3 results about "Euclidean distance matrix" patented technology

The application discloses a kind of underwater wireless sensor network node positioning method based on matrix completion, steps are as follows: S1: the Euclidean distance between two different sensor nodes is obtained by TOA ranging method, so as to obtain the Euclidean distance matrix of underwater sensor network node;S2: the Euclidean distance matrix is recovered and completed using the matrix completion algorithm based on non-convex rank approximation, and the distance measurement between all nodes in the network is obtained;S3: the relative coordinates of all nodes underwater are calculated using MDS-MAP algorithm, the relative coordinates of conventional nodes are converted into absolute coordinates by the position of a part of anchor nodes, and the coordinates of conventional nodes are output.The application can not only accurately recover and complete the missing distance matrix, but also effectively handle the influence of Gaussian noise and outlier noise, with good robustness and accuracy.Using the position information of a small number of anchor nodes, the relative position is converted into absolute position, with high node positioning accuracy.

This invention discloses a method for topology identification of low-voltage distribution network areas based on a dual-modal liquid graph neural network, comprising the following steps: macroscopic topology localization of user nodes in the low-voltage distribution network area; construction of a dynamic nearest neighbor spatial topology adjacency matrix; construction of a discrete liquid graph neural network to calculate the Euclidean distance matrix of depth spatial features; calculation of the explicit waveform trend distance matrix; execution of dual-modal weighted fusion to generate the final fused distance matrix; application of an agglomerative hierarchical clustering algorithm, combined with intelligent search for optimal cluster numbers using contour coefficients, to output the final topology structure of micrometer boxes. This invention, through a discrete truncation mechanism and a dual-modal weighted fusion model, can effectively improve the accuracy of micro-topology identification of low-voltage distribution network areas while avoiding complex continuous differential solutions; combined with embedded space optimization training and intelligent optimization of contour coefficients, it can obtain better meter box clustering results within the candidate cluster number search interval, improving the accuracy and stability of topology identification.

The application provides a wafer defect detection method and system based on YOLO-Label comparison, comprising: obtaining a wafer image to be detected, performing adaptive accelerated non-local mean filtering denoising and adaptive multi-scale gradient enhancement Canny edge detection on the wafer image to be detected, and obtaining a binary edge image; inputting the binary edge image into a pre-trained target detection model to obtain target positioning information in the YOLO-Label format, wherein the model is obtained by training a YOLOv8n network that is lightened by Ghost convolution and is adapted to a single channel, using a wafer binary image sample set; comparing the target positioning information of the image to be detected with standard positioning information of a defect-free image, extracting feature points of a bounding box, calculating an Euclidean distance matrix, and based on threshold matching, determining missing defects or redundant defects. The application can realize high-precision, high-efficiency and high-robustness wafer defect automatic detection with less labeled data under complex imaging conditions, and significantly improves the detection speed and integrity.