA algorithm, and separating out each single fault characteristic signal; and 4, carrying out corresponding diagnosis on each separated single fault characteristic signal. According to the method, impulse noise and white noise can be effectively filtered out, noise is reduced, the signal-to-noise ratio is increased, effective extraction of fault feature signals is achieved, and the method isan effective diagnosis method for the coupling fault of the rotary machine system.
Owner:LUOYANG NORMAL UNIV
Electromechanical equipment bearing fault prediction method based on Bayesian network of transfer learning
The invention discloses an electromechanical equipment bearing fault prediction method based on a Bayesian network of transfer learning. The method specifically comprises the following steps: carrying out noise removal processing on an original acquisition signal through CEEMDAN and FastICA technologies to form a reconstructed original signal; introducing migration learning, clsutering original signals by a neural network to obtain a signal set classified according to fault types, taking the signal set as a target domain data set and as input of a Bayesian network, and selecting a reference sample set from a source domain as a training set of the source domain Bayesian network; based on the covariable shift theory, using the training data of the source domain to complete the goal of minimizing the parameter loss value on the target domain, and optimizing the maximum likelihood estimation of the Bayesian network on the target domain; and returning a result by output of the Bayesian network on the target domain and is visually embodied, giving an alarm in an abnormal state, returning parameter information to mechanical equipment in a decline state, and indicating a machine to adjust self-adjusting parameters such as the rotating speed within a certain range.
Owner:ANHUI UNIVERSITY
TSP strong industrial electrical interference suppression method based on SVD-ICA
ActiveCN108957550AReduce lossesReduce the cost of repeated acquisitionsSeismic signal processingDecompositionFundamental frequency
The invention relates to a TSP strong industrial electrical interference suppression method based on SVD-ICA. Firstly, a hankel matrix is built for single-channel data interfered by strong industrialpower; SVD decomposition is performed on the built hankel matrix then, and an industrial electrical interference signal is rebuilt then; and, finally, sine and cosine signals as the FastICA input signals are respectively built according to industrial electrical interference frequency, and an effective signal is rebuilt through processing of a FastICA output component by utilizing the cross-correlation technology. It is verified that effective suppression of the fundamental frequency and harmonic interference of the industrial electric power can be achieved; and, compared with the existing industrial electrical interference suppression method, the TSP strong industrial electrical interference suppression method based on the SVD-ICA has the advantages of good noise suppression modification,small damage to the effective signal, no phase shift during processing and no influence on target positioning accuracy. Data processing is fast through the method, the cost of repeated collection dueto too large industrial electrical interference can be reduced, construction efficiency is improved then, and construction cost is saved at the same time; and the data quality is improved without changing collection and construction conditions, and the method is of great significance for improving the accuracy of geological prediction.
Owner:JILIN UNIV
SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance
The invention relates to the technical field of transfer learning and adaptive neural network clustering bearing fault analysis, and discloses an SOM network clustering electromechanical equipment bearing fault analysis method based on transfer learning and manifold distance. The specific process is as follows: original acquisition signals are de-noised by CEEDAN and FastICA technologies to form reconstructed original signals, and the reconstructed original signals are used as the input of the SOM adaptive neural network; in the SOM network, a manifold distance is considered and utilized to calculate a similarity matrix, a transfer learning model mechanism is introduced, weight parameters of SOM neural network nodes of a source domain and a target domain of a data sample are shared, and a target domain clustering task function and neuron weights are optimized through a source domain training result; and a clustering task of the target domain sample is finished through an SOM neural network target domain output layer result, thus obtaining a clustering result, and outputting a mechanical fault diagnosis type.
Owner:ANHUI UNIVERSITY
Multi-channel electroencephalogram signal eye artifact automatic removal method and storage medium
PendingCN114176605ASolve the lossTroubleshoot setup issuesDiagnostic recording/measuringSensorsFastICARadiology
The invention relates to a multi-channel electroencephalogram signal eye artifact automatic removal method and a storage medium, and the method comprises the steps: S1, processing an original EEG signal through FastICA and a differential evolution algorithm, so as to obtain a plurality of independent components; s2, processing the independent component containing the ocular artifacts by using wavelet transform and the differential evolution algorithm to obtain an ocular artifact component; and S3, based on wavelet reconstruction and inverse transformation, according to the ocular artifact component, obtaining an EEG signal without ocular artifacts. The invention relates to a method for automatically removing ocular artifacts by combining a differential evolution algorithm, FastICA and wavelet transform. According to the method, wavelet decomposition is adopted to decompose artifact components in FastICA, artifacts can be further separated out, useful information in EEG signals is reserved to the maximum extent, and the problem that the useful information is lost in the automatic artifact removing process is solved.
