22results about How to "Accurate reconstruction" patented technology

A kind of time domain ground penetrating radar intelligent forward method based on physical parameter guidance, comprising the following steps: step one: build underground medium model and generate supervision data;Step two: the ground penetrating radar GPR forward simulation is expressed as the mapping problem of " physical parameter field to radar profile ";Step three: build PhyTANet network based on encoder decoder;Step four: the PhyTANet network obtained by step three is trained, fine-tuned;From the above steps, finally, after training fine-tuning ends, the dielectric constant and conductivity distribution of any given underground medium are input, and the corresponding prediction result is obtained.The purpose of the present application is to solve the technical problems of the existing ground penetrating radar forward technology, such as insufficient accuracy, insufficient modeling of key physical parameter coupling relationship and limited time domain feature expression in complex heterogeneous medium, and the present application is proposed.

This invention discloses a broadband oscillation disturbance source localization method based on compressed sensing and graph convolutional neural networks (GCNs), comprising two stages: offline training and online localization. In the offline training stage, electrical quantities under subsynchronous / supersynchronous broadband oscillation modes are obtained using measurement data from actual systems or simulation examples, constructing a broadband oscillation offline sample library. Compressed sensing is used to encode and compress the electrical quantities in the offline sample library, obtaining a training set and a test set. The constructed GCN localization model is trained using the training set until the test set reaches the target localization accuracy, resulting in a broadband oscillation localization model. In the online localization stage, a substation collects electrical quantity data and compresses and encodes it using compressed sensing technology; the substation data is uploaded to the master station; at the master station, the feature matrix and system adjacency matrix are input into the trained GCN localization model, and the oscillation source location is output. This invention's method is applicable to broadband oscillation disturbance source localization under various operating conditions.

This invention relates to the field of machine vision technology and discloses a robust extraction method for the center line of structured light in fillet welds. It aims to solve the problem of missegmentation and loss of corner features caused by traditional algorithms relying on geometric connectivity segmentation under strong arc light and high reflectivity interference. The invention first obtains a light stripe backbone mask through multi-scale morphological filtering and clustering screening; secondly, it constructs a scattered data pool by performing global coarse extraction within the mask; subsequently, it uses a sequential random sampling consensus algorithm to adaptively extract the equations of the upper and lower main lines and calculate the theoretical intersection point through a "fit-stripping" iterative mechanism; finally, it extends along the main line towards the intersection point and performs independent pixel grayscale thresholding and sub-pixel fine extraction within a local window of the original image. This invention completely eliminates the risk of missegmentation, accurately repairing large-scale reflective fractures while perfectly preserving and reconstructing the sharp corner features of the fillet weld.

This invention relates to the field of optical detection technology and discloses a method for measuring the three-dimensional topography of an object surface based on optical interferometry. The method includes: acquiring a mixed temporal-domain interferometric signal sequence during axial scanning; extracting seed pixels and constructing a spatial topological connectivity feature matrix; introducing a fractional Fourier transform to construct a joint decoupling objective function containing a frequency-domain nonlinear phase distortion evaluation index and a temporal-domain physical invariant evaluation index; performing multi-level optimization for the seed pixels to lock the globally optimal rotation angle; performing array adaptive decoupling in conjunction with the spatial topological connectivity feature matrix to generate the optimal rotation angle matrix; and performing signal decoupling and reconstruction on the mixed signal to extract the absolute surface height of each pixel to generate three-dimensional topography data. This invention solves the phase decoupling problem caused by thin-film interferometric aliasing through multi-dimensional joint decoupling and spatial prior constraints, improving measurement accuracy while reducing the computational overhead of large pixel arrays.

The application discloses a kind of active implantable medical device in-vivo radio frequency response modeling method based on equivalent lumped element model, by constructing and matching the electromagnetic characteristics of the actual conductive path of AIMD and surrounding tissue, equivalent lumped element model (LEM), the key factors such as the multiscale electrical characteristics of the geometry size of implantable medical device and its in homogeneous, non-uniform even real anatomic tissue environment are parameterized characterization, so as to quickly and accurately reconstruct the radio frequency transfer function of implant.The method of the application effectively breaks through the bottleneck of the existing Tier 3 evaluation method, meets the technical requirements of the latest standard based on Tier 4 evaluation method for modeling the radio frequency transfer function of implant in the environment closely related to clinical tissue, and provides a new technical path and implementation means for efficient and accurate evaluation of the radio frequency heating risk of implantable medical device in the magnetic resonance environment.

