110 results about "Independent component analysis" patented technology

The invention provides a method for extracting electrical characteristic signals of electrical equipment in a complex working condition environment, and belongs to the technical field of electrical equipment detection.The method comprises the steps that a multi-channel ultrasonic sensor array is arranged to collect partial discharge signals, background noise is eliminated through adaptive noise cancellation processing, and an ultra-sparse frequency band energy distribution vector is constructed; the method comprises the following steps: calling a self-adaptive time-frequency analysis model to extract instantaneous frequency, amplitude and phase parameters to form a micro-hour-frequency characteristic matrix, separating independent source signals through independent component analysis, calculating a kurtosis value and a skewness value, fusing multi-domain characteristics to construct a transient stationary comprehensive characteristic vector, matching with a standard discharge characteristic vector library to identify the discharge type and intensity, and calculating the discharge intensity. A corresponding prediction algorithm is selected according to the discharge mode, multi-parameter coupling optimization adjustment is started under a certain condition, an electrical characteristic signal description vector is finally constructed, and the technical problem that the partial discharge signal of the power equipment is difficult to accurately extract under a complex working condition environment is solved.

The invention discloses a fan operation abnormal vibration monitoring method and system based on multi-sensor fusion, and relates to the technical field of fan operation monitoring, and the method comprises the following steps: deploying a plurality of types of sensors on a fan, collecting the data of each sensor in real time, carrying out the synchronous processing of the sensor data through employing an IEEE1588 protocol, carrying out the preprocessing through combining wavelet denoising, and carrying out the monitoring of the abnormal vibration of the fan. Extracting feature data by using a principal component PCA combined independent component ICA analysis method; according to the method, multiple types of sensors are deployed on the fan, the actual conditions of the offshore wind field are considered, fine processing and feature extraction are carried out after multi-source signals are collected in real time, the abnormal vibration condition of the fan in the coastal or offshore wind field under the complex environment is accurately monitored and reliably recognized, and therefore the early abnormal features of fan operation can be captured in time; normal changes and real fault anomalies caused by environmental factors are effectively distinguished, and early discovery of faults is further realized.

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention discloses an intelligent control system for a tea processing process and a control method based on technological parameter optimization, and relates to the technical field of tea processing. By integrating the high-precision sensor and the intelligent decision-making module, comprehensive monitoring and real-time optimization of key technological parameters of tea processing are realized, data accuracy is ensured through pyroelectric infrared and SAW humidity sensors and the like, and the intelligent decision-making module automatically adjusts the processing parameters by using a deep belief network and an ant colony algorithm, so that the processing accuracy is improved. The production efficiency is improved, manual errors are reduced, meanwhile, technological parameters are accurately controlled by adopting an advanced data processing algorithm, variation mode decomposition and independent component analysis ensure stable tea quality, in addition, external and internal data are integrated to optimize the processing technology, heating, ventilation and other parameters are accurately controlled, and energy consumption and cost are reduced.

The invention discloses a cable partial discharge signal noise separation mode identification method and system, and particularly relates to the technical field of cable partial discharge detection, in a multi-cable stacking environment, partial discharge signals are synchronously collected through multiple types of sensors such as a high-frequency current sensor, an ultrasonic sensor and an electromagnetic antenna; performing band-pass filtering, wavelet denoising and multi-channel synchronous alignment on the acquired signals to suppress background noise and retain key features of partial discharge pulses; separating the mixed signals by adopting independent component analysis; calculating a decision coefficient based on an inter-channel correlation coefficient, a signal-noise power ratio and an amplitude dynamic range, and judging whether to introduce a denoising method based on deep learning; for the waveform after noise separation, executing an amplitude re-calibration process so as to correct amplitude scaling uncertainty, and extracting multi-dimensional time domain, frequency domain and phase features; and a support vector machine and other machine learning algorithms are combined to realize automatic identification of partial discharge types.

