5results about How to "Fast training convergence" patented technology

This application discloses a method and apparatus for constructing a multi-scale adaptive gating MambaPlus network, belonging to the field of artificial intelligence and machine learning technology. The method includes: initializing the network configuration and constructing the basic structure; preprocessing the input data to generate a standard dataset; mapping the input data to the hidden space via an input mapping layer, and extracting backbone features from the Mamba backbone; constructing at least two parallel scale branches in the hidden space to obtain a multi-scale feature set; inputting the backbone features and multi-scale features into an adaptive gating module, dynamically allocating weights and adaptively fusing them through a hierarchical gating mechanism to generate fused features; further enhancing the features through cross-scale attention and feedforward enhancement, and then superimposing the residuals to generate the final discriminative features; finally, completing category prediction and model training evaluation. This application, while retaining the advantages of Mamba's long-range dependency modeling, addresses the problems of insufficient utilization of multi-scale information, poor adaptive feature fusion, and low robustness in complex scenarios.

This invention discloses a method and system for publishing multimodal data in industrial production for artificial intelligence, belonging to the field of industrial intelligence and data processing technology. The method includes: collecting multimodal data and performing hardware-level time synchronization and preprocessing to obtain a synchronized data stream; extracting multimodal features from the synchronized data stream in parallel and using an attention mechanism to fuse networks and generate a unified feature vector; constructing a five-dimensional quality assessment system and intelligently enhancing it; performing structured annotation and version management; adapting and publishing datasets according to AI tasks; implementing multi-level data anonymization and fine-grained access control, embedding invisible digital watermarks, and generating audit logs; and enabling multi-terminal access through a publishing platform. This invention solves the problems of difficult data alignment, poor quality, and low reusability, achieving secure data sharing and providing a high-quality, standardized data infrastructure for industrial AI applications.

The application discloses a soft-hard interbedded rock mechanical parameter prediction method and system based on a residual attention network, relates to the technical field of rock mechanical parameter prediction, and has the advantages that the traditional neural network is prone to gradient disappearance when processing a deep network, which influences the training effect; the existing method lacks an attention mechanism and cannot effectively identify and strengthen key features; and the modeling capability for interlayer interaction is insufficient; the application provides a soft-hard interbedded rock mechanical parameter prediction method based on a residual attention network, which comprises the following steps: obtaining structure parameters and target mechanical parameters of a soft-hard interbedded rock sample; converting the structure parameters into an enhanced feature vector; constructing a residual attention network model; inputting the enhanced feature vector into the residual attention network model; and outputting a mechanical parameter prediction result and reliability evaluation information.

The application discloses a flame and smoke identification method, and relates to the technical field of computer vision, and the method comprises the following steps: generating a boundary box of a flame and smoke target through a YOLOv target detection framework; extracting multi-scale visual features by using an image encoder of a SegmentAnything Model (SAM); encoding boundary box coordinates into a prompt embedding vector; fusing the visual features and the prompt embedding by using a mask decoder to generate a segmentation mask and a confidence score; confirming the target when the score exceeds a threshold value, and superimposing the boundary box and the segmentation mask and outputting to a fire-fighting early warning system.

The invention provides a tomato shelf life prediction method based on a hyperspectral and RGB imaging technology. The tomato shelf life prediction method comprises the following steps: S1, obtaining a hyperspectral image and an RGB image of a tomato sample; s2, preprocessing the hyperspectral image to obtain hyperspectral feature data; s3, extracting color feature data and texture feature data of the RGB image; s4, splicing the data obtained in the steps S2 and S3, performing standardization processing, and dividing the data into a training set, a test set and a verification set; s5, constructing an MLP model; screening the target feature set; s6, training the MLP model to obtain a tomato shelf life identification model; s6, obtaining a hyperspectral image and an RGB image of the detected tomato, performing preprocessing, and screening data according to the target feature set; and S7, splicing the screened color feature data, texture feature data and hyperspectral feature data of the detected tomatoes, inputting the spliced data into the tomato shelf life recognition model for recognition, and outputting the shelf life of the detected tomatoes. According to the invention, the accuracy of tomato shelf life identification is improved.