This invention discloses an online detection method and device for the trajectory of sphere polishing, relating to the field of precision machining online detection. The method includes: S1, acquiring a continuous image sequence during the sphere polishing process at time i using a high-speed camera; S2, preprocessing and feature recognition of the continuous image sequence; S3, a rotation angular velocity measurement module calculating the rotation angle and rotation angular velocity to obtain theoretical values; and a three-dimensional trajectory reconstruction module radially projecting discrete trajectory points onto a sphere surface determined based on the first point, to achieve visualized three-dimensional trajectory reconstruction under geometric constraints, thus completing the online detection of the sphere polishing trajectory. This invention combines a transparent grinding disc structure, a multi-view observation layout, and a dedicated image processing algorithm to achieve real-time, high-precision, and high-efficiency online detection of the sphere's rotational motion parameters and trajectory during the polishing process.

This disclosure provides a method, apparatus, electronic device, and storage medium for lung segment reconstruction. It calls a preset algorithm to reconstruct lung segments from lung imaging data, generating initial lung segment reconstruction data. The reconstructed data is then validated to identify deviated lung segments with reconstruction deviations at their junctions. Based on the reconstruction instructions for these deviated lung segments, a lung segment assembly is reconstructed. Based on the data collected from the lung segment assembly, the junctions of each lung segment within the assembly are reconstructed and repaired to obtain the final lung segment reconstruction data. Compared to related technologies, this disclosure, by calling a preset algorithm for lung segment reconstruction, can utilize advanced algorithmic technology to initially outline the lung segment structure. Then, by validating the reconstructed data, it can accurately identify deviated lung segments with reconstruction deviations at their junctions. For these deviated lung segments, a lung segment assembly is reconstructed according to their specific reconstruction instructions, ensuring accurate reconstruction when dealing with problematic areas.

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 table structure recognition method and device, a storage medium and an electronic device. The method comprises the following steps: obtaining a target picture containing a target table object; using a predetermined target key point prediction model to predict key points of the target picture by using a key point heat map prediction method, to obtain coordinate values of each key point and type information of each key point; using a predetermined target key point matching model to match each key point based on the coordinate values of each key point and the type information of each key point, to obtain a plurality of key point sets; and performing table reconstruction based on the coordinate values in each key point set, to obtain a recognition result of the target table object. In the application, the key point prediction model and the key point matching model are used, so that a plurality of key point sets corresponding to each cell can be accurately obtained, and the target table object can be accurately reconstructed based on the plurality of key point sets.

This invention discloses a digital prediction and reverse causal chain diagnosis method for hydrogen refueling station fires, belonging to the field of fire prediction and diagnosis technology. Based on the environmental parameters and leakage source parameters of the hydrogen refueling station, a multivariate coupled operating condition is designed to simulate a hydrogen refueling station fire and construct a database. Based on the Transformer model, a learnable location encoding mechanism is designed to dynamically fuse spatiotemporal coordinate features, training a fire development prediction model. By fusing temperature sensor data through a spatiotemporally coupled Transformer model, a diffusion generation architecture with residual constraints is constructed, training a fire temperature visualization prediction model. Based on the temperature sensor data and environmental parameters of the hydrogen refueling station, combined with the fire development prediction model and the fire temperature visualization prediction model, digital prediction of hydrogen refueling station fires is achieved. A reverse causal chain diagnosis method based on fault trees and multi-source data is established to achieve second-level root cause diagnosis. This invention is used for more accurate prediction of hydrogen refueling station fires and reverse causal chain diagnosis.

This invention discloses a method for dynamically detecting the morphology of a cavity based on interlayer fracture vibrations, belonging to the field of cavity construction and monitoring technology for salt cavern gas storage. The method involves drilling and trenching to locate the cavity center and mudstone interlayer, deploying monitoring points and burying distributed monitoring optical fibers in the outer region of the cavity wall to capture the vibration wave signals of the interlayer fracture, recording the time difference and fluctuation intensity differences; analyzing the signal data of the propagation path to establish a three-dimensional coordinate system; and establishing a system of equations based on the time difference between the primary fracture vibration and the secondary vibration from the rock impact to calculate the three-dimensional coordinates of the fracture point and characteristic points of the cavity wall; then reconstructing the real-time contour of the cavity based on the layered symmetrical fracture characteristics and signal energy characteristics, and verifying it with other signals from the fracture layer and correcting the accuracy of the monitoring equipment. This method does not require interruption of the cavity construction operation, has high spatiotemporal resolution, and can accurately invert the cavity morphology, providing a basis for dynamic adjustment of the flow velocity and direction in water-soluble cavity construction, effectively reducing the risk of cavity collapse and excessive lateral dissolution.

