139 results about "Component analysis" patented technology

This application belongs to the field of coherent diffraction imaging technology, specifically disclosing an ultra-low dose coherent diffraction imaging method and apparatus. Based on a blind source separation strategy, this application uses principal component analysis to process the acquired diffraction signals, performing noise separation and updating in reciprocal space. This effectively separates mixed noise energy into noise components, thus avoiding crosstalk to the reconstruction process and significantly improving the convergence stability and robustness of coherent diffraction imaging when reconstructing diffraction signals with extremely low signal-to-noise ratios under ultra-low exposure doses. Simultaneously, this application constructs noise components separately for each scanning position and correlates noise components at different positions through low-dimensional spatial projection, achieving non-stationary noise separation. This enables more effective handling of random noise caused by the low quantum efficiency of ultra-short band detectors, thus maintaining extremely high noise robustness and reconstruction accuracy even under ultra-low exposure doses, achieving an effective improvement in resolution.

A system for a website building system (WBS) includes a component analysis and visualization (CCAV) module to analyze elements of an underlying application built using the WBS and to generate a visual system architecture diagram (SAD) representing the hierarchical arrangement of the elements. The CCAV module includes a site object build engine to gather and analyze information on the elements and to create a site object representation, a software bill of materials builder to create a software bill of materials from the site object consisting at least of assets and configuration data for the underlying application and a SAD renderer to create and display the SAD where the SAD includes a static application architecture diagram representing the at least assets and configuration data and an aggregated application architecture diagram showing an overlay of at least one of: software, hardware usage, and business data changes affecting the underlying application.

This invention discloses a method and system for predicting reservoir types, comprising: obtaining a cross-interpretation chart of sandstone and mudstone transverse and longitudinal wave impedances based on the lithological distribution information, P-wave impedance curves, and S-wave impedance curves of the target layer in the target well; constructing new elastic impedance curves for distinguishing sandstone and mudstone based on P-wave and S-wave characteristics using principal component analysis based on the sandstone and mudstone transverse and longitudinal wave impedances in the cross-interpretation chart; constructing a new three-dimensional elastic impedance data volume based on the new elastic impedance curves and the three-dimensional inversion data volume of transverse and longitudinal wave impedances; and predicting the reservoir type of the entire well section of the target well based on the new three-dimensional inversion data volume of elastic impedances. This invention can construct elastic impedance attributes more suitable for reservoir identification based on transverse and longitudinal wave impedances, and can realize reservoir interpretation using a single parameter, overcoming the problem of low sandstone identification accuracy under impedance overlap, and improving the accuracy and efficiency of sandstone reservoir identification.

The embodiment of the application discloses a sample analyzer and a control method thereof, which are provided with a sample inlet device, a detection carrier, a sample distribution device, a dilution device, an imaging device and a controller. The detection carrier comprises a first bearing chamber and a second bearing chamber. The height of the second bearing chamber is less than that of the first bearing chamber. The first image information is obtained by imaging and analyzing the sample to be detected in the first bearing chamber and / or the second bearing chamber. Whether the sample to be detected is diluted is determined according to the first image information. If not, the detection result is output according to the first image information. If yes, the sample to be detected is diluted to obtain a diluted sample, and the diluted sample is distributed to the first bearing chamber and the second bearing chamber. The second image information is obtained by imaging and analyzing the diluted sample in the first bearing chamber and / or the second bearing chamber. The detection result is output according to the second image information. The sample analyzer and the control method thereof improve the accuracy of component analysis.

This invention relates to a finite element model assembly method and system based on the DMIG principle. The method includes: Step S1: Performing finite element modeling on the original structural model according to the structural composition and analysis requirements, and extracting specified component models; Step S2: Remodeling the extracted component models according to functional needs to obtain reconstructed component models; Step S3: Assembling and analyzing the reconstructed component models with the remaining structural model based on the DMIG principle. The method provided by this invention improves the accuracy and efficiency of local component analysis.