The invention relates to the technical field of vibration monitoring, in particular to a rotating machine vibration protection method and system based on particle swarm optimization, and the method comprises the steps: obtaining a corrected load torque based on a polynomial fitting temperature compensation algorithm in combination with a real-time environment temperature and a sensor output voltage, and synchronously calculating an actually measured vibration amplitude; configuring the number of variational mode decomposition layers according to the real-time rotating speed of the rotor to obtain a preprocessed vibration signal, adjusting the number of independent component analysis iterations according to the load torque, separating the preprocessed vibration signal to obtain independent vibration components, and generating a feature vector; calibrating adaptive resonance theory network parameters based on a particle swarm optimization algorithm, obtaining a normalized feature vector, calculating the similarity with a fault feature template, outputting a diagnosis result, calculating the deviation between an actually measured vibration amplitude and a normal vibration threshold, and performing compensation according to the diagnosis result and the deviation. According to the scheme, the fault diagnosis accuracy of the rotary mechanical vibration protection system is effectively improved through multi-working-condition self-adaptive diagnosis.

The invention discloses an electroencephalogram fatigue detection method based on fusion of a graph convolutional network and Transform, and belongs to the technical field of artificial intelligence and electroencephalogram signal analysis. The method comprises the steps that multichannel electroencephalogram signals are collected, and band-pass filtering, power frequency notch, independent component analysis, standardization and other preprocessing are conducted on the signals; constructing an inter-channel graph structure based on a Pearson's correlation coefficient, and extracting spatial features by using a graph convolutional network; inputting the spatial features of the plurality of continuous time windows into Transform to carry out time sequence modeling; and finally, realizing fatigue state recognition through a full-connection network and a Softmax classifier. According to the method, the spatial topological structure and the time dynamic evolution of the EEG signal can be modeled at the same time, the accuracy and the real-time performance of fatigue detection are remarkably improved, and the method has good generalization ability and edge deployment ability and is suitable for traffic driving monitoring, intelligent health and other scenes.

The invention relates to the technical field of safety intelligent monitoring of constructional engineering, and discloses a safety assessment and early warning method and system for a dangerous and large engineering project based on multi-source data. According to the multi-source data-based dangerous engineering project safety assessment early warning method, sensor data, video monitoring data, equipment operation data and environment data are subjected to quality assessment and standardization processing; real-time fusion of multi-source data is realized by adopting a weighted fusion technology; performing dimension reduction compression on the high-dimensional features through principal component analysis and independent component analysis, and compressing feature dimensions from hundreds of dimensions to dozens of dimensions; performing risk assessment by adopting a hybrid analysis method combining an expert rule and machine learning; rapid early warning response is realized through adaptive threshold adjustment, and system performance is continuously optimized through incremental learning. According to the method, high-precision and high-real-time safety assessment early warning can be realized under the condition of limited computing resources, and the safety management and control level of the dangerous engineering is effectively improved.

The invention discloses a leakage monitoring method based on an LNG fuel gas system. The leakage monitoring method comprises the steps that a self-adaptive frequency modulation continuous wave active acoustic scanning network is established; blind source separation is carried out on mixed signals in acoustic scanning network abnormal events by adopting self-adaptive kernel independent component analysis; constructing a leakage feature mapping model of the physical information neural network; leakage source accurate positioning and quantification based on acoustic tomography and Bayesian reasoning are carried out; multi-modal decision fusion is carried out based on the multi-dimensional data sources received in parallel, a false alarm suppression mechanism is set, and time continuity verification and space consistency verification are carried out; establishing a reinforcement learning model for autonomously optimizing a monitoring strategy according to environment change and system state, and realizing adaptive optimization; the strategy network after self-adaptive optimization is deployed at the cloud, actions are generated regularly according to the current state, the actions are issued to the regional gateway and the edge node for execution, and iterative updating is carried out, so that the monitoring accuracy in a complex environment is improved, and the false alarm rate is reduced.

The invention discloses a phase modifier bearing fault diagnosis method, which belongs to the technical field of power equipment fault diagnosis, and comprises the following steps of: analyzing independent components, performing blind source separation on a received phase modifier bearing vibration signal, and extracting three types of independent source signals of impact, abrasion and noise; the gradient driving window length is self-adaptive, and the window length of short-time Fourier transform is dynamically adjusted based on the instantaneous frequency gradient of the independent source signal; wavelet packet frequency band energy screening: performing wavelet packet decomposition on the signal after window length adaptive processing, screening a fault characteristic frequency band based on an energy contribution rate, and reconstructing the signal; and enhancing stochastic resonance, and inputting the reconstructed signal into a stochastic resonance system. According to the method, independent component analysis, gradient driving window length self-adaption, wavelet packet frequency band energy screening and stochastic resonance enhanced fourth-order diagnosis chain are constructed, so that multi-stage cooperative processing of phase modifier bearing faults is realized, and the technical problems of time-frequency resolution contradiction, insufficient feature decoupling and weak generalization ability are effectively solved.

