27 results about "Kernel method" patented technology

The invention discloses a semi-supervised multi-view clustering method based on double-constraint concept decomposition, which relates to the technical field of machine learning, combines point constraint with pairwise constraint, constructs a double-constraint algorithm based on prior label information of a complementary supervision mechanism, and realizes maximum utilization and progressive propagation of limited supervision information. And based on a concept decomposition framework, breaking through the limitation of a traditional method on non-negativity of data, processing complex data distribution in combination with a kernel method, obtaining a consensus matrix of multi-view data fusion as low-dimensional representation of views, performing clustering by adopting a k-means algorithm, and outputting a final category division result. According to the method, effective processing of complex multi-view data is realized, finally, the performance of a multi-view clustering task is comprehensively improved, and the bottlenecks in clustering precision, data adaptability and supervision information utilization efficiency in the prior art are broken through.

This invention discloses a drought early warning method based on energy flux, causal relationship analysis, and machine learning. The method includes the following steps: S1, acquiring energy flux and drought indicators, determining the optimal lag time through causal reasoning, and constructing a multi-order lag feature set; S2, establishing a tree model, using regression / kernel methods and a time series model candidate set, optimizing hyperparameters through particle swarm optimization, integrating two layers in a stacked manner, adaptively optimizing the performance of comprehensive regression and event recognition, and outputting a predicted sequence; S3, setting multi-level early warning rules according to drought thresholds, mapping drought levels, and evaluating effectiveness through statistical precision and recall; S4, calculating contribution using an additive feature attribution algorithm, identifying nonlinear thresholds to form sensitivity analysis, and improving interpretability. This invention achieves a 7-11 month early warning prediction of drought based on energy flux, with a drought early warning recall rate of 66.67%-75.86%, significantly improving the accuracy and interpretability of drought early warning.

The invention provides a Mendel randomization nonlinear causal inference method based on genome research, and relates to the technical field of biological genetic data analysis, and the method comprises the following steps: obtaining a tool variable, an exposure factor and a result variable, and carrying out data preprocessing to form a structured input matrix; respectively constructing a tool variable nonlinear model and a causal effect nonlinear model; calculating a loss function of the model, and adding a regularization item for joint optimization; optimizing model parameters by using a GBDT kernel method; updating the model based on the optimized model parameters and drawing a nonlinear response curve of the exposure factors and the result variables; replacing the tool variable with a pseudo tool variable to repeat the above steps to verify a causal inference result, and inferring a causal effect according to the verified causal inference result; according to the method, the limitation of assuming a linear relation and processing high-dimensional data in a traditional causal inference method can be overcome, and a more accurate, robust and interpretable causal inference result is provided.

The invention discloses a distributed MIMO radar AOA target positioning method based on kernel recursive least squares, and the method comprises the steps: carrying out the nonlinear mapping of AOA measurement data obtained by a radar to a high-dimensional RKHS based on a kernel method, converting a nonlinear target position estimation problem into a linear parameter estimation problem in the RKHS, carrying out the online learning through employing the obtained AOA measurement data, and carrying out the positioning of a target position. And outputting a position estimation result of the target on line to complete target positioning. According to the method, effective target positioning precision can be provided under the condition that measurement error prior information is unknown, target positioning precision higher than that of an existing method can be obtained under the condition that measurement errors are large, and the problem that timeliness still needs to be guaranteed under the condition that the requirement for radar target positioning precision is high is solved.

This solution discloses a new method for identifying RNA pseudouridine sites. This method proposes to use a variety of feature representation techniques to extract sequence features, and then uses the SVM-RFE method for feature selection to compress the feature space and optimize the feature subset. The best feature set after feature selection is input into the kernel method KeMRF based on polynomial random forest to identify pseudouridine sites in the sequence. As a newly proposed classification method, compared with the traditional random forest, KeMRF not only optimizes the discriminant criterion for node splitting, but also combines with an easy-to-interpret kernel method, making the classification performance more superior. This method reduces the training time of the model, improves the classification performance of the model, and further enhances the accuracy of identifying pseudouridine sites.