The present application relates to the technical field of marine remote sensing and water depth inversion, and discloses a water depth inversion method fusing multi-temporal information and geographic spatial perception mechanism, single-temporal remote sensing images of the same water area at different time points are acquired first to construct a multi-temporal remote sensing image sequence, then a robust principal component analysis method RPCA is used to fuse and process the multi-temporal remote sensing image sequence to obtain a fused image; then a plurality of core units are cascaded together to complete step-by-step feature extraction, and the last core unit is connected to a regression head to output continuous water depth values, thereby completing the construction of a water depth inversion network model; finally, the water depth inversion network model is trained by using the fused image combined with the geographic spatial position, and the trained water depth inversion network model is used to perform water depth inversion on the data-processed remote sensing image to be detected.

The application relates to the technical field of coal quality sorting, and discloses a precision sorting and regulation method and system suitable for complex coal quality, which comprises the following steps: determining the component parameters of each component based on a component analysis model and coal quality parameters, comparing all the component parameters in a historical regulation database, determining the fan rotating speed of a blower based on the similarity of the component parameters when a calling strategy is determined, constructing a wind speed mapping model based on a sample data set and the historical regulation database when a compensation strategy is determined, determining whether the stability of the fan rotating speed is qualified based on wind speed fluctuation frequency, determining the fan rotating speed correlation result based on the valve opening and a correlation rule algorithm, determining the rotating speed regulation coefficient of the fan rotating speed according to the fan rotating speed correlation result, and adjusting the fan rotating speed according to the rotating speed regulation coefficient; and the reliability of sorting is ensured by determining the component parameters and judging the stability of the fan rotating speed.

The application provides a method and system for removing electro-physiological signal gradient artifacts in a magnetic resonance environment, and relates to the technical field of magnetic resonance image processing. The method comprises: collecting an electro-physiological signal of a magnetic resonance scan and a scan synchronization signal; aligning and windowing the electro-physiological signal based on the scan synchronization signal to obtain a windowed sequence; constructing an artifact template based on the windowed sequence; combining the windowed sequence and the artifact template to calculate a first residual; performing principal component analysis on the artifact template and the first residual to obtain a second residual; performing logarithmic domain conversion and polarity extraction operations on the second residual to obtain a logarithmic domain sequence and a polarity symbol layer; inputting the logarithmic domain sequence into a deep learning denoising network to output a de-artifact logarithmic domain sequence; and performing inverse logarithmic transformation and polarity recovery operations on the de-artifact logarithmic domain sequence and the polarity symbol layer, respectively, to determine a de-artifact signal.

This disclosure relates to the field of insulating material testing, including a method and system for evaluating the nonlinear strength of insulating materials based on high-frequency component analysis. The method involves applying an excitation electrical signal of a first preset frequency to the insulating material under test; acquiring the response signal obtained by the insulating material in response to the excitation electrical signal based on a second preset frequency; performing time-frequency transformation on the response signal to obtain the frequency characteristics of various preset higher harmonic components; and determining the evaluation result of the nonlinear strength of the insulating material based on the frequency characteristics of these higher harmonic components. This method allows for obtaining the evaluation result of the nonlinear strength with a single excitation and response acquisition; it eliminates the need for multi-voltage point step testing in traditional techniques, shortening the testing cycle, improving testing efficiency, and enabling efficient, accurate, and standardized determination of the nonlinear strength evaluation result.

This invention discloses a real-time monitoring method for vehicle lane-crossing behavior based on image semantic segmentation, comprising the following steps: acquiring traffic video data to obtain a road image sequence and performing preprocessing; inputting the image semantic segmentation model for semantic segmentation processing to obtain a semantic segmentation result image; performing morphological processing and connected component analysis to obtain the set of pixel boundaries of the vehicle region and the set of pixel boundaries of the lane lines; calculating the spatial position data of the lane lines and performing perspective correction processing to obtain perspective-corrected lane line spatial position data; generating the position region of the vehicle region in the image coordinate system; performing target association matching to generate vehicle motion trajectory data; establishing spatial relationship data and forming relative position changes; performing temporal consistency analysis and real-time determination, and outputting the vehicle lane-crossing behavior detection result, thereby achieving continuous, accurate, and real-time monitoring of vehicle lane-crossing behavior.

