3results about How to "Improve classification ability" patented technology

The invention relates to the technical field of ensemble learning, in particular to a production identification screening method and system based on a machine-made sand dry method, and the method comprises the steps: obtaining a first vector of each placer particle sample; obtaining a hierarchical clustering tree, and obtaining an integrated weight of a base classifier of each dimension in a dimension set of each class cluster of each hierarchy of the hierarchical clustering tree; obtaining a base classifier of each level of the hierarchical clustering tree in each dimension; obtaining an integrated classifier of each level of the hierarchical clustering tree; obtaining a classification error rate of an integrated classifier of each level of the hierarchical clustering tree; obtaining the comprehensive weight of the integrated classifier of each level; obtaining a total classifier of the training set; and obtaining the production quality of the ore sand particles to be analyzed. According to the method, the difference influence of different characteristics of the ore sand particles is reduced, and the efficiency and accuracy of production quality evaluation of the to-be-analyzed ore sand particles are improved.

This invention relates to the field of machine learning, providing a method, system, and electronic device for rock damage identification. The method includes: acquiring multi-stage damage data of a rock under test, wherein the multi-stage damage data is acquired through a piezoelectric sensing element disposed on or inside the surface of the rock under test; constructing an original dataset based on the multi-stage damage data; and inputting the original dataset into a pre-constructed convolutional neural network model to obtain the damage identification result of the rock under test. This invention addresses the shortcomings of related technologies, such as the easy loss of key damage details like local spectral deformation and peak shift, enabling real-time, non-destructive, and continuous monitoring of rock damage. It automatically extracts damage features and accurately classifies damage at each cycle stage from initial loading to final failure, providing reliable technical support for early warning of geotechnical engineering disasters.

The invention discloses an array type iris face multi-mode acquisition and recognition system, which belongs to the technical field of biological feature recognition and comprises a main control board and a multi-core processor adopting an ARM framework. The main control board is connected with a binocular face enhancement camera, a binocular distance measurement camera, 16 groups of iris acquisition modules, 16 groups of near-infrared light supplement lamps, a three-color LED indicating lamp, a touch display screen and a real-time living body detection and anti-fraud module based on deep learning. According to the method, the iris features and the face features are deeply fused by adopting a multi-dimensional feature fusion and matching algorithm, so that comprehensive and accurate identity feature vectors are formed. The iris has extremely high uniqueness and stability, the face feature provides an additional identity information dimension, and the fusion of the iris and the face feature greatly improves the accuracy of identity recognition. Meanwhile, the real-time living body detection and anti-fraud module based on deep learning is trained by a large number of real samples and fraud samples, and has strong feature extraction and classification capabilities.