The application discloses a semi-supervised multi-view clustering method based on double-constraint concept decomposition, relates to the technical field of machine learning, combines point constraints with pair constraints, constructs a double-constraint algorithm based on prior label information of a complementary supervision mechanism, realizes maximum utilization and progressive propagation of limited supervision information, breaks through the limitation of data non-negativity of a traditional method by relying on a concept decomposition framework, processes complex data distribution by combining a kernel method, obtains a consensus matrix of multi-view data fusion as low-dimensional representation of a view, adopts a k-means algorithm for clustering, and outputs a final class division result. The application realizes effective processing of complex multi-view data, finally comprehensively improves the performance of a multi-view clustering task, and breaks through the bottleneck of the prior art in clustering precision, data adaptability and supervision information utilization efficiency.

This invention discloses a method for predicting the load of stochastic workflows with missing data. The method includes: a workflow preprocessing stage, where the workflow is transformed into an embedded vector form and clustered based on historical data similarity; a workflow missing value completion and load prediction stage, where a kernel density missing data filling method based on common features is used to fill in missing data, and a workflow load analysis and prediction method is proposed based on the characteristics of the computing network-cloud-edge-device structure and historical information on workflow computation, storage, and communication; a cluster modeling and feature engineering stage; and a cluster load prediction stage, where this invention combines kernel methods and Kalman filtering techniques to predict missing values ​​and workflow load, optimizing the robustness and generalization of the prediction. This method has broad application value and promising prospects in the fields of load prediction and workflow scheduling.

Embodiments of the present disclosure provide functionality to tensor network framework designed for quantum kernel methods and demonstration of tensor network effectiveness at scaling this application. Quantum kernels capture the distance between data points in quantum feature space by evaluating the quantum state overlaps associated with each data point. It has been found that expressing data in quantum feature space may produce more separable data that improves the results of linear classifiers. The different kernel elements may be computed independently, and parallel processing may be exploited to significantly reduce computational time, enabling to train on more data. Thus, quantum kernels continue to improve classification metrics with the addition of more training data and more features.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for quantum machine learning. In one aspect, the method includes obtaining, by a quantum computing device, a training dataset of quantum data points; computing, by the quantum computing device, a kernel matrix representing a similarity between quantum data points included in the training dataset, including computing a value of a kernel function for each pair of quantum data points in the training dataset, wherein the kernel function is based on a reduced density matrix of the quantum data points; and providing, by the quantum computing device, the kernel matrix to a classical processor, wherein the classical processor uses the kernel matrix to perform a training algorithm to construct a machine learning model.

The invention provides an electromagnetic full-wave inverse scattering imaging method based on shape prior information, and relates to the field of machine learning and electromagnetic full-wave inverse scattering. Aiming at the problem that shape prior, nonlinear modeling and physical interpretability are difficult to effectively integrate and realize in the field of electromagnetic full-wave inverse scattering imaging, the invention integrates shape prior, physical consistency and a kernel method, and relates to the field of machine learning and electromagnetic full-wave inverse scattering. According to the method, singular value decomposition (SVD) is utilized, a low-dimensional shape parameter is adopted to carry out continuous microparameter modeling on a target boundary, and a high-dimensional pixel space is mapped to a structured prior space. Physical constraints are enforced to ensure consistency between state variables and measurement data. In addition, a kernel method is introduced to carry out nonlinear modeling on state-residual mapping, and a dual-time-scale random optimization strategy is adopted to balance parameter flexibility and physical interpretability. According to the method, excellent real-time reconstruction performance is realized in a high-noise and complex-structure scene, and a new stable and explainable method is provided for electromagnetic full-wave inverse scattering imaging.

The application provides a quantum kernel method, a classification method, a data coding method and related systems and devices, which are applied to a quantum computer or a quantum simulator. The method comprises: encoding any two m-dimensional data in a plurality of m-dimensional data into n quantum bits through a quantum circuit corresponding to a preset kernel function, m and n are positive integers, and m>n; measuring the encoded n quantum bits to obtain quantum kernel inner products of the two encoded m-dimensional data in a Hilbert space, thereby obtaining a quantum kernel matrix corresponding to the plurality of m-dimensional data. In the application, multi-dimensional data is encoded into a smaller number of quantum bits without compressing data feature information as much as possible, the calculation of the quantum kernel method is completed, the running time and the memory space are saved through the compression of the number of bits, and the calculation efficiency is improved.

