169 results about "Independent component analysis" patented technology

The invention discloses a morphological gradient region replacement method based on SAM semantic segmentation and user guidance, and relates to the technical field of computer vision and image processing, and the method comprises the steps: carrying out the semantic segmentation of a to-be-processed image through an SAM model, extracting a multi-level semantic feature, carrying out the standardization and dimension reduction, extracting a causal factor based on independent component analysis, and carrying out the user guidance. A directed causal factor association graph is generated through Granger causal relationship test, and a causal attribution probability graph is generated through reverse mapping; constructing a structured causal graph, and generating a causal mask through a graph convolutional network; encoding the original interaction signal into a guide thermodynamic diagram; constructing a diffusion equation, forming a gradual change control equation by dynamically fusing and guiding the intensity distribution of the thermodynamic diagram and an image semantic diffusion item, and iteratively solving the gradual change control equation; generating an anisotropic morphological operation kernel according to the geometric curvature characteristics of each region in the replacement mask; and fusing the optimized replacement mask with the target content based on a gradient domain optimization algorithm to generate a gradient replacement image.

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 discloses a power industry electroencephalogram cap-oriented key channel screening method and system, and the method comprises the following steps: firstly, collecting electroencephalogram signals of a power worker in typical states of waking, fatigue and the like by adopting a 32 or 64 channel electroencephalogram cap, and carrying out the preprocessing operations of band-pass filtering, ICA (Independent Component Analysis), signal-to-noise ratio evaluation and the like; artifacts and inferior channels are removed, and then multi-dimensional features such as the frequency domain, the time domain, the entropy value and statistics of each channel are extracted to comprehensively score the importance of the channels. Constructing a plurality of channel subsets, verifying the recognition performance of the channel subsets through a classification model, and screening out a group of key channels with the highest information expression capability in cognitive state discrimination; according to the method, the number of electroencephalogram channels is reduced, the complexity and calculation overhead of acquisition equipment are reduced, the feasibility of real-time deployment of the model in an electric power field is improved, and the method has relatively high industry adaptability and engineering application value and is suitable for various key scenes with relatively high requirements on personnel state perception.

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 an intelligent civil engineering construction management method and system, and relates to the technical field of civil engineering construction management, and the method comprises the following steps: obtaining the vibration data of N construction devices and the mixed vibration data of M monitoring points; and judging whether the mixed vibration data is greater than a preset threshold, if so, performing multi-feature matching with a preset vibration fingerprint database, determining responsible equipment according to a matching result, and outputting contribution degrees and vibration measured values in a descending order. According to the method, environmental interference is eliminated through preprocessing, multi-dimensional decomposition of the mixed vibration data is realized through independent component analysis, and double criteria of time domain waveform difference and frequency domain energy matching degree are combined, so that the recognition accuracy of responsible equipment is remarkably improved, and the recognition efficiency is improved. A three-dimensional geologic model is further constructed through ground penetrating radar scanning and soil layer test data, spatial distribution and attenuation characteristics of different soil layers are accurately reflected, and misjudgment of responsible equipment caused by soil layer difference is avoided.

The invention belongs to the technical field of signal processing, and particularly discloses an acoustic emission signal denoising method based on multi-method fusion, and the method comprises the steps: collecting an acoustic emission signal through an acoustic emission sensor; a band-pass frequency range is determined by adjusting the frequency range based on a plurality of evaluation indexes, and the evaluation indexes are determined by analyzing the difference between the first signal sample and the second signal sample; the first signal sample and the second signal sample are samples collected when no cavitation phenomenon exists and when the cavitation phenomenon exists respectively; wavelet threshold de-noising is carried out, and a signal after background noise is removed is obtained; through independent component analysis, a target signal and an interference signal are separated. According to the invention, by combining a plurality of denoising technologies, accurate suppression of different types of noise is realized, the denoising effect can be ensured in a complex environment, the signal-to-noise ratio of the acoustic emission signal is effectively improved, and the quality and reliability of the signal are ensured.

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 requests to protect a motor imagery electroencephalogram signal enhancement method based on graph attention. The method comprises the following steps: firstly, carrying out multi-stage preprocessing on an original electroencephalogram signal, including band-pass filtering and baseline drifting, and removing artifacts in combination with independent component analysis, then, extracting time information of each channel by utilizing a one-dimensional convolutional network so as to capture time sequence characteristics in a motor imagery process, on the basis, constructing a graph attention network, taking the electroencephalogram signal channels as nodes, and taking the electroencephalogram signal channels as the nodes; the method comprises the following steps of: dynamically modeling and optimizing the internal relation between channels, strengthening motor imagery related channels such as C3, C4 and Cz by combining priori knowledge, further introducing a time attention network to identify and enhance key time slices rich in discrimination information, and finally, forming a high-dimensional feature matrix by fusing space-enhanced and time-enhanced electroencephalogram features, so as to realize the recognition and enhancement of the motor imagery related channels. And inputting into a classifier for pattern recognition.