Owner:中国人民解放军火箭军工程大学
Automatic weather station real-time data quality control method based on PSR-FastICA-OS_ELM
The invention discloses an automatic weather station real-time data quality control method based on PSR-FastICA-OS_ELM. According to the method, firstly, single station real-time air temperature and a fixed length historical air temperature sequence are collected, and a collection signal is formed; then the collection signal is subjected to basic quality control; the phase space reconstruction is carried out; a reconstructed high-dimensional vector space signal is subjected to rapid independent component analysis, purposes of denoising and redundancy elimination are achieved, and an unbiased signal of a source signal in a high-dimensional vector space is obtained; finally, through an online sequential extreme learning machine, the reconstruction model of a time sequence collection sequence is constructed, and a denoised air temperature reconstruction value is obtained. According to the comparison of the denoised air temperature reconstruction value with an actual observation value, the condition correction is carried out, and the quality control of the air temperature of the time is completed. According to the method, the requirement of real-time data collection is satisfied, and the quality of real-time automatic weather station observation data is improved.
Owner:NANJING UNIV OF INFORMATION SCI & TECH
Structural modal parameter identification method based on wavelet and ICA improved EEMD
The invention discloses a structural modal parameter identification method based on wavelet and ICA improved EEMD. The method comprises steps of eliminating noise residues in the IMFs after classic EEMD decomposition by using a wavelet soft threshold denoising method in wavelets; after FastICA in ICA is used for processing, obtaining new IMFs, and removing modal aliasing; judging a real IMF component by using a correlation coefficient between the new IMFs and the original signal; removing a false mode, then obtaining free vibration response of each real IMFs by using a random decrement technology, and finally, identifying the frequency and damping ratio of the structure by using a method of combining Hilbert transform (HT) and a least square fitting technology. The method has the advantages that the problems of white noise residues after EEMD decomposition, modal aliasing, effective IMFs screening and the like are solved; wavelet soft threshold denoising in wavelet transform and FastICA in ICA are combined to improve the EEMD, and the problems of excessive false modes, noise residues and true IMF judgment in the classic EEMD process are solved.
Owner:LIAONING UNIVERSITY OF PETROLEUM AND CHEMICAL TECHNOLOGY
Speech enhancement method and device based on neural network, terminal and medium
PendingCN112562716AEasy to separateGuaranteed intelligibilitySpeech analysisFastICABack propagation neural network
The invention provides a speech enhancement method and device based on a neural network, a terminal and a medium. The method comprises the following steps: acquiring multiple paths of noisy speech signals acquired by each array element of a microphone array; unmixing the multiple paths of noisy speech signals by adopting a fast independent component analysis algorithm fastICA to obtain multiple paths of separation signals; predicting an optimal over-subtraction factor by adopting an error back propagation neural network BPNN; and according to the optimal over-subtraction factor predicted by the neural network, carrying out voice processing on the path of separation signal with the highest signal-to-noise ratio after unmixing by adopting a multi-window spectral subtraction method to obtaina voice enhancement signal. According to the invention, the BPNN is adopted to predict the optimal over-subtraction factor, so that the silence zone is smoother, and the speech quality is improved.
Owner:LANZHOU JIAOTONG UNIV
A Blind Extraction Method of Mechanical Vibration Fault Characteristics in Time Domain
ActiveCN104198187BSolve the problem of order uncertaintyAccurate and effective extractionMachine bearings testingSpecial data processing applicationsAlgorithmSignal subspace
Owner:KUNMING UNIV OF SCI & TECH
A line selection method for single-phase ground fault in small current grounding system based on fastica
The invention relates to the technical field of grounding fault identification, in particular to a line selection method based on a FastICA for a low current grounding system in a single-phase grounding fault. According to the method, the FastICA method is utilized to process zero-sequence current of the grounding fault, three observation channels are arranged to separate fault steady-state components, transient-state components and noise signals from observation signals, the line fault characteristics are obvious after processing, and accurate line selection can be conducted on a fault line according to the energy of line separation variables. Because the noise signals are separated at the same time, the method is reliable, high in anti-interference capability and capable of not being affected by interference signals; meanwhile, for different initial phase angles of the fault, satisfactory line selection results can all be obtained by means of the method. By using the method to decompose and analyze the zero-sequence current of the fault, the cancellation or compensation among the components of the signals can be avoided, so that the fault characteristics are obvious to achieve the accurate line selection, and the method is widely applicable to the low current grounding system.