The application provides a method for rapidly detecting seed germination rate, comprising: obtaining a plurality of seed samples with known germination rates, performing volatile organic compound detection on each sample to obtain volatile component data, and after data cleaning and arrangement, constructing a model of a machine learning algorithm of an integrated model, a linear model and a kernel method; using standardized feature data to establish an optimal prediction model of seed germination rate, which can be used for testing of unknown seed germination rate samples. Compared with a traditional manual statistical detection method, the detection period of the traditional method is compressed to within 2 hours, the detection efficiency is greatly improved, the manual germination and grain-by-grain counting operations are omitted, the labor input is significantly reduced, the human subjective error is reduced, the objectivity and repeatability of the detection result are greatly improved, and the error with the actual measurement value is within ± 5%. The method can be widely applied to seed quality detection scenes of various crops, and has good industry application and promotion value.

This invention discloses a soft measurement modeling method for multiphase intermittent processes based on an improved geodesic manifold kernel. The method includes: collecting platform parameters from a cloud server; projecting source and target domain data onto a common manifold subspace using a geodesic manifold kernel method based on linear local tangent space arrangement to reduce the data distribution differences between the source and target domains; using a time-series-based fuzzy clustering method to divide the source domain data into phases, obtaining phase division points; and establishing sub-phase soft measurement models based on different phase characteristics using different modeling methods. For phases with slowly changing process characteristics, partial least squares regression is used to establish the soft measurement model; for phases with rapidly changing process characteristics, partial least squares regression based on real-time learning is used to establish the soft measurement model. This method establishes sub-phase soft measurement models for multiphase intermittent processes, enabling the estimation of difficult-to-measure variables and improving the performance of the soft measurement model under varying operating conditions.

According to the present invention, the estimation accuracy of machine learning by a quantum kernel method is improved. A quantum computer (3) estimates a kernel function k(xk,xl) of two feature vectors xk, xl (xk, xl have n elements in total, and n is an integer of 2 or more) using the two feature vectors xk, xl and phase parameters φp ,q(x) (xp and xq are the p-th and q-th elements, respectively, in one of the two feature vectors xk, xl). The phase parameters φp, q(x) are calculated by φp, q(x)=(π-αpxp)×(π-αqxq) using adjustment parameters αp and αq.

The invention belongs to the technical field of image processing, and particularly relates to a hyper-spectral image classification method for semantic-guided kernel low-rank sparse preserving projection, which comprises the following steps: firstly, acquiring a training sample set and a test sample set of a hyper-spectral image, and calculating a kernel matrix and a cross kernel matrix of the hyper-spectral image; further constructing a kernel method dimension reduction model objective function fusing a sparse reconstruction error term, a sparse regularization term, a semantic guidance low-rank constraint term and a spatial adaptive manifold regularization term; the complex objective function is efficiently solved by adopting an alternating direction multiplier method, and an optimal projection matrix is finally obtained by introducing an auxiliary variable and alternately updating a projection matrix coefficient, a sparse coding matrix and other variables; and mapping the test sample to a low-dimensional feature space by using the projection matrix so as to finish classification. According to the method, through semantic guidance and spatial-spectral information adaptive fusion, the discrimination ability and classification precision of dimension reduction features are significantly improved.

Methods and systems for using a cloud-managed state store are disclosed. A stream of data is received via a network. State information for a real-time computation workload is stored in a cloud-managed state store. The real-time computation workload is scaled out by utilizing the cloud-managed state store to retrieve state information. The stream of data is processed using a processing engine and utilizing the retrieved state information from the cloud-managed state store. Results of processing the stream of data are stored in the cloud-managed state store.

The invention discloses a seismic physical model material ratio parameter modeling method based on artificial intelligence. The method comprises the steps of S1, experimental design and data acquisition; s2, performing SVR forward modeling based on a kernel method, and constructing a nonlinear mapping model between a matching matrix of the epoxy resin, the silicone rubber and the talcum powder and physical parameters of the test block based on support vector regression of the kernel method; s3, a data enhancement strategy is carried out, Latin hypercube sampling is carried out in the design space of the material ratio to generate K groups, and K is larger than or equal to 100 new ratio parameters; and S4, performing reverse modeling based on a machine learning method, and constructing an inverse problem solving model for reversely deducing the material ratio parameter X from the target physical parameter Y. The method supports multi-target joint prediction, automatically meets the proportion constraint, has accuracy, robustness and engineering practicability, and provides an efficient and reliable solution for intelligent design of seismic physical simulation materials.

