4results about How to "Increased computational burden" patented technology

The invention discloses an electron microscope image drift distance rapid calculation method based on Fourier transform, and the method comprises the steps: obtaining two electron microscope images with the same size, and naming the images as sum; respectively carrying out Fourier transform on the image sum to obtain a phase difference between the image and the frequency domain; fourier transform is carried out on the phase difference, and the drift distance of the image is determined according to the distance between the diffraction points in the frequency domain. According to the method, through the Fourier transform method, the problems of high calculation complexity and low processing speed in the prior art are effectively solved, and the calculation efficiency can be remarkably improved while the precision is ensured.

The application discloses a monocular hand-eye robot vision servo method for three-dimensional space dynamic depth estimation, comprising establishing a parameterized vision servo system model, constructing an estimation error driven adaptive law and executing position-based vision servo control. The application establishes a parameterized model of the robot-camera-target unification, estimates the depth changes caused by the target motion and the robot motion in the same framework, which enables the application to effectively deal with the more common and more complex working conditions that both the robot and the target are moving. The application solves the problem that the nonlinear time-varying depth is difficult to measure in the monocular camera vision servo without prior geometric knowledge of the observed object and under the condition that both the target and the robot are moving, and realizes the rapid convergence of the control error and the estimation error in the hand-eye robot system.

A meshless large deformation simulation method using displacement-pore pressure non-uniform support domain interpolation is disclosed, belonging to the field of numerical simulation technology for civil engineering and hydraulic engineering. The steps are as follows: S1, read in the node coordinates, background mesh information, material information, and load information of the meshless model; S2, generate Gaussian points within the background mesh, calculate the displacement support domain and pore pressure support domain of the Gaussian points, and search for meshless nodes within each support domain; S3, calculate the displacement shape function and pore pressure shape function of the Gaussian points based on the nodes within the displacement and pore pressure support domains, and determine the displacement and pore pressure at each Gaussian point; S4, calculate the matrices required in the meshless governing equations; S5, conduct meshless large deformation analysis based on the shape function matrix and strain matrix. This invention improves simulation accuracy by calculating displacement and pore pressure shape functions through nodes within different support domains, and is of great significance for simulating large deformation of soil weakening and liquefaction in practical engineering.

The invention discloses an unmanned aerial vehicle image detection confrontation sample generation method and system based on a black box normal form. The method comprises the steps of S1, candidate region generation: extracting multi-scale features from an input unmanned aerial vehicle image, and generating a candidate region set containing potential target frame coordinates through feature fusion and transformation; s2, target category retrieval and allocation: allocating a target category with the highest attack efficiency to each candidate region in the candidate region set based on pre-constructed object category correlation prior information to form a target category set; and S3, adversarial sample generation: combining the candidate region set with the target category set, generating a virtual instance set, and adding the virtual instance set into the original input image to obtain a final adversarial sample image. According to the method, efficient black box attacks are realized, calculation delay and energy consumption are innovatively induced, the attack pertinence is high, the concealment is high, the attack success rate is improved, the adaptability is high, and a new view angle is provided for security assessment and defense.