15 results about "Bayesian framework" patented technology

This invention provides a germline mutation detection method and system with a low false positive rate. The system is computer-executed and includes: first, performing a PCR repeat cluster consistency test on sequence alignment files generated from high-throughput sequencing reads, down-regulating the base count weights of inconsistent sites within the cluster; then, based on the sample-specific background error baseline, calculating the variation confidence index of each genomic site using an empirical Bayesian framework to obtain candidate single nucleotide variants (SNPs); obtaining candidate insertion / deletion variants through read clustering and physical verification of insertion fragment lengths; subsequently, performing a dual-engine cross-feedback iteration on the two candidate types until convergence, integrating and filtering, and outputting a structured mutation detection report. This invention significantly reduces the false positive rate of both SNPs and insertion / deletion variants while maintaining sensitivity, and improves the detection capability of complex insertion / deletion variants.

This invention relates to the field of wind power equipment fault diagnosis technology, and provides a method for extracting fault features from wind turbine gearboxes based on Gaussian mixture modeling. The method includes: modeling a mathematical model of the wind turbine gearbox observation signal by superimposing impact fault feature vectors and multi-source noise vectors, wherein the fault feature vector is the product of a redundant dictionary D and a sparse coefficient vector; modeling the multi-source noise vector as a Gaussian mixture distribution; constructing an objective function within a Bayesian framework to solve for the sparse coefficient vector in the mathematical model using maximum a posteriori probability estimation; simplifying the objective function; and using the EM algorithm and ADMM algorithm in a joint alternating iterative solution to obtain the optimized sparse coefficient vector; and reconstructing the impact fault feature vector in the wind turbine gearbox observation signal. This method improves the accuracy and robustness of extracting fault features from the observation signal of offshore wind turbine gearboxes.

The present application belongs to the technical field of image processing, and particularly relates to a multi-light source estimation method based on human visual color perception mechanism. In order to solve the problems of spectral coupling and edge blur which are extremely challenging in multi-light source estimation, the feedforward and feedback mechanisms in the human visual'retina-LGN-V1' color perception pathway are modeled as energy function optimization problems under the Bayesian framework, and the semi-quadratic splitting (HQS) algorithm is used for expansion solution, so as to realize the integration of human visual mechanism into the design of data-driven model. In combination with the carefully designed multi-scale opponent color initialization and color cross-attention optimization module, the framework significantly improves the estimation accuracy of complex spatial light distribution.

This invention proposes a safety assessment method for delaminated composite materials based on the principle of damage non-propagation, belonging to the fields of composite material delaminated damage modeling and composite structure safety assessment. The method includes: constructing ultimate load envelopes that meet the damage non-propagation requirement under different damage sizes; for readily available binary observation data in practical engineering, i.e., whether damage propagates under specific damage sizes and load conditions, connecting discrete observations with a continuous parameter space through a latent variable model; using the Probit link function to map the predicted difference between the load and the ultimate load into a damage propagation probability; and constructing a likelihood model using a Bernoulli likelihood function; and optimizing the ultimate load envelope within a Bayesian framework. This invention can effectively support safety assessment and maintenance decisions for composite material structures in aerospace and other fields.

The application discloses a ship radiation noise line spectrum estimation method based on multi-snapshot sparse Bayesian learning, and belongs to the field of underwater acoustic signal processing; the method utilizes the sparse characteristics of the ship radiation noise line spectrum, that is, the line spectrum is not uniformly distributed in the whole frequency range, but is discrete and sparse, converts the line spectrum estimation problem into a sparse signal recovery problem, so as to solve the problems in the background technology, under the condition that the data sample length is limited and the sparsity is unknown, the Bayesian framework utilizes probability modeling to adaptively adjust the hyperparameters from the global optimization angle, can effectively reduce the sidelobe, realizes high resolution, reduces the background noise fluctuation variance through multiple observation snapshots statistical average, enhances the signal-to-noise ratio of the line spectrum output, and improves the line spectrum feature detection performance.

