3 results about "Canonical correlation" patented technology

This invention discloses a fire-fighting pipe fitting leakage risk early warning system based on big data analysis, comprising the following steps: collecting multi-dimensional time-series data such as pressure, flow rate, temperature, and humidity; constructing a data processing and modeling workflow; employing kernel canonical correlation analysis to extract nonlinear correlation features between different monitoring parameters to identify weak correlation changes before leakage; and constructing an anomaly measurement mechanism based on the maximum correlation entropy criterion to quantify the degree of feature shift. By fusing the above correlation features and entropy information, a dynamic risk index is generated and compared with a dynamic threshold to achieve real-time early warning of leakage risk, effectively supporting early fault detection and intelligent assessment of fire-fighting pipe fittings. This invention achieves dynamic perception and intelligent judgment of fire-fighting pipe fitting leakage risk, possessing data-driven early warning capabilities.

This invention relates to the field of power equipment testing technology, and more particularly to a method for identifying partial discharge types in transformers. The method first acquires partial discharge pulse signals using an ultra-high frequency sensor, and obtains a standard time-domain signal sequence through denoising and normalization preprocessing. Then, it extracts two types of features in parallel: first, it uses an improved adaptive noise complete set empirical mode decomposition to obtain key intrinsic mode components, and calculates multi-scale permutation entropy to form a first feature subset; second, it converts the signal into a time-frequency distribution image, and extracts high-dimensional deep features through a pre-trained deep convolutional neural network to form a second feature subset. Finally, it uses kernel canonical correlation analysis to fuse the features, and after dimensionality reduction, obtains a low-dimensional discriminative feature vector, which is input into a cascaded forest ensemble classifier containing a channel attention module to complete the identification. This invention mines features from multiple dimensions, improving feature discrimination and recognition accuracy, and has strong anti-interference capabilities, providing reliable support for transformer fault diagnosis and ensuring the stable operation of power systems.

The application discloses a single-cell annotation method and device based on canonical correlation analysis, equipment and medium, including: first, the original peptide sequence and physical and chemical characteristics are acquired, the peptide segment confidence weight is calculated through the uncertainty estimation model based on the convolutional neural network, and the peptide segment is quantitatively weighted and aggregated into a protein expression matrix to realize preliminary noise reduction; subsequently, a cell adjacency graph is constructed and multi-view enhanced data is generated, feature extraction is carried out by using a graph convolution network combined with a graph canonical correlation analysis (GCCA) objective function, and batch effects are eliminated by forcing the decorrelation between feature dimensions, so that cell invariant embedding representation across datasets is extracted; finally, the embedding representation is used to realize high-precision cell type annotation through a label migration algorithm. The application effectively solves the problems of high noise and batch effects in single-cell proteome data, and significantly improves the accuracy of cell heterogeneity analysis and tumor microenvironment characterization.