3results about How to "Reduce feature redundancy" patented technology

This invention discloses a method and device for diagnosing the health status of flow batteries based on particle swarm optimization (PSO), aiming to solve the problems of difficult modeling and poor real-time performance of traditional physical model-driven methods, and the unscientific feature selection and insufficient prediction stability of existing data-driven methods. The system is data-driven at its core, collecting multi-dimensional data such as voltage, current, and capacity of the flow battery during operation through sensors. After preprocessing, LASSO and grey relational analysis are used to jointly screen the optimal subset of health features. An extreme learning machine (ELM) is built as the main model for SOH prediction, using the ReLU activation function to improve nonlinear expression capabilities, and introducing a particle swarm optimization algorithm to optimize the weights and biases of the ELM, improving the instability caused by random initialization.

This application discloses a sound source localization model training method, a sound source localization method, and an apparatus, belonging to the field of sound signal processing technology. This application is applied to sound source localization scenarios, such as predicting the spatial position of a loosely connected component, wherein the component is mounted on a mechanical connection structure. This application uses sound signals as the data processing object, trains a sound source localization model based on pre-built training samples, and achieves automatic sound source localization based on the trained model. Since this method requires no manual intervention, compared to manual inspection, it not only saves labor costs and avoids the risks of working at heights, but is also more efficient, enabling real-time monitoring of the fastening status of the connected component.

The application discloses a liver cancer prognosis evaluation model generation method and related devices, and relates to the technical field of data processing. The method obtains medical multi-modal data of a liver cancer patient, the medical multi-modal data including macroscopic magnetic resonance imaging data and microscopic pathological whole slice image data; based on a graph neural network, the medical multi-modal data is converted into multi-scale graph structure data and is subjected to cross-scale topological alignment through a GroMoVe optimal transport algorithm to obtain topological fusion features; the topological fusion features are input into a causal intervention module constructed based on a structural causal model, a counterfactual generation is performed to eliminate confounding factors, and causal invariant features are extracted; a continuous time evolution component is trained based on the causal invariant features, the continuous time evolution component represents time dynamic changes of a tumor recurrence risk based on a neural ordinary differential equation, and a liver cancer prognosis evaluation model is obtained. The application improves liver cancer recurrence prediction accuracy, enhances model generalization robustness and interpretability.