180 results about "Volumetric data" patented technology

Automatic volumetric quantification can be performed for various parameters of an object by providing volumetric data, such as three-dimensional image data, to at least one neural network. A network can extract features from the data that can be used to infer a point cloud representative of the surface of the object. One or more loss functions can be used to adjust the relevant network parameters. The network can also attempt to infer a segmentation mask for the object, indicating which data values correspond to the object of interest. Since the network performs the segmentation and point cloud generation in parallel, updates to the network parameters can impact the segmentation process, effectively constraining the segmentation based on the inferred shape of the object. Ensuring that the segmentation mask corresponds closely to the surface of the object can cause the segmentation process to be more accurate than conventional segmentation processes alone.

The invention discloses a vacuum cavity pressure maintaining test and leak rate rapid estimation method and system, and the method comprises the steps: achieving the test preparation of multiple sections through a public metering manifold and a multi-way isolation valve, and building a section volume database; the pressure and temperature data are synchronously collected in a fixed sampling period by exhausting air to preset pressure and isolating a pump end; obtaining an equivalent pressure change rate through temperature normalization and deflation baseline deduction in combination with filtering and regression algorithms, and performing conversion to obtain a standardized leak rate; step-by-step detection is carried out on each functional section of the cavity through a segmented bipartite isolation strategy and volume correction, and rapid positioning of a suspicious leakage section is realized; through double-threshold determination of an early warning threshold and an unqualified threshold, a detection report is automatically generated and written into a historical database, and a detection-determination-maintenance closed loop is formed. The method can improve the precision of leak rate detection, and is suitable for vacuum coating equipment, semiconductor process equipment and industrial vacuum treatment devices.

The invention provides a DWS sorting control method and device based on multi-modal perception and logistics equipment, and relates to the technical field of logistics equipment, and the DWS sorting control method based on multi-modal perception comprises the steps that bar code image data, actual volume data and actual weight data of parcels to be sorted are obtained; mailing information is determined, and target volume data and target weight data are determined; comparing the actual volume data with the target volume data to determine a first difference value; comparing the actual weight data with the target weight data to determine a second difference value; acquiring three-dimensional point cloud data of the parcels to be sorted; reconstructing surface morphological characteristics of the parcels to be sorted, and extracting geometric parameters of abnormal regions; determining a sorting abnormity type of the to-be-sorted parcel; and a sorting deviation correction instruction is generated, and the sorting deviation correction instruction is sent to the sorting and transporting device so as to adjust the conveying path or the sorting target grid of the to-be-sorted packages. According to the method, the sorting abnormity response efficiency and the deviation correction precision can be improved.

A method for assigning train blocks at a railroad merchandise yard includes determining, using a first optimization model and historical train block volume data, a first list of train block assignments for a plurality of train blocks and a plurality of classification tracks of a classification bowl. The method further includes displaying the first list of train block assignments generated by the first optimization model if the volume of the train blocks is not greater than the total available track length of the classification tracks. The method further includes determining and displaying, using a second optimization model and the historical train block volume data, a second list of train block assignments for the plurality of train blocks and the plurality of classification tracks of the classification bowl if the volume of the plurality of train blocks is greater than the total available track length of the plurality of classification tracks.

Embodiments of the present disclosure are directed to providing fuel emissions monitoring and / or management for an aerial vehicle. In an example, fuel consumption data and carbon emissions data for an aerial vehicle is determined. The fuel consumption data provides a comparison between (i) first fuel consumption data associated with a first type of aviation fuel being utilized by the aerial vehicle and (ii) second fuel consumption data associated with a second type of aviation fuel that is not being utilized by the aerial vehicle. The carbon emissions data is based on (i) volume data indicative of a real-time volume of the first type of aviation fuel, (ii) a first carbon emissions factor for the first type of aviation fuel, and (iii) a second carbon emissions factor for the second type of aviation fuel. In another example, a rendering of the fuel consumption data is caused via a display.

