10 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.

The invention discloses a real-time assembly error recognition and early warning method based on multi-dimensional dynamic threshold adjustment. The method comprises the following steps: extracting a multi-modal assembly state descriptor and a field assembly information sequence; field assembly information fragments are intercepted for similarity calculation; constructing a time sequence prediction module, and taking the field assembly information sequence as input to predict the assembly state of the next frame; constructing a weighted Euclidean distance matrix of the field fragment and the template fragment, and calculating a minimum cumulative distance to quantify the difference between the two sequences; a dynamic threshold model is established based on statistical process control, and whether errors exist in the assembly process or not is judged according to the model; based on an incremental dynamic time planning algorithm and a real-time data acquisition module, a real-time identification system is constructed to realize early warning and standard step pushing, and a real-time adaptive quality control closed loop is formed. According to the method, a real-time and self-adaptive quality control closed-loop system is formed through integration with multi-mode sensing and AR guide technologies, and an effective solution is provided for a complex industrial assembly scene.

The invention discloses a packaging process parameter data processing and consistency evaluation method and system, and relates to the technical field of data processing and quality consistency evaluation. According to the method, multi-source data fusion is carried out on the side wall morphology features after wafer scribing and the infrared thermal image dynamic features in the parallel seam welding process, and refined space-time two-dimensional fragmentation processing is carried out on the seam welding process, so that the problem that the packaging process state cannot be comprehensively reflected by a traditional single index is effectively solved; according to the method, multi-dimensional characteristic parameters are mapped to a unified coding interval and principal component dimensionality reduction is carried out to generate a fingerprint code capable of representing the comprehensive process state of a device, and then statistical analysis and heat map visualization of an Euclidean distance matrix are utilized to realize objective quantification of process consistency in batches and visual positioning of systematic deviation.

The invention belongs to the technical field of water supply network monitoring, and particularly relates to a water supply network leakage detection method based on a Kriging interpolation method and a CatBoost model, and the method comprises the steps: firstly constructing a network hydraulic model, and calculating an inter-node Euclidean distance matrix; a hydraulic model is used for simulating multiple times of leakage, and pressure data of pressure value monitoring points and corresponding leakage labels are collected to form training samples; based on a Kriging interpolation method, fusing the pressure data and the distance matrix, estimating a pressure value of a non-monitoring point, and constructing a pressure feature set covering nodes of the whole network; performing importance sorting and screening on the features to obtain an optimal feature subset; combining the data with leakage labels to form a training sample matrix, and inputting the training sample matrix into a CatBoost model for training; and utilizing the trained model to realize the identification of the leakage area and grade according to the real-time pressure data. According to the invention, based on the pressure data of a small number of monitoring points, the independent metering partition where leakage occurs can be positioned more accurately, and the severity of the leakage can be judged.

The application provides a mobile network cooperative positioning method based on tensor completion, acquires relative distances between unknown nodes in a wireless sensor network, constructs incomplete and noisy Euclidean distance matrices at different times, obtains Euclidean distance tensors according to the incomplete and noisy Euclidean distance matrices at different times and complete Euclidean distance matrices at different times, completes and denoises the Euclidean distance tensors, obtains denoised and completed Euclidean distance matrices according to the denoised and completed Euclidean distance tensors, obtains relative coordinates of the unknown nodes through multi-dimensional scaling of the denoised and completed Euclidean distance matrices, and obtains global positions of the unknown nodes through coordinate registration of the relative coordinates of the unknown nodes by means of Procrustes analysis, so that the Euclidean distance matrices are accurately recovered, and the positioning accuracy of the unknown nodes is improved.

This application proposes an analytical device and equipment for mental illness. The device divides the whole brain of the subject into Regions of Interest (ROIs) and obtains the centroid coordinates and BOLD time series of each ROI. It processes fMRI data using a sliding window method, calculates the correlation between ROIs within the time window to obtain a time-resolved functional connectivity (FC) matrix, processes DTI data using a probabilistic tracking method to obtain a structural functional connectivity (SC) matrix, obtains a Euclidean distance matrix based on the ROI centroid coordinates, and obtains several structural index matrices based on the SC matrix. It constructs an edge-pair feature matrix based on these matrices, where each element corresponds to a pair of ROIs. It performs PCA on the edge-pair feature matrix and maps the PCA results back to ROI pairs to obtain a secondary edge-pair feature matrix. Based on multiple linear regression, it estimates the predictive power from the secondary edge-pair feature matrix to the FC matrix, obtaining a dynamic structural-functional coupling time series matrix for each subject. Based on this matrix, mental illness analysis can be performed, fully considering the dynamic changes in functional connectivity over time, which is beneficial for comprehensively assessing brain network abnormalities related to mental illness.

The application provides an adaptive DBSCAN abnormal battery identification method based on a Euclidean distance without prior weight, belongs to the technical field of battery anomaly detection of energy storage power stations under data driving, and comprises the following steps: preprocessing original data; taking the preprocessed data as input, obtaining the no-prior weight w of each attribute of the original input data of the battery of the energy storage power station; obtaining the multivariate time series Euclidean distance matrix dist with the no-prior weight w; based on dist, respectively performing MDS low-dimensional embedding and adaptive selection of DBSCAN clustering algorithm parameters; performing DBSCAN clustering abnormal battery identification; and finally outputting a sequence composed of the clustering labels of each battery, wherein the label value of-1 indicates that the battery corresponding to the serial number is an abnormal battery. The application can effectively mine the potential information of the distribution of original battery data, better measure the differences between the input attributes of the multivariate time series, solve the problem of unsatisfactory clustering outlier identification caused by small differences between input parameters, and improve the accuracy of abnormal battery identification of the energy storage power station.

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.

The invention relates to the technical field of Internet of Things node positioning, and discloses an Internet of Things positioning data completion method, which comprises the following steps: acquiring a distance matrix of partial observation between nodes; extracting observation distances from each node to all other nodes to form a one-dimensional distance sequence, and converting the one-dimensional distance sequence into a Hankel matrix through a sliding window; constructing a three-dimensional Euclidean distance observation tensor, namely, an observation EDT tensor; establishing a joint optimization model based on structured prior and deep low-rank prior to obtain a complete EDT tensor; solving the joint optimization model by adopting an alternating direction multiplier method framework based on Plug-and-Play, and iteratively updating and outputting a complete EDT tensor; the complete Euclidean distance matrix is inversely converted into the complete Euclidean distance matrix for node positioning of the Internet of Things, and the method aims at improving the data recovery precision and robustness under the condition that the distance measurement information is seriously lost.