14results about How to "Improve reconstruction accuracy" patented technology

The application relates to the technical field of image processing, in particular to an image super-resolution reconstruction method based on a multi-scale content perception mixer, which is realized by using an adaptive processing mechanism. The method comprises the following steps: shallow feature extraction is performed on a low-resolution image to be reconstructed, so as to obtain an initial shallow feature map; feature enhancement based on a feature pyramid and an attention mechanism is performed on the shallow feature map, so as to obtain a deep feature map; multi-scale content perception prediction is performed based on the deep feature map, so as to generate guide information for guiding calculation allocation, the guide information comprising a window classification binary mask and a window size; different image regions are allocated to different calculation paths for processing based on the guide information; the feature maps output by the calculation paths are recombined and fused, and then enlarged to a target resolution, so that a high-resolution image is finally obtained. The method realizes accurate classification of image regions and on-demand allocation of calculation resources, and significantly reduces the calculation complexity and the memory occupation.

This invention proposes a hyperspectral image fusion and reconstruction method based on cross-modal interaction and difference perception. The steps are as follows: acquiring training and testing sample sets; constructing a reconstruction network model based on cross-modal interaction and difference perception; iteratively training the reconstruction network model; and obtaining the reconstruction results of the hyperspectral image. The residual module of this invention utilizes multiple cross-modal interaction modules (CIMB) to perform cross-modal collaborative interaction on the spectral-spatial feature maps of LR-HSI and HR-MSI, enabling the mining of interaction features and contextual features between the two modalities. The difference perception fusion module (DPFM) performs difference perception and fusion on the cross-modal interaction features output by CIMB, effectively enhancing key details while mitigating distortion caused by differences between the two modal features, thus significantly improving reconstruction accuracy.

This invention provides a method and apparatus for reconstructing the three-dimensional temperature and humidity field of a tropical cyclone based on deep learning and sparse observations. The method includes: acquiring radiosonde observation data and satellite remote sensing data for a target tropical cyclone region; mapping the radiosonde observation data onto a two-dimensional grid on multiple preset pressure layers to generate an initial temperature and humidity field containing missing data regions, and generating a mask to identify the missing data locations; interpolating and aligning the satellite remote sensing data onto the two-dimensional grid to form an auxiliary remote sensing field; combining the initial temperature and humidity field, the mask, and the auxiliary remote sensing field at each pressure layer to form a multi-channel input tensor, which is then input into a pre-trained deep neural network model. The model infers and completes the missing data regions, outputting the complete two-dimensional temperature and humidity field for the current layer; and vertically synthesizing the complete two-dimensional temperature and humidity fields corresponding to all pressure layers to obtain the three-dimensional temperature and humidity field of the target tropical cyclone.

This invention provides a method and system for geomagnetic data reconstruction based on reference channel constraints and a physical information neural network. The method includes: S1: segmenting the geomagnetic data to be processed and inputting it into a classification model to identify it as high-quality geomagnetic data segments or noisy / anomaly-containing geomagnetic data segments; S2: inputting the high-quality geomagnetic data segments into an MVMD model based on reference channel constraints to obtain a series of modal components; S3: inputting each modal component into an MFFE-LSTM-PI network model to obtain a predicted value, and then superimposing the predicted values ​​corresponding to each modal component to obtain the geomagnetic data reconstructed for the next time period of the high-quality geomagnetic data segment. The technical solution of this invention combines MVMD and MFFE-LSTM-PI networks to achieve multi-band decomposition and reconstruction, and further optimizes the classification model by using an Improve-TCN network for preliminary processing of geomagnetic data to achieve geomagnetic signal-to-noise identification.