The invention relates to the technical field of atmospheric electric field simulation, and discloses an atmospheric electric field diurnal variation simulation method and system based on solar irradiance, and the method comprises the steps: obtaining historical atmospheric electric field data and synchronous environmental factor data, employing the time sequence analysis and neural network technology to extract time sequence features, generating a coupling data set, and obtaining a coupling data set; a multi-parameter model is constructed based on the coupled data set, and the multi-parameter model can accurately simulate the hysteresis effect of environmental factors on the atmospheric electric field through optimization of a convolutional neural network and a memory kernel function; independent component analysis is adopted to separate direct and scattering components of irradiance, a component action vector is generated, and the prediction performance of a multi-parameter model can be further optimized; according to the invention, efficient simulation and accurate prediction of the daily variation of the atmospheric electric field can be realized.

The invention discloses a brain-computer interface-based closed-loop transcranial magnetic stimulation treatment system and method, and the system comprises an electroencephalogram monitoring module which is used for collecting an electroencephalogram signal of a subject in real time; the transcranial magnetic stimulation module is used for transmitting pulse stimulation to a brain target spot of a subject; the control module is in communication connection with the electroencephalogram monitoring module and the transcranial magnetic stimulation module, and the control module is configured to receive the electroencephalogram signals output by the electroencephalogram monitoring module, preprocess the electroencephalogram signals by adopting a signal processing algorithm to remove electromagnetic interference, and send the preprocessed electroencephalogram signals to the transcranial magnetic stimulation module; the signal processing algorithm comprises but is not limited to fast continuous wavelet transform (FCWT) and fast independent component analysis; according to the method, a closed-loop monitoring mechanism is adopted, electroencephalogram oscillation is recognized in real time, stimulation is dynamically triggered, the stimulation opportunity is accurately matched with the brain excitation state, and the treatment pertinence is improved; the signals are preprocessed through FCWT and an algorithm, electromagnetic interference and artifacts are effectively removed, and the accuracy of electroencephalogram oscillation feature extraction is guaranteed.

The invention relates to the technical field of brain-computer interfaces, in particular to a wearable helmet brain-computer control system and a wearable helmet brain-computer control method. The wearable helmet brain-computer control system comprises a multi-mode sensor assembly, an inertial measurement unit, an inertial measurement unit, an inertial measurement unit and a brain-computer interface control module, and the multi-mode sensor assembly is used for synchronously collecting electroencephalogram, electrocardiogram and electromyogram physiological signals and head movement data of the inertial measurement unit; the motion artifact removing module is used for predicting motion artifacts through a Hammerstein-Wiener model on the basis of IMU (Inertial Measurement Unit) data, separating artifact components in mixed electroencephalogram signals by combining an independent component analysis model, and reconstructing pure electroencephalogram signals; the central processor is used for carrying out feature extraction and decoding on the pure electroencephalogram signal and generating a control instruction; the control instruction interface is used for outputting the control instruction to external equipment; the storage module is used for storing an original signal and a processing result; the wireless transmission module is used for realizing data interaction with a terminal; the core problems that a traditional device is poor in signal quality and unstable in control in a dynamic environment are effectively solved.

This invention discloses a preprocessing method for highly polluted pediatric EEG data, belonging to the field of EEG signal processing technology. The invention proposes a robust motion artifact detection method based on a multi-channel voting mechanism. This method overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision-making, automatically identifying time periods requiring restoration. A comprehensive preprocessing workflow integrating PCHIP interpolation restoration, adaptive Bayesian wavelet denoising, bandpass filtering, and independent component analysis is designed. This workflow is validated using real pediatric continuous task EEG data. This invention demonstrates excellent performance in preserving frequency band information and improving the weighted signal-to-noise ratio, achieving an average accuracy of 96.4% (205 test cases) and a maximum of 100% based on permutation entropy feature classification. This invention effectively solves the preprocessing challenge of pediatric EEG data in high-noise environments, significantly improving the accuracy and reliability of subsequent classification tasks.