The application discloses a kind of robust prestack seismic inversion methods and devices based on coherence constraint algorithm, belong to unconventional oil and gas exploration and development field, comprising: through the extraction of seismic coherence attribute of centrifugal window of space transformation;Based on the approximate formula of azimuthal anisotropy reflection coefficient, the forward operator of fracture reservoir is deduced and constructed, and the positive problem of prestack seismic inversion prediction is clear;Based on the prestack seismic inversion prediction positive problem, under the Bayesian framework, the coherence attribute is added to the objective function of inverse problem as the prior information of seismic inversion prediction, and stable prestack and poststack joint seismic inversion prediction is carried out.The application not only considers the influence of large-scale fracture in post-stack, but also considers the azimuthal anisotropy characteristics caused by small-scale fracture, which can greatly improve the stability of anisotropy parameter inversion of fracture reservoir, and has better predictability for small-scale fracture associated with large-scale fracture.

The application provides a non-invasive coronary blood flow evaluation method based on multi-phase CTA and Bayesian inference, comprising the following steps: acquiring multi-phase coronary CTA images of a target to be evaluated, and extracting coronary vessel deformation waveform data from the multi-phase coronary CTA images; generating posterior probability distribution estimation results of coronary blood flow rate through a pre-established Bayesian inference framework and the coronary vessel deformation waveform data; and generating coronary blood flow rate evaluation results corresponding to the target to be evaluated according to the posterior probability distribution estimation results. The application uses a Bayesian framework to simultaneously estimate multiple physiological parameters, and identifies parameter interactions through posterior correlation analysis, thereby improving inversion accuracy.

The application provides a model for identifying allele imbalance markers driving tumor evolution based on a hierarchical Bayesian framework and a construction method thereof, and belongs to the field of information technology.The application provides a construction method of a model for identifying allele imbalance markers driving tumor evolution based on a hierarchical Bayesian framework, a three-level Bayesian hierarchical model is constructed, global noise, subclone specificity, regional discreteness and allele single nucleotide mutation (SNV) site observation are jointly modeled, and a reparameterization correction allele copy number deviation is introduced.On this basis, a Markov Monte Carlo (MCMC) sampling is used to obtain a posterior distribution of parameters, and a sample comparison method of a posterior distribution probability of each parameter including a true allele imbalance coefficient and a Kullback-Leibler divergence is provided, which can be used for identifying allele imbalance and analyzing markers driving evolution.

The application relates to the technical field of non-invasive detection of cardiac electrophysiology. A cardiac abnormal beat source positioning method, device, equipment and medium are disclosed. The method comprises the following steps: synchronously collecting MCG signals and ECG signals; based on a preset feature extraction model, performing feature extraction on the MCG signals and the ECG signals to obtain an ECG time sequence feature sequence and an MCG space-time feature set; obtaining MCG sensor coordinates and ECG electrode coordinates, and based on a preset space coordinate mapping model, performing space alignment processing on the MCG sensor coordinates and the ECG electrode coordinates; based on a preset cardiac geometry and electrical conduction model, determining an abnormal beat source according to a mapping relationship between the ECG time sequence feature sequence, the MCG space-time feature set, the MCG sensor coordinates and the ECG electrode coordinates by means of a Bayesian framework model, a deep learning model and a sparse inversion model. The application realizes non-invasive cardiac electrical activity source positioning with high space-time resolution, high stability and individualization.

The application discloses a teammate adaptive method and system based on a Bayesian progressive intention alignment, fuses natural language priori and likelihood estimation of teammate interaction behavior in a unified Bayesian framework, realizes rapid convergence of teammate intention and efficient alignment in a single round of interaction, fully utilizes high-level semantic information in language description, and overcomes hysteresis and ambiguity when intention is inferred by relying on a state-action sequence in a traditional way. The application effectively solves intention confusion and low-efficiency adaptation problems of an existing method in a multi-intention scene. Overall, the application has the advantages of fast adaptation speed, high inference precision and no need of additional demonstration, and has a wide application prospect in a human-machine cooperation scene.

