35 results about "Kernel principal component analysis" patented technology

The application relates to a method and device for monitoring over-error of a capacitive voltage transformer, electronic equipment and a computer readable storage medium. The method comprises: performing kernel principal component analysis based on historical sample data to determine an SPE statistical quantity threshold; calculating the SPE statistical quantity of current sample data collected in real time; if the SPE statistical quantity is less than or equal to the SPE statistical quantity threshold, determining that the operation state of the transformer group is normal; and if the SPE statistical quantity is greater than the SPE statistical quantity threshold, calculating the contribution rate of each element in the current sample data to the square prediction error statistical quantity, determining a fault element, and locating an over-error transformer in the transformer group. The application effectively processes the nonlinear problem of data by using KPCA, more accurately extracts data features, realizes fault element separation, accurately locates the over-error CVT, performs online real-time monitoring on the CVT error, and improves the stability and reliability of the power system.

The invention provides a third industry user-oriented load adjustable characteristic evaluation and interactive regulation and control method, and aims to improve the demand side resource adjustable capability and support supply and demand coordinated regulation and control under a novel power system. The method comprises the following steps: firstly, collecting typical three-product user loads and multi-source heterogeneous data, after preprocessing and feature extraction, realizing feature dimension reduction by adopting kernel principal component analysis, and completing typical user load clustering through a K-Means clustering algorithm; and secondly, constructing a multi-dimensional user portrait evaluation system based on a clustering result, and quantitatively evaluating the adjustable potential of the typical user load in combination with a multi-dimensional time sequence load model. And finally, establishing a multi-constraint optimization scheduling model, introducing a particle swarm optimization algorithm for solving, and outputting targeted regulation and control strategies and regulation instructions of different time periods and different devices. According to the method, refined evaluation and rapid scheduling optimization of the three-production user load adjustable potential are effectively realized.

This invention provides a method for predicting aging based on metabolite profiles using nonlocal variance enhancement, relating to the fields of medical data analysis and metabolomics. The method acquires LC-MS and / or GC-MS metabolite profile data and the actual age of the subject to be predicted. It performs missing data checks and intra-class normalization on the data, constructs a class-constrained nonlocal variance enhancement feature extraction model, and combines multi-kernel principal component analysis and linear multi-view fusion to obtain fused metabolite profile features. The fused features are then input into a support vector regression model to output predicted age values. The aging degree is determined based on the difference between the predicted and actual age values, which improves the feature expression ability and interpretability of metabolite profile age prediction.

The invention discloses a high-voltage circuit breaker health state assessment method and system based on multi-source data fusion, and the method comprises the steps: constructing a health index model based on multi-source data fusion, and carrying out the effective dimension reduction of complex features in the multi-source data of a high-voltage circuit breaker through the kernel principal component analysis; and the dimensionality-reduced features are fused through a weighted mahalanobis distance to obtain a one-dimensional health index capable of comprehensively reflecting the operation state of the equipment. On the basis, the degradation starting point is identified based on the degradation rate, and the normal state and the potential fault state of the equipment are divided, so that the problems of dependence on a single data source, incomplete evaluation, incapability of early identification of performance degradation and the like in the prior art are solved, and the change of equipment performance can be identified earlier; and accurate evaluation and predictive maintenance of the health state of the high-voltage circuit breaker are realized.

This invention discloses a method and system for detecting fertilizers, relating to the field of fertilizer detection technology using image extraction. The method includes: segmenting particle images using a multi-scale sliding window, extracting hue, saturation, and brightness components, and combining this with an illumination compensation model to eliminate illumination effects and noise interference. Then, high-dimensional feature vectors are compressed using kernel principal component analysis to construct gradient direction distribution and texture consistency analysis, accurately locating defect areas. Subsequently, by comparing the local features of defective and normal areas, local features of the defective area are extracted, and the texture consistency and color component differences of adjacent sub-blocks are analyzed to output the defect location coordinates and a comprehensive quality rating. This invention establishes a fertilizer visual feature quantification system adapted to complex working conditions, achieving a stable mapping between particle physical properties and imaging features, and improving the accuracy of automated detection.

