248 results about "Volumetric data" patented technology

The invention relates to an electromagnetic shielding method and shielding system for power distribution, and belongs to the technical field of electromagnetic shielding. Comprising the following steps: establishing a shielding model corresponding to the shielded room according to volume data of the shielded room; simulating and positioning electromagnetic wave conditions in the shielded room in the shielding model; selecting key detection points; constructing a phase difference matrix; shielding requirements of different waveforms in the same frequency band are distinguished, leakage path positioning based on a waveguide equation is carried out, and a core leakage area and an auxiliary protection area are divided. According to the electromagnetic shielding method and shielding system for power distribution provided by the invention, the dynamic adaptability of electromagnetic shielding and the detection capability of transient electromagnetic pulses are remarkably improved, and a multi-dimensional risk quantification and differentiation reinforcement strategy is realized. Meanwhile, intelligent materials and a dynamic optimization mechanism are adopted, the material utilization rate and the protection effect are improved, and a reinforcing effect feedback closed loop is formed.

The invention discloses an early lung cancer screening method based on transfer learning. The method comprises the following steps: S1, obtaining preprocessed three-dimensional volume data; s2, generating a multi-scale enhanced feature map; s3, obtaining a preliminary micro-nodule detection result; s4, obtaining a first-stage optimization model; s5, performing second-stage training on the first-stage optimization model by adopting a double-normal-form transfer learning strategy to obtain a transfer optimization model; s6, embedding an adversarial domain adaptive module in the migration optimization model to obtain a cross-domain robust model; and S7, in a clinical reasoning stage, outputting a micro-nodule detection result for the new chest low-dose spiral CT original image data by using the cross-domain robust model. According to the invention, the model can adaptively emphasize the local gradient difference between the lung micronodule and the background texture, thereby improving the discrimination capability of the weak boundary region.

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 discloses a personalized logistics service quotation method and system based on a rule engine. The method comprises the steps of (1) data acquisition and preprocessing, (2) rule matching and selection, and (3) pricing calculation. And (4) outputting and optimizing a result. The system comprises a data input module, an image processing module, a rule management module, a rule engine module and a result output module. The data input module is responsible for receiving logistics related information input by a user, including but not limited to weight, volume, order quantity and the like of articles. Volume data can be obtained by a user by uploading a picture or shooting an object in real time by using a camera; through a flexible pricing mode and an intelligent image identification technology, logistics cost calculation can be efficiently and accurately completed. The working process of the system covers key links such as data acquisition and preprocessing, rule matching and selection, pricing calculation, result output and optimization and the like, and the pricing demand in a complex scene is realized.

The invention relates to the technical field of data processing, and discloses a pipeline corrosion repairing method and system based on artificial intelligence and a storage medium. The method comprises the following steps: acquiring a corrosion image through a pipeline endoscope, and obtaining a standardized feature data set through Gaussian filtering and histogram equalization processing; inputting a semantic segmentation network for identification and segmentation to obtain corrosion three-dimensional coordinates and volume data; according to the coordinates and chemical factors, mortar proportioning parameters are obtained through calculation; inputting the volume data into a depth-thickness mapping network to obtain repair thickness distribution; and jet parameter setting and construction control are conducted according to the thickness distribution, and pipeline corrosion repairing is completed. The technical problems that in an existing pipeline corrosion repairing method, corrosion recognition precision is low, repairing parameters lack pertinence, multi-point corrosion collaborative optimization is insufficient, and the intelligent degree of construction control is low are solved.

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 system for processing an object using a programmable motion device is disclosed. The system includes an end effector of a programmable motion device for gripping objects from a feed container, and a control system for determining payload protection for selected objects. The payload protection includes point cloud data regarding volumetric data, the volumetric data including a volume occupied by the selected object, and the payload protection is determined in response to at least one characteristic of the selected object and provided specific to the selected object.

