3 results about "Mixture modeling" patented technology

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.

Methods for identifying disease subgroups are described. The methods may comprise, for example, receiving subject data for a plurality of subjects diagnosed with the disease; creating a plurality of candidate best fit latent class or mixture models by: i) providing an estimate of a number of subgroups; ii) generating a set of models, each model of the set comprising the same estimate of the number of subgroups; iii) selecting a candidate best fit model from the set; and iv) repeating (i) - (iii) at least once using a different estimate of the number of subgroups to obtain a plurality of candidate best fit models; selecting a best fit model from the plurality of candidate best fit models based on a fit statistic; and applying the best fit model to the subject data to identify a number of subgroups for the disease and an associated genomic profile for each subgroup.

This application discloses a method, apparatus, and device for pruning deep neural networks based on Gaussian mixture modeling, relating to the field of deep neural network pruning technology. It addresses the problem that single pruning strategies in existing technologies can easily lead to model accuracy collapse or convergence difficulties. The solution includes: obtaining a pre-trained deep neural network model; performing forward propagation on the deep neural network model based on sample data to obtain the activation feature values ​​of each neuron in each network layer of the deep neural network model; dividing each neuron into effective neurons and candidate neurons based on the activation feature values; for candidate neurons, using a Gaussian mixture model to fit the distribution of the activation feature values ​​of the candidate neurons to distinguish between signal distributions representing effective information and noise distributions representing redundant information; and pruning and removing candidate neurons belonging to the noise distribution based on the fitting results.