The application discloses an IGBT residual life prediction method based on multi-feature fusion and KPCA optimization. The method first collects IGBT collector current and voltage and other multi-source signals, extracts time domain, frequency domain, time-frequency domain and derived statistical domain features after pretreatment; then adopts a two-stage strategy of comprehensive evaluation index preliminary screening and mutual information regression fine screening to eliminate redundant features and retain high correlation features. On this basis, nonlinear dimension reduction is carried out by using kernel principal component analysis to construct a high-robustness health index, and the exponential weighted moving average and adaptive gradient detection are combined to accurately divide the degradation into three stages. Finally, the CNN-BiLSTM model is used to deeply mine the time sequence degradation features, and the MC Dropout algorithm is introduced to realize the accurate prediction and uncertainty quantification of the residual life. The application effectively solves the one-sidedness of single-source signal representation and the feature redundancy interference problem, and significantly improves the prediction accuracy and reliability.

The invention provides a seismic data filtering method and device based on kernel principal component analysis and a medium, and belongs to the field of seismic data processing. The method comprises the following steps: extracting a trend time difference attribute of an input three-dimensional seismic data volume; setting a surface element size and extracting a corresponding line data volume according to the surface element size; selecting a target point and calculating surface element coordinates according to the trend time difference data; setting the size of a time window and extracting a small three-dimensional data volume in combination with surface element coordinates; and performing kernel principal component analysis on the small three-dimensional data volume to obtain a first principal component, and taking central point data of the first principal component as filtered data of the target point. According to the method, original data are mapped into a high-dimensional space through nonlinear mapping by adopting kernel principal component analysis, so that nonlinear structures and characteristics in the original data can be better reserved; in addition, the trend time difference attribute of seismic data is also considered, geologic structure data can be more accurately obtained by opening up a time window along the event trend, the problem of data discontinuity caused by the influence of stratum inclination is improved, and the filtering effect is improved.

The invention discloses a construction method for a CO2 emission prediction model in the iron and steel industry. The construction method comprises the steps of data collection, cleaning and normalization processing; normalized CO2 emission data is input into an empirical mode decomposition (EMD) module, the non-stationarity of the data is reduced, and kernel principal component analysis (KPCA) processing is carried out on each IMF component to extract nonlinear features of the IMF component and reduce the dimensionality of the IMF component. And taking the IMF component after dimension reduction and the residual term as input, constructing a long short-term memory (LSTM) network for training and prediction, modeling and predicting the CO2 emission data after dimension reduction through an LSTM model obtained through training, and finally obtaining a prediction result of the CO2 emission. According to the method, the EMD method, the KPCA method and the LSTM method are combined, and the problems that according to an existing prediction method, data non-stationarity and non-linear feature mining is not deep, the feature extraction efficiency is low, and the long sequence data processing capacity is limited are effectively solved.

The application relates to the field of medical information technology and discloses a psychological disease treatment effect evaluation method and system adopting data analysis. The method comprises the following steps: classifying and standardizing physiological parameters based on a model; obtaining a dimension reduction vector of a sleep quality parameter through kernel principal component analysis, and then obtaining a cross-modal correlation feature through nonlinear mapping of the sleep quality parameter and a voice emotion parameter; calculating a correlation coefficient, adjusting a kernel function parameter in combination with an emotional stress parameter if the correlation coefficient exceeds a threshold value, and then reextracting the feature; obtaining a dynamic weight coefficient through the cross-modal correlation feature and a weight mechanism, and weighting a treatment effect index; and identifying an index abnormal mode through abnormal detection to generate a personalized evaluation report. Through multi-dimensional physiological data analysis and dynamic adjustment, the application solves the problems of lacking objective multi-dimensional continuous monitoring and dynamic personalized analysis in evaluation, and improves the evaluation accuracy and the personalized level.

The application relates to a kind of industrial robot position error and its interval prediction method and electronic equipment, the method comprises the following steps: constructing data set containing joint angle vector and position error;Using kernel principal component analysis, joint angle vector is projected to high-dimensional space, feature dimension is upgraded;After feature dimension is upgraded, feature and corresponding position error are respectively used as the input and label of bayesian neural network, and bayesian neural network is trained;Test set data is input to trained bayesian neural network after being upgraded according to the same method, and the prediction of position error and its confidence interval is output.Compared with prior art, the application has the advantages of realizing the prediction of robot position error and interval, more accurate prediction value and good applicability.