Provided is a method for labeling volumetric data, including obtaining, with a computing device, access to volumetric data in a hierarchical spatial data structure, displaying, with the computing device, a multiresolution representation of a three-dimensional image volume in an extended reality environment configured to display volumetric data using the hierarchical spatial data structure, receiving, with the computing device, a selection of a region of interest of the image volume, receiving, with the computing device, user input defining a brush trajectory within the region of interest, determining, with the computing device, which voxels within the image volume that intersect the brush trajectory within the region of interest; and labeling, with the computing device, the identified voxels in memory.

Disclosed herein is a system for monitoring urine output (UO) of a patient. The system includes a UO module configured to couple with a collection container. The UO module includes (i) a frame configured for anchoring to frame rails of a bed, (ii) a load cell configured to measure a weight of UO within the container and (iii) logic stored in memory that determines volumetric UO data from the load cell and transmits the UO data across a network to an external entity. An intermediate coupling device between the collection container and the UO module is configured to couple with the collection container and inhibit decoupling from the collection container. The intermediate coupling device includes an identification device attached thereto, identification device including patient information, and the logic links the UO data to the patient information.

This invention relates to a global feature tracking method for time-varying data based on unsupervised segmentation. It constructs a volume data segmentation network to segment the input volume data and achieve global tracking. This invention introduces deep learning to segment volume data, separating feature domains from the background. This enables automatic and accurate tracking of specific features in complex data without any manual annotation, reducing tracking complexity and improving tracking accuracy. It introduces a global feature tracking method; by selecting a target feature, it can track the trajectories of features similar to the target feature across all time steps. Users can select any feature at any time step to track features of interest in time-varying data. It can track extracted features from a global perspective, avoiding tracking errors and defects caused by local tracking methods. Furthermore, it adds the tracking of spatiotemporally similar features, simultaneously tracking the complete paths of spatially similar features of the target feature, improving feature tracking accuracy.

This invention discloses a method, apparatus, equipment, and storage medium for managing coal gas consumption, comprising: standardizing the initial calorific value data of sample coal gas to determine standard calorific value data, and storing the standard calorific value data in a calorific value dataset; determining the constituent coal gas of the sample coal gas from candidate coal gas according to the standard calorific value data; the candidate coal gas includes blast furnace gas, coke oven gas, and converter gas; determining the calorific value state volume data of the sample coal gas corresponding to the standard calorific value data according to the standard calorific value data and the unit volume heat data of the sample coal gas, and storing the calorific value state volume data in a volume dataset; training a machine learning model based on the calorific value dataset, the volume dataset, and the constituent coal gas of the sample coal gas, and determining a coal gas consumption evaluation model based on the training results; the coal gas consumption evaluation model is used to evaluate the consumption of the coal gas to be evaluated. This can improve the efficiency and accuracy of evaluating coal gas consumption.

A view of geometry captured in image data generated by an imaging sensor is compared with a description of the geometry in a volumetric data structure. The volumetric data structure describes the volume at a plurality of levels of detail and includes entries describing voxels defining subvolumes of the volume at multiple levels of detail. The volumetric data structure includes a first entry to describe voxels at a lowest one of the levels of detail and further includes a number of second entries to describe voxels at a higher, second level of detail, the voxels at the second level of detail representing subvolumes of the voxels at the first level of detail. Each of these entries include bits to indicate whether a corresponding one of the voxels is at least partially occupied with the geometry. One or more of these entries are used in the comparison with the image data.

This invention discloses a BIM-based method and system for calculating the quantity of outdoor pipe trenches, comprising: reading pipe parameters from the BIM model of the pipe components through a preset script program; automatically calculating the cross-sectional dimensions of the pipe trench based on the pipe diameter by calling a pre-constructed pipe trench cross-sectional parameter rule model; generating a pipe trench cross-sectional contour family by calling a parametric family library based on the pipe trench cross-sectional dimensions and the spatial direction coordinates of the pipe, and stretching the path along the pipe direction to generate a three-dimensional pipe trench model consistent with the pipe path; acquiring the volume data of the three-dimensional pipe trench model, calculating the excavation quantity of the pipe trench based on the volume data, and setting the excavation quantity into preset attribute fields to achieve dynamic statistics of the quantity. This invention can reduce the manual drawing and calculation process, improve the efficiency and accuracy of quantity calculation, realize the linkage update of model and data, and enhance the automatic adaptation capability to standard parameters.

