10 results about "Independent component analysis algorithm" patented technology

This application discloses a method, apparatus, device, and storage medium for classifying users of a motor imagery brain-computer interface. The method includes using independent component analysis (ICA) to decompose the user's target EEG signal, obtaining multiple initial independent components. Feature data is extracted from each initial independent component. Based on the feature data, a first target independent component corresponding to contralateral event-related desynchronization and a second target independent component corresponding to ipsilateral event-related synchronization are determined from the initial independent components. Finally, the classification result of the motor imagery brain-computer interface user is obtained. This scheme improves the accuracy and reliability of classification by using ICA-related neurodynamic modeling and classifying users based on multi-dimensional neural indicators obtained from feature data. Furthermore, by combining multiple feature data for automated selection of independent components, the subjectivity issues caused by manual intervention are avoided, improving the applicability and scalability of the classification method.

The invention relates to the technical field of intelligent sensing systems, and discloses a method and a device for detecting the bearing capacity of a pavement engineering construction material, and the method comprises the steps: synchronously collecting a mixed signal and a pure noise signal through a main measurement sensor and a reference noise sensor, constructing a multi-dimensional observation matrix, and carrying out the centralization and whitening preprocessing, thereby obtaining the bearing capacity of the pavement engineering construction material. And blind source separation is carried out by adopting an independent component analysis algorithm based on maximized non-Gaussian property, a target bearing capacity response signal with a super Gaussian characteristic is identified according to a kurtosis value of each source signal component, and then a dynamic deformation modulus is calculated. The system comprises a sensor array module, a synchronous acquisition module, a data processing module based on FPGA hardware acceleration and a result output storage module. According to the invention, strong coherent vibration noise can be effectively suppressed, signal details are completely reserved, and high-precision and real-time bearing capacity detection is realized.

This invention relates to a method for separating vibration and flow noise in the underwater noise spectrum of a ship propeller, comprising: constructing a "frequency label" for vibration noise by fabricating two propeller models with identical geometry but significantly different material properties; a dual-model difference stage: calculating the absolute difference in the noise spectra of the two models and generating a vibration noise mask based on modal analysis results; a blind source separation stage: for the residual signal after difference, using an improved Independent Component Analysis (ICA) algorithm to further separate residual interference by utilizing the statistical independence of vibration noise; introducing a convolution kernel function in this stage to enhance the algorithm's adaptability to time-delayed signals; and introducing Cepstrum analysis technology to perform phase correction on the complex spectrum of the replacement frequency band. This method solves the significant shortcomings of existing noise control technologies in the coupling separation of vibration and flow noise, enabling the separation of the components of underwater noise from ship propellers, obtaining the noise components caused by blade vibration and the flow noise components separately.

The invention provides an on-line monitoring method for abnormal vibration of thermal energy storage equipment, and belongs to the technical field of thermal energy storage equipment. A multi-source sensor array comprising low-frequency and high-frequency vibration sensors is arranged at key parts of the thermal energy storage equipment, and noise interference is suppressed in a differential arrangement mode; a multi-source coupling vibration signal is separated by using an independent component analysis algorithm, time-frequency analysis is performed by combining wavelet transform to extract vibration characteristics, and a phase change vibration characteristic mechanism equation is established to calculate material physical parameter changes in a phase change process. A neural network identification model based on dynamic topology reconstruction sparse connection learning and probability graph model structured prediction is constructed, and a vibration anomaly discrimination threshold system is established to realize anomaly early warning and adaptive optimization. The technical problem that the abnormal vibration mode is difficult to accurately separate and identify by the multi-source coupling vibration signal in the phase change process of the thermal energy storage equipment is solved.

This invention belongs to the field of geological disaster monitoring technology, specifically disclosing a method and system for quantitative inversion of ground fissures based on optical fiber and independent component analysis. The method includes: acquiring distributed optical fiber time-series strain monitoring data to construct an observation matrix; determining the number of components through principal component analysis; using spatial location as the observation dimension, employing independent component analysis to separate blind sources, extracting spatial independent components and corresponding time score matrices, thus decoupling the ground fissure signal from the background deformation signal; selecting components exhibiting continuous high amplitude as ground fissure strain anomaly components, and setting a threshold based on these components and the background noise level, defining intervals where the amplitude continuously exceeds the threshold as anomaly intervals; within these intervals, numerically integrating the anomaly components along the spatial direction to obtain the quantitative change in ground fissure width. This invention achieves automatic and accurate positioning of ground fissures and highly reliable quantitative inversion of their width under complex geological conditions.