The invention provides a coal-fired unit group comprehensive energy consumption curve generation method based on kernel principal component analysis. According to the coal-fired unit group comprehensive energy consumption curve generation method based on kernel principal component analysis, the real energy consumption characteristics of a pure condensing type coal-fired unit group can be reflected more accurately, the stability and reliability of the comprehensive energy consumption curve are improved, and scientific measurement and calculation of variable cost and market mechanism design in the electricity market are effectively supported.

This application discloses a method for determining model parameters, a method for classifying vehicle abnormal noises, an apparatus, and a device. The method includes: acquiring a training dataset of vehicle abnormal noise features; generating an initial population based on the training dataset; the population containing multiple individuals; constructing a kernel principal component analysis (KPC) model based on preset model parameters; the KPC model is used to extract target abnormal noise feature vectors from the vehicle's abnormal noise features for vehicle abnormal noise classification; determining an objective function based on the KPC model; iteratively updating the initial population based on the objective function to obtain an updated population; determining the optimal individual from the updated population; and determining the target model parameters of the KPC model based on the optimal individual and a preset parameter range. This method can determine the target model parameters of the KPC model most suitable for the current abnormal noise environment, thereby improving the classification accuracy of vehicle abnormal noise classification.

The invention provides a fire range extraction method based on a nuclear combustion index and adaptive spatial spectrum optimization, and relates to the technical field of remote sensing image processing, and the method comprises the steps: collecting remote sensing image data; mapping a spectral vector corresponding to the fire image data set to a high-dimensional space based on a Gaussian radial basis kernel function, and performing kernel principal component analysis on the spectral vector to construct a kernel difference combustion index; according to kernel density estimation, performing adaptive threshold segmentation on the kernel difference combustion index to obtain an initial fire pixel detection result; constructing a spectrum space composite kernel similarity matrix, and optimizing the initial fire pixel detection result in a conditional random field according to the spectrum space composite kernel similarity matrix to obtain a transition fire pixel detection result; and performing morphological operation and small patch area filtering on the transition fire pixel detection result to obtain a predicted fire pixel detection result. According to the invention, the overall precision of fire range extraction can be improved.

The invention discloses a gear remaining life prediction method based on multi-feature fusion and an Informer model, and belongs to the technical field of mechanical equipment fault prediction and health management. The method comprises the following steps: firstly, acquiring data to obtain a full-life-cycle vibration signal of a gear; then, extracting an initial feature set capable of representing performance degradation from multiple dimensions of a time domain, a frequency domain and a time-frequency domain; then, a feature fusion method based on kernel principal component analysis is adopted to carry out dimension reduction and fusion on the high-dimensional initial features, and single, sensitive and monotonous health indexes are constructed; and finally, the constructed health index sequence is used as input, and the residual service life of the gear is directly predicted by using the strong long sequence time sequence prediction capability of the Informer model. According to the method, the characterization capability of health indexes is effectively improved through multi-feature fusion, the limitation of a traditional recurrent neural network in long sequence prediction is overcome by using the Informer model, and more accurate and more stable prediction of the gear remaining life is realized.

The invention discloses a multivariate quality detection method and system based on wavelet packet decomposition and kernel principal component analysis, social security and a medium, and the method comprises the steps: setting a collection frequency, and obtaining a rolling bearing vibration signal data set through collecting vibration data of different positions in real time; the method comprises the following steps: carrying out multilayer wavelet packet decomposition on a rolling bearing vibration signal, and mapping nonlinear data to a high-dimensional space by using a Gaussian kernel function on high-frequency and low-frequency signals decomposed from the vibration signal and sub-bands after wavelet packet decomposition; performing nonlinear dimensionality reduction modeling on the sub-band signals one by one, extracting principal components, calculating corresponding principal component scores, and calculating corresponding Tstatistics; determining the control limit of each sub-band signal by calculating the historical data distribution of the Tstatistics; and comparing the Tstatistic calculated in real time with the control limit, judging whether the process is abnormal or not, and if the Tstatistic exceeds the control limit, judging that the process is abnormal. According to the invention, weak anomalies under complex working conditions can be effectively identified.