The invention discloses a clinical specimen self-adaptive liquid taking device, and relates to the technical field of medical auxiliary equipment, and the clinical specimen self-adaptive liquid taking device comprises a mobile operation table, a volume measurement module, a processing module, a liquid taking metering module and a man-machine interaction module. According to the device, through deep fusion of the volume measurement module and the temperature compensation unit, the precision limitation of traditional manual estimation of the sample volume is broken through, the liquid and tissue density can be dynamically corrected according to the environment temperature, and accurate measurement of the sample volume is achieved; the processing module automatically calculates the amount of the stationary liquid meeting the industry specification based on the accurate volume data, and through real-time feedback and closed-loop control of an electromagnetic flowmeter of the liquid taking and metering module, it is ensured that the amount of the stationary liquid meets the requirement for complete immersion of the specimen, excessive waste is avoided, and the cost is reduced. Autolysis and deterioration of specimen tissues caused by insufficient stationary liquid are effectively prevented, a high-quality specimen basis is provided for pathological diagnosis, and the accuracy of diagnosis results is guaranteed.

Machine learning algorithms can interpret three-dimensional sonar data to provide more precise and accurate determination of seafloor depths and in-water target detection and classification. The models apply architectures for interpreting volumetric data to three-dimensional forward-looking sonar data. A baseline set of training data is generated using traditional image and signal processing techniques, and used to train and evaluate a machine learning model, which is further improved by additional inputs to improve both seafloor and in-water target detection.

The application discloses a kind of coal mine underground sealed roadway gas content real-time determination system and determination method, belong to coal mine gas content determination field;The method comprises: before roadway sealing, distribute distributed sensing optical fiber network and determine initial volume;After sealing, continuously monitor surrounding rock strain and internal gas pressure, temperature and gas concentration;Periodically inject known amount of inert gas into sealed space, use the change of gas concentration, pressure and temperature before and after injection, based on gas state equation to invert current absolute volume;Based on optical fiber strain data, calculate volume estimate through geometric and physical model, fuse the above two volume data, obtain continuous, high-precision optimal dynamic volume;Finally, fuse dynamic volume, concentration, pressure and temperature data, calculate gas total content and its change rate in standard state in real time, realize hierarchical early warning and gas resource, risk quantification management.

The invention relates to a computer-implemented method for visualizing a three-dimensional object represented by volume data (VD) to a user in the form of a two-dimensional visualization image (VB) composed of a plurality of visualization pixels (VP). The method has the following steps: providing an image synthesis algorithm which is composed for visualizing the three-dimensional object by mapping the volume data onto the visualization pixels; detecting a current viewing direction of the user; determining a focal point region (FB), wherein the focal point region is defined as a region of the visualization image on which the user is focused based on the current viewing direction; adjusting the image synthesis algorithm such that a computational effort required for mapping the visualization pixels outside the focal point region is less than a computational effort required for mapping the visualization pixels within the focal point region; and mapping the volume data onto the visualization pixels by means of the adjusted image synthesis algorithm.

The application provides a mechanical arm control method based on visual processing, comprising: acquiring original point cloud data of a target object, processing the original point cloud data by using a three-dimensional reconstruction algorithm to obtain spatial form and volume data of the target object; acquiring a terminal position of a mechanical arm, obtaining a spatial distance by calculating a spatial straight line distance between the terminal position and three-dimensional coordinates of a fragile part; if the spatial distance is less than or equal to a preset distance threshold, it is determined that a current stage is a close distance action stage, and the material attribute and surface roughness of the target object in the close distance action stage are acquired; the material attribute, the surface roughness and the volume data are processed by using a support vector machine to obtain a basic clamping strength; a final control instruction is acquired, and a terminal executor of the mechanical arm is driven to perform a grabbing action on the target object according to the final control instruction.

Provided herein are methods, apparatuses, computer program products, and systems for generating textured surfaces using computed tomography. One method can include generating a computer data structure representing three-dimensional surfaces of a physical object from (i) segmentation of volumetric data reconstructed from radiographs, (ii) position information and orientation information of the physical object, and (iii) size information of the physical object; adding texture data to the three-dimensional surfaces represented in the computer data structure using images captured by at least one optical camera, the images being of the physical object at different rotational orientations, by projecting data of the images onto the three-dimensional surfaces using the size information, the position information, and the orientation information of the physical object and predetermined position, location, and parameters of the at least one optical camera; and providing the computer data structure for processing of the three-dimensional surfaces.