This invention discloses a renewable energy data clustering method and system based on optical quantum computing. First, a renewable energy data clustering optimization problem based on a quadratic unconstrained binary optimization (QUBO) model is constructed based on a discrete optimization objective function. Then, the objective function is matrix-transformed, and the kernel method is used to optimize the clustering of the objective function terms, converting them into matrix form to construct a new matrix-transformed clustering model. Finally, the new QUBO optimization problem is solved using CIM, and the result is transformed into the optimal clustering result. This invention's method, by constructing a QUBO model and combining it with a kernel method to improve data similarity measurement, transforms the renewable energy data clustering problem into a form suitable for CIM solving, thus avoiding the computational bottleneck of classical computational methods in high-dimensional data processing. This method not only preserves the nonlinear characteristics of the data but also achieves efficient clustering optimization computation, significantly improving computational efficiency and clustering accuracy.

Method for determining a measured value of a measurement quantity in process automation technology in a liquid or gaseous medium by means of an optical sensor, which has at least one transmitter (17.1) for sending transmitted light with at least two wavelengths (1-7), and a receiver (17.2) associated with the transmitter (17.1) for receiving received light, comprising the steps: - Taking a sample (13) of the medium (15), - Mixing the sample (13) with one or more reagents (16), - Applying an excitation signal to the transmitter (17.1) to generate the transmitted light, wherein the transmitted light is converted into the received light by interaction, in particular by absorption, with the mixed sample (13) depending on the measured quantity, - Generating a receiver signal using the receiver (17.2) from the converted received light, - Determining the measured value based on the receiver signal and a calibration function, characterized by the fact that The aging of the reagents is taken into account when determining the measured value. In particular, the calibration function includes a term that accounts for aging. the reagents taken into account, where the calibration function is created using a kernel method, where the kernel method is the Support Vector Machine or the Kernel Fisher discriminant, where the measured value c(x) is given by c ( x ) = a 0 + ∑ i = 1 N ai ⋅ k ( x , xi ) is calculated using the kernel function k ( x , xi ) = exp ( − μ | | xi − x | | 2 ) , with x a data vector comprising the receiver signal of at least two wavelengths, x i Support vectors, a0, ai coefficients, µ is a kernel parameter, and N is the number of support vectors as a natural number, in particular between 50 and 300.

The invention relates to the technical field of satellite communication, and particularly discloses a satellite communication anti-interference method and system based on a kernel method and FastICA, and the method comprises the steps: building a post-nonlinear hybrid model, so as to simulate the nonlinear signal distortion caused by an amplitude limiter in satellite communication, the post-nonlinear mixing model comprises a linear mixing stage and a nonlinear compression stage; a kernel method is adopted to map observation signals to a high-dimensional regeneration kernel Hilbert space, nonlinear features are represented through a quartic polynomial kernel function, and a nonlinear mixing problem is converted into a linear separable form; in a high-dimensional kernel space, based on a FastICA algorithm, a communication signal and an interference signal are separated through a non-Gaussian criterion of maximizing negentropy; in combination with regularization pre-whitening processing and a symmetric fixed point iterative optimization strategy, a separation matrix is updated; and outputting the separated communication signal to realize nonlinear interference suppression.

The invention discloses a graph neural network feature and label propagation method for multi-view data, and the method comprises the following steps: S1, initializing a model optimization variable, and evaluating adjacent matrixes of all graphs through employing a kernel method; s2, according to node features in the multi-view data, spreading the node features in the multi-view data in a graph structure by using a message passing mechanism of a graph neural network; s3, according to selection of parallel or cooperative training of feature and label propagation, label information is propagated in a graph structure through different multi-view label propagation rules; and S4, predicting the label of the test sample in combination with the results of the feature propagation and the label propagation. According to the method, information can be effectively integrated in multi-view data, the accuracy of label prediction is improved, and the method is suitable for complex graph structure data processing tasks.

The application discloses a multi-source image data feature extraction method, system, device and storage medium, original view data is mapped into a high-dimensional space through a kernel method, and a multi-source data set is constructed; the self-expression learning of each view data and the similarity between the view data are used as constraints to calculate the corresponding graph information matrix of each view; for the graph information matrix corresponding to different views, the importance of different views is considered to give corresponding weights, and an optimal consistency graph adjacency matrix is calculated through a self-expression learning and adaptive fusion method; based on a graph constraint canonical correlation analysis algorithm, and through the fused graph structure information as a constraint, multi-view data canonical correlation analysis is carried out, common consistency features of each view data are fused, and a complete feature extraction objective function is constructed. Nonlinear information of data can be better captured, sample data is linearly separable in the high-dimensional space, and similarity calculation and graph structure construction are facilitated.

