14 results about "Kernel method" patented technology

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.

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 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.

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.

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.

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 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 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 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.