This invention relates to a dynamic scanning compensation method and DNA synthesis device based on inkjet printing, belonging to the field of DNA synthesis. By applying threshold segmentation, connected component analysis, and circular Hough transform to a standard analysis image, a circular region for each reaction site is obtained. Based on the distance of all edge points of each reaction site from the center of the circular boundary, it is determined whether each reaction site meets the roundness requirements, thus obtaining information on missing reaction sites. Simultaneously, to address potential inaccuracies after the circular Hough transform, the results are calibrated using the YOLO target detection model based on a convolutional neural network, achieving high-precision detection of missing reaction sites suitable for industrial applications. Furthermore, by adding recognition weights for defective reaction sites such as those with "partially excessive distances from the center" during YOLOv6 model training, the compatibility between the two is further improved, enhancing detection accuracy and meeting the requirements of high-precision DNA synthesis.

The present disclosure relates to a fan fault detection method, device, medium and equipment. The method comprises: acquiring current working condition data of the fan; determining whether the fan has a fault according to the acquired current working condition data, predetermined normal working condition data, a principal component analysis (PCA) algorithm, a Gaussian mixture clustering (GMM) algorithm and a kernel density estimation (KDE) algorithm. In this way, it can be accurately and reliably determined whether the fan has a fault, and the false alarm rate is reduced.

This invention relates to the field of micro-motion surface wave exploration methods, and discloses an automatic extraction method for dispersion curves of micro-motion surface waves based on connected component analysis. The method includes cross-correlation extraction of virtual source surface wave shot gathers, frequency-wavenumber domain amplitude spectrum calculation, adaptive threshold binarization, connected component analysis, frequency-wavenumber domain surface wave mask generation, mask-amplitude matrix element multiplication, phase velocity picking, Hampel filtering, and automatic generation of dispersion curves. This invention utilizes image connected component analysis and other operations to process the amplitude spectrum of micro-motion surface waves, separating the surface wave signal region from the frequency-wavenumber domain amplitude spectrum image. It automatically identifies discrete phase velocity points from images containing only surface wave dispersion energy stripes and uses Hampel filtering to remove distortion points, ultimately achieving high-precision automated measurement of the dispersion curve.

The present application relates to the technical field of detection data processing, and particularly relates to a textile fiber component analysis system and method based on machine learning; the direction gradient attention module provided in the embodiment has strong direction bias in feature representation before generating a candidate region proposal, which makes the region proposal network more easily locate the geometric center of the fiber according to the extension direction of the fiber, so as to generate a more compact and accurate candidate box, and lay a foundation for subsequent disentanglement of the adhered fiber. By focusing attention on several main directions related to the fiber, the direction gradient attention module naturally suppresses the edge response from random direction background noise, purifies the features, and improves the robustness of the model.

This invention relates to the field of machine vision inspection technology, specifically a machine vision-based online image detection method for material particle size. The method includes: acquiring a grayscale image sequence of material flow from a conveyor belt; enhancing the image by suppressing background noise; segmenting particle regions and extracting particle contours based on edge information; constructing pseudo-boundaries by combining particle edge curvature changes and local grayscale minimum distribution characteristics; and separating adhered and overlapping particles using an improved connected component analysis algorithm. The method calculates geometric features such as particle projected area and equivalent diameter, determines particle size levels by comparing with grading thresholds, and statistically analyzes the proportion of particles at each size. Finally, it calculates continuous mass flow distribution data by combining the conveyor belt running speed and generates an online detection report. This method effectively solves the problem of particle stacking and segmentation, stabilizes and refines particle recognition accuracy, and achieves integrated real-time detection of material particle size and conveying flow rate, making it suitable for continuous material conveying operations in industrial settings.