The application discloses a reflection type laminated imaging method based on off-axis diffraction and a related device, relates to the field of laminated diffraction imaging, and comprises the following steps: acquiring diffraction images of different scanning positions in a target region of a sample; according to the diffraction images, applying an improved rPIE iterative algorithm to perform diffraction light field reconstruction to obtain the amplitude and phase of the sample reconstruction; and the improved rPIE iterative algorithm refers to replacing a diffraction propagation model in a traditional rPIE iterative algorithm with a parallel plane off-axis diffraction transmission model. In the application, the diffraction propagation model in the traditional rPIE iterative algorithm is replaced with the parallel plane off-axis diffraction transmission model, a relatively simple light field transmission model is established and applied to realize the reconstruction of the reflection type laminated imaging, no complex interpolation calculation is involved, the reconstruction method is simplified, and the reconstruction accuracy is improved from the aspects of the definition, contrast and resolution of the reconstruction result.

The application belongs to the technical field of structural health management, and particularly relates to a multi-sensor failure compensation method based on high-dimensional feature reconstruction, comprising: initializing algorithm parameters, acquiring monitoring data by using multi-sensors, and converting the monitoring data into a matrix; standardizing observation values of the matrix to obtain a standardized data matrix, and obtaining a covariance matrix in a subspace after dimension reduction; based on an ADMM method, decomposing the covariance matrix in the subspace after dimension reduction to obtain a low-rank matrix and a sparse matrix; reconstructing missing sensor data based on the low-rank matrix and the sparse matrix to obtain a reconstructed sensor vector. The aviation aircraft sensor failure compensation algorithm based on high-dimensional feature reconstruction fully considers the coupling relationship between sensors and real-time requirements, significantly improves the prediction accuracy under the condition of missing fault sensors, and is suitable for wide application of various aircrafts such as civil aviation passenger planes and unmanned aerial vehicles.

This disclosure provides a model training method, comprising: extracting features of an image, the features including channel dimension, height dimension, and width dimension; dividing the features into multiple feature blocks along the channel dimension; calculating entropy constraints of the features based on the encoding entropy of each feature block; and training an encoder based on the entropy constraints of the features, the encoder being used to generate compressed codes for the image. This disclosure also provides a method for training a generator using the encoder trained by this model training method, and for generating compressed codes for input image labels based on the generator.

The application provides a curve reconstruction method and device based on deep learning, equipment and medium, the method comprises the following steps: obtaining the logging curve data of the non-expanded section, and establishing a training set and a test set; a density curve reconstruction model is established and the training set is used to train the density curve reconstruction model, the density curve reconstruction model is a deep learning model; the test set is used to test the density curve reconstruction model, and a reconstructed density curve is generated, the density curve reconstruction model is adjusted until the correlation of the reconstructed density curve and the original density curve of the non-expanded section and the relative error of the density curve reconstruction model reach a preset condition; the density curve of the target well is reconstructed by using the adjusted density curve reconstruction model. The application simplifies the operation process, especially in complex formation conditions, can accurately reflect the actual situation of the formation, and improves the calculation accuracy of the reservoir parameters.

The present application relates to the technical field of fluid conveying pipeline temperature control, and specifically discloses an intelligent temperature control method and system for electric heating hoses in extremely cold environments, aiming to solve the technical problems of existing temperature field sensing blind spots and the inability of fault diagnosis to distinguish between external working condition interference and hardware failure. First, the hose configuration and real-time operation data are obtained, a temperature field reconstruction model based on boundary condition online identification is constructed to reconstruct the full-length temperature distribution of the hose, then the thermal response feature vector composed of the heating response time, cooling response time and equivalent heat dissipation coefficient of the heating section is extracted and a historical normal value baseline is established, the precise distinction between working conditions and faults is realized based on the feature vector, and finally the adaptive collaborative temperature control is completed in combination with the reconstructed temperature distribution and the diagnosis result. The temperature field sensing blind spots can be eliminated, the fault false alarm rate is reduced, the aging of the heating element can be early warned to realize predictive maintenance, the temperature control strategy can be adapted to environmental changes, and the safe transportation of easily frozen media in extremely cold environments is effectively ensured.