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 discloses a traffic flow prediction method, device and equipment for a text travel scene, and a storage medium, and relates to the technical field of intelligent traffic, and the traffic flow prediction method for the text travel scene comprises the steps: carrying out the rapid independent component analysis and decomposition, multi-scale wavelet transformation and standardization processing of historical traffic flow data, and obtaining a traffic flow prediction result; standardized features are obtained; extracting local space-time features through a target convolutional neural network optimized by chaotic evolution and an efficient local attention mechanism, and extracting global time features by using a hierarchical attention network and a low-rank attention mechanism; dynamically fusing the two features through a multi-head attention mechanism to obtain a fused feature; and generating a prediction base vector based on the historical time sequence and the to-be-predicted time period, and performing matrix operation and destandardization on the fusion feature and the prediction base vector to obtain a traffic flow prediction result. According to the method, the multi-scale spatial-temporal features can be efficiently fused, and the calculation complexity is reduced, so that the accuracy and robustness of traffic flow prediction are improved.

The invention discloses a broadband oscillation uniform suppression method and system for an offshore wind power flexible direct current grid-connected system, and relates to the technical field of offshore wind power flexible direct current grid-connected control, and the method comprises the steps: carrying out the multi-source signal decoupling and frequency domain joint mapping of broadband oscillation characteristic parameters, and generating real-time signal characteristic data; performing impedance cooperative tuning and energy transmission path reconstruction processing on the real-time signal characteristic data to generate port energy absorption parameters; performing asymmetric coupling attenuation and time domain sequence reforming on the port energy absorption parameters to generate port post-dissipation electrical state parameters; processing the electrical state parameters after port dissipation through a switch sequence collaborative application to generate source end spectrum optimization parameters; after global parameters are subjected to feature analysis and classification, the construction of real-time signal feature data is realized by adopting independent component analysis and frequency domain joint mapping, so that not only is the decoupling and uniformity of multi-source signals enhanced, but also an accurate basis is provided for subsequent regulation and control.

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 provides a continuous identity authentication method based on multichannel PPG signals in an uncontrolled environment. Firstly, a smart watch is used for collecting wrist double-channel green light, red light and infrared light PPG signals and motion sensor data, and original information is obtained. Aiming at the problem that an uncontrolled environment signal is easily interfered, noise reduction and optimization are carried out on the signal through abnormal value processing, FIR high-pass, band-pass and multi-band-pass filtering and ICA independent component analysis, so that motion artifacts are effectively inhibited, and the signal quality is improved. Then, based on pulse wave valley positioning segmentation signals, a multi-channel standardized data set is constructed; and finally, by means of an Inception-LSTM neural network fusing multi-scale feature extraction and time sequence modeling, end-to-end learning of biological features is carried out, and a high-precision identity authentication decision in a dynamic environment is realized. According to the method, the robustness is verified in seven motion state simulation scenes, and the authentication accuracy and reliability in an uncontrolled environment are remarkably improved.

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 discloses an intelligent micro-grid multi-mode collaborative optimization method and system, and belongs to the field of intelligent power grids. The method comprises the following steps: collecting electrical data of a common connection point and a distributed unit, and quantifying a harmonic responsibility coefficient by using an improved complex independent component analysis and partial coherence method; constructing a dynamic electricity price game model, and solving an optimal excitation coefficient through a non-cooperative game; and building a multi-objective optimization model, correcting model prediction control parameters by means of a digital twinborn training reinforcement learning agent, and outputting a scheduling instruction in a rolling optimization manner to realize multi-mode switching. The system comprises a data acquisition layer, a regional collaboration layer, a cloud computing and digital twinning layer and an intelligent optimization core layer. According to the method, the electric energy quality and economic dispatching are fused, and the optimization intelligence and safety of the micro-grid are improved.