The application discloses a cord steel decarburization layer quality prediction method and system, and the method comprises the following steps: S1, acquiring sample data in a cord steel production process, wherein the sample data comprises scalar data and time series data, and the scalar data is lengthened to obtain multivariate time series data; S2, fitting the multivariate time series data into multivariate function type data by using a B-spline basis function; S3, projecting the multivariate function type data into a high-dimensional function type feature space by using a function type data kernel method; S4, calculating a function type data projection hyperplane that minimizes the intra-class distance and maximizes the inter-class distance based on the function type features in the high dimension; and S5, projecting each sample to obtain a final sample classification result. According to the application, the cord steel decarburization layer quality can be accurately and timely predicted.

In view of the problem that the optimization method based on the subspace is insufficient in precision in the field of full-wave inverse scattering imaging, a kernel subspace optimization method for full-wave inverse scattering high-precision imaging is disclosed, which relates to the fields of machine learning and full-wave inverse scattering. The method combines the robust framework of the traditional subspace optimization method and the adaptive learning advantage of the kernel method, and transfers the iteration of the original subspace optimization method to the reproducing kernel Hilbert space, so that the evolution in the learning iteration process is learned with the least mean square error as the learning criterion. The kernel-based subspace optimization method combines the mature and interpretable framework of the subspace optimization method with the adaptive learning ability of the kernel method, and the method provides a robust and general algorithm for full-wave inverse scattering in the fields of medical imaging, geophysical exploration and nondestructive testing, and can provide accurate reconstruction results.

The application discloses an industrial process fault monitoring method based on a time information enhanced graph convolutional autoencoder, designs a variable-guided time regularization Gaussian kernel method to measure the correlation degree between variables, simultaneously introduces a sparse maximization function to adaptively reserve significant variable correlation, constructs a space perception graph structure across time, and comprehensively represents the spatial dependence between industrial process variables and the spatial dependence across time; a multilayer graph convolutional autoencoder network is constructed to extract local spatiotemporal correlation features, simultaneously a convolutional long short-term memory network is connected between the encoding layer and the decoding layer to capture long-term time dependence information of the sequence, the extraction capability of the spatiotemporal correlation features is enhanced, and data reconstruction is assisted. The method can fully extract the spatiotemporal correlation features of normal data of an industrial process, improves the reconstruction capability of the model on the normal data, realizes higher monitoring precision under an unsupervised task of an industrial process with a complex spatiotemporal correlation relationship, and is superior to the prior art.

The invention provides a channel water level prediction method of an online kernel echo state network based on an unsupervised learning filter, relates to the technical field of water level prediction, and solves the problem of model prediction uncertainty by designing a kernel reserve state through a kernel method to replace traditional random weight initialization. An online learning mechanism capable of dynamically increasing hidden neurons according to new input data is also provided, the limitation that a traditional neural network structure is fixed is broken through, and the model can adaptively learn a new water level change rule; an unsupervised learning filter based on Mean-shift and KD-tree is introduced, the distance between new data and an existing knowledge center point is calculated in real time, and intelligent decision is to add new neurons to learn new features, only update connection weights to finely adjust the model or delete redundant data, so that automatic online updating and construction of the model are realized, and meanwhile, the learning efficiency of the model is improved. Infinite expansion of a network structure is avoided, and the precision and efficiency of medium-and-long-term water level prediction are remarkably improved.

The application provides a channel water level prediction method based on an online kernel echo state network of an unsupervised learning filter, relates to the technical field of water level prediction, and solves the problem of model prediction uncertainty by designing a kernel reserve state through a kernel method to replace traditional random weight initialization; an online learning mechanism of dynamically increasing hidden neurons according to new input data is further provided, the limitation of a fixed traditional neural network structure is broken through, the model can adaptively learn new water level change rules, a non-supervised learning filter based on Mean-shift and KD-tree is introduced, the distance between new data and an existing knowledge center point is calculated in real time, and it is intelligently decided whether new neurons are increased to learn new features, only connection weights are updated to fine-tune the model, or redundant data is deleted, so that the model is automatically and online updated and constructed, unlimited expansion of the network structure is avoided, and the precision and efficiency of medium and long term water level prediction are significantly improved.