This invention belongs to the field of aviation equipment testing technology, specifically involving a fault diagnosis method and system for aircraft electrical boxes based on multi-dimensional feature and rule matching. It includes acquiring multi-dimensional operating parameters such as coil voltage, coil current, contact resistance, and pull-in / release time through a global clock synchronization mechanism. Based on massive amounts of normal data, a normal parameter space model is constructed using principal component analysis. Simultaneously, based on fault tree theory, a mapping rule base is established between typical fault modes and abnormal combinations of original parameters. During the diagnosis process, the T² statistic and Q statistic are used to comprehensively capture the collaborative anomaly characteristics between parameters. Even if a single parameter in the original data does not exceed the standard, it can be identified as long as the multi-dimensional parameter linkage deviates from the normal parameter space model, thus overcoming the limitations of traditional single-parameter judgment. This invention fundamentally solves the problem of parameter collaborative anomaly failure in traditional methods, significantly reducing the risk of missed latent faults and providing reliable assurance for flight safety.

This invention discloses a method for identifying cavitation bubbles in high-velocity clouds based on adaptive multi-threshold segmentation, belonging to the field of cavitation bubble image recognition and fluid measurement technology. The method first uses a high-speed camera to acquire images of the evolution of dense bubble groups in a high-velocity cloud cavitation flow field. Based on the image's grayscale distribution characteristics, the OTSU adaptive global thresholding method is used to determine the background noise threshold and the scattered light noise threshold, respectively. On this basis, an adaptive multi-threshold segmentation algorithm is constructed, combining grayscale threshold sequences and bubble diameter threshold sequences for multi-round identification. Connected component analysis and area compensation are used to separate adherent bubbles and accurately extract small-scale bubbles. This invention can adapt to environments with high flow rates, non-uniform illumination, and uneven bubble distribution, achieving high-precision, non-invasive identification and statistical analysis of tiny bubbles inside cloud cavitation.

This application provides a method, apparatus, and electronic device for determining image region attribution. The method includes: obtaining a first binary matrix and a corresponding second binary matrix; constructing a first-order matrix and a second-order matrix based on the first and second binary matrices; performing dilation processing on the first-order and second-order matrices to obtain a first dilated matrix and a second dilated matrix; in an image processing scenario, the dilation processing includes adaptive structuring element dilation processing combined with boundary features after image preprocessing; performing an intersection operation on the first and second dilated matrices to obtain candidate associated regions; and generating a region attribution determination result through connected component analysis. This application can optimize the adaptability of image processing scenarios and meet the high-precision and real-time requirements of image region attribution determination in different scenarios such as industrial inspection.

A method and device for generating a training dataset of a bio-signal denoising artificial intelligence (AI) model are provided. According to an embodiment, the method includes receiving first bio-signal data measured in a non-noise-suppressed environment and second bio-signal data measured in a noise-suppressed environment, performing component analysis of the first bio-signal data, and determining noise component data by separating target component data from the first bio-signal data, generating third bio-signal data by combining the noise component data with the second bio-signal data, and determining the second bio-signal data as ground truth data of the training dataset and determining the third bio-signal data as noisy data of the training dataset to determine the training dataset.

The application provides a house source three-dimensional modeling method, equipment and a storage medium, wherein the method comprises using a camera to shoot a first video of the interior of a house, and splitting the first video into multiple indoor images. The indoor image set is subjected to semantic segmentation, and movable obstacles in the indoor image set are removed according to K labels. The active structure in the house interior structure image set is marked according to the K labels, and the two sides of the active structure are subjected to connected component analysis. The house interior structure image set is converted into M three-dimensional point clouds, and the M three-dimensional point clouds are reconstructed based on the spatial connection relationship. N reconstructed point clouds are subjected to Poisson reconstruction, and the interior three-dimensional model of the house source is obtained. The application adopts the semantic segmentation mode to exclude the influence of furniture and other movable obstacles on the house structure, uses the active structure to improve the accuracy of the spatial relationship between different regions in modeling, and represents the space separated by the active structure, so that the house is comprehensively and accurately three-dimensionally modeled.