The application discloses an online monitoring method of a voltage transformer based on independent component analysis, which samples information of historical data, steady-state data and real-time data of signals output by the voltage transformer, constructs a data set from the historical data, and imports the data set into an initial SDAE network model based on a sparse denoising autoencoder to perform dynamic training, uses the steady-state data to fine-tune parameters based on the SDAE network model obtained through offline training, and thus obtains encoding data of the SDAE network model; uses an independent component analysis method to perform independent component decomposition by taking the encoding data as input; calculates sample statistics and overall statistical threshold, compares real-time statistics with the overall statistical threshold, and if the real-time statistics are lower than the overall statistical threshold, it is determined that the state of the voltage transformer is normal in this round of judgment; otherwise, it is considered that there is an abnormal voltage transformer in the voltage transformer group.

Traditionally, feature extraction and thickness quantification of Pulsed eddy current (PEC) composite signals from multiple concentric pipes in oil and gas wells or similar, have been approached via pre-built frequency domain inversion models that do not utilize in-situ data and are subject to many unknown variables. We apply independent component analysis (ICA) with known applications in other areas including facial feature extraction, in a novel fashion to determine from in-situ data, the original independent PEC signals corresponding to each pipe, from their composite signal, hence allowing the thickness of each pipe to be reliably quantified. Since ICA is unsupervised, no prior modelling with synthetic or out-of-sample data is required, rather in-situ data is utilized. The results are consistent across logging tool manufacturers as prior knowledge of each tool sensor / physics is not required.

The invention belongs to the technical field of electroencephalogram signal analysis, and particularly relates to an electroencephalogram signal-based fatigue state detection system, which comprises a signal acquisition module, a signal preprocessing module, a feature extraction module and a fatigue state judgment model, the signal acquisition module is used for establishing stable low-impedance connection between each electrode and scalp by wearing an electrode cap and using conductive paste, and recording an original EEG signal; the signal preprocessing module is used for carrying out band-pass filtering and self-adaptive wave trapping on an original EEG signal and then removing physiological artifacts through self-adaptive filtering and independent component analysis; the feature extraction module is used for extracting artificially designed frequency domain, time domain and nonlinear features from the preprocessed signals, automatically learning deep features through 1D-CNN and an attention mechanism, and splicing the two types of features to form a fusion feature vector; the fatigue state judgment model calculates a fatigue score based on the fusion feature vector, and obtains a comprehensive score through time smoothing and trend analysis.

The invention relates to the technical field of industrial process soft measurement and quality control, and discloses a paper quality prediction method based on multivariate statistical latent variable fusion and space-time transformation, which comprises the following steps: acquiring space-time sequence data of a multi-source sensor in a papermaking process, constructing a space-time diagram structure reflecting a topological relation of equipment, and preprocessing. Then, multi-view latent variables are extracted through non-negative matrix factorization, independent component analysis and robust principal component analysis, attention fusion is conducted on the latent variables through an LV fusion module, and fusion latent variables are obtained; and inputting the fusion latent variable and original node data into a multi-scale convolution auto-encoder to obtain spatial feature embedding, and inputting the spatial feature embedding and the fusion latent variable into a space-time Transform module together to realize joint modeling of space correlation and time dependence. And finally, outputting a paper quality predicted value through a linear regression module. The method can achieve the accurate prediction of the paper quality under a high-dimensional and multi-noise working condition, and is suitable for online monitoring and modeling updating.

The invention relates to the technical field of quality tracing, and discloses a precision part quality intelligent tracing method and system based on reinforcement learning, and the method comprises the steps: integrating environment monitoring, part pollution detection and process space-time information, and generating a multi-dimensional feature tensor; analyzing the tensor by using a multi-source causal discovery algorithm, identifying a pollution source causal relationship, and outputting an initial causal network; extreme working condition pollution simulation data is generated by means of the conditional generative adversarial network; analyzing and decomposing the mixed pollution signal by using independent components to obtain a pollution source contribution degree vector; using a domain adaptation algorithm to generate working condition invariant pollution characteristics; inputting a distributed robust reinforcement learning network, and calculating a traceability strategy; verifying the propagation trajectory through importance sampling correction and particle tracking; evaluating uncertainty by Monte Carlo sampling and the like, and outputting an all-working-condition pollution source positioning result with a reliability score; the technical problem of accurate tracing of multi-source cross contamination under extreme working conditions is solved.

