17 results about "Numerical reconstruction" patented technology

The application discloses a pollen monitoring method based on Fourier-wavelet transform fusion, and belongs to the technical field of environmental monitoring, and specifically comprises the following steps: S1, pollen sampling is carried out by using a sampler, and a hologram of the pollen sample is obtained; S2, numerical reconstruction is carried out on the obtained hologram, Fourier transform and frequency domain transfer function are used to simulate the propagation of light, and a complex amplitude distribution on a reconstruction plane is obtained; S3, two-dimensional wavelet decomposition is carried out on the reconstructed image, and wavelet coefficient variance is calculated; S4, multi-scale depth fusion reconstruction is carried out, the maximum wavelet coefficient variance is selected by traversing different reconstruction depths, and an optimal reconstructed image is obtained through fusion; S5, post-processing is carried out on the reconstructed image; and S6, particles are detected on the processed image, morphological features are extracted, and pollen particles are classified, counted and visualized based on the features. The application significantly improves the accuracy of reproduction and identification of pollen particles in the lensless digital holographic technology.

This invention relates to a membrane electrode design optimization method based on three-dimensional reconstruction of a catalytic layer with variable resolution, comprising the following steps: constructing a numerical reconstruction model of the catalytic layer based on existing experimental characterization results; validating the effectiveness of the numerical reconstruction model of the catalytic layer; if the verification is successful, proceeding to the next step; otherwise, adjusting the growth probability of each phase and returning to the previous step to adjust the numerical reconstruction model of the catalytic layer; adjusting the parameters of the numerical reconstruction model of the catalytic layer, and analyzing the catalyst utilization and transport characteristics within the catalytic layer after parameter adjustment; and determining electrode parameters based on the analysis results to guide the fabrication of the membrane electrode. Compared with existing technologies, this invention has advantages such as reduced membrane electrode fabrication costs and strong method versatility.

This invention discloses a method for 3D reconstruction of coaxial digital holographic particle fields combining structural tensors and guided filtering. Specifically, the method involves: normalizing the coaxial digital hologram and performing multi-distance numerical reconstruction to obtain a 3D reconstruction intensity stack; constructing a focus response using structural tensors and generating spatially consistent fusion weights through guided filtering, fusing them to obtain an extended depth-of-field image and a particle soft depth map; constructing a candidate score map based on the extended depth-of-field image to achieve candidate localization, adhesion separation, and diffraction ring suppression; and finely focusing and segmenting the measured particle size within the depth neighborhood, ultimately outputting the particle's 3D coordinates and size parameters. This invention aims to utilize the effective characterization capability of structural tensors for focusing features and the edge-preserving properties of guided filtering to construct a fusion method for multi-depth particle field reconstruction results. This method enhances particle target features while suppressing artifacts and background interference, improving particle localization and measurement accuracy, and achieving higher-precision 3D reconstruction of coaxial digital holographic particle fields.

In a digital holographic wrapped phase aberration compensation method based on deep learning, a random Zernike polynomial coefficient and a corresponding wrapped phase map are generated by a computer and are respectively treated as a learning label and a network to train a neural network model. A digital holographic optical setup is built to record a hologram of a sample to be measured, the wrapped phase map is inputted into the trained neural network model after numerical reconstruction, and the Zernike polynomial coefficient is outputted to reconstruct a phase aberration distribution and to compensate complex amplitude in a spatial domain. Phase filtering and unwrapping are performed on the compensated wrapped phase map, and Zernike polynomial fitting based on background segmentation is performed on the unwrapped phase to compensate for residual aberration.

This invention discloses a method and system for identifying and reconstructing suspended spans of subsea pipelines based on distributed fiber optic strain sensing, belonging to the field of subsea pipeline integrity management and structural health monitoring technology. The invention deploys distributed fiber optic strain sensors longitudinally along the monitored pipeline segment, dispersing them circumferentially to form measurement positions, thereby separating strain and determining the principal bending plane and curvature. The curvature energy ratio extrapolation is used as the convergence criterion to determine the boundary of the suspended span influence region. A simply supported conjugate beam model is established within the suspended span influence region to obtain the foundation displacement field and rotation field reflecting the elastic deformation of the suspended span. Addressing the limitation of the conjugate beam method in reconstructing the rigid body displacement effect generated by pipe-soil interaction, a zero-curvature rigid body follow-up displacement correction method is proposed. This method compensates for the missing rigid body displacement components in the real displacement field by constructing a piecewise linear correction term. This invention overcomes the difficulty of conventional numerical reconstruction relying on ideal boundaries and improves the computational accuracy of spatial displacement field reconstruction.