------18 / 05 / 2565------(OCR) This invention provides a system for processing and analyzing data by a component analysis (CAC) configurator for generating system reports, including an electronic user interface configured for receiving a set of information from the user, a large data repository storing a number of data attributes, layers of base data sets defining essential elements embedded throughout data structures that emphasize that a number of data attributes and data structures are specified using functional objects, thus enabling the user to define a number of components where the base data set is specified from the large data repository based on the information, a processor configured for processing information using at least one decision tree data model to extract a number of data attributes from the large data repository, and a controller connected to the processor and encoded with instructions that enable the controller to function as a bot for controlling various system components for data processing and analysis;A definition processor configured for collecting data definitions related to a number of attributes and storing these data definitions in a data definition database; and an execution processor configured for generating at least one query based on the data definitions and extracting a number of reports based on these queries from a large data repository, where at least one report is generated based on the extracted data (Figure 1). ------------ This invention provides a system for processing and analyzing data using a data arranger. The component analysis (CAC) model for building system architectures includes interfaces. An electronic user configured to receive a set of information from users, storage area. Big datasets store a number of data characteristics, layers of base data sets for definition. The essence is embedded throughout the data structure, emphasizing the importance of data characteristics. Numbers and data structures are specified using functional objects, thus enabling users to... This will define a number of components where the base dataset has been specified from a large data storage area. Based on the information, the processor is configured for processing information using... At least one decision tree data model is needed to extract a number of data features. From the large data storage area, the system also includes a controller that is made to connect to the processor and... It is encoded with commands that allow the controller to function as a bot for various control purposes. System components for data processing and analysis; data processors. A structured definition for collecting data definitions related to a number of attributes. And the storage of data definitions in a database of data definitions; and the processing unit for performing the actions. It is structured for constructing at least one question based on data definitions and data separation. In a number of reports, based on queries from large data archives, at least according to... The report was created based on the extracted data. Figure 1;

This invention discloses a satellite target attitude estimation method based on geometric feature constraints in ISAR images, comprising the following steps: segmenting typical satellite components in sequential ISAR images using a Conv-LSTM network and extracting component scattering centers using an attribute scattering center model; estimating the direction and length of the satellite's main axis using Principal Component Analysis (PCA); extracting the axis features of the solar panels using DBSCAN clustering and L-shaped rectangle search algorithms; and finally, constructing a projection geometric model of the target from a 3D to a 2D ISAR imaging plane by combining radar line-of-sight information, establishing an optimization objective function, and solving for the satellite attitude angles using a particle swarm optimization algorithm. This invention fully integrates the spatiotemporal information of sequential images with the geometric constraints of the target, effectively overcoming the projection variation problem caused by target motion in ISAR imaging, achieving high-precision satellite attitude estimation, and possessing strong anti-interference capabilities and practical application value.

The application discloses a power equipment overheating fault early warning method based on principal component analysis, and relates to the technical field of power equipment state monitoring, comprising: collecting multi-source monitoring data, performing preprocessing, and generating standardized time series data set; performing principal component analysis on the standardized data, and obtaining score sequence and characteristic vector; constructing a thermodynamic evolution interval, comparing evolution trajectories, and generating deviation judgment results; calculating disturbance propagation speed, dynamically modeling change relationship, and generating break judgment results; reconstructing residual time series data, inputting improved MTGNN, and identifying propagation path and source node; and comprehensively integrating all early warning indexes, and outputting overheating grade and overheating point position. The application realizes early warning of power equipment overheating fault and accurate positioning of potential overheating points by performing principal component analysis on multi-source operation data of power equipment and combining graph structure time series modeling to jointly analyze abnormal thermal evolution characteristics and propagation behavior.