The application discloses a periodic description blade profile global fairing reconstruction method. First, the application generates initial profile fitting curves according to curve control vertex data and node vector data, and refines the node vector of the profile fitting curve in a least square progressive iterative approximation fitting process by using a curvature guided profile subdivision method according to the discrete curvature distribution of the profile value points, so that the curve control vertex distribution is fitted to the profile shape characteristics, thereby obtaining the subdivided profile fitting curve. Finally, according to the local support of the base function, the subdivided profile fitting curve is updated and fitted in a region-by-region and successive manner by using a curvature constraint adaptive local fitting method until a global fairing periodic reconstruction curve meeting the accuracy requirement is obtained. The application finally generates a global fairing reconstruction curve which highly accurately approximates the profile value points, guarantees the reconstruction fitting efficiency, and realizes the closed characteristic by the periodic base function, and can be effectively used for subsequent T-spline blade surface modeling.

The application discloses a kind of object optimal tilt angle calculation method and application of X-ray CL three-dimensional reconstruction, and the method comprises:1, the geometric model of three-dimensional reconstruction algorithm of X-ray CL scanning mode is established,2, the geometric parameter relationship of CL scanning mode is determined, the projection address expression is solved,3, projection address relative error formula is calculated according to projection address expression,4, the influence of object tilt angle on projection address relative error is calculated, and gradient descent method is used, the relationship between object thickness and optimal tilt angle is solved, and polynomial fitting method is used, and object thickness and optimal tilt angle relationship formula are fitted.The object tilt angle of the application is optimized in the process of CL three-dimensional reconstruction, can improve the accuracy of CL three-dimensional reconstruction algorithm, and reduce the interlaced phenomenon after reconstruction.

This invention relates to the field of fluid dynamics data reconstruction and intelligent computing technology, specifically a method and system for super-resolution reconstruction of vortex flow fields based on residual U-Net. The invention first performs bicubic downsampling on the high-resolution flow field and interpolates it back to its original size to construct a degenerate input. This input is then fed into a network containing an encoder, bottleneck layer, decoder, skip connections, and global residual connections. The output residual field is added to the input to obtain the reconstructed flow field. During training, a combined loss consisting of mean square error and gradient loss is used for supervised optimization. This scheme can effectively recover small-scale vortices, shear layer boundaries, and high-gradient textures in low-resolution flow fields, reduce over-smoothing, and improve reconstruction accuracy and physical structure realism. It is suitable for recovering flow field data after compressed transmission, enhancing low-precision measurements, and reconstructing numerical simulation results.

This invention provides a method and storage medium for 3D reconstruction of petrochemical pipelines based on multi-view optimization, relating to the fields of petrochemical pipeline inspection and 3D reconstruction. The method involves acquiring multiple pipeline images around the target petrochemical pipeline to construct a multi-view image set; preprocessing the multi-view image set; extracting features from each image to obtain a feature point set and feature descriptors; performing initial matching to obtain initial feature matching pairs; optimizing the process to obtain the optimal feature matching pairs; calibrating camera parameters to eliminate lens distortion and accumulated errors; generating a sparse point cloud using an improved motion recovery structure algorithm; removing redundant and noise points to obtain an optimized sparse point cloud; generating a dense point cloud using a dense reconstruction algorithm; optimizing the process to obtain an optimized dense point cloud; constructing an initial 3D model; and post-processing to obtain the final 3D model of the target petrochemical pipeline; and storing the data on a storage medium. This invention provides reliable 3D model support for the digital management, fault detection, maintenance, and repair of petrochemical pipelines.

The application discloses a kind of based on the dual hand reconstruction method of multi-scale color information and depth information fusion.Utilize dual-flow feature extraction model respectively to extract monocular RGB-D image multi-scale color feature, left and right hand corresponding center point feature and multi-scale depth feature;Then use pyramid type depth fusion network to fuse multi-scale color feature, left and right hand corresponding center point feature and multi-scale depth feature, obtain left and right hand corresponding global fusion feature;Finally, according to left and right hand corresponding global fusion feature, using GCN decoder generates dual hand grid in camera coordinate system.The application can fully fuse color information and depth information to reconstruct both hands, reduce the blur effect in RGB image and the noise in depth image, greatly improve the reconstruction accuracy, and enhance the robustness of model;Meanwhile, the application can accurately reconstruct three-dimensional hand grid with real depth and scale in camera space, overcome the limitation of relying on root node output.