The application provides a kind of long-distance tunnel gate pump large-scale series water delivery system hydrodynamic efficient simulation method, first, the one-dimensional hydrodynamic equation is discretized by using Godunov type finite volume method, the second-order accuracy in time is realized by using Runge-Kutta method, the flux at the boundary is solved by using HLL approximate Riemann solution, the spatial second-order accuracy is realized by numerical reconstruction in the grid, then different methods are used for coupling of channel section and internal boundary according to different internal boundary characteristics of long-distance tunnel gate pump large-scale series water delivery system, then the model is parallelized by using OpenMP method.The application is based on the finite volume method to discretize one-dimensional shallow water equation, and proposes a double-layer coupling method for large-scale unsteady flow numerical calculation, which includes inner layer solution and outer layer iteration, to solve the problem of long time-consuming in explicit format calculation, and develops full-line parallel calculation by using CPU, realizes accurate simulation of water surface line in stable state of water delivery and efficient derivation of hydraulic response in water delivery transition process, and provides technical support for efficient operation of water delivery system.

This invention discloses a method and system for optimizing spatial collaborative bearing structure support in coal mine roadways, relating to the field of support parameter optimization technology. The method includes the following steps: collecting geomechanical parameters and constructing a three-dimensional initial geostress field numerical model; reconstructing, in the model, a first bearing structure formed by constrained concrete supports, a second bearing structure formed by high-strength UHPC spray coating, and a third bearing structure formed by grouting anchors and their reinforced surrounding rock, and defining their constitutive relations; applying tunneling disturbance and mining support pressure for simulation and extracting collaborative deformation response data; constructing a collaborative bearing capacity evaluation function to quantify the collaborative working performance of the structure; and optimizing the support parameters using an optimization algorithm, with the goal of maximizing the minimum value of the evaluation function, and outputting the optimal support parameters. This invention, through numerical reconstruction and dynamic collaborative coefficient inversion, achieves quantitative evaluation and parameter optimization of the bearing capacity of the triple structure, improving the stability of roadways in fractured soft rock.

The invention belongs to the technical field of wind tunnel tests, and discloses an inner wall global profile numerical reconstruction method and system based on a flexible plate outer wall local displacement field. According to the method, the finite element model based on the Kirchhoff thin plate theory is established, the local displacement field of the non-reinforcing rib area of the outer wall of the flexible plate is adopted as input, and the engineering obstacle that measuring points are difficult to arrange in the reinforcing rib area connected with the actuating rod is avoided. The result shows that when the input length of the non-reinforcing-rib area in the length direction is not less than 75% of the total length of a single non-reinforcing-rib area, the root-mean-square error of the reconstructed inner wall displacement and the simulation result is less than 0.04 mm, the maximum absolute error is less than 0.13 mm, and the flexible-wall nozzle profile measurement precision requirement is met.

The invention discloses a lithium ion battery negative electrode microstructure modeling method and device, a medium and equipment, and relates to the field of lithium ion batteries, and the method comprises the following steps: imaging a negative electrode material of a lithium ion battery to obtain structural information of the negative electrode material; according to the structural information of the negative electrode material, extracting geometrical characteristics of particles and pores in the structural information, performing morphological analysis on the extracted geometrical characteristics, and determining particle morphology and pore distribution; performing coordinate transformation on each particle based on particle morphology and pore distribution in a reconstruction calculation domain, randomly generating an additive on the surface of the particle or around the pore until a preset porosity is reached, and generating a plurality of two-dimensional microstructure slice images reflecting the microstructure characteristics of the negative electrode material by combining a numerical reconstruction technology; and performing three-dimensional Gaussian blur processing on the plurality of two-dimensional microstructure slice images, and converting the two-dimensional microstructure slice images into a three-dimensional microstructure model used for describing the microstructure of the negative electrode material in combination with a visualization technology.

The invention relates to a method for reconstructing a turbulence wake of a square-section building from random sparse data, and the method comprises the steps: constructing a generative adversarial network architecture with a U-Net as a generator and a convolutional neural network as a discriminator, and enabling the generative adversarial network architecture to be used for building end-to-end mapping from low-dimensional sparse input to high-dimensional full-field output; performing tensorization coding on sparse wind speed values acquired by a sensor at any moment and corresponding space coordinates to form dual-channel input including a wind speed value channel and a position information channel; in the training process, the vortex street periodicity of the square-section building streaming is used as physical prior, and the phase consistency constraint based on the phase difference is applied to the U-Net bottleneck tensor to embed periodicity information; a combined training strategy is adopted, and a combined loss function composed of adversarial loss, numerical reconstruction error loss and phase consistency constraint loss is used for alternately optimizing the generator and the discriminator; and after training, verifying the generalization and interpretability of the model.

