2 results about "Cluster cell" patented technology

This invention discloses a comprehensive temperature sensing method for energy storage battery clusters based on a hybrid expert model. The method includes: acquiring full thermal-electrical data of the target battery cluster; using the acquired full thermal-electrical data as input, classifying all cells using a clustering algorithm, selecting the central cell of the cluster, and installing a temperature sensor at the central cell to form a sparse temperature measurement arrangement; processing the acquired data using a sliding window with a preset time window length to construct a model input feature matrix; constructing a multi-source time-series feature matrix and a T-MoE model, using a multi-gated, multi-task structure for classification output; training the T-MoE model based on the main loss function and auxiliary loss function; and outputting the temperature of unmeasured cells or the temperature distribution of the entire cluster cells based on the trained T-MoE model. This invention enables high-precision, stable, and scalable sensing of the temperature distribution of large-scale energy storage battery clusters under low-sensor configuration conditions.

This application relates to the field of cell type identification technology and discloses a cell type identification method based on multi-omics decoupled representation and graph embedding, including the following steps: Step 1, acquiring single-cell multi-omics data and preprocessing it; Step 2, obtaining cell spectrum embedding representation using a graph embedding model, calculating single-cell sample similarity based on the spectrum embedding representation, and then constructing a sample similarity graph; Step 3, using a variational autoencoder model to map different omics data to the same dimensional space for data integration, obtaining a single-cell shared latent representation; Step 4, clustering cells using a Gaussian mixture model based on the shared latent representation to obtain cell cluster assignments; Step 5, constructing an objective function based on maximum likelihood estimation. Through a scalable model architecture and high-quality cell type identification, this method improves the performance efficiency of single-cell multi-omics data integration and analysis, providing new directions and possibilities for single-cell multi-omics data integration.