6 results about "Hypercomplex number" patented technology

The invention provides a ship detection-oriented multi-source remote sensing image fusion method, a ship detection-oriented multi-source remote sensing image fusion medium and a ship detection-oriented multi-source remote sensing image fusion system, and belongs to the technical field of remote sensing images. Constructing an electromagnetic scattering constraint model based on physical optical approximation and a geometric diffraction theory to calculate a predicted value of the backscattering cross section of the target, introducing a fusion cost function, generating a fusion weight matrix by adopting an adaptive feature weighting model, and performing multi-scale fusion on the image through hypercomplex wavelet transform; and iteratively optimizing the fusion result by using an information diffusion heat conduction equation, and performing physical consistency correction to output an enhanced fusion image, thereby solving the technical problem of poor physical consistency of the fusion result caused by lack of physical model constraint when the synthetic aperture radar image and the multispectral image are fused.

The present disclosure relates to a system and methodology designed for the spectroscopic detection of materials found on surfaces or dispersed in air. Some embodiments utilize a multi-modal sensor suite that operates through a progressive and multi-level acquisition pipeline, escalating across different sensing modalities such as visible, thermal, and hyperspectral. Data acquired at each level can be fully integrated and fused for efficient and precise material detection. Some embodiments use a physics-constrained computational model that uses latent hypercomplex states and learnable hypercomplex algebra to accurately disentangle and reconstruct spectral signatures, enabling robust substance identification. The system is adaptable to a range of hardware platforms, such as handheld devices, UAVs, and robots.

The invention discloses a nonlinear rotor system dynamic response and sensitivity calculation method and device, equipment and a medium, and the method comprises the steps: S1, carrying out the dynamic modeling of a nonlinear rotor system according to the structural characteristics of the nonlinear rotor system, and building a motion equation of the nonlinear rotor system; s2, establishing a hypercomplex form of the motion equation of the nonlinear rotor system to obtain a hypercomplex motion equation of the nonlinear rotor system, and performing dimension expansion conversion on the motion equation of the nonlinear rotor system of a complex matrix into an equivalent real number matrix by using a complex matrix expression; and S3, solving the established hypercomplex motion equation of the nonlinear rotor system, extracting the real part value and the imaginary part value of a calculation result, and obtaining the response of the system and the corresponding sensitivity of each order. According to the method, the jacobian matrix is automatically updated and solved in the iteration process, the accuracy of the calculation result is ensured, the response sensitivity and the dynamic response sensitivity of the nonlinear rotor system can be calculated at the same time, and the calculation efficiency is high.

A data processing method, apparatus, and related equipment relate to the field of artificial intelligence technology. The method involves acquiring raw data, which serves as input to an AI model including an attention layer and a feedforward layer; generating input data for the attention layer based on the raw data, wherein the values ​​of the elements in the input data are hypercomplex numbers, including real and imaginary parts; performing calculations on the input data in the attention layer to obtain a first calculation result, wherein the values ​​of the elements in the first calculation result are hypercomplex numbers; and executing the feedforward layer based on the first calculation result. Since the imaginary part of the hypercomplex number can indicate features such as signal amplitude, phase, or frequency corresponding to the raw data, hypercomplex numbers can retain more dimensional features of the raw data compared to real numbers. Therefore, performing calculations based on hypercomplex numbers in the attention layer can improve the accuracy of the calculation results output by the attention layer, thereby improving the overall training or inference accuracy of the AI ​​model.

The present disclosure relates to a system and methodology designed for the spectroscopic detection of materials found on surfaces or dispersed in air. Some embodiments utilize a multi-modal sensor suite that operates through a progressive and multi-level acquisition pipeline, escalating across different sensing modalities such as visible, thermal, and hyperspectral. Data acquired at each level can be fully integrated and fused for efficient and precise material detection. Some embodiments use a physics-constrained computational model that uses latent hypercomplex states and learnable hypercomplex algebra to accurately disentangle and reconstruct spectral signatures, enabling robust substance identification. The system is adaptable to a range of hardware platforms, such as handheld devices, UAVs, and robots.

This application discloses a method, apparatus, equipment, and medium for calculating the dynamic response and sensitivity of a nonlinear rotor system. The method includes the following steps: S1, based on the structural characteristics of the nonlinear rotor system, performing dynamic modeling of the nonlinear rotor system and establishing the equations of motion for the nonlinear rotor system; S2, establishing the hypercomplex form of the equations of motion for the nonlinear rotor system to obtain the hypercomplex equations of motion for the nonlinear rotor system, using complex matrix expression, and expanding the dimension of the complex matrix equations of motion for the nonlinear rotor system to convert them into equivalent real matrix equations; S3, solving the established hypercomplex equations of motion for the nonlinear rotor system, extracting the real and imaginary parts of the calculation results, and obtaining the system response and corresponding sensitivities of each order. This application achieves automatic updating and solving of the Jacobian matrix during the iteration process, ensuring the accuracy of the calculation results, and can simultaneously calculate the response and dynamic response sensitivity of the nonlinear rotor system, resulting in high computational efficiency.