28results about How to "Efficient reconstruction" patented technology

The invention discloses a point cloud data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring initial point cloud data to be sent; dividing the initial point cloud data into a group of point cloud blocks; performing graphical representation on the basis of the point cloud data in each point cloud block in the group of point cloud blocks to generate a group of graph structure data, one graph structure data in the group of graph structure data corresponding to one point cloud block in the group of point cloud blocks, and the graph structure data being used for representing features of the corresponding point cloud block; and encoding the group of graph structure data to obtain a group of data packets, and sending the group of data packets to a target terminal so as to perform decoding reconstruction at the target terminal to obtain target point cloud data. The technical problems that the processing efficiency of the point cloud data is low and the effect cannot be quickly achieved in the related technology are solved.

The invention discloses a drug target combinability prediction method based on variational coding, and belongs to the field of bioinformatics. According to the method, efficient extraction of drug and target features, construction of potential feature space of the drug and target features and prediction of the interaction relationship of the drug and target features are realized through a dual-stage coupled end-to-end neural network. The model is mainly composed of two stages, in the first stage, potential representations of drug and target features are obtained through variational compression and feature extraction, and in the second stage, a drug target action matrix is reconstructed through an up-down sampling comprehensive deep neural network. Through the architecture, the chemical characteristics of the drug and the biological characteristics of the target can be effectively integrated, and the accuracy of interactive relationship prediction is improved. The invention provides a novel and efficient solution for deep analysis of biological big data at the same time.

The invention provides a magnetic resonance image reconstruction method and device, an imaging system and a storage medium, and the method comprises the steps: inputting to-be-reconstructed undersampling K space data into an image reconstruction model, and obtaining a reconstructed image; and the image reconstruction model is trained by taking model training loss value minimization as a target: performing inverse Fourier transform and down-sampling on the full-sampling K-space data to obtain a full-sampling image and under-sampling K-space data, and performing randomization, interpolation and inverse Fourier transform on the under-sampling K-space data in sequence to obtain a frequency domain training image; and fusing an undersampling training image obtained by inverse Fourier transform of the undersampling K space data and an undersampling image obtained by sequentially carrying out multiple times of second downsampling and interpolation on the undersampling training image to obtain an image domain training image, and calculating a loss value of the training image obtained by fusing the frequency domain and the image domain and the full-sampling image. According to the invention, through adaptive filling and reconstruction of the image domain and the frequency domain, efficient, rapid and high-quality magnetic resonance image recovery is realized.

The invention discloses a turbine blade surface field rapid prediction method based on a multi-channel self-attention network, and relates to the technical field of data prediction. The technical problems that in the prior art, an existing turbine blade surface field prediction technology is long in consumed time, high in calculation cost and insufficient in precision, and obvious temperature and pressure distribution nonuniformity exists are solved. According to the technical scheme, the turbine blade surface field rapid prediction model building method based on the multi-channel self-attention network comprises the steps that turbine blade surface field data are collected; collecting a preset multi-channel self-attention network architecture; performing pretreatment; performing feature extraction and modeling on the turbine blade surface field according to the processed multi-channel self-attention network architecture; and according to the feature extraction and modeling results, establishing a'mapping relation from boundary conditions to a temperature field and a pressure field on the surface of the turbine blade '. The method is suitable for predicting the temperature field and the pressure field on the surface of the turbine blade.

The invention discloses a decentralized application-oriented smart contract program generation method, device and equipment and a storage medium, and belongs to the field of block chain smart contracts, and the method comprises the steps of persisting an executable code and state data of a fragmented smart contract in block data; in response to a calling instruction for the fragmented smart contract, extracting the executable code and the state data persisted in the block data to execute the fragmented smart contract, and updating the state data persisted in the block data according to execution of the fragmented smart contract; and in response to a reconstruction instruction for the fragment smart contract in execution, extracting persistent executable codes and state data from the block data, and reconstructing the fragment smart contract in execution through the extracted executable codes and state data. The problems of complex cross-fragment calling, low execution speed and difficult fault recovery of the smart contract are solved, and the performance and stability of the block chain system in decentralized application processing are improved.