This invention relates to a six-DOF microgravity quality assessment method based on mission phase-driven approaches, belonging to the field of spacecraft control and ground simulation. It constructs a multi-physics fusion six-DOF microgravity quality index, divides the on-orbit service mission phase using a finite state machine and matches dynamic weights and assessment thresholds, combines digital twins to achieve virtual-real consistency verification, employs rapid independent component analysis and extended state observers to complete disturbance decoupling and contribution rate quantification, and uses a graph neural network long short-term memory network model and Shapley sum interpretation algorithm to achieve pre-experiment performance prediction and in-experiment anomaly diagnosis. This invention addresses the technical problems of existing microgravity ground simulation assessments, such as the disconnect between static thresholds and on-orbit mission requirements, the inability of a single index to cover multi-DOF coupling characteristics, the lack of virtual-real consistency verification, the inability to quantify and attribute disturbance sources, over-reliance on expert experience, and post-event offline assessment.

The embodiment of the application discloses a vibration tracing method for an engineering structure, which comprises the following steps: selecting a plurality of to-be-determined monitoring points according to the significant position of structure vibration and point category; optimizing the arrangement of the plurality of to-be-determined monitoring points according to the correlation coefficient and energy weight between the responses of the plurality of to-be-determined monitoring points; separating the responses of the plurality of to-be-determined monitoring points by using variational mode decomposition independent component analysis to obtain a plurality of monitoring responses; identifying the plurality of monitoring responses according to a support vector machine model to determine the vibration source category corresponding to the components of the plurality of monitoring responses; traversing all the vibration source categories to determine the contribution degree of the components of the plurality of monitoring responses to the response energy, and obtaining the vibration source causing the vibration of the engineering structure. Through the scientific monitoring point selection, optimized arrangement, signal separation and vibration source identification technology, the precise tracing of the vibration source of the engineering structure is realized, and strong support is provided for the health monitoring and vibration control of the engineering structure.

The application provides a fetal electrocardio monitoring system based on data analysis, and belongs to the technical field of electrocardio monitoring, and specifically comprises the following steps: collecting a mixed signal, removing power frequency interference by using an IIR notch filter, removing baseline drift by using a median filter, and removing artificial pulse interference by using empirical mode decomposition; extracting a maternal signal by using an independent component analysis method, constructing a singular value transfer matrix, taking the maternal abdominal wall electrocardio signal as spatial filtering, offsetting the maternal signal by singular value decomposition; extracting a fetal signal by using the independent component analysis method again; determining a fetal electrocardio R wave peak by using a time domain nonlinear transformation threshold method, performing clustering detection on the R wave peak, calculating a fetal heart rate according to the time difference of RR intervals; analyzing low frequency components LF and high frequency components HF in a maternal heart rate signal, calculating an LF / HF value of the maternal heart rate, and judging whether the maternal body has an anxiety emotion; and the application realizes more convenient and accurate fetal heart monitoring.

The method comprises the following steps: constructing a baseline observation network comprising a Beidou receiver and a continuously operating reference station in a target construction site; collecting real-time observation data of a control point in the target construction site by using the Beidou receiver; performing orbit evolution and independent component analysis based on the real-time observation data to obtain real-time coordinates of the control point and a real-time displacement amount of the control point relative to a reference position; performing early warning in response to the real-time displacement amount being greater than a preset displacement threshold; updating the coordinates of the control point and pushing the updated coordinates to a field lofting terminal for display. The method can realize the transition from manual re-measurement to intelligent perception, improve the positioning accuracy and stability of Beidou in complex environments, and meet the requirements of high-level construction control.

The application discloses a fan operation abnormal vibration monitoring method and system based on multi-sensor fusion, relates to the technical field of fan operation monitoring, and comprises the following steps: deploying multiple types of sensors on a fan, collecting sensor data in real time, adopting IEEE1588 protocol for sensor data synchronization processing, combining wavelet denoising for pretreatment, and then extracting characteristic data by using a principal component PCA combined independent component ICA analysis method; the application considers the actual conditions of offshore wind farms by deploying multiple types of sensors on the fan, finely processes and extracts features after collecting multi-source signals in real time, accurately monitors and reliably identifies abnormal vibration conditions of the fan in a complex environment in a coastal or offshore wind farm, so that early abnormal features of fan operation can be captured in time, normal changes caused by environmental factors and real fault abnormalities can be effectively distinguished, and early fault discovery is further realized.