The invention discloses a series lithium battery pack internal short circuit fault diagnosis method, system and device and a medium, and the method comprises the steps: employing the historical voltage data of a single cell of a lithium battery pack as the input of a kernel principal component analysis model, and employing a first square prediction error in a residual subspace as an internal short circuit detection parameter; according to the first square prediction error, a self-adaptive square prediction error threshold value based on kernel density estimation is obtained, and the self-adaptive square prediction error threshold value serves as an internal short circuit detection threshold value; and inputting the obtained voltage data of the single battery cell into the kernel principal component analysis model, obtaining a second square prediction error, comparing the second square prediction error with an adaptive square prediction error threshold, and judging whether an internal short circuit fault occurs or not. According to the method, voltage data feature mining is taken as a core, the algorithm structure is simple, the calculated amount is small, a complex mechanism model does not need to be constructed, the diagnosis time is short, the precision is high, and real-time identification and early warning of the short-circuit fault in the battery cell can be realized.

The application discloses a kind of based on new health index bearing life prediction method, specifically related to bearing residual life prediction technical field, comprising the following steps: S1, the vibration signal of rolling bearing is collected using vibration sensor, and the vibration signal is pretreated;S2, the health index of bearing is constructed from the multi-domain feature extracted from the vibration signal after pretreatment;S3, establish health index degradation model, set failure threshold, then according to the probability distribution of the time when the first failure threshold is reached by equipment degradation, determine the prediction of bearing residual life;The present application reduces the fluctuation caused by noise through completely self-adaptive noise ensemble empirical mode decomposition and kernel principal component analysis;Compared with the current residual service life prediction method, multi-domain features are extracted, so that the bearing health index constructed contains rich degradation information, and the accuracy of the prediction result is improved.

The application discloses a multi-feature extraction and fusion method for magnetic anomaly detection, which is a method for pre-processing data of a magnetic anomaly signal, selecting time-frequency features, statistical features and target magnetic moment features of the magnetic anomaly signal as multi-features of the magnetic anomaly signal, performing multi-feature fusion in a feature layer by using a kernel principal component analysis method, performing data processing on the magnetic anomaly signal, and providing effective input information for a neural network framework. The method can detect the magnetic anomaly signal under a low signal-to-noise ratio, improves detection efficiency, reduces a false rate, and improves performance of the neural network.

The invention provides a low-cost bridge structure health monitoring and intelligent sensing method based on multivariate data fusion, and relates to the technical field of bridge structure health monitoring. VMD parameters are adaptively optimized by using a fruit fly optimization algorithm to realize vibration signal noise reduction, key features are extracted in combination with kernel principal component analysis, and a Fisher discriminant analysis model is constructed to realize intelligent diagnosis; the method can effectively improve the accuracy of bridge disease recognition, has the advantages of low deployment cost, high algorithm adaptability, stable diagnosis effect and the like, and is suitable for long-term monitoring and early disease recognition of a bridge structure.

The invention discloses a low-altitude traffic flow prediction method and system based on machine learning, and belongs to the technical field of low-altitude traffic management. The method comprises the following steps: deploying a monitoring network composed of a low-altitude traffic management platform, a monitoring station, a Beidou positioning system and a navigation unit communication system, and obtaining multi-source heterogeneous data; constructing a data processing model, performing normalization and kernel principal component analysis dimension reduction processing on the data, and extracting a principal component information set; analyzing a low-altitude traffic road network feature structure based on the set, and combining historical and current traffic flow information to construct a multi-dimensional prediction system; through the route time period flow analysis model and the road resistance prediction function, accurate analysis and prediction of the traffic flow and the comprehensive road resistance cost of any route are realized. According to the method, the low-altitude traffic flow change trend can be effectively predicted, and reliable data support and decision basis are provided for planning and management of low-altitude flight activities.