The invention discloses a neural radiation field incremental optimal view selection method based on hybrid uncertainty estimation. The method comprises the following steps: inputting an RGB image set; calculating a 3D position and an observation direction; a step of initializing NeRF training; based on the 5D coordinate (x, d) of the residual image set, threshold sampling is carried out through a modified NeRF network, the color and opacity of the RGB image are calculated, the color and opacity are integrated into Beta distribution, and rendering uncertainty is calculated; inputting a 3D coordinate x of a residual image set, judging whether a track is a plane or a non-plane through a classifier, and estimating position information by using a Voronoi graph algorithm to obtain position uncertainty; an optimal view selection step: performing normalization and summation on the rendering uncertainty and the position uncertainty, and calculating the hybrid uncertainty of each RGB image; and selecting the image with the highest hybrid uncertainty, adding the image into a training set, and repeating the process until a specific reconstruction effect or a preset image number limit is achieved.

The present application relates to the technical field of computer graphics, computer vision and three-dimensional reconstruction, and particularly relates to an immersive scene reconstruction rendering method and system based on adaptive 3D Gaussian sputtering, which acquires scene initial point cloud data and camera parameters, constructs a 3D Gaussian point cloud model containing multiple attributes and generates sky point cloud; based on the mechanism of pulse neurons, the opacity of Gaussian points is adaptively controlled, and according to the gradient information and geometric attributes of Gaussian points, splitting, copying or pruning operations are performed to optimize the distribution of point cloud; through a deep learning network, the camera pose is corrected, large-scale scenes are divided into blocks for parallel training, and a bilateral grid post-processing is combined with multiple loss functions to optimize rendering, and the sky point cloud is subjected to exclusive constraint, so that the technical defects of the prior art are solved, and the immersive scene application requirements in the fields of VR / AR, digital twin and the like are met.

The application discloses a kind of based on structure-detail separation bidirectional recurrent neural network super-resolution method, belong to video super-resolution technical field.The application mainly includes the following steps:1, the spatial feature, structure feature and detail feature of each frame picture of low-resolution video are extracted;2, the forward and backward optical flow of each frame picture is calculated;3, the spatial feature, structure / detail feature and forward optical flow of each frame picture are input into network, and the forward structure / detail feature of current frame is obtained;4, the spatial feature, structure / detail feature, backward optical flow and forward structure / detail feature of each frame picture are input into network, and the backward structure / detail feature of current frame is obtained;5, the spatial feature, structure / detail feature and forward and backward structure / detail feature of each frame picture are calculated to obtain reconstruction structure / detail feature, and high-resolution result is obtained by calculating the result with spatial feature.The video super-resolution method based on the application can obtain high-quality output with more details.

A training method for an MRI reconstruction network based on speedup ratio segmentation, firstly, by deeply segmenting the target speedup ratio, a series of MRI data with different speedup ratios are constructed; secondly, based on a sequential training method, from low speedup ratio to high speedup ratio, the mapping relationship between each undersampled MRI image and the fully sampled MRI image is sequentially learned, and only a small number of iterative trainings are performed for each speedup ratio; finally, multiple iterative trainings are performed for the target speedup ratio. The method provided by the present invention can effectively utilize small-scale training samples to reconstruct high-multiple undersampled MRI images, and has strong practicality.

The invention relates to a fault recovery method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: if a virtual network element in a network meets a fault condition, determining an actual restart number of a target time point; and initiating restart of the virtual network element corresponding to the actual restart number of the target time point at the target time point, and determining the restart success rate of the virtual network element at the target time point. And based on the restart success rate of the target time point and a preset number adjustment strategy, determining an actual restart number of a next target time point of the target time point, and re-executing restart of the virtual network element corresponding to the restart number of the target time point at the target time point. And determining a restart success rate of the virtual network element at the target time point until the virtual network element is determined not to meet the fault condition, so that a restart storm of the virtual network element after the local side equipment in the network has a fault can be avoided, and rapid recovery of the fault is realized.

