3results about How to "Realize classification recognition" patented technology

The application discloses a kind of sonar image classification methods of combined SLIC superpixel and graph attention network.It includes the following steps: according to the imaging principle of two-dimensional forward-looking sonar and side scan sonar respectively and prior information when imaging: the sonar image after correction is based on the image pre-segmentation of improved DeepLabV3+ network, SLIC superpixel algorithm is used to construct Graph (graph) structure data: construct the sonar image classification model based on GAT (graph attention network), the sonar graph structure data built is sent into network to complete the training and test of model;Verify the importance of pixel feature and spatial position feature.The application discloses a kind of sonar image classification methods of combined SLIC superpixel and graph attention network, which fully utilizes the space position relationship of acoustic shadow area, target area and shadow area by SLIC superpixel method and graph attention network, so as to realize higher accuracy of sonar image classification identification of sonar image by combining pixel feature and spatial geometric feature.

The embodiment of the application provides a robot control method and device based on an electroencephalogram signal, a computer readable storage medium and a computer program product, the method comprising: controlling an electroencephalogram acquisition device to collect original electroencephalogram signals in real time, preprocessing the original electroencephalogram signals to obtain target electroencephalogram signals, and the preprocessing at least comprising band-pass filtering; using a multi-band filter bank to perform multi-band decomposition on the target electroencephalogram signals into a plurality of sub-bands of different frequency ranges, and performing frequency domain transformation on each sub-band to obtain multi-band frequency domain features; inputting the multi-band frequency domain features into a pre-trained deep learning-based Transformer network to obtain a target category, the Transformer network modeling the correlation between different sub-bands based on a self-attention mechanism to realize feature fusion and deep feature extraction of the multi-band frequency domain features; and generating and outputting a control instruction based on the target category. The embodiment can improve the accuracy and stability of robot control.

This invention discloses a visual inspection system and method for transformer tank panels in new energy applications, belonging to the field of transformer tank manufacturing and inspection technology. The system includes a flexible steady-state clamping and conveying mechanism, a transient pulse excitation mechanism, a high-frequency multimodal visual acquisition mechanism, and a data processing and spatiotemporal response judgment module. By applying a controlled transient thermal pulse to the area to be inspected and simultaneously acquiring static three-dimensional reference surfaces and dynamic three-dimensional response surface sequences, spatial registration and differential processing are performed to obtain a transient out-of-plane displacement field. Welding defects are then identified based on preset spatiotemporal continuity anomaly judgment conditions, and the system combines two-dimensional surface texture images to classify and identify internal latent defects and surface penetration defects. This invention is suitable for online inspection of transformer tank panels.