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 present invention discloses a multi-modal autoencoder intelligent detection method and device for spare parts integrating 3D point clouds, which relates to the field of image processing. The method includes: S1, obtaining the volume, weight, and 3D point cloud data of spare parts of each model; performing normalization and 2D depth image conversion; S2, cropping and feature extraction on the 2D depth image, and performing feature fusion with the normalized weight and volume data to obtain multi-modal features; S3, performing grayscale processing to obtain a multi-modal feature grayscale image; S4, dividing the training set and the test set; training an autoencoder model using the training set; S5, inputting the test set into the trained autoencoder model to obtain the feature codes of spare parts of each model; S6, inputting the multi-modal feature grayscale image of the spare parts to be detected into the trained autoencoder model to obtain the feature codes of the spare parts to be detected, and performing feature comparison to obtain the model information. The present invention uses an autoencoder to extract multi-modal features integrating 3D point clouds, improving the accuracy of spare part detection.

The invention provides a refrigerator cabinet polyurethane foam density test analyzer and a density test method. The density test analyzer is configured to obtain to-be-tested sample information input by a user; pre-treating the sample based on the information of the sample to be detected; putting the pretreated sample at a weight testing position for weighing to obtain weight data, and then putting the sample block into the volume measuring cup for volume measurement to obtain volume data; and calculating and displaying the density based on the weight data and the volume data. According to the invention, through cooperation of the automatic weighing module and the intelligent data processing system, high-precision rapid detection of the density of the polyurethane foam is realized, the measurement error is controlled within + / -0.5%, and the detection efficiency is significantly improved; and meanwhile, through information binding storage and dynamic analysis functions, a complete quality tracing system is constructed, so that the process abnormity response speed is increased by 70%.

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.

A picking method, a picking vehicle and a picking system. The picking method includes: obtaining a current volume data and a current weight data of a box; generating a current arranging position data of the box based on the current volume data, the current weight data and an arranging position model of an artificial intelligence model. The arranging position model includes multiple arranging date each including a predetermined volume data, a predetermined weight data and a predetermined arranging position data that are corresponding to one another; and moving the box to a target arranging position via a picking vehicle based on the current arranging position data.

The invention relates to an asphalt pavement pore and crack identification method and system based on a CT image, and a storage medium. The method comprises the following steps: obtaining an asphalt pavement two-dimensional image; wherein the two-dimensional image is obtained by scanning a core sample at the crack of the asphalt pavement through CT equipment; processing the two-dimensional image by adopting a three-dimensional reconstruction algorithm, converting two-dimensional image data into three-dimensional volume data, and generating a three-dimensional structure model of the asphalt pavement; extracting three-dimensional volume data in the three-dimensional structure model, and calculating a volume form factor; according to the volume form factor, classifying pores and cracks of the asphalt pavement by using an automatic classification algorithm; and outputting a classification result to realize the recognition of the pores and cracks of the asphalt pavement.

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.

The invention discloses a waste-oriented detection and classification system, and particularly relates to the technical field of waste detection, if a sensor module detects waste, a signal is output to trigger an intelligent identification module to work, the intelligent identification module performs preliminary garbage classification through a picture shot by a camera, and the picture is transmitted to a main control core through a serial port; the intelligent recognition module and the main control core train a built-in Rep-YOLOv8s model of the intelligent recognition module through the upper computer to adjust algorithm parameter setting, the sensor module provides weight and volume data for the main control core, after data fusion, the data are compared with parameters set in a database, and an accurate classification result is obtained and fed back to the display module in real time. When the classification result is abnormal or the sensor data exceeds a normal range, giving an alarm prompt; one stepping motor is controlled by the stepping motor module to rotate the garbage can to a designated area, and the other stepping motor is controlled to drive the garbage baffle to achieve precise throwing.

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.

A computer-implemented method is disclosed for generating and displaying predictive synthetic digital radiographs (DRs). The method comprises receiving pre-operative volume data for at least a portion of a patient's anatomy, the data including voxels with radiographic density or other volumetric values. A 3D model of the anatomy is generated, and a 3D model of an implant is placed at a planned position or orientation within the anatomical model. The post-implantation model is translated into planned post-implantation volume data by changing the voxel values corresponding to the implant. One or more predicted synthetic DRs are created from this volume data and presented on a display to show the implant in its planned position and / or orientation.

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.