The invention relates to the technical field of artificial intelligence, comparative learning and semantic communication, and provides a semantic communication method and system based on improved comparative learning, and a terminal. The method is completed based on a trained auto-encoder model, and comprises the following steps of: configuring a plurality of texts with different lengths, and sequentially inputting the plurality of texts with different lengths into a semantic communication encoder to obtain a semantic encoding sequence; the semantic coding sequence is transmitted into a fuzzy semantic coding sequence through a channel, and the fuzzy semantic coding sequence is input into a semantic communication decoder for semantic decoding; constructing a contrast learning positive sample pair and a contrast learning negative sample pair; based on the positive sample pair and the negative sample pair, an improved contrast learning loss function is adopted to train an auto-encoder model, and when a loss value does not decrease in continuous preset times of training, the training is completed; and deploying the trained auto-encoder model to a channel, and performing semantic communication. According to the method, alignment and uniformity of comparative learning can be ensured, and semantic communication has better anti-noise robustness.

The application provides a frequency variational autoencoder-based image super-resolution reconstruction method, device and equipment, and relates to the technical field of image processing. The method comprises the following steps: performing frequency domain decomposition on an input low-resolution image through a low-frequency extractor of a frequency variational autoencoder, extracting low-frequency features, and performing up-sampling interpolation on the low-resolution image to obtain a low-frequency image consistent with a target high-resolution size; sampling latent variables from a relevant distribution of low frequencies and high frequencies learned by a relevant encoder of the frequency variational autoencoder, wherein the relevant distribution represents the statistical correlation between the low-frequency information of the low-resolution image and the high-frequency information of a high-resolution image; inputting the latent variables and the low-frequency features into a high-frequency decoder of the frequency variational autoencoder for decoding to generate a high-frequency detail image; and superimposing and fusing the high-frequency detail image and the low-frequency image to obtain a final high-resolution reconstructed image.

This invention belongs to the field of blade trajectory monitoring technology, specifically involving a method for dynamic monitoring of the three-dimensional trajectory of wind turbine blades by fusing imagery and laser point cloud data. The steps include: fixing a camera and scanner with a rigid connection device for use as a dynamic monitoring system for the three-dimensional trajectory of the wind turbine blade; calibrating the camera; calibrating the relative position and attitude between the camera and scanner; acquiring image data and laser point cloud data during normal operation of the wind turbine; performing motion compensation correction on the laser point cloud data; calculating the three-dimensional coordinates corresponding to the monitoring point; obtaining a time-series image-side coordinate sequence of the monitoring point; and calculating the three-dimensional coordinate sequence corresponding to the time-series image-side coordinate sequence of the monitoring point, i.e., the three-dimensional motion trajectory of the monitoring point on the wind turbine blade during operation. This invention uses imagery and laser point cloud technology to monitor the three-dimensional trajectory of wind turbine blades, requiring no auxiliary markers, eliminating complex calibration, offering flexible deployment and high accuracy, and efficiently supporting blade safety assessment and maintenance decisions.

The application discloses a five-dimensional seismic data interpolation method and system based on coordinates, constructs a point-by-point seismic data interpolation model based on a NeRF theory, modifies an MLP framework by combining an additional convolution layer on the basis of the point-by-point interpolation model, constructs a profile-by-profile seismic data interpolation model, introduces kernel norm regularization to construct an objective function, reads original five-dimensional seismic data D obs and inputs the five-dimensional seismic data with missing traces into the network to realize reconstruction of the five-dimensional seismic data. The application eliminates the need for additional labeled data, utilizes unique characteristics of seismic data, enables a convolution network decoder to output data profile by profile, can significantly improve data processing efficiency by 40 times, and increases kernel norm regularization in the objective function, so that the noise resistance and robustness of the model can be effectively improved.

