5results about How to "Improve clustering accuracy" patented technology

This application relates to an image processing method, apparatus, electronic device, and storage medium, which can be applied to the field of mapping. The method includes: acquiring an image of an object to be clustered and a target image of an object; determining a first object image to be segmented and a second object image to be unsegmented from the image of the object to be clustered and the target image of the object; the size of the first object image is larger than the size of the second object image; segmenting the first object image to obtain at least two sub-images; inputting the second object image and each sub-image into an image matching model for image matching processing to obtain a first matching result; and determining a target image set of the image of the object to be clustered based on the first matching result. According to the technical solution of this application, the accuracy and efficiency of image clustering can be improved.

This application provides a mask layout correction method, system, medium, program product, and terminal based on a combination of membrane computing and the DBSCAN algorithm. The method involves obtaining an initial layout, performing preliminary clustering on the initial layout using a preset clustering method to obtain several partitions and their corresponding density and contour features; obtaining initial density and contour parameters for DBSCAN clustering; iteratively optimizing the initial clustering parameters of the DBSCAN clustering using a preset membrane computing algorithm based on the density and contour features of each partition to obtain the optimal clustering parameters for each partition; performing DBSCAN clustering on each partition based on the corresponding optimal clustering parameters to obtain the core region, boundary region, and sparse region of the initial layout; and correcting the core region, boundary region, and sparse region based on a preset correction strategy to obtain the corrected layout of the initial layout. This improves the accuracy of mask layout correction.

This invention relates to the field of data processing technology, specifically to an artificial intelligence-based production line scheduling optimization method. The method obtains the adjustment time and corresponding change magnitude based on the data change characteristics of parameter adjustment curves; it obtains the process difference significance based on the distribution characteristics of the adjustment times of parameter adjustment curves for all orders under the same process and the change magnitudes of all adjustment times; it obtains the target process based on the difference significance of all processes; and it obtains the comprehensive difference degree based on the difference characteristics of the parameter adjustment curves of the target process between different orders. This invention clusters all orders based on the comprehensive difference degree and order production time to obtain different order clusters; and it schedules and allocates production lines based on all order clusters, which can reduce equipment wear and tear and improve production efficiency.

The application discloses a kind of dam deformation prediction method, equipment, storage medium and product, the prediction method includes preprocessing to dam monitoring data;The environmental data after pretreatment is extracted to depth feature, and environment characteristic is obtained;The reservoir water level after pretreatment and environment characteristic are carried out cluster analysis, and according to the time domain division of time factor and dam survey point displacement according to cluster analysis result, obtain the cluster division result of different time periods;According to each cluster division result, respectively construct sample data set;Each sample data set is used to train and test Transformer-LSTM model respectively, and the dam deformation prediction model of each time period is obtained;Obtain the data to be predicted, and the dam deformation prediction model corresponding to the time period of the data to be predicted is used to predict the prediction data, and the dam deformation prediction result is obtained.The dam deformation prediction precision of the application is improved.

The application provides a single-cell rare cell type identification method and system based on a divide-and-conquer strategy, comprising the following steps: S1, using a variational autoencoder to encode single-cell transcriptome data into Gaussian distribution hidden variables; S2, performing spectral clustering on the Gaussian distribution hidden variables to obtain a single-cell coarse-grained clustering result; and S3, optimizing the single-cell coarse-grained clustering result based on intra-class cluster loss and inter-class cluster loss until the intra-class cluster loss and the inter-class cluster loss are minimized to obtain a single-cell clustering result containing rare cell types. The application can overcome the deficiency that previous single-cell clustering and identification methods do not optimize rare cell types, leading to misclassification or difficulty in identifying rare cell types.