The application belongs to the technical field of industrial automatic control, and discloses a leakage rate evaluation method and system suitable for constant pressure water supply pipe network. The leakage rate evaluation method suitable for constant pressure water supply pipe network comprises the following steps: according to the characteristics of the constant pressure water supply system, using the Lagrange proportional load method and the Newton iteration method, the water consumption signal reference vector and the leakage signal reference vector are separated from the total table flow signal and the pressure signal of the pressurizing equipment; and taking the separated water consumption signal and the leakage signal as the constraint conditions, the constraint independent component analysis algorithm is combined to carry out iteration, so that the separated water consumption signal and the leakage signal are obtained. Even if the current industrial park, station and other field sections do not have automatic flow and pressure collection remote devices, the application only needs to install a common pressure gauge at the water pump during the shutdown and maintenance time, install a common water gauge at the water pump outlet pipe, and send personnel to record the reading at regular time, so that the collection cost of observation data is extremely low.

This invention belongs to the field of data processing, specifically relating to a power metering method based on big data self-diagnosis. The invention discloses a power metering method based on big data self-diagnosis, which includes: real-time acquisition of multi-dimensional heterogeneous sensing data; training a dynamic floating baseline for metering using a spatiotemporal graph convolutional network; applying variational mode decomposition and independent component analysis algorithms to decouple the metering data stream into load characteristics, environmental disturbances, and hardware drift components; comparing the hardware drift components with the floating baseline in real time to calculate an error correction coefficient; and converting this coefficient into a digital compensation factor applied to the data processing module to achieve online correction of the metering results and synchronously generate a health diagnosis report. This invention, by constructing a dynamic baseline model and feature decoupling algorithms, achieves real-time self-detection of metering accuracy and digital closed-loop self-healing, eliminating external environmental interference and improving the robustness, management level, and operation and maintenance efficiency of the power metering system.

The invention provides an unmanned aerial vehicle aerial photovoltaic module detection method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining the hyperspectral image data of a photovoltaic module; performing correction processing on the hyperspectral image data to obtain corrected image data; performing dimension reduction on the corrected image data through an independent component analysis algorithm to obtain an independent component image; sampling is carried out on the independent component image, and a training sample is determined; training the U-Net model through the training sample; inputting the whole independent component image into the trained U-Net model, and outputting a classification result graph; according to the classification result graph, the operation state of the photovoltaic module is evaluated, and a photovoltaic module inspection report is output. According to the invention, through the hyperspectral image and the U-Net model, efficient and accurate detection and automatic inspection of the operation state of the photovoltaic module are realized.

The invention discloses a rapid fault detection and analysis method for a secondary control cable of a power system. The method comprises the following detection and analysis steps: S1, collecting an impact discharge sound mixed signal with noise; s2, carrying out wavelet transform multi-scale decomposition to obtain wavelet coefficients and connecting the wavelet coefficients in series; s3, separating the two paths of series one-dimensional wavelet coefficients by adopting a fast independent component analysis algorithm, and judging the separated signals based on kurtosis; s4, carrying out threshold processing on the discriminated signal, and obtaining a denoised impact discharge sound signal by adopting wavelet reconstruction; s5, judging starting points of the sound and the magnetic signal, and obtaining an acoustic-magnetic time difference according to the starting point of the magnetic signal and the sound starting point; s6, obtaining the distance between the fault point and the detection instrument based on the acoustic-magnetic time difference and the propagation rate of the impact discharge sound, and achieving the positioning detection of the fault point. Cable faults can be rapidly detected, working efficiency is improved, and safety and stability of a power system are guaranteed.

The application discloses a new energy power facility detection data management and analysis platform, comprising: a multi-source heterogeneous high-frequency synchronous induction system, each sensor node of which is equipped with a Beidou timing module, so that sampling error is controlled within 20ns by taking a second pulse as a sampling trigger source and adding a time stamp; an environment-driven dynamic reference flow form construction system for extracting low-dimensional embedding coordinates of environment characteristics through nonlinear flow form learning and constructing an ideal output hyper surface changing with the environment; and an endogenous performance deviation decoupling extraction system for calculating a residual matrix of real-time operation characteristics and ideal operation states and adopting an independent component analysis algorithm to decouple and extract an endogenous attenuation characteristic vector representing equipment intrinsic performance attenuation from the residual matrix. The application changes the monitoring reference from a fixed threshold to a dynamic baseline fluctuating with the environment, effectively separates environmental interference and equipment real performance attenuation, and improves diagnostic accuracy and preventive maintenance capability.