The invention discloses a point cloud geometric compression method based on deep learning, and the method comprises the following steps: (1) segmenting an initial point cloud according to the shape, so as to ensure that each block corresponds to different parts of an object; (2) applying a k-Means clustering algorithm to the segmented point cloud data to further refine the blocks so as to enable the points between the blocks to be relatively balanced; (3) inputting the subdivided point cloud data into a Point Net + + network for feature extraction so as to capture local geometric features; (4) constructing a graph structure in the processed point cloud data to enhance feature interaction between blocks; (5) fusing the extracted point cloud features by using a graph attention mechanism, and focusing on parts with similar shapes in adjacent blocks; and (6) adding point information into the features, and carrying out arithmetic coding and transmission on the features. And (7) finally, gradually reconstructing each block according to the features, and interpolating the point cloud blocks according to the point information to obtain a final reconstructed point cloud. Through the method, the geometric compression and reconstruction performance of the point cloud is remarkably improved.

The invention discloses a video super-resolution method and system based on strip-shaped and global time-space attenuation attention, and belongs to the technical field of video super-resolution. The method comprises the following steps of: 1, extracting spatial features of an original video frame image after enhancement and performing up-sampling; 2, calculating spatial strip attention and time strip attention on the upsampling spatial features; 3, processing the spatial strip-shaped attention and the time strip-shaped attention by using depth separable convolution and a residual structure to obtain final attention outputs, and performing fusion processing on the two attention outputs to obtain fusion features; and 4, calculating the global space-time attenuation attention of the spatial feature map, and fusing the spatial features, the fusion features and the global space-time attenuation attention to obtain a final high-resolution reconstruction result. Based on the video super-resolution method provided by the invention, the PSNR / SSIM can be improved on a common data set, and sharp detail information can be recovered.

The invention discloses an incremental grid reconstruction method based on adaptive parameters, and the method comprises the steps: firstly obtaining point cloud data, carrying out the linear point cloud segmentation of an obtained point cloud based on a point index, dividing the point cloud into m subsets, and enabling the adjacent subsets to have an overlapping region; then density estimation is carried out on the segmented point cloud subsets, and downsampling and normal estimation are carried out; and finally, constructing a triangular mesh for each point cloud subset by adopting an incremental method. According to the method, through point cloud segmentation and overlapping region introduction, the problem of computing resource limitation is solved, and the consistency and continuity of a processing result are ensured through the design of the overlapping region; the dynamic adjustment of down-sampling and normal estimation is realized through adaptive parameters obtained by density estimation, so that the retention of important geometric features is ensured, and the overall processing efficiency is improved; dynamic vertex combination and patch reconstruction are realized by defining a vertex mapping function and a distance threshold.

This invention discloses a neural radiation field rendering method and apparatus based on neural basis functions and tensor decomposition, belonging to the field of computer vision technology. The method includes: acquiring multi-view images and their camera parameters to construct a dataset; sampling the radiation field from tensor decomposition according to the camera parameters under a selected training viewpoint to obtain density feature tensors and texture feature tensors, which are then converted into density values ​​and neural basis functions, respectively; calculating the affine matrix and bias through a coefficient network based on the neural basis functions and performing affine transformations in conjunction with the observation direction to obtain coefficients; inputting the neural basis functions and coefficients into a neural hybrid network to obtain the color of a single sampling point; calculating pixel colors using volumetric rendering techniques and training the entire network; setting a new viewpoint and acquiring corresponding camera parameters; and rendering the image using the trained network parameters. This invention can efficiently reconstruct high-quality 3D scenes from multi-view image data, effectively reconstruct complex surface reflection effects, and achieve realistic image rendering from new viewpoints.

