14 results about "Radial basis function kernel" patented technology

The invention belongs to the technical field of electric power information physical system security, and discloses an urban power grid information physical system security situation early warning method based on cross-space risk propagation. A power grid physical layer and information layer coupling model and a cellular space false data injection attack model are constructed, and a fault cross-space propagation mechanism is simulated through an event-driven cellular automaton theory; an integrated kernel extreme learning machine model is adopted to predict the operation state of the power grid, multi-dimensional data fusion is realized through a radial basis kernel function, and an integrated structure is adopted to fuse a plurality of model prediction results so as to improve the model prediction precision; and establishing an early warning system including voltage out-of-limit, line overload and load loss indexes, and dynamically distributing weights and dividing early warning grades in combination with an entropy weight method. According to the method, the problem of low precision of urban power grid security situation early warning under multivariate disturbance is solved, and the accuracy and robustness of security situation early warning can be effectively improved.

This invention relates to the field of computational toxicology, and in particular to a precise method for comparing the consistency of animal and non-animal toxicity evaluation results. This method acquires in vivo baseline data from animals and in vitro test data from non-animals. It employs a dynamic time warping algorithm and radial basis function kernel function to construct correction coefficients and perform equivalent mapping to generate a converted sequence. Based on a binary classification mapping of toxicity thresholds, a confusion matrix is ​​constructed to calculate sensitivity, specificity, and predicted values. Each indicator is treated as an independent source of evidence, and the Mahalanobis distance is calculated using the covariance matrix to generate a basic probability allocation function. The D-S evidence theory combination rule is applied for fusion, and a secondary factor allocation is introduced when there is conflict. Finally, the result is compared with a preset threshold to determine the feasibility of substitution. This invention adaptively eliminates the nonlinear misalignment difference between in vivo and in vitro dose responses, providing an objective quantitative judgment standard for toxicological substitution verification.

The application discloses a kind of based on multi-axis state signal's numerical control processing technology identification method and system, belong to machining process type identification technical field.Method includes to workpiece is standardized clamping positioning;Make numerical control machine tool execute standardization preoperation program;Adopt non-synchronous acquisition mode based on working condition trigger, obtain multi-axis current data;Introduce timestamp alignment algorithm to realize the consistency of multi-axis current data on time sequence;For the multi-axis current data of timestamp alignment, combined with empirical mode decomposition and wavelet threshold denoising algorithm, construct adaptive selective denoising preprocessing framework, determine the current data of sample segmentation, and utilize multi-axis synchronous sliding window technology, obtain three-dimensional data set;Process feature set is constructed;Adopt principal component analysis to process feature set is nonlinear feature dimension reduction, and combined with grid optimization's radial basis function kernel support vector mechanism constructs multi-class process identification model.The application effectively realizes the automatic identification of multiple processing technology.

The invention relates to the technical field of energy storage, discloses an SVR intelligent vehicle power battery remaining life prediction method based on multi-source information, and aims to solve the problem of low prediction precision caused by single input of an existing data driving method. The method comprises the following steps: collecting multi-source information of the running state of the power battery, wherein the multi-source information comprises performance degradation characteristics and running historical characteristics; features with high correlation with the battery capacity retention rate are screened through Pearson correlation analysis, and a target input feature vector is constructed; training a support vector regression model based on the screening features, and processing a nonlinear degradation relationship by adopting a radial basis function kernel; and predicting and calculating the remaining service life by using the training model. According to the method, state information and history information are fused, intelligent feature screening is assisted to reduce noise interference, more comprehensive data dimensions are provided for a prediction model, prediction accuracy, robustness and generalization ability are remarkably improved, and the method has outstanding substantive features and industrial application value.