The invention provides a short-term power load interval prediction method based on kernel principal component regression analysis. The method comprises the following steps: acquiring historical power load data and corresponding historical power load influence data; clustering the historical power load influence data, and determining the historical power load influence data corresponding to each power consumption scene according to a clustering result; for the historical power load influence data corresponding to each power consumption scene, performing dimension reduction based on kernel principal component analysis, and extracting main power load influence characteristics corresponding to each power consumption scene; constructing a load interval prediction model based on the main power load influence characteristics corresponding to each power consumption scene and the corresponding historical power load data; and performing interval prediction on the short-term power load of the power system according to the load interval prediction model corresponding to each power consumption scene. The method can provide an interval prediction result for short-term power load prediction of the power system, overcomes the uncertainty of the prediction result, and gives consideration to the prediction efficiency.

This invention discloses an offline diagnostic method for nonlinear feature compression, initial screening, and hierarchical evaluation of sampling channels of substation voltage transformers. Addressing the offline risk assessment needs of substation voltage transformer sampling channels, this method first constructs a unified output health discriminant factor by combining kernel principal component analysis and entropy weighting, reducing redundancy and enhancing cross-site robustness. An extreme learning machine is introduced to perform offline initial screening of sample vectors, focusing on abnormal sampling channel segments that meet the anomaly judgment criteria to improve batch processing efficiency. Based on fuzzy membership functions and a monotonic mapping relationship between predefined risk levels and scores, multi-level risk classification of initial screening candidate samples is achieved. Furthermore, trend quantities are combined to correct risk evolution for risk decision-making, ensuring that the score remains stable near the boundary zone and is more sensitive to abrupt changes. This method effectively improves the offline data fault diagnosis and location capabilities of voltage measurement links under conditions of multiple disturbances and non-stationary operation.

The invention discloses a multispectral-based insulator smudginess detection and evaluation method, which comprises the following steps of: firstly, performing multi-angle image acquisition on an insulator by using an imaging system carrying a visible light camera and an infrared camera, and completing preprocessing such as image denoising, enhancement, registration and radiometric calibration; secondly, accurately segmenting an insulator region from the preprocessed visible light image by using a Sobel algorithm and a watershed algorithm, and extracting spectral line features; carrying out spectral line feature screening and fusion by adopting an iterative information variable preserving algorithm, carrying out secondary fusion and dimensionality reduction through kernel principal component analysis in combination with features such as colors and textures, and constructing a fusion feature set for identifying smudginess types; and finally, combining the infrared texture features to construct an integrated learning model, and outputting the smudginess degree.

The application provides a kind of sensing control system fault prediction method and device, it is related to sensing control technical field.The application obtains multiple equipment sets by dividing equipment in sensing control system according to working phase;Then real-time acquisition parameter information of equipment in each equipment set;According to parameter information of equipment in each equipment set respectively, using the health assessment model corresponding to each equipment set is evaluated, and the evaluation result of each working phase is obtained;According to the evaluation result of each working phase, determine fault feature from parameter information of equipment in each equipment set;Finally, according to fault feature, using the fault prediction model constructed based on kernel principal component analysis algorithm and random forest algorithm to predict the fault of sensing control system, and the system fault prediction result is obtained.The fault prediction result is more accurate, and the fault feature obtained is effective fault feature, which further improves the accuracy of system fault prediction, and improves the reliability of the whole sensing control system.

The application discloses a photovoltaic inverter operation abnormality detection method based on a kernel principal component recombination technology, how to further mine directly effective features reflecting abnormal changes by recombination on a plurality of nonlinear principal components extracted by kernel principal component analysis, so as to realize abnormality detection on the operation state of the photovoltaic inverter by monitoring the fluctuation of the features. Specifically, the method of the application generates recombination features that can best reflect the difference by recombining the elements in the kernel principal component vector through maximum-minimum difference feature analysis, and the recombination features are used for photovoltaic inverter operation abnormality detection. Compared with the conventional method of using a nonlinear modeling algorithm to perform abnormality detection, the method of the application obtains the recombination features that can best reflect the difference between online data and normal data in real time through recombination. Therefore, the method of the application has the significant features of self-adaptation and updating change in theory.