The invention provides a method and a system for intelligent reconstruction of a Java website and a storable medium. The method comprises the following steps: receiving original data of the Java website for preprocessing; the file content of the original data is intelligently analyzed, and a website building rule is automatically generated; obtaining a database reconstruction mode through the backup format of the database backup, the database type and the database version; establishing a database reconstruction environment and a Java website reconstruction environment according to a database reconstruction mode and a website establishment rule, and deploying and establishing a Java website and a database by using original data; and running the Java website, if the starting fails or the Java website cannot be normally accessed, performing exception analysis and automatically repairing the exception, and re-adjusting the website building rule and the database configuration information for reconstruction until the Java website is normally accessed and a background is logged in. According to the method, an integrated Java website reconstruction environment is provided, intelligent analysis and deployment can be carried out, abnormity during reconstruction can be automatically repaired, the Java website can be quickly and efficiently reconstructed, the readability and editability are improved, and the use threshold is reduced.

The present application relates to the technical field of antenna testing, and in particular to a spherical near-field antenna measurement truncation error reduction method, device and equipment. The spherical near-field electric field vector data of a measured antenna is obtained. The truncated spherical near-field electric field data is subjected to fast Fourier transform in the pitch angle direction to obtain first frequency domain data. The first frequency domain data is subjected to filtering processing to obtain second frequency domain data. The second frequency domain data is subjected to inverse fast Fourier transform to obtain near-field reconstruction data, and the near-field reconstruction data replaces the truncated spherical near-field measurement data. Through frequency domain transformation, filtering processing and inverse transformation on the truncated spherical near-field measurement data, the truncated data part can be effectively reconstructed, so that the real far-field radiation characteristics of the non-truncated region of the antenna can be more accurately reflected after near-far field conversion. This method can compensate for the data loss problem caused by limited measurement range.

The application provides a TDLAS combined detection method, which is based on an existing gas two-dimensional concentration distribution reconstruction method based on TDLAS remote detection, and combines a TDLAS in-situ detection system and a TDLAS remote detection system to detect indoor gas concentration, optimizes the gas detection method, corrects the iteration result by using in-situ detection data in each iteration of the reconstruction process, improves the image reconstruction algorithm, and proposes a gas two-dimensional concentration distribution reconstruction method based on TDLAS combined detection. The application improves the reconstruction accuracy and reduces the reconstruction time.

The present invention provides an image atmospheric turbulence elimination method and system, and relates to the field of image processing technology. The method includes: building a light field imaging device and collecting a target image of a target object; building a turbulence simulation scene, collecting a turbulence image and wavefront information of the target object in the turbulence simulation scene; building a turbulence image reconstruction network, and training the turbulence image reconstruction network, building a total loss function, optimizing the trained turbulence image reconstruction network through the total loss function, and obtaining an optimized turbulence image reconstruction network; reconstructing the turbulence image to be reconstructed through the optimized turbulence image reconstruction network to obtain a reconstructed target image. The mapping relationship between the turbulence image and the corresponding target image is learned through the constructed turbulence image reconstruction network, and the network is optimized in conjunction with the wavefront information, and the wavefront aberration of the turbulence image is corrected to reconstruct the target image.

The invention provides a communication signal enhancement method, device, equipment and system and a storage medium, and the method applied to a signal receiving end comprises the steps: carrying out the oversampling of a received to-be-enhanced signal through an oversampling frequency, and obtaining a first signal; wherein the to-be-enhanced signal is a signal which is transmitted to the signal receiving end through a communication channel after the to-be-transmitted signal is subjected to cyclic shift spread spectrum processing and modulation processing by the signal transmitting end; acquiring signal feature information corresponding to the first signal; performing optimization configuration on a pre-established adaptive stochastic resonance system based on the signal feature information; processing the first signal based on the configured adaptive stochastic resonance system to obtain a second signal; and performing demodulation processing and cyclic shift dispreading processing on the second signal to obtain an enhanced signal. Through the technical scheme of the invention, the integrity and reliability of data transmission can be ensured.