The invention relates to the field of machine learning, in particular to a residual service life prediction method and system based on a deep Gaussian process and meta-learning, a medium and equipment, and the method comprises the steps: obtaining original data to construct a multi-task data set; a multi-layer depth Gaussian process model is constructed based on a multi-task data set, a radial basis function kernel is used as a covariance function, and a multi-task Gaussian likelihood function is used for modeling uncertainty of RUL prediction; meta-training is carried out on a PHM data set, the model is optimized through internal circulation and external circulation, and an Adam optimizer is adopted to learn cross-task generalization weights; transferring the last layer of parameters and likelihood function parameters obtained by meta-training to a fan gearbox data set and an NASA aero-engine data set based on the same meta-learning model framework, and performing fine tuning on a support set of the test data set; and performing RUL prediction on the test sets of the fan gear data set and the NASA aero-engine gear data set by using the weights obtained after fine tuning, and outputting a prediction mean value, a variance, a confidence band and a prediction band.

A traffic flow prediction and signal timing optimization method based on Gaussian process regression comprises the following steps: 1, collecting mixed traffic flow data through a traffic detection unit and an Internet of Vehicles terminal, constructing a time sequence data set in combination with historical data, and analyzing and determining a difference interval by using an autocorrelation function to eliminate seasonal influence; 2, establishing a short-time traffic flow prediction model based on Gaussian process regression, and fusing a radial basis function kernel and a periodic kernel to quantify prediction uncertainty; 3, constructing a signal timing optimization model taking average delay, parking times and traffic capacity as multiple targets, wherein constraint conditions comprise green light time and a period duration range; and 4, an improved multi-group genetic algorithm is combined with a simulated annealing solution model, and the global optimization capability is improved through parallel evolution and a probability hopping mechanism. Through data prediction-optimization closed-loop control, the intersection delay is remarkably reduced, the traffic capacity is improved, and the method is suitable for a dynamic traffic scene of mixed driving of intelligent network connection vehicles and manual driving.

This invention discloses a method for short-term wind power prediction under low-temperature and cold wave weather. In the feature extraction and selection stage, meteorological data around the wind farm are collected, feature correlation values ​​are calculated, and key features are screened. Subsequently, a meteorological-power mapping model is established using a Support Vector Machine (SVM) model and a Radial Basis Function (RBF) kernel. The model is then optimized through training, validation, and evaluation index analysis. For low-temperature and cold wave weather, comprehensive judgment conditions are set, and model parameters are adaptively adjusted based on prediction errors to improve model adaptability. Simultaneously, a real-time data-driven update mechanism is established, collecting data in real time and incrementally training the model. Performance is evaluated and feedback adjustments are made, forming a closed-loop optimization system. This method effectively improves the accuracy of wind power prediction under low-temperature and cold wave weather, enhances model adaptability and stability, provides strong support for wind power grid connection and grid dispatch, helps improve wind power operating efficiency, and promotes the development of the new energy power generation field.

The invention discloses an open vocabulary multi-target tracking method and system for a foggy day traffic scene, and the method comprises the steps: generating a cross-modal target suggestion through a vision-language combined modeling-based open vocabulary detector through a re-parameterized vision-language path aggregation network and region-text comparative learning; the method can flexibly recognize the unlabeled unknown category target in the training set, breaks through the dependence of a traditional detector on a fixed category label, and effectively solves the problems of target appearance feature degradation and background noise interference in a foggy day scene. The traditional NMS algorithm is optimized by combining the distance of the center points of the bounding boxes and the information of the overlapping regions, false detection and missing detection are remarkably reduced, the accuracy of target box screening is improved, and a solid foundation is laid for follow-up track association. A self-adaptive trajectory interpolation method based on Gaussian process regression is introduced, a nonlinear motion mode of a target is captured through a radial basis function kernel, a missing trajectory is dynamically repaired, and continuity and stability of trajectory prediction are enhanced.

The invention discloses a standard cell library file automatic generation method based on a Gaussian process. The method comprises the following steps: extracting Liberty data; classification is carried out according to the size of Liberty training data, and a direct lookup table prediction method or an index-based point-by-point enhancement prediction method is selected to carry out prediction; inputting the training data set into a Gaussian process model for modeling, and selecting a radial basis function kernel as a covariance function; for the PVT of the prediction target, selecting a direct lookup table prediction method or an index-based point-by-point enhancement prediction method to carry out corresponding data format processing; inputting the coded target input features into a trained Gaussian process model for prediction, and obtaining a corresponding target output value and a corresponding prediction variance; related parameters in the lookup table are updated until the prediction tasks of all the arc structures in the standard cell library are completed, and the complete lookup table automatic generation process is achieved.

