3 results about "Random mixing" patented technology

This invention relates to the fields of computer vision and machine learning, specifically to a lightweight classification and segmentation method and system for LiDAR point clouds based on GRSNet. The method includes: acquiring LiDAR point cloud data; generating a representative point set using a sampling method based on the golden ratio to reduce preprocessing complexity; inputting the point set into a GRSNet network for feature extraction, which consists of multiple processing stages. Each stage constructs a local region through a Sample_and_Group module, uses two SA_Ghost Block modules to learn shared weights and extract deep aggregation features, and performs feature aggregation through a Mixed Pooling module with randomly selected pooling methods; finally, classification or segmentation is completed based on the extracted features. This invention, through innovative sampling methods, a lightweight dual-path feature extraction module, and a randomized mixed pooling mechanism, significantly reduces the number of model parameters and computational complexity while maintaining high-precision classification and segmentation performance.

This invention discloses a robust relative navigation method for aircraft based on a hybrid distribution under non-Gaussian noise, belonging to the technical field of computation, estimation, or counting. To address the problem of filter divergence caused by non-stationary heavy-tail noise in time-varying environments during relative navigation, this invention introduces a Dirichlet random mixture vector fusion of Gaussian, Student's t, and multivariate K-distributions, proposing a Gaussian-Student's t-multivariate K-distribution modeling of measurement likelihood. Then, by minimizing the KLD of the true posterior probability density function and the approximate posterior probability density function using variational Bayesian techniques, the approximate posterior estimates of the aircraft's relative motion state and filter parameters are obtained, yielding the target's state information relative to the aircraft and solving for relative position and velocity. Finally, a nonlinear filter based on the Gaussian-Student's t-multivariate K-distribution is derived to improve relative navigation accuracy for angle-only relative navigation of aircraft in time-varying environments.

The application discloses a kind of generalized theory numerical calculation method of the thermal conductivity of multiphase random mixing porous medium, the method includes the following steps: step 1, obtain the porosity of soil sample;Step 2, obtain the thermal conductivity of soil particles;Step 3, obtain the effective thermal conductivity of soil sample and the full state thermal conductivity surface under the porosity of soil sample;Step 4, obtain the volumetric water content and ice content of soil sample.The application innovatively proposes the strategy of using extreme state to calculate the thermal conductivity of soil particles, realizes the double check of parameters, eliminates the calculation error caused by the difference of soil mineral composition, and can obtain the effective thermal conductivity of soil body by calculation using conventional soil parameters.Using the method of the application, the thermal conductivity of soil body can be predicted with high precision only by using easily obtained physical parameters, and online monitoring and inversion analysis can be carried out.This is especially suitable for large-scale engineering projects and soil thermal response prediction, which greatly saves time and economic cost.