8 results about "Gibbs sampling" patented technology

This invention discloses a multi-level measurement partitioning method for tracking multiple extended targets. Addressing the low accuracy of traditional measurement partitioning when targets are close together or intersecting, this invention first establishes an extended target state representation and observation model, constructing an ET-GP-GMPHD filter. Based on predicted intensity, the measurement set is partitioned in three levels: known target measurements are assigned using k-means clustering guided by Gaussian feature contour points; new targets are identified through SOMST adaptive clustering for the remaining measurements; and multi-partition hypotheses are generated using Gibbs sampling. Finally, the partitioning results are used to update the filter and extract the target state. This invention achieves accurate measurement partitioning for close targets, significantly improving tracking accuracy and robustness.

The application belongs to the technical field of power systems, and provides a Monte Carlo-based reliability evaluation method for an IES-containing distribution network, comprising: firstly, generating a system fault state sequence covering three scenarios of a comprehensive energy system, a distribution network and simultaneous faults of both by using Markov chain Monte Carlo simulation combined with Gibbs sampling; secondly, constructing a differentiated load reduction model with tie-line power as a coupling variable for different fault types, and iteratively solving an optimal reduction scheme by using a hierarchical distributed optimization strategy and a target cascade analysis method; thirdly, aggregating and calculating expected power supply shortage, average power outage frequency and average power outage duration based on the scheme results to obtain three reliability indexes; and finally, judging the indexes by using a variance coefficient as a convergence criterion, outputting an evaluation result if the accuracy is met, or continuing iteration until convergence. The application significantly improves the accuracy and efficiency of the reliability evaluation of the IES-containing distribution network, and provides direct technical support for system configuration and dispatching strategy optimization.

This invention discloses a method and system for predicting the remaining lifespan of individual sensors based on Bayesian statistics, relating to the field of equipment health status monitoring and lifespan prediction technology. The invention provides a method comprising: establishing a general degradation model and prior distribution using historical degradation data; collecting monitoring data of a specific target sensor under real or accelerated stress in the field; updating the posterior distribution of model parameters based on Bayesian statistical inference and Gibbs sampling to generate a specific degradation model for that individual sensor; and calculating the remaining lifespan based on this specific model and a failure threshold. This invention achieves a breakthrough from "group lifespan assessment" to "accurate prediction of individual lifespan" through data-driven adaptive correction, significantly reducing over-maintenance costs and enhancing equipment operational safety.

The application discloses a review usefulness prediction method considering seed information and causality, comprising the following steps: obtaining review texts and corresponding non-text data, thereby constructing a review data set D; obtaining user review preferences, thereby constructing a seed topic word distribution φ s ; constructing a Bayesian seed topic regression model based on the review data set D and the seed topic word distribution φ s ; initializing all parameters in steps S2 and S3 based on the review data set D, and performing parameter inference on a document topic distribution, a topic word distribution and a review usefulness prediction distribution by using an EM algorithm and a Gibbs sampling method. The application allows users to guide the theme discovery process by adding seed information, thereby quickly and accurately mining beneficial themes that are concerned by users, and meanwhile, the prediction accuracy is improved by jointly modeling the review texts and the review related data, so that the application can be widely applied to the fields of causality inference and linguistics.

The present application relates to the field of data processing, more particularly, the present application relates to a kind of industrial internet platform multi-source heterogeneous data fusion processing method and system, method includes: from multiple heterogeneous data sources collection multi-source text, pre-processing obtains global vocabulary;Based on industry standard classification tree, the shortest path edge number of word to each standard classification node is calculated;The distribution significance of word is calculated, and the path correlation density is obtained by combining structure attenuation factor, and then the guide coefficient of each text belonging to each standard classification node is constructed;Each standard classification node is set as a theme, and the guide coefficient is taken as priori constraint and is integrated into probability calculation in gibbs sampling process, to obtain the probability that text belongs to each theme;Through confidence threshold screening, the fusion label set of each text is output.The present application realizes the automatic alignment of multi-source heterogeneous data and industry standard classification system, improves the automation level and business availability of data fusion.

This invention provides a method for estimating the health status of lithium-ion batteries based on Gibbs Variational Inference Multiple Imputation (GVIMI). The method includes: constructing a lithium-ion battery degradation model with missing data; transforming the data imputation problem into a posterior approximation problem based on Gibbs variational inference; optimizing the variational lower bound using a mini-batch data approximation method; obtaining an approximate posterior distribution by maximizing the variational lower bound; and establishing a multiple imputation and health status prediction model. This invention uses a hybrid method combining Gibbs sampling and variational inference as the underlying imputator, introduces a variationally complete conditional model, and uses Gibbs variational inference to approximate the posterior distribution of the model's latent variables, optimizing the imputation model with missing data. This improves the accuracy and robustness of lithium-ion battery health status estimation in scenarios with missing data.