The present disclosure relates to a wavelength selection method and system for passive spectral calibration of an ultraviolet spectral instrument in orbit, the method comprising: establishing a standard line list of preset wavelength bands, and preprocessing, performing single-line Gaussian fitting and local baseline modeling, using asymmetry and wavelength deviation for quality control, calculating the quality weight of each standard line, and calculating the similarity kernel of each standard line and other standard lines; based on the constraints of the wavelength range endpoints, the minimum distance constraints of two standard lines, and the constraints of abnormal standard line rejection, the standard lines in the set of standard lines after quality control are screened by using the determinant point process; further screening according to the joint evaluation of cross-day consistency and information amount; under the Bayesian framework, the standard lines are linearly regressed by a variable error model to obtain the finally selected standard lines. The present disclosure has high accuracy and high stability.

A method for predicting future blood glucose concentrations for an individual patient includes selecting an individualized nonlinear physiological model of glucose-insulin dynamics, the selected model having a plurality of model parameters for which values ​​are determined; estimating values ​​for each of the model parameters in the plurality of model parameters, a first subset of the model parameters having values ​​estimated from a priori population data and a second subset of the model parameters having values ​​personalized for the individual patient by applying a parameter estimation technique to a priori information and data for the individual patient to obtain posterior information; and applying a nonlinear prediction technique to the selected model using the estimated values ​​for each of the model parameters to obtain a predicted blood glucose concentration for the individual patient at a future time.

This invention discloses a design flood correction method coupling climate trends and extreme events, belonging to the field of next-generation information technology. The method includes the following steps: initiating a non-stationary analysis mechanism through a three-dimensional metric for climate change significance; constructing a shifted enhanced flood dataset based on watershed similarity screening and function shifting; employing a Bayesian framework to comprehensively utilize watershed information and shifted information to construct a main non-stationary flood correction model considering climate trends, and a tail-end extreme flood correction model; finally, outputting the complete posterior distribution of flood design values ​​through dynamic coupling of the main and tail aspects, thus achieving design flood correction coupling climate trends and extreme events. This invention improves the robustness and engineering applicability of long-return-period flood design results, providing solid scientific support for the correction of watershed flood control standards and the safety assessment of major water-related projects under changing environments.

The application relates to the technical field of bioinformatics and medical data processing, and discloses a pulmonary arterial hypertension target point mining verification simulation system integrating multi-omics data, which comprises a data input layer receiving transcriptome data, adopts a linear regression model based on an empirical Bayesian framework to perform batch correction and output a standardized gene expression matrix; a differential analysis module extracts a differentially expressed gene set; a weighted gene co-expression network analysis module constructs a topological overlap matrix based on a soft threshold parameter and outputs a core module gene set; a protein interaction network analysis module obtains an intersection of the two sets, calls a molecular complex detection algorithm to output a high-connectivity target point feature set; and a multi-machine learning integrated screening module adopts an elastic network regression, a support vector machine recursive feature elimination and a random forest model to independently reduce dimensions and extract. The application overcomes the overfitting bias of a single algorithm and improves the accuracy of target point identification.

This invention relates to the field of new energy power generation prediction technology, specifically, to a method for constructing new energy power prediction samples and training models. By simultaneously collecting historical actual power data, measured meteorological data, and historical numerical weather prediction data, measured meteorological sample sets and NWP sample sets are constructed respectively. The two sample sets are merged and source identification features are added to train a hybrid prediction model. A hierarchical model is established based on a Bayesian framework to analyze the systematic bias and random error distribution of NWP data, generating diverse virtual NWP samples and constructing a data augmentation training set. The augmented prediction model is trained using the augmented training set and then weighted and integrated with the baseline model, NWP model, and hybrid model to output the final prediction model. This invention, through innovative sample construction and training mechanisms, effectively solves the problem of inconsistent distribution between training and inference data, significantly improving the accuracy and robustness of power prediction.