This invention discloses a method, apparatus, and system for tilt attitude calibration of a vertical hydrophone array based on the ray-mapping method. The method includes: sequentially performing a pre-search estimation of the receiving-transmitting distance of each hydrophone in order from shallow to deep depth, minimizing the error between the theoretically calculated arrival time difference between the direct wave and the reflected wave from the sea surface and the actual extracted time difference, thereby obtaining the optimal distance estimate for each hydrophone in the pre-search stage; recalculating the optimal direct wave arrival time for each hydrophone using the optimal distance estimate obtained from the pre-search; and again, sequentially fine-tuning and optimizing the receiving-transmitting distance of each hydrophone in order from shallow to deep depth, iteratively searching for the distance estimate that most closely matches the theoretical direct wave arrival time calculated based on the pre-search estimate with the actual measured value. This invention provides a marine vertical receiving array attitude calibration scheme that is independent of external assistance, highly accurate, robust, and computationally efficient.

The application belongs to the field of magnetic particle imaging, and particularly relates to an MPI acceleration calibration method based on a system matrix super-resolution network, aiming to solve the problems of long calibration time and poor accuracy of reconstructed MPI images of the existing magnetic particle imaging system matrix reconstruction method. The method comprises the following steps: placing a unit MNP sample in the imaging field of view of a magnetic particle imaging device for sparse calibration scanning, and constructing a system matrix as a low-resolution system matrix; inputting the low-resolution system matrix into a system matrix super-resolution network model after pretreatment, to obtain a super-resolved system matrix row set; performing post-processing on the super-resolved system matrix row set to obtain a high-resolution system matrix; and combining the high-resolution system matrix to solve the magnetic particle concentration distribution of a target object to be imaged, and reconstruct an MPI image of the target object to be imaged. The application can greatly reduce the calibration time while accurately reconstructing the MPI image.

This invention discloses a method, system, device, and medium for data acquisition of industrial IoT devices, aiming to solve problems such as resource waste or loss of key features caused by fixed sampling frequencies. The method includes: acquiring raw signals and performing standardization processing; calculating the intensity of nonlinear fluctuations and prediction residuals of the signals, and evaluating the sampling completeness coefficient; extracting features to identify the device's operating modes; combining the completeness coefficient and mode identification results, calculating the target sampling frequency through multi-objective optimization and achieving seamless frequency switching; and adaptively compensating the evaluation model based on reconstruction errors. The system includes modules for data perception, sampling evaluation, mode identification, frequency decision-making, acquisition execution, and closed-loop feedback. This application achieves deep adaptation between the sampling frequency and signal characteristics, significantly reducing the communication and storage burden while ensuring reconstruction accuracy, and improving the system's adaptive capability and fault warning accuracy.

The application provides a prefabricated component demolding transfer robot path planning method and system, relates to the technical field of robot path planning, and the method comprises the following steps: acquiring electrostatic signal data, and performing inverse finite element analysis and manifold learning dimension reduction to generate gradient distribution data and geometric contour data; then the gradient distribution data is subjected to smoothing treatment to generate a first repulsion potential field; then a second repulsion potential field is constructed on the component surface region according to three-dimensional laser point clouds; then an attractive potential field is generated based on the three-dimensional pose information of the prefabricated component and the geometric constraint of the grabbing path, and the three potential fields are adaptively weighted and superimposed to construct a composite artificial potential field; finally, combined with a hybrid A* algorithm and a Reeds-Shepp curve model, node connection and trajectory feasibility checking are performed to complete state node evaluation and exploration in the composite artificial potential field, so as to generate trajectory planning data. The application can realize undisturbed accurate grabbing path planning.

A machine learning-based model and method for predicting the composition, process, and microstructure of difficult-to-deform alloys is presented, relating to the fields of intelligent material design and hot working technology. This method first constructs a composition-data-image coupling matrix, cleaning up data noise while preserving the true mechanical response. To address the problem of unbalanced data-image distribution, a non-full-coverage sampling strategy of "typical microstructure-key process" is proposed, supplementing images in low, medium, and high power dissipation regions to preserve the true sparse distribution. During the training phase, supervised and semi-supervised branch collaborative learning is employed, enabling the model to capture the microstructural differences of different alloys even with small sample conditions. This achieves the nonlinear mapping between power dissipation coefficient and dynamic recrystallization, quantitatively revealing the recrystallization mechanism transformation, and outputting the optimal hot working window. This solves the problems of narrow hot working process windows and unclear microstructure-property mapping in difficult-to-deform alloys, and is applied to the intelligent process optimization of NiTiHfx-based shape memory alloys.

This invention discloses a method and system for cross-scale representation of road texture based on PTUNs networks to reconstruct resolution accuracy. It relates to the field of road texture representation technology. The method involves collecting high-resolution texture data and preprocessing it; downsampling to obtain texture datasets of different resolutions; constructing a PTUNs network; training the constructed PTUNs network based on the texture datasets of different resolutions; inputting low-resolution texture data into the trained PTUNs network to reconstruct small-scale details lost in the low-resolution texture data, outputting high-resolution texture data; and performing cross-scale representation of the low-resolution texture based on the high-resolution texture data. This invention can capture subtle road texture features even under resolution limitations, achieving fine-grained understanding and representation of textures. The PTUNs network can bridge the gap between high and low resolution road textures, achieving accurate reconstruction of even smaller-scale texture details.

The invention discloses a permanent magnet synchronous motor and a single-resistor current sampling method. The permanent magnet synchronous motor controls a three-phase inverter based on an SVPWM strategy, and the method comprises the following steps: in a current control period, acquiring flow directions of A, B and C three-phase current; according to the flow direction of the ABC three-phase current and a sector where a target voltage vector is located, whether the minimum time window of single-resistor sampling in the current control period can cancel dead time or not is determined; and according to the determined minimum time window, determining whether to adjust the two-phase sampling in the current control period. According to the scheme provided by the invention, sampling can be carried out by directly utilizing the dead time under some conditions, so that the number of times of PWM waveform adjustment is reduced, the stability of the system is improved, and the collected current is closer to the same moment.

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.

This invention belongs to the field of 3D reconstruction technology and provides a deep learning-based single-frame white light 3D reconstruction method to solve the problems of traditional structured light 3D detection technology, such as reliance on projection equipment, limited application scenarios, and insufficient accuracy of existing single-frame white light 3D reconstruction techniques. This invention combines a projection physics model with a generative neural network to construct a virtual projection model. The virtual projection model is used to predict single-frame white light images of the object under natural illumination, generating a four-step phase-shifting fringe pattern. Then, 3D reconstruction is achieved based on the fringe projection contouring principle. This invention achieves high-speed, high-precision 3D reconstruction under single-frame white light input without the need for projection equipment, reducing system deployment costs and complexity, and can be effectively applied to miniaturized and mobile terminal scenarios.

The present disclosure provides a model training method, a rendering image generation method and a device thereof. The method comprises: obtaining a target training sample; the target training sample comprises: an initial reference image, initial noise corresponding to each view angle in N view angles, and M initial material channel images determined based on an initial material of an initial three-dimensional model in each view angle in N view angles; performing fusion processing on the M initial material channel images in each view angle and the initial noise corresponding to each view angle to obtain an initial noisy image of each view angle; inputting the initial reference image and the initial noisy image of each view angle into a to-be-trained generative model to obtain M estimated material channel images in each view angle and estimated noise corresponding to each view angle; obtaining a target loss value based on the estimated material channel image and / or the estimated noise; and performing model training on the to-be-trained generative model using the target loss value to obtain a target generative model.

This disclosure relates to a method and system for decoupling and acquiring a three-dimensional magnetic field in an ion battery. The method includes: acquiring time-series data during the charging and discharging process of the ion battery; performing standardized preprocessing on the time-series data to obtain a preprocessed standardized dataset; constructing an initial neural network model, wherein the initial neural network model includes a long short-term memory network layer, a time attention mechanism layer, and a fully connected output layer connected sequentially; using the preprocessed standardized dataset as training samples, iteratively training the initial neural network model through a joint optimization algorithm to obtain a trained magnetic field decoupling model; acquiring real-time time-series data of the ion battery; and processing the real-time time-series data sequentially through the long short-term memory network layer, time attention mechanism layer, and fully connected output layer in the magnetic field decoupling model to obtain a decoupled three-dimensional magnetic field feature vector. This disclosure can achieve accurate acquisition of a three-dimensional magnetic field.