14 results about "3d analysis" patented technology

Techniques are discussed herein for generating three-dimensional (3D) representations of an environment based on two-dimensional (2D) image data, and using the 3D representations to perform 3D object detection and other 3D analyses of the environment. 2D image data may be received, along with depth estimation data associated with the 2D image data. Using the 2D image data and associated depth data, an image-based object detector may generate 3D representations, including point clouds and / or 3D pixel grids, for the 2D image or particular regions of interest. In some examples, a 3D point cloud may be generated by projecting pixels from the 2D image into 3D space followed by a trained 3D convolutional neural network (CNN) performing object detection. Additionally or alternatively, a top-down view of a 3D pixel grid representation may be used to perform object detection using 2D convolutions.

Increase resolution with a simple configuration. [Solution] The imaging device (1) includes a first laser light source (11) that emits a first laser beam (L1), a second laser light source (12) that emits a second laser beam (L2), a zoom lens (13) that can switch between a first focal length (f1) and a second focal length (f2), and a sensor (14) that detects reflected light from an object (2) irradiated with the first laser beam (L1) or the second laser beam (L2) through the zoom lens (13). The second focal length (f2) is longer than the first focal length (f1). When the focal length of the zoom lens (13) is the first focal length (f1), the first laser light source (11) is positioned to satisfy the Scheinproof condition. When the focal length of the zoom lens (13) is the second focal length (f2), the second laser light source (12) is positioned to satisfy the Scheinproof condition.

This invention relates to the field of automotive braking technology, and specifically to a method for analyzing braking noise in an automotive braking system. The method comprises the following steps: S1: Obtaining the rotational stiffness of the brake pads during braking; S2: Establishing a 1D model of the automotive braking system based on the rotational stiffness of the brake pads to obtain the relative angular velocity between the brake disc and brake pads during the complete braking process; S3: Dividing the relative angular velocity interval between the brake disc and brake pads during the complete braking process into n equal intervals; S4: Establishing a 3D analysis model of the automotive braking system's braking noise based on the stick-slip effect between the brake disc and brake pads, based on the interval division of the relative angular velocity; S5: Obtaining the results of the braking noise analysis of the automotive braking system based on the stick-slip effect between the brake disc and brake pads during the complete braking process; S6: Verifying and correcting the 1D model of the automotive braking system and the 3D simulation analysis model of the automotive braking system's braking noise based on actual experimental results.

The present disclosure discloses a four-dimensional assimilation method for wind farm data based on physical constraints and generative AI, belonging to the technical field of numerical weather prediction and wind energy prediction. The method includes: collecting various types of wind-detecting observation data from a wind farm and performing quality control; performing multi-scale preprocessing and uncertainty encoding on raw data of background field; encoding the wind-detecting observation data into conditional features; using the raw data of background field and the conditional features as joint conditional inputs to drive a generative model, and performing a reverse denoising sampling to generate a preliminary 3D analysis field; applying soft constraints and a projection correction based on physical constraints such as MO similarity theory and wake model; and performing multi-sample generation and statistical integration to output a mean and a uncertainty of analysis field and products for key height layers of wind power.

The invention provides an intelligent-detection tunnel karst cave roof stability evaluation method and system, and the method comprises the steps: exploring the geological state of a tunnel karst cave roof through deep drilling, carrying out the distance measurement through a surface leveling laser range finder, receiving a signal reflected by the surface of a natural object, carrying out the distance measurement, and constructing a three-dimensional solid model of a tunnel karst cave; performing 3D analysis processing, filtering and interpretation on the original point cloud data of the karst cave, and establishing a numerical analysis model of the karst cave based on a finite element simulation program; according to the actual tunnel external load and the serialized virtual tunnel external load, tunnel karst cave roof mechanical calculation and finite element simulation calculation analysis are carried out, and karst cave roof stability analysis sequence data are generated; and according to the mechanical calculation sequence data of the tunnel karst cave roof, statistically analyzing the maximum principal stress data of the tunnel karst cave roof, and judging whether the stability of the karst cave roof meets the maximum load requirement outside the tunnel.

The application provides a tunnel karst cave roof stability evaluation method and system for intelligent detection, wherein the geological state of the tunnel karst cave roof is explored through deep drilling, ranging is performed through a surface leveling laser range finder, signals reflected by natural surfaces are received for ranging, and a three-dimensional entity model of the tunnel karst cave is constructed; original point cloud data of the karst cave are processed through 3D analysis, filtering and interpretation, and a numerical analysis model of the karst cave is established based on a finite element simulation program; mechanical calculation and finite element simulation calculation and analysis of the tunnel karst cave roof are performed according to actual external loads of the tunnel and serialized virtual external loads of the tunnel, and sequence data of the stability analysis of the karst cave roof are generated; and whether the stability of the karst cave roof meets the requirement of the maximum external load of the tunnel is judged according to the sequence data of the mechanical calculation of the tunnel karst cave roof and statistical analysis of the maximum principal stress data of the tunnel karst cave roof.