The application discloses a digital holographic wrapped phase distortion compensation method based on deep learning. Random Zernike polynomial coefficients and corresponding wrapped phase maps are generated by a computer, and are used as learning labels and network training neural network models respectively; a digital holographic light path is built to record a hologram of a sample to be measured, and after numerical reconstruction, the wrapped phase map is input into the trained neural network model to output Zernike polynomial coefficients to reconstruct a phase distortion distribution, and the object light reconstruction amplitude is compensated in a spatial domain; after compensation, the wrapped phase map is subjected to phase filtering and unwrapping, and the unwrapped phase is subjected to Zernike polynomial fitting based on background segmentation to compensate residual distortion. The method only trains the network with a simulation data set, compensates most of the distortion before phase unwrapping, improves the reliability of the wrapped phase data, greatly improves the phase recovery accuracy, and has the advantages of fast calculation speed and accurate distortion compensation.

The application provides a target reconstruction method based on a differentiable SAR image renderer, which is used for reconstructing a three-dimensional target through a differentiable renderer according to a SAR image, and comprises the following steps: step S1, defining a three-dimensional rendering scene; step S2, reconstructing the target, in step S2, a contour map is used for reconstructing a non-ground target, and an illumination map and a shadow are used for reconstructing a ground target, wherein the reconstruction process in step S2 comprises the following steps: step S2-1, according to the target type, extracting a contour map or an illumination map and a shadow from the SAR image as a true value and inputting the true value into the differentiable renderer as a rendering target; step S2-2, the initialization input of the differentiable renderer is a spherical mesh, the error between the rendering image of the differentiable renderer and the true value is transmitted to the input scene parameters in the reverse direction along the forward rendering pipeline of the differentiable renderer, the numerical value of the scene parameters is modified multiple times through a gradient descent algorithm, and a three-dimensional scene conforming to the target two-dimensional image is reconstructed.

The invention discloses a high-bandwidth holographic three-dimensional imaging method based on a Kramers-Kronig relationship, and the method comprises the steps: building and arranging a Fresnel digital holographic imaging system, and collecting a current interference mode, thereby obtaining a Fresnel digital hologram. By means of the Kramers-Kronig relationship, the light field distribution carrying the complete wavefront information of the three-dimensional sample at the position of the camera can be calculated from the Fresnel digital hologram under the condition of higher space bandwidth utilization rate, and then the wavefront information of the three-dimensional sample can be calculated by means of an automatic focusing algorithm and diffraction propagation calculation. Therefore, clear images of the three-dimensional sample at different spatial positions can be reconstructed in the computer, and high-bandwidth holographic three-dimensional imaging of the three-dimensional sample is realized. The method breaks through the limitation of the traditional off-axis holographic interference technology on the space bandwidth utilization rate, does not need to add a physical filtering device in an optical path, and can obtain high-contrast and low-noise sample three-dimensional information only through numerical reconstruction and diffraction propagation calculation of a computer. And rapid and stable high-bandwidth holographic three-dimensional imaging of the target object is realized.

The invention relates to the field of atmospheric wind field modeling, and provides a full-scale wind field numerical reconstruction method for low-altitude torrent accompanying wind direction vertical shear in order to improve calculation efficiency, which directly takes target statistical characteristics as a driving target, concentrates calculation resources for a most critical structural wind field interaction area, and improves the calculation efficiency. By iteratively adjusting the parameter of a single control point of the entrance boundary, the wind field at the target position can be quickly converged to the predetermined feature, and high-resolution solution does not need to be performed on the whole atmospheric boundary layer, so that the order of magnitude improvement of the calculation efficiency is realized on the premise of ensuring the reconstruction precision of the wind field at the target position.

This invention discloses a deep learning-based digital holographic phase image stitching method. The method includes: acquiring multi-field holographic interferograms, obtaining a single-field phase image through numerical reconstruction, and then linearly mapping it to a grayscale image to create a synthetic dataset; using semi-supervised training of a noise-resistant registration network, calling the obtained registered grayscale image pairs and their binary masks, and then inputting them into a baseline compensation fusion network, using unsupervised training of the fusion model; inputting the registered grayscale image pairs into the trained image stitching model to obtain a stitched grayscale image, which, after grayscale-phase conversion, yields a large field-of-view phase image. This invention uses only a synthetic dataset to train the network, avoiding the difficulty of manually annotating the registration matrix. Through a semi-supervised training strategy and noise-resistant registration network design, it greatly improves registration accuracy, and baseline compensation further optimizes the fusion effect, showing great application potential in digital holographic microscopy quantitative measurement field-of-view stitching.

The present application belongs to the technical field of wind tunnel test, and discloses an inner wall global profile numerical reconstruction method and system based on local displacement field of the outer wall of a flexible plate. The present application establishes a finite element model based on Kirchhoff thin plate theory, and uses the local displacement field of the non-stiffener area of the outer wall of the flexible plate as input, thereby avoiding the engineering obstacles of arranging measuring points in the stiffener area connected with the actuating rod. The results show that when the input length of the non-stiffener area along the length direction is not less than 75% of the full length of a single non-stiffener area, the root mean square error of the reconstructed inner wall displacement and the simulation result is less than 0.04 mm, and the maximum absolute error is less than 0.13 mm, thereby meeting the flexible wall nozzle profile measurement accuracy requirements.