This application relates to the field of bearing remaining life prediction technology, and discloses a method and system for predicting bearing remaining life. The method includes: acquiring multi-channel vibration signals of the bearing during operation; extracting multi-domain features from the vibration signals of each channel to obtain a multi-domain feature vector at the current moment; the multi-domain features include at least one of the following: time-domain features, frequency-domain features, time-frequency-domain features, envelope spectrum features, cepstral features, and bearing-specific features; performing standardization, principal component analysis dimensionality reduction, moving mean smoothing, and difference target transformation on the multi-domain feature vector in sequence to obtain a preprocessed low-dimensional feature vector; inputting the low-dimensional feature vector into a feature prediction model to output the difference prediction value for the next moment; and obtaining the degradation feature prediction value for the next moment based on the difference prediction value and the current low-dimensional feature vector; and calculating the remaining life of the bearing based on the degradation feature prediction value for the next moment. This application significantly improves the stability and accuracy of the prediction.

This application discloses a method and system for image segmentation. One method for image segmentation includes: acquiring a two-dimensional image to be segmented and a first user cue corresponding to the two-dimensional image to be segmented; generating a two-dimensional affinity map based on the two-dimensional image to be segmented using a two-dimensional affinity prediction module; converting the first user cue into a first cue map; generating a first two-dimensional mask based on the two-dimensional affinity map and the first cue map using a two-dimensional mask prediction module; and performing connected component analysis on the first two-dimensional mask to generate a first segmented two-dimensional image, wherein different regions in the first segmented two-dimensional image have different labels.

This invention relates to the field of biomedical imaging data processing technology, and discloses an AgInS quantum dot bioimaging image enhancement and recognition system and method. The system includes an excitation light source, a gated camera, a timing controller, and a data processing unit. The method includes: receiving image frames under different delay time windows to construct a three-dimensional spatiotemporal data matrix; orthogonalizing pre-calibrated long and short lifetime prior vectors to generate an initial target basis, and calculating the inner product with pixel temporal vectors to generate an initial target weight matrix; segmenting and eroding the matrix to extract a high-confidence background mask, and inversely mapping and averaging to generate a dynamic background basis; re-orthogonalizing and calculating the inner product using the dynamic background basis to obtain the final target weight matrix; numerically mapping the matrix and performing connected component analysis to output the results. This invention achieves real-time adaptive updating of background reference features, improving target contrast and target recognition accuracy in complex living environments.

This invention discloses a static vector model generation method based on distillation, comprising: obtaining all independent lexical units in the basic model's word segmenter; inputting each independent lexical unit individually into the basic model for forward propagation to extract representations and constructing an initial embedding matrix; applying principal component analysis to the initial embedding matrix for dimensionality reduction; and using a smooth inverse frequency weighting algorithm based on Zipf's law, employing vocabulary ranking as a proxy variable to calculate lexical probabilities, and performing weighted optimization on the dimensionality-reduced embedding matrix to generate the target static vector model. This invention eliminates the dependence of traditional model distillation on massive amounts of data and GPUs, simplifies complex Transformer nonlinear inference to an extremely fast table lookup operation through computational collapse, and retains most of the deep semantic understanding capabilities of the original model while reducing model size and increasing CPU inference speed.

This invention discloses a method, apparatus, equipment, and storage medium for predicting the minimum miscibility pressure of crude oil. The method includes acquiring the target crude oil for which miscibility pressure prediction is to be performed and the target gas for injecting into the target crude oil for oil displacement. The target crude oil and target gas are subjected to component analysis to determine the necessary component parameters for predicting the minimum miscibility pressure. Based on a pre-fitted target miscibility pressure prediction model, the target minimum miscibility pressure for the miscibility of the target crude oil and target gas is determined according to the necessary component parameters. The target miscibility pressure prediction model is divided into a first miscibility pressure prediction model and a second miscibility pressure prediction model based on the gas type of the target gas. This invention is applicable to injection gas scenarios involving pure CO2 or flue gas containing different impurities. By separately calculating the miscibility pressure contributed by the oil phase itself and the miscibility pressure contributed by the injection gas, the accuracy and applicability of minimum miscibility pressure prediction can be significantly improved.