Owner:GUANGXI UNIV
A kurtosis-based fastica and approximation solution domain for stlbo motor bearing fault diagnosis
ActiveCN113742638BEnhanced signal characteristicsReduce noise interferenceMachine part testingCharacter and pattern recognitionCorrection algorithmLearning factor
The invention relates to a kurtosis-based FastICA and an STLBO motor bearing fault diagnosis method approximating the solution domain. The method of approximating the solution domain is combined with the initial training of BPNN to determine the upper and lower bounds of network structure parameters and TLBO initialization population; finally, the TLBO correction algorithm of adaptive dynamic learning factor is integrated, and the optimal weights and thresholds are iteratively searched for input into BPNN. The invention can enhance signal characteristics, reduce noise interference, and improve the recognition rate of fault diagnosis; for unknown sources, it does not need too much prior knowledge and theoretical reserves, and has strong generalization; It is necessary to set specific parameters and dynamically change the learning factor according to the current number of iterations, which can improve the convergence speed of the algorithm, and at the same time avoid falling into local convergence, which is convenient for debugging and simple in calculation.
Owner:NANTONG UNIVERSITY
A Speech Enhancement Method of Small Aperture Array Microphone
InactiveCN103413555BImproved noise suppressionEnhanced inhibitory effectSpeech analysisFastICAVoice source
The invention discloses a method for enhancing microphone voice through a small-bore array. According to the method, under the situation that priori knowledge such as a voice signal DOA and an array topological structure is unknown, according to the FastICA basic principle, an objective function with a separation matrix as a variable is built for an output variable, the output variable is maximized, independent isolated components are separated, and then post-processing is conducted. In the process, estimation on a voice source signal direction is removed, and calculated amount is saved.
Owner:LIAONING UNIVERSITY OF TECHNOLOGY
A dual-core micro-processing system for single-channel gearbox with multiple fault separation
ActiveCN109916625BReduce noiseHigh-speed and effective processing systemMachine part testingPrincipal component analysisDual core
The invention relates to a single-channel gearbox multi-fault separation dual-core micro-processing system and belongs to the gearbox fault diagnosis technology and signal processing technology field.The system of the invention is the dual-core micro-processing system of DSP and ARM. A DSP core includes a phase space reconstruction parameter estimation module, a phase space reconstruction module,a reconstruction parameter adjustment module, a reconstruction signal separation module, and a Fourier transform module. An ARM core includes a time frequency domain spectrogram drawing module. A time delay reconstruction method is used to expand, which avoids human intervention of a reconstructed signal taking experience as a judgment standard. A principal component analysis method is used to reduce influences of a noise and redundancy in a gearbox on the reconstructed signal. A limited support sample kernel function and FastICA fusion algorithm is used to the estimate a source signal probability density function, and then a nonlinear function according with a statistical characteristic of a source signal is obtained. And finally the signal is effectively separated. In the invention, theinfluence of improper selection of reconstruction parameters and a separated nonlinear function is greatly reduced on the aspect of gearbox multi-fault separation.
Owner:CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
A Distributed Harmonic Source Identification Method Based on Single-frequency Current Transmission Characteristics
The invention relates to a distributed harmonic source identification method based on singe-frequency current transmission characteristics. The method comprises the following steps that (1) a current harmonic source identification model is established; (2) constraint conditions are exported; (3) a current harmonic observation matrix is established and preprocessed; (4) the harmonic source maximum identification frequency T and a non-Gaussianity threshold value F in an electricity network are set, and k is made to be equal to 0; (5) the k is made to be equal to k+1, and a FastICA method is used for solving a mixing matrix Wh; (6) the inverse matrix Wh-1 of the Wh is calculated, the phase position and the row order of the Wh-1 are adjusted according to the constraint conditions, the Wh-1 is denoted as Ph, if the Ph meets the constraint conditions, the step (7) is executed, and otherwise the step (5) is executed again; (7) the reverse matrix of the Ph is calculated, a demixing matrix Wh=Ph-1 is updated, the non-Gaussianity sum phi of identification results is calculated, if phi is no smaller than the threshold value F, the demixing matrix Wh is output, the harmonic source identification result is calculated, the positions of main harmonic sources are analyzed, iteration is ended, and otherwise the step (8) is executed; (8) when k is smaller than or equal to T, the step (5) is executed again, when k is larger than T, the demixing matrix Wh and the non-Gaussianity threshold value are output, and the positions of the main harmonic sources in the network are further analyzed.
Owner:TSINGHUA UNIV
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