3 results about "Porcupine" patented technology

This invention belongs to the field of vehicle navigation technology and provides a method and system for intelligent vehicle navigation. The method involves acquiring 3D point cloud data to be processed during vehicle navigation; spatially partitioning the 3D point cloud data and solving for the normal vectors of each point; calculating the angles between the normal vectors using the obtained normal vectors; and constructing a clustering algorithm that integrates geometric features, along with a clustering objective function, based on these angles. The method employs the first defense mechanism of the crowned porcupine algorithm to improve the hunting phase of the black-winged kite algorithm, obtaining a local optimization strategy. It combines the aggregation behavior of the artificial fish swarm algorithm with the migration mechanism of the black-winged kite algorithm to obtain a global optimization strategy, resulting in a combined intelligent algorithm. This combined intelligent algorithm is used to improve the clustering algorithm, iteratively optimizing the cluster centers until the point cloud cluster division results are obtained. Vehicle navigation is then performed based on the point cloud cluster division results. This invention improves the accuracy of point classification in intelligent vehicle navigation.

A multi-model power system inertia probabilistic prediction method based on porcupine optimization and adaptive kernel density estimation includes the following steps: acquiring power system inertia-related characteristic variables, constructing a data sample set for inertia prediction, and building a CNN-BiLSTM-MHAM deep learning model based on the data sample set; optimizing key hyperparameters of the model using the porcupine optimization algorithm based on the constructed CNN-BiLSTM-MHAM deep learning model to obtain optimized model structure parameters and inertia prediction results; constructing an error database based on the error between the inertia prediction results and actual values; and using the constructed error database, probabilistically modeling the prediction error using the adaptive bandwidth kernel density estimation method, and generating the probability interval for inertia prediction by combining the Bootstrap resampling method, thereby realizing the quantification and probabilistic expression of the uncertainty of the inertia prediction results. This method not only significantly improves the accuracy of power system inertia prediction but also more effectively characterizes the uncertainty and probability distribution characteristics of inertia fluctuations.

This invention discloses a method and system for correcting structural finite element models based on the Crucian porcupine optimization algorithm, belonging to the field of structural health monitoring technology. It aims to solve the technical problems of slow convergence speed and insufficient accuracy in traditional model correction. The main technical features are: first, acquiring measured modal data of the structure and extracting the calculated modal parameters of the finite element model; second, constructing a weighted objective function that includes the difference between frequency error and modal confidence criteria; finally, using this objective function as a fitness standard, and employing the four defense processes of the Crucian porcupine optimization algorithm to perform global optimization iteration on the model parameters until the convergence condition is met, thus achieving high-precision model parameter updates through intelligent optimization algorithms.