The present application discloses a quasi-3D design method for a centrifugal compressor impeller based on an inverse design algorithm, wherein the method includes making a preliminary guess of an unknown flow channel wall surface through a ball-and-spine algorithm; generating a computational grid; performing a quasi-3D analysis of the impeller meridian plane by specifying input parameters to obtain an inner target decompression distribution; calculating the difference between the current inner decompression distribution and the target inner decompression distribution; determining whether the current inner decompression distribution is close to the target inner decompression distribution, if so, stopping the deformation of the flow channel wall surface and obtaining the target shape; if not, calculating the displacement of the flow channel wall surface and updating the geometric shape of the flow channel wall surface, and returning to computational grid generation. The present application is beneficial in that it provides a quasi-3D design method for a centrifugal compressor impeller based on an inverse design algorithm, which improves the impeller performance by improving the meridian plane through inverse design of the flow channel decompression distribution.

The invention discloses a subtropical forest above-ground biomass estimation method and a related device, and relates to the technical field of quantitative remote sensing, and the method comprises the steps: obtaining multi-source remote sensing and field survey data and historical ground feature spectral information of a target subtropical forest region; based on photons in the laser radar data, extracting attributes of the target subtropical forest region; according to spectral information in the optical image data, vegetation index features are calculated based on historical ground feature spectral information; determining surface reflectance based on wave band information in the optical image data; according to the surface reflectance of each single wave band, extracting texture features by adopting a gray-level co-occurrence matrix; according to the topographic data, utilizing a 3D analysis tool to extract topographic factors; constructing a multivariable regression model according to the attributes, the vegetation index features, the texture features and the topographic factors; the multivariable regression model is a machine learning model based on XGBoost, estimates the above-ground biomass based on the multivariable regression model, and outputs an estimation result. According to the method, the above-ground biomass can be estimated more accurately.

A method includes obtaining a first imaging scan and a second imaging scan of a single subject's brain. The first imaging scan is converted to a first data set and the second imaging scan is converted to a second data set. A sequence-adaptive multi-modal segmentation algorithm is applied to the first data set and the second data set. The sequence-adaptive multi-modal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labeled data set and a second labeled data set. The first labeled data set and the second labeled data set are automatically co-registered to one another to generate a transformation matrix based on the first labeled data set and the second labeled data set. The transformation matrix is applied to align the first data set and the second data set.

The present disclosure generally relates to systems and methods for spatial barcoding or identification of molecules in a sample. Certain embodiments relate to multi-dimensional spatiomics tools with targeted or non-targeted biomolecules, highly parallel barcoding, and subcellular resolution. Some aspects may use light to introduce spatial barcodes into biomolecules and sequence them to decode their content and location within cells and tissue blocks. Spatial barcoding may be accomplished using a light beam that may address pixels in 3D space without any dead zone between the pixels. Characteristics of these non-targeted RNA measurements, tissue 3D analysis, parallel barcoding, no dead zone and the likelihood of single cell sequencing provide a new way to acquire molecular and spatial information from tissue, e.g., for discovery in cancer biology, immunology, neuroscience and developmental biology.

The present application relates to the technical field of computer algorithm field, especially to a kind of mass spectrum peak detection analysis method and its device.The mass spectrum peak detection analysis method specifically includes the following steps: step S1: mass spectrum data is regarded as three-dimensional point cloud, and mass spectrum data is divided into window;Step S2: point cloud data extraction is carried out in each window area, and point cloud data is pretreated;Step S3: the point cloud data after pretreatment is analyzed based on the end-to-end multi-task network of deep learning.By the present application, 3D analysis of mass spectrum data can be realized, and the accuracy of mass spectrum peak quantitative analysis can be effectively guaranteed.

Sports shoe system (1) with integrated sensors, comprising a sensor insole (10) with piezoelectric pressure sensors (21-28) for measuring the pressure distribution under the foot and with bioimpedance electrodes (11-14) for measuring the electrical body impedance, characterized in that the sensor insole (10) and an electronic module (30) connected to it are jointly designed to calculate body weight from the pressure sensor signals (21-28), to determine body composition from the bioimpedance signals (11-14), to perform a three-dimensional reconstruction (60) of the foot position and gait pattern and to automatically generate personalized recommendations for orthopedic insoles (70) from this, wherein all measurements are performed simultaneously during sporting activity.

The application discloses a method for constructing a dental restoration model based on an intraoral scanner, and applies to the technical field of data processing, and comprises the following steps: a desktop scanner is used to scan a prepared body model to obtain a digital reference model; an intraoral scanner is used to scan the prepared body model to obtain a digital experimental model, and 3D comparison analysis is performed on the model to determine the mean absolute deviation, the maximum positive average deviation and the maximum negative average deviation and obtain a color-coded graph; 3D analysis is repeatedly performed according to preset analysis conditions; all 3D analysis results are stored in a database, data deviation value analysis is performed, and a modeling data deviation adjustment library is determined; user scanning data is obtained, the user scanning data is adjusted by using the modeling data deviation adjustment library to determine modeling correction data, and the modeling correction data is used to construct a restoration model. The technical problem that the intraoral scanner has low modeling precision and poor dental restoration effect when modeling the internal structure of the oral cavity in the prior art is solved.

Methods and apparatus for large area 3D analysis of a sample using glancing incidence FIB milling are disclosed herein. An example method includes at least: milling a sample with a focused ion beam at a shallow angle and at a plurality of rotational orientations to remove a layer of the sample and expose a surface, and after milling, imaging the exposed surface of the sample with a charged particle beam.