7 results about "Mixture modeling" patented technology

The invention discloses a human body posture estimation result generation method and device based on a generative model, relates to the technical field of image processing, effectively recovers a complete skeleton structure, realizes accurate recognition of human body postures in high-shielding and high-dynamic change scenes such as an electric power production field, and improves the adaptability to shielding scenes. The method comprises the following steps: acquiring a to-be-identified video stream, and performing initial attitude estimation on each frame of video image in the to-be-identified video stream by using a pre-trained attitude detection network model to obtain an initial skeleton point sequence; fitting the initial skeleton point sequence by adopting a Gaussian mixture modeling method to obtain a defect skeleton point sequence; inputting the defective skeleton point sequence into a pre-trained inverse diffusion network model, and optimizing the defective skeleton point sequence in combination with the adjacent frame skeleton point coding information of each frame of video image in the inverse diffusion network model to obtain a complete skeleton point sequence; and generating and outputting a human body posture estimation result based on the complete skeleton point sequence.

The invention provides double-granularity alignment efficient partial correlation video retrieval based on implicit fragment modeling and semantic decomposition, and aims to solve the problems of information redundancy and low efficiency in an existing video modeling method, the problem of granularity mismatching between sentence representation and video frame features and the problem that alignment between a text and a video is not refined enough. According to the method, the expression and modeling of video data are optimized by introducing a structure combining a Gaussian mixture model and Transform, and multi-scale local details with short time span are adaptively integrated by introducing a window attention mechanism and a cross attention mechanism, so that finer features are obtained, and cross-modal similarity score calculation of texts and videos is facilitated. The method comprises the following steps: data preprocessing and frame segmentation: preprocessing and segmenting an input video into frames, and preparing for feature extraction after each frame of image is subjected to standardization processing; implicit modeling is carried out on the fragment-level features, uniformly sampled frame-level visual features are input into a Gaussian mixture modeling module, adjacent frame focusing modeling is carried out through a multi-scale Gaussian attention mechanism, and fragment-level representation of different receptive fields is implicitly formed. Enhancing local details of the frame-level features, performing convolution operation on each frame by adopting windows with different scales, and calculating a feature relationship in each local window; local features of all scales are fused through a cross attention mechanism, semantic expression of frame features is enhanced, and it is ensured that each video frame can capture important local details.

A method for lane-level road congestion identification and forecasting includes identifying lane-level road congestion using Gaussian mixture modeling, predicting lane-level road congestion using a 2D Markov chain, and identifying routes and route changes for a host vehicle and applying the route and route changes to improve a host vehicle estimated time of arrival (ETA) at a predetermined finish location such that the ETA is shorter than a predetermined threshold.

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.

The invention discloses an image generation method and system based on Gaussian mixture modeling, and belongs to the technical field of image generation, and the method comprises the steps: obtaining a plurality of image samples; encoding each image sample to obtain an implicit feature of each image sample after encoding; clustering the coded hidden features of all the image samples to obtain a plurality of feature clusters; generating Gaussian mixture distribution of all feature clusters; using Gaussian mixture distribution to randomly generate a new hidden feature vector; and decoding the generated new hidden feature vector to obtain an amplified sample. The problem that the current image generation quality is poor is solved, and high-quality generation of the image amplification sample is realized.