The invention discloses an SPR (Surface Plasmon Resonance) signal classification system based on a support vector machine algorithm, and the system comprises the following modules: a signal collection module which is used for collecting the intensity of reflected light and environmental parameters; the preprocessing module is used for resampling, denoising, correcting and normalizing the signals; the feature extraction module is used for extracting time domain, frequency domain and Fresnel model-based physical features; the model training module is used for training by adopting a radial basis kernel function containing a physical constraint term to obtain an optimal classification model; the online self-adaptive updating module is used for monitoring distribution change based on a sliding window and dynamically updating model parameters; and the classification and output module is used for executing classification reasoning and outputting signal categories and alarm control. According to the SPR signal classification method, the physical constraint kernel function is introduced, and an online self-adaptive updating mechanism is combined, so that the physical interpretability and dynamic stability of SPR signal classification are realized, and the classification accuracy and real-time performance are remarkably improved.

This invention discloses a self-learning judgment method for gas sensing data based on NB-IoT, comprising the following steps: acquiring and preprocessing data through a gas sensor to generate standardized samples; extracting features using a sparse autoencoder and training and updating the model using unlabeled data; initializing an improved ridge regression model based on labeled data, and constructing an initial dictionary and regression coefficients using a radial basis function kernel; during real-time data input, the model quickly outputs gas state judgment results, while updating the memory matrix using a moving average; and recursively updating the regression coefficients and dictionary using labeled data. This invention's method has high real-time performance, accuracy, and adaptability, effectively improving the intelligence level of gas monitoring.

The invention discloses a protein function discrimination and similarity calculation method based on cooperation of a protein language model and a support vector machine, and belongs to the field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, carrying out standardized pretreatment on a protein sequence, converting the protein sequence into a space partition format, filtering non-standard amino acid characters, and then generating an input tensor and an attention mask tensor through filling and truncation; inputting the tensor into a pre-trained protein language model, extracting the hidden state of the last layer of Transform module, and performing mean pooling to obtain a global feature vector; constructing a data set in combination with the feature vectors and category labels, and performing stratified sampling to divide the data set into a training set and a verification set; training a support vector machine model with a radial basis function kernel by using the training set, optimizing hyper-parameters through grid search, and evaluating performance by using the verification set; and repeating preprocessing and feature extraction on a to-be-detected sequence, inputting a trained model output function category, and quantifying the similarity with known protein by using a decision function value.

This invention relates to the field of data processing, specifically to an intelligent early warning method and system for sintering furnaces used in powder metallurgy. The method includes: collecting historical normal operation data and dividing it into multiple windows; extracting multi-dimensional features for each window, including a structural feature vector based on the singular values ​​of the autocorrelation matrix, a coupling feature vector based on the cross-correlation matrix, an inertial-coupling dominant feature, and an inertial-coupling consistency vector; using the structural feature vector as the first sub-vector and the remaining features as the second sub-vector, employing a cosine kernel and a radial basis function kernel respectively, and determining the weights of the two kernels to construct a combined kernel function to train a single-class support vector machine; collecting data in real time and extracting identical features, and substituting them into a decision function to determine whether an early warning is triggered. This invention, through multi-dimensional feature extraction and adaptive combined kernel functions, can effectively distinguish between normal fluctuations and abnormal precursors, improving the accuracy of early warnings for sintering furnace anomalies.

Systems and methods for multiband linearization using kernel regression are provided. In some embodiments, a method includes, for each band of the multiband transmitter: transforming a group of input signals from one or more bands into a constructed input vector space to provide transformed input signals; predistorting the transformed input signals to provide a respective group of predistorted input signals in accordance with a Radial Basis Function (RBF) kernel regression; and transmitting the respective group of predistorted input signals. In this way, some advantages include a semi blind approach as one need not to account for the non-linearity order as in Volterra-based DPD for example, only the memory depth is needed to be incorporated to the input vector space. The computational complexity of DPD is reduced compared to Volterra-based DPD. Implementation complexity is relaxed by means of using a 1D Lookup Table implementation regardless of the number of bands.