The invention provides an independent component repeatability analysis method based on correlation tensor clustering, and belongs to the technical field of signal processing. The method comprises the following steps: obtaining a subject information matrix of a plurality of subject data based on a principal component analysis method; performing multiple decomposition on the tested information matrix by using an independent component analysis method, and extracting a component matrix and a coefficient matrix; splicing the component matrix and the coefficient matrix to construct a rank-one matrix, and stacking a plurality of rank-one matrixes to form a third-order tensor; and on the basis of the third-order tensor, analyzing and evaluating the repeatability of the components based on correlation tensor clustering. According to the method, the repeatability of the time sequence and the space distribution is considered at the same time, so that the stability and the reliability of ICA extraction components are evaluated more comprehensively. According to the method, the limitation that only the repeatability of spatial distribution is concerned is solved, components stably appearing in different operations can be effectively identified through multiple ICA decomposition and tensor clustering analysis based on correlation coefficients, and the accuracy of component extraction and the precision of repeatability evaluation are improved.

The invention relates to the technical field of intelligent power grids, and discloses a multi-electric-quantity output relay protection secondary circuit test method and system, and the method comprises the steps: collecting an environment noise time sequence, obtaining a noise spectrum, and setting a filter parameter set; outputting a test signal including the feature code signal based on the noise spectrum and the filter parameter set; executing a plurality of groups of test sequences by using the test signals to obtain segmented signals; performing real-time noise suppression on the segmented signals to obtain enhanced signals; extracting a feature vector from the enhanced signal; obtaining a fault type and a fault type position coordinate based on the feature vector; according to the invention, through noise spectrum analysis and independent component analysis, high anti-interference capability and real-time noise suppression of a test signal are realized, a composite fault is successfully separated, a fault type is identified, and accurate positioning of the fault is realized through a cross-correlation function and a coordinate optimization algorithm.

The invention provides a method for determining a noise component demarcation point in an electric signal of power transformation equipment, which belongs to the technical field of power transformation equipment, and comprises the following steps of: firstly, carrying out time domain sampling and standardization on the electric signal through a data preprocessing module of a hardware unit, and then extracting a frequency domain feature by utilizing fast Fourier transform; blind source separation is carried out through independent component analysis, and an adversarial neural network model is constructed to carry out deep analysis on independent component signals. The generator network generates a noise feature vector, and the discriminator network evaluates a noise component proportion and sets an adaptive threshold to mark a noise dominant signal. And finally, time-frequency energy density distribution characteristics are obtained through wavelet transformation, the energy concentration degree and the frequency bandwidth are calculated, noise component demarcation points are accurately determined, accurate recognition and processing of the electric signal noise of the power transformation equipment are achieved, and the problems that in the prior art, an artificially designed characteristic extraction algorithm is often depended on, self-adaptability is lacked, and the noise is poor are solved. And the effect is not good in a complex and changeable noise environment.

The invention discloses a subway station passenger flow buffer area risk identification and collaborative management method and system, and relates to the field of analog simulation, and the method comprises the steps: building a three-dimensional simulation scene of each subarea passenger flow buffer area of a subway station based on individual motion simulation software Massmotion of a social force model, and obtaining the data of the subway station passenger flow buffer area; adopting a partition independent component analysis PIPCA algorithm to obtain a dominant variable influencing each partition passenger flow system; according to the dominant variable, optimizing the weight and bias parameters of a pre-constructed nonlinear sub-regression neural network NARX through a moss growth algorithm MGO, and obtaining a subway passenger flow buffer area risk identification model for predicting the passenger flow density of each buffer area and a bottleneck period; constructing a target function and a constraint condition, and selecting an optimal control strategy under the constraint condition of meeting the passenger flow volume demand; and selecting the control scheme with the lowest total congestion risk as a final implementation scheme by adopting a congestion risk identification method. The congestion risk of the buffer area is efficiently identified.

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.

The invention provides a terahertz time-domain spectral signal noise reduction method and system for transformer aged insulating oil, and the method comprises the steps: collecting a terahertz time-domain spectral signal of an aged insulating oil sample, and carrying out the parameter optimization of variational mode decomposition through a whale optimization algorithm, according to the method, an optimized variational mode decomposition algorithm is adopted to decompose terahertz time-domain spectral signals of aged insulating oil into a series of intrinsic mode function components, then independent component analysis is adopted to separate the intrinsic mode function components into noise and effective signals, and finally the effective signals are screened out by calculating information entropy of independent components. And reconstructing the terahertz time-domain spectral signal of the aged insulating oil after noise reduction. The technical problems that the wavelet basis function and the decomposition layer number of wavelet noise reduction are difficult to determine, the noise reduction effect is restricted by the endpoint effect and mode aliasing problem of empirical mode decomposition, and the noise reduction effect of variational mode decomposition is poor are solved.