The invention discloses an electroencephalogram signal reconstruction method and system based on artifact removal. The method comprises the steps that multi-channel electroencephalogram signals are obtained and subjected to filtering preprocessing; blind source separation is carried out through independent component analysis, and statistical independent signal source components and a hybrid matrix are obtained through decomposition; constructing an equivalent current dipole model to calculate a space artifact index, evaluating signal complexity in combination with a time sequence sample entropy, and judging and identifying an artifact component and a neural activity component through weighted fusion and a self-adaptive threshold value; neural activity components are reserved, artifact components are zeroed, and pure electroencephalogram signals are reconstructed through mixed matrix inverse transformation. According to the method, accurate identification and effective removal of the electroencephalogram artifacts are realized, and high-quality data are provided for electroencephalogram analysis.

The application discloses an online self-adaptive seizure prediction method and system, relates to the technical field of medical signal processing and artificial intelligence, and comprises the following steps: acquiring multi-channel electroencephalogram signals of a target user; constructing an online self-adaptive seizure prediction model according to spatially constrained independent component analysis, brain function network and a transfer learning mechanism; inputting the multi-channel electroencephalogram signals into the online self-adaptive seizure prediction model, identifying and predicting a pre-seizure state, and obtaining a prediction result; and performing online early warning judgment according to the prediction result, and triggering an alarm if early warning conditions are met. The application solves the problem of unstable early warning effect caused by the variability of electroencephalogram data of epilepsy, realizes online seizure prediction with clinical accuracy and rapidness, and provides a basis for the treatment of intractable epilepsy and the research on the seizure mechanism of epilepsy.

The invention provides a signal noise reduction method for acoustic monitoring of a tool wear state in a milling process, which relates to the technical field of intelligent manufacturing, and is characterized in that main shaft noise is eliminated through spectral characteristics of IMF components obtained through adaptive noise complete set empirical mode decomposition in combination with spectral characteristics of signals acquired by a multi-scene milling test; first-stage noise reduction is realized; performing fast independent component analysis on the residual IMF components after the first-stage noise reduction to realize blind source separation, and identifying and removing random noise components in combination with a fuzzy entropy threshold criterion to realize second-stage noise reduction; reconstructing a de-noised signal by using the residual signal components after the second-stage noise reduction, and screening out features conforming to a tool wear trend through a Kendall rank correlation coefficient; and inputting the screened features conforming to the tool wear trend into a convolutional neural network to realize high-precision intelligent monitoring of the tool wear state based on the sound signal. According to the invention, stable denoising performance is maintained under different working conditions.

A sound source separation system is configured to perform blind source separation of individual sound source signals by an independent component analysis (ICA) method from a plurality of mixed signals in which two or more sound source signals are mixed. The sound source separation system includes an n number of microphones, where n≥2; a virtual microphone signal generator configured to generate, from output signals of the n number of the microphones, an m number of the virtual microphone signals that are output signals of the m number of virtual unidirectional microphones having directivities in different directions, where m>n; and an ICA processor configured to separate an L number of the sound source signals that are signals of the L number of different sound sources, by the ICA method, from the m number of the virtual microphone signals that are the plurality of the mixed signals, where L>n and L≤m.

The application discloses a pre-processing method based on high-pollution children's electroencephalogram data and belongs to the technical field of electroencephalogram signal processing. The application proposes a robust motion artifact detection method based on a multi-channel voting mechanism, overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision, and automatically identifies the repair period. A set of comprehensive preprocessing procedures are designed, which integrate PCHIP interpolation repair, adaptive Bayesian wavelet denoising, band-pass filtering and independent component analysis. The procedures are verified based on real children's continuous task electroencephalogram data. The application performs excellently in preserving frequency band information and improving weighted signal-to-noise ratio, and the average accuracy rate based on permutation entropy feature classification reaches 96.4% (205 test sets), and the highest is 100%. The application effectively solves the pre-processing problem of children's electroencephalogram data in a high-noise environment, and significantly improves the accuracy and reliability of subsequent classification tasks.