58 results about "Quantitative measure" patented technology

Embodiments of the present disclosure provide a system, method, and application for estimating a physiological age of a subject. The system comprises: at least one storage device configured to store a set of instructions; at least one processor in communication with the at least one storage device, wherein the at least one processor is configured to perform operations when executing the set of instructions, the operations include: obtaining quantified abundance levels of one or more target metabolites from a plurality of metabolites in a sample of the subject via a quantitative measurement device; the plurality of target metabolites comprises metabolites listed in Table A; estimating the physiological age of the subject using a predictive model based on the quantified abundance levels of each of the one or more target metabolites.

Disclosed herein is a method and system for analyzing wellbore fluid samples using impedance spectroscopy to identify chemical constituents and detect contaminants. The approach involves collecting fluid samples from a wellbore and performing chemical impedance spectroscopy by applying multiple discrete frequencies of alternating current, generating impedance spectra that are transformed into Nyquist and Bode plots for analysis. A data processing workflow handles raw impedance data through steps of labeling, cleaning incomplete sweeps, temperature thresholding, and temperature compensation to a reference standard. A hybrid modeling approach that integrates physics-based modeling (using a modified Randles circuit) with advanced data analytics techniques (utilizing Gaussian Process Regression) is utilized. Derived electrical properties, particularly susceptance and permittivity, are used to enhance the predictive capabilities of the model. This enables quantitative measurement of contaminants including mud filtrate and various salt types in wellbore fluids.

The invention relates to a quantitative measurement system and method for mechanical properties of soft tissue and electronic equipment, and the system is characterized in that a pre-negative pressure cavity is in preset negative pressure, and a direct negative pressure cavity is in sealed contact with the measured soft tissue; the first control part loads and maintains target negative pressure to the direct negative pressure chamber according to a preset time sequence; the second control piece is used for releasing pressure and resetting the direct negative pressure chamber; the pressure acquisition part acquires the actual pressure in the cavity; the displacement acquisition part acquires the displacement of the tested soft tissue at the opening of the direct negative pressure chamber; the state detection piece detects the actual state between the direct negative pressure chamber and the detected soft tissue; the control part generates a control instruction and generates soft tissue mechanical property quantitative measurement data in combination with actual intracavity pressure and displacement. Therefore, the problems of high maintenance cost, strong operation dependence, poor repeatability, difficulty in programming scheduling of key test conditions, poor flexibility and configurability and the like caused by large volume of the gas circuit and the detection unit in the related technology are solved.

The invention discloses a finger knuckle buckling force measuring device which comprises a base, a sliding table, a palm fixing assembly and a measuring assembly. The sliding table is arranged on the base, and the palm fixing assembly is arranged on the sliding table and can slide in the horizontal X direction and the horizontal Y direction. The palm fixing assembly is provided with at least one limiting part, the measuring assembly is arranged on the base and comprises a pressure measurer, and the vertical position of the pressure measurer is adjustable in the vertical direction. According to the finger knuckle buckling force measuring device, quantitative measurement of single finger knuckle buckling force can be achieved, the accuracy of a measurement result can be guaranteed by limiting a palm and an arm, the uniformity of measurement positions can be achieved, and the comparison significance of data is enhanced. And through the sliding table structure, the device can be suitable for patients with different body types, and has good flexibility.

The method for establishing a high-dimensional Raman spectrum dimension reduction training model in noninvasive detection is a method for dimension reduction training from high-dimensional spectral data to low-dimensional characteristic peak data. The original data set is obtained by collecting Raman spectrum and test results while collecting human venous blood for testing. Abnormal spectra are removed through t test and Z test. The spectral data are baseline corrected through the least square method. The noise in the spectrum is eliminated through discrete wavelet transform. Dynamic dimension reduction is realized through pre-characteristic peak screening, wavelet method, recursion method and verification to generate a pre-training data set. The present application solves the problems of inaccurate characteristic peak selection, uncertain device system error, characteristic peak superposition of mixed substances and non-support of dynamic characteristic peak optimization, realizes the accuracy of the characteristic peak in iteration can be continuously optimized, and provides innovative achievements and foundation for later quantitative measurement of mixed substances.

This invention discloses a shear force distribution demonstration device and measurement method, belonging to the field of structural engineering teaching technology. The device includes a base, a support mechanism, a loading mechanism, a demonstration mechanism, and a measurement mechanism. The support mechanism is a frame structure with sliding rails; the loading mechanism has replaceable supports with switchable constraints; the demonstration mechanism uses replaceable vertical columns with four-jaw chuck sleeves; and the measurement mechanism includes a strain measurement module, an S-shaped tension sensor, and a positioning ruler. This device adopts a fully modular, quick-release design, allowing for rapid switching between series and parallel systems, column constraint forms, and parameters. It simultaneously demonstrates the core knowledge points of inter-story force transmission in series and intra-story force distribution in parallel, verifying the shear force distribution law through measured data. This invention is easy to operate, combining visualization and quantitative measurement, effectively solving the pain points of abstract and difficult-to-understand traditional teaching methods, and significantly improving teaching effectiveness.

The invention belongs to the technical field of fan gear box defect detection, and relates to a fan gear box defect real-time detection and three-dimensional measurement method and a related device. Comprising the following steps: acquiring a left eye image frame and a right eye image frame in the fan gearbox in real time to form an image sequence; performing defect detection on the image sequence, identifying a defect region in the image sequence and determining a defect category to obtain a defect detection result including the defect region and category information; based on the left eye image frame and the right eye image frame, reconstructing a three-dimensional surface point cloud of the gearbox by adopting a binocular stereoscopic vision algorithm; extracting three-dimensional point cloud data corresponding to the defect area from the three-dimensional surface point cloud according to a defect detection result; and based on the three-dimensional point cloud data corresponding to the defect area, carrying out quantitative measurement on defect size parameters. According to the invention, automatic and intelligent identification and high-precision three-dimensional quantitative analysis of internal defects of the fan gearbox are realized.

Described herein is the design and implementation of an instrumented user device to produce quantitative metrics of patient fall risk. In some embodiments, a regression algorithm is used to estimate postural sway velocity, which is an effective predictor of fall risk. The instrumented user device enables continuous patient monitoring outside of the clinic and could provide a long-term, quantitative measure of fall risk.

A wearable garment with sensors attached to obtain physiological data. The sensors are incorporated to form a body area sensor network to obtain the data. This provides patients with improved health monitoring by aggregating multiple interconnected nodes on a human body for sensorimotor measurements and provides patients with quantitative measurements of their progress. The data is obtained in a way that allows for the number of transmissions to be reduced thereby conserving the energy of the wearable devices. This is made possible by each sensor reducing the number of samples by eliminating predictable samples and configuring the sensors to pack the data efficiently. A neural network can determine whether a sample can be skipped or needs to be reported. A long short term memory architecture creates a waveform for a given snapshot of samples based on the previous samples regardless of whether these samples were reported or predicted.

A handheld palpation device for telemedicine examinations includes a handle that applies pressure to a patient's body through a palpation head. The handle includes an interior space and an opening to the interior space. One end of the palpation head is installed in the opening, and the other end of the palpation head comprises a circular soft head part which is configured to be in contact with the body of the patient when pressure is applied. A pressure pad located in the interior space acquires voltage data when applying pressure. A sensor chip located in the interior space receives voltage data from the pressure pad, calculates one or more quantitative pressure measurements, and transmits the quantitative measurements to a communication chip connected to the sensor chip, which transmits the quantitative pressure measurements to an external device for analysis, a clinical assessment is provided based on the pressure measurements using machine learning.

The application discloses a kind of gas impurity concentration quantitative measurement method, equipment and medium, it is related to gas detection field, the method comprises: obtaining the response signal of B impurity in A gas, A impurity in B gas;Using mass dilution method, with D gas as diluent gas, dilute A gas and B gas respectively, obtain the A dilution mixed gas of first set concentration value and the B dilution mixed gas of second set concentration value;Obtain the response signal of A component in A dilution mixed gas and B component in B dilution mixed gas;The measurement model of bidirectional iterative intelligent algorithm is constructed;Using the measurement model of bidirectional iterative intelligent algorithm, according to four response signals, gas mass data and molecular weight data in dilution process, obtain the measurement result of B impurity concentration in A gas and A impurity concentration in B gas.The application can solve the quantitative measurement problem of corresponding impurity of two gases being impurity each other.

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

Systems and methods for determining wind speed are disclosed. The system includes a video capture device (e.g., a smartphone camera, a drone-mounted camera, or a satellite) to capture a video time-series of a pre-existing flexible object, such as natural vegetation, interacting with a wind flow. A processor is configured to analyze the video time-series to extract kinematic features of the object's motion and determine a quantitative wind speed. In one embodiment, a physics-informed neural network is used, which is trained to infer the object's mechanical properties (such as stiffness or species) from the video to solve for the wind speed. In another embodiment, a neural network is configured to receive a set of statistical feature maps, derived from the video's temporal signals, to determine the wind speed. In a further embodiment, the wind speed is determined by applying a first-principles physical formula based on the discovery that the fluctuating motion of individual leaves becomes decoupled from the branch structure at certain wind speeds, allowing for a quantitative measurement based on leaf size and measured leaf speed.

The application discloses a cross-scale composite wavefront sensing method based on a deep learning model, combines a Shack-Hartmann wavefront sensor (SHWFS) and off-axis digital holography (OADH) through a composite wavefront sensing system, fuses data of a wavefront measured by the composite wavefront sensing system by using an MSWM-Net model based on a deep neural network, realizes complementary advantages of performances of the two measurement systems, and further obtains a high-confidence reconstructed wavefront. When measuring a wavefront aberration containing different spatial frequencies, the method not only solves the problem of low phase accuracy in SHWFS measurement, but also solves the limitation of wrapping the wavefront in a period in OADH measurement. The feasibility and effectiveness of the method in wavefront detection are verified through tests. The application is more easy to realize quantitative measurement of atmospheric turbulence aberration, can provide design input parameters for an atmospheric turbulence aberration correction system, and can be used for distortion wavefront detection of the atmospheric turbulence correction system.

This invention relates to a trajectory recognition method based on distributed acoustic sensing, belonging to the field of vehicle trajectory recognition technology. The trajectory recognition method based on distributed acoustic sensing utilizes a distributed acoustic sensing system to achieve high-precision quantitative measurement of external mechanical disturbances by demodulating the phase change of Rayleigh backscattered light. Its unique feature lies in that the trajectory recognition method includes the following steps: Step 1, acquiring low SNR vibration signals collected by the DAS system within a unit acquisition time; Step 2, in VTR Net, employing the Global-Local Feature Collaborative Modeling Mechanism (GL-FCM) and the Multi-Resolution Attention Feature Map Recovery Architecture (AG-FMR); Step 3, using the CEH-FA model robustness enhancement method for vehicle trajectory recognition to identify and finally output the vehicle trajectory.

The application discloses a position calibration method and a position calibration device, and belongs to the technical field of equipment calibration. The position calibration method comprises the following steps: attaching a standard customized block to a target hole, wherein a center point is arranged on the surface of the side of the standard customized block away from the target hole, and the center point is located on the central axis of the target hole; controlling an electric wrench to move to the surface of the standard customized block and controlling the wrench head of the electric wrench to extrude the surface to obtain a position point extruded by the wrench head and the surface; the central axis of the electric wrench is perpendicular to the plane where the surface is located; and the position of the electric wrench is calibrated based on the position point and the center point. The position calibration method changes the qualitative judgment of the position offset condition into quantitative measurement, replaces the traditional eye observation, is more accurate in the debugging of the equipment with high precision requirements, and avoids the qualitative difference in individual debugging, and the quantitative measurement process is simple and convenient to operate.

1. Name of the product in this design: Blood glucose testing instrument. 2. Purpose of this design: This design is for patient self-use to perform in vitro quantitative measurement of blood glucose in peripheral blood. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

A wearable garment with sensors attached to obtain physiological data. The sensors are incorporated to form a body area sensor network to obtain the data. This provides