The invention belongs to the technical field of fault detection and diagnosis in a flow industrial process, and discloses a paper machine fault detection and diagnosis method based on multi-section sliding window kernel reconstruction analysis, which comprises the following steps of: constructing a sample through a sliding window mechanism, and performing nonlinear dimension reduction and reconstruction error extraction by adopting kernel principal component analysis; the modeling capability of the model for the complex nonlinear relationship is enhanced; a kernel density estimation method is introduced on the basis of reconstruction error statistics to adaptively set a control limit, and sensitive detection of tiny faults is achieved; by performing variable-level weighted analysis on the reconstruction error, main abnormal variables when the fault occurs are identified, and high-precision fault source positioning is realized. In order to verify the effectiveness of the method, the method is applied to typical fault scenes of multiple working sections in the papermaking process, the experimental result shows that the method can accurately detect and diagnose key abnormal variables in various faults, and the method has high fault interpretability and diagnosis reliability. The method is suitable for abnormal identification and process monitoring of key variables in a complex industrial process.

The invention discloses a shale gas-bearing characteristic modeling method and system, and the method comprises the steps: carrying out the preprocessing of collected logging parameters, and obtaining the preprocessed logging parameters; identifying the preprocessed logging parameters through correlation analysis to obtain logging parameters significantly related to the shale gas content Vg, and screening sensitive parameters; performing principal component extraction and dimension reduction on the sensitivity parameters through a KPCA kernel principal component analysis method; and on the basis of the data subjected to dimension reduction, multiple models for predicting the shale gas content are constructed, and the multiple models are evaluated and compared to determine a final prediction model.

The invention provides an equipment residual life prediction data preprocessing method based on multi-scale dynamic label re-calibration. The method comprises the following steps: S1, multi-scale time delay embedding; s2, kernel principal component analysis health index construction; s3, zero-phase finite impulse response filtering is carried out; s4, performing multi-scale HI fusion and dynamic slope calibration; and S5, dynamic segmentation RUL label reconstruction is carried out. According to the method, through multi-scale time delay embedding, the performance of degradation dynamics on different time granularities is captured, and the information content of health indexes is enriched. KPCA and zero-phase FIR filtering are combined, a smooth and continuous health index curve capable of reflecting nonlinear degradation essence is effectively extracted, and a foundation is laid for accurate FPT recognition. According to the method, the FPT is calibrated and positioned through the dynamic slope, and the segmented RUL tag is reconstructed according to the FPT, so that the limitation of a fixed threshold method is overcome, and the tag can more truly reflect the degradation behavior of individual equipment.

The application belongs to the technical field of electromechanical system simulation, and particularly relates to an electromechanical simulation model evaluation method based on AI analysis, comprising the following steps: data preprocessing, AI evaluation engine construction, dynamic index evaluation and closed-loop optimization; an electromechanical simulation model evaluation system based on AI analysis, comprising the following hierarchical architecture: a data preprocessing layer, an AI evaluation engine layer, a dynamic evaluation layer and a closed-loop optimization layer, one of kernel principal component analysis or variational autoencoder is introduced for nonlinear feature extraction, game theory Nash equilibrium is introduced for dynamic distribution of model weights, and a sliding window anomaly detection is introduced for dynamic adjustment of threshold values. The application initiates the "physical rules + AI reasoning" dual-drive evaluation architecture of electromechanical simulation models; the dynamic weight distribution algorithm improves the evaluation efficiency compared with traditional methods; three-level indexes are gradually converged to realize comprehensive evaluation from local performance to global efficiency.

A hyperspectral image classification method and system, (1) using information divergence to measure the similarity between bands and based on the idea of clustering to divide the subspace, and finally the spectral average value of the subspace is stacked as the spectral feature output; (2) using relative total variation technology under different parameter settings to extract multi-scale spatial features; (3) using kernel principal component analysis to reduce the dimension of data, and obtaining the final space-spectrum joint feature; (4) using the SVM model with RBF kernel to classify the space-spectrum joint feature, and proving that the algorithm improves the classification accuracy on two hyperspectral data sets.