The present invention discloses a method for constructing a basic large model of low-altitude unmanned aerial vehicle hyperspectral remote sensing, including constructing a data set, wherein the sample is a H×W×D hyperspectral sample; constructing a three-dimensional extended MAE model, including an image block layer, a 3D position encoding layer, a dynamic mask layer, a MAE model, and a HED model; constructing a total loss function L; training the three-dimensional extended MAE model to obtain a large spectral model, which is used to reconstruct the hyperspectral sample to obtain a predicted hyperspectral image. The present invention extends the traditional two-dimensional MAE model to three dimensions, combines the dynamic occlusion rate strategy and 3D position encoding to enable the reconstructed image to better express the spectral characteristics of different objects and surfaces, and then combines the HED model to enhance the processing of edge details and provide more accurate physical boundaries. The present invention is suitable for accurate material identification and classification, and is particularly suitable for complex environmental monitoring and resource assessment tasks in the fields of precision agriculture, environmental protection, and disaster management.

The application discloses a space-based infrared dim small target detection method based on a vector signal, in order to overcome the low timeliness and large resource consumption bottleneck of the traditional "imaging-caching-preprocessing-detection" link, skips the image caching and preprocessing link, and directly takes the DN value vector signal read out by the detector as input. Wherein, the two-dimensional or higher-dimensional features of the target are recovered from the one-dimensional vector signal through the network based on the CNN and BERT mechanisms and with the help of the Markov random field model; the network based on the self-supervised U-Net structure is used to decouple the target and background clutter features, and the multi-scale detection mechanism is combined to selectively enhance the target features to complete the detection; the network is lightened through the multi-teacher knowledge distillation architecture, and pruning and quantization are combined to obtain a lightened detection model suitable for the resource limited environment on the satellite. The application realizes the "detection and calculation integration", and significantly improves the timeliness and resource efficiency of the space-based infrared dim small target detection.

The invention provides a high-definition micro blood flow field measurement method which is characterized in that a microbubble trajectory field is highlighted based on an ultrasound contrast image sequence, then a high-resolution microbubble displacement field time sequence is obtained through speckle correlation strategy estimation, and a powerful tool is provided for real-time and non-invasive observation of capillary blood flow dynamics evolution. Compared with a traditional micro blood flow field reconstruction strategy based on microbubble positioning, the micro blood flow field reconstruction speed can be greatly increased in a non-positioning mode.

The invention belongs to the technical field of magnetic particle imaging, particularly relates to a magnetic nanoparticle device and method for three-dimensional rapid and direct imaging of a nervous system, and aims to solve the problems of low time resolution, low imaging quality, low speed, large calculation amount and complex calculation mode of magnetic particle signal detection in the conventional magnetic particle imaging technology. According to the device, a signal generator 1 outputs a voltage signal, amplifies the voltage signal and then accesses a selection coil and a driving coil; a signal generator 2 outputs a voltage signal and then accesses an excitation coil; the number of the selection coils is two, and the two selection coils serve as a pair and are arranged in the Y direction. The six driving coils, the six exciting coils and the six receiving coils are arranged in the X direction, the Y direction and the Z direction respectively, and every two coils serve as a pair. And the high-pass filter filters the signal received by the receiving coil and imports the signal into a computer through a signal acquisition card to reconstruct a magnetic particle image. According to the method, the time resolution of magnetic particle signal detection is improved, and the method for effectively reconstructing the MPI time sequence video is provided.

The invention provides an image super-resolution method based on a novel network convolution architecture, and aims to improve the spatial resolution and detail recovery capability of an image. According to the method, a super-resolution generative adversarial network is taken as a baseline model, and the network is improved and optimized by using a novel network convolution architecture. In the improved network, batch normalization in the original network is replaced by a new layer normalization module, so that the network can perform independent normalization on a single sample, the dependence on statistical information of the whole batch is avoided, and the feature extraction capability is enhanced. In addition, a new module is introduced in the method, the structures of the generator and the discriminator are further optimized, and the performance of the network during high-resolution image generation is improved. According to the method, the paired image data sets are adopted for training, the model obtained after improved network training can effectively reconstruct the high-quality image, the definition and detail performance of the image are remarkably improved, and good adaptability and wide application prospects are shown.