Methods and systems for characterizing a biological structure including: obtaining image data for the biological structure using an imaging system; constructing a volumetric dataset of the biological structure based on the image data; determining at least one of a plurality of sections along a plane or curve within the volumetric dataset; and characterizing, using a trained algorithm and based on at least one of the plurality of sections, the biological structure for potential tissue type or disease state.

Various methods and processes, apparatuses or systems, and media for enabling skill-based contract assignment for completing a particular project are disclosed. A processor trains a model on a set of known criteria data, a plurality of dimensions data, and volume data; and receives a request, via a user interface, from a user to assign the contract for completing the project by selecting criteria determining data. The model applies a weight to the selected criteria determining data; generates a forced-rank list of subject matter experts (SMEs) with rankings and contact information; and transmits the forced-rank list to the user interface. The processor receives user input from the user, via the user interface, establishing an agreement to select an SME from the forced-rank list; transmits an electronic message to the selected SME to accept the agreement; and sets the agreement into a contract on a blockchain to ensure accuracy and encryption.

An apparatus includes a memory to store volumetric data representing a volumetric image of an anatomical region having a volume of interest (VOI), and a processing device operatively coupled to the memory. The processing device is to determine, based on the volumetric data, several voxels of the volumetric image. Each voxel has a respective uncertainty value representing a probability that the voxel belongs to the VOI. The processing device is further to generate beam control data to direct a treatment beam relative to the VOI based on the uncertainty values to meet a predetermined quality metric.

The invention discloses an image segmentation method for evaluating a non-small cell lung cancer neutron treatment dose, and relates to the technical field of neutron therapy, and the method comprises the steps: carrying out the training of a 3D U-Net network model through a medical image data set, updating the network parameters and hyper-parameters in the 3D U-Net network model, so as to obtain a trained 3DU-Net network model, and carrying out the evaluation of the neutron treatment dose of the non-small cell lung cancer. In the aspect of processing three-dimensional volume data in an image, information of a space direction can be better reserved, segmentation of a three-dimensional structure can be more accurately carried out, and the segmentation form can accurately reflect an anatomical structure of a patient, a distribution condition of 10B in a body and a radionuclide dynamics condition. Therefore, the information in the medical image can be accurately and fully displayed, and application is facilitated.

This invention relates to the field of cross-border trade technology, specifically to an intelligent order generation method and system based on dynamic container matching. It includes: acquiring a user's terminal access request and extracting the user's nationality metadata; mapping product information data in the source language to product description data in the target language that matches the nationality metadata; receiving the user's order request and generating a pending verification order, where each target product in the pending verification order is associated with real-time updated 3D geometric feature data; performing multi-dimensional spatial stacking simulation and volume superposition calculation to generate total order volume data including a recommended stacking map; dynamically matching the total order volume data with a preset set of volume parameters for various standard container types to solve for the optimal container combination scheme that fits the total order volume data, and sending it to the user's terminal for confirmation to complete the automated order entry. This invention significantly improves the accuracy of orders and the efficiency of container loading in cross-border trade.

Systems, methods, and apparatuses for encoding and / or decoding video are provided in various implementations. An encoder generates and / or receives a plurality of discrete input views. The encoder generates a volumetric data representation based on the plurality of discrete input views. The encoder further generates a plurality of pseudo views and / or a plurality of error pseudo views. The encoder transmits a bitstream to a decoder. The bitstream comprises an encoded volumetric data representation with a plurality of encoded pseudo views and / or a plurality of encoded error pseudo views. The decoder uses the plurality of pseudo views and / or a plurality of error pseudo views to optimize the volumetric data representation and generate an improved volumetric data representation. The decoder generates a plurality of new views based on the improved volumetric data representation. The decoder renders the plurality of new views, generates a final video data, and outputs the final video data.

A method for assigning train blocks at a railroad merchandise yard includes determining, using a first optimization model and historical train block volume data, a first list of train block assignments for a plurality of train blocks and a plurality of classification tracks of a classification bowl. The method further includes displaying the first list of train block assignments generated by the first optimization model if the volume of the train blocks is not greater than the total available track length of the classification tracks. The method further includes determining and displaying, using a second optimization model and the historical train block volume data, a second list of train block assignments for the plurality of train blocks and the plurality of classification tracks of the classification bowl if the volume of the plurality of train blocks is greater than the total available track length of the plurality of classification tracks.

The invention discloses an intelligent digital volume-dependent deformation measurement system and method for three-dimensional complex deformation. The system comprises a three-dimensional volume data preprocessing unit, a multi-scale Fourier feature mapping unit, an adaptive neural network prediction unit, a complex deformation compatibility verification unit and an intelligent result analysis unit. According to the method, the overall deformation mode is captured through a low-frequency branch (sigma 1 = 1.0), local details and discontinuous features are captured through a high-frequency branch (sigma 2 is learnable), three-dimensional coordinate input is expanded into high-dimensional feature representation, and the expression ability of the network to the complex deformation mode is remarkably enhanced. Numerical derivation errors are avoided through end-to-end combined prediction, and synchronous high-precision measurement of the displacement field and the strain field is achieved. The method is specially designed for complex modes such as stress concentration deformation, discontinuous deformation and multi-scale deformation, compared with a traditional method, the measurement precision is improved by 1-2 orders of magnitude, and the method is suitable for complex deformation analysis in the fields of material science, civil engineering, aerospace, biomedicine and the like.

A computer implementation method for generating volume datasets representing medical volumes for use in rendering processes. [Solution] A computer implementation method for generating a display of a volume dataset representing medical volume for use in rendering. Multiple sets of functions representing the volume dataset are generated in different rendering configurations, and a given function is determined based on the multiple sets of functions, having a 3D position for each volume dataset and one or more respective visualization attribute values ​​to be stored during the display. The given function is stored in the display in association with selection data configured to allow selection of one or more of the given functions for use during the rendering process. The selection data is based on one or more rendering configurations. A computer implementation method for rendering an image of a volume dataset representing medical volume using the above display is also described.

A computer-implemented method for use in determining a radiation dose distribution in a medical volume, includes: receiving a volumetric dataset representing the medical volume; performing a selection process of a rendering process for generating a visualization of the volumetric dataset to select at least one sample point in the volumetric dataset on which to perform a radiation determination process; and performing, for the at least one sample point in the volumetric dataset, the radiation dose determination process to determine a radiation dose at the at least one sample point. The method may also include visualizing the volumetric dataset and / or the radiation dose distribution.

A system and / or method uses a trained U-Net neural network to remove flow artifacts from optical coherence tomography (OCT) angiography (OCTA) data. The trained U-Net receives OCT structural volume data and OCTA volume data as input, but expands the OCTA volume data to include depth information. The U-Net applies dynamic pooling along the depth direction and gives greater weight to the portion of the data that follows (e.g., along the contour) the selection of retinal layers. In this way, the U-Net applies context-different computations at different axial locations, at least in part, based on depth index information and / or (e.g., proximity) the selected retinal layers. Compared to the input OCTA data, the U-Net output is OCT volume data with reduced flow artifacts.

The invention discloses a two-phase immersion liquid cooling gas-phase pressure adjusting method and system based on PID control, and relates to the technical field of liquid cooling gas pressure adjustment. The PID control-based two-phase immersion liquid cooling gas phase pressure regulation method comprises the following steps: S1, acquiring and preprocessing environment temperature data, pressure and volume data and execution feedback data in a liquid cooling gas pressure regulation process, and constructing a standardized gas-liquid control data set; s2, the gas phase space pressure intensity state is evaluated, and the inflation and deflation operation force of the elastic air bag is dynamically adjusted; s3, the air pressure adjusting trend is analyzed, and the air inlet and outlet rate of the two-way pump is adjusted in real time; and S4, comprehensively analyzing the current closed gas phase space state, and driving the volume adjustment instruction to adjust the amplitude. The problem that accurate matching control of evaporation and condensation processes is difficult to support due to the fact that a target pressure value lacks a joint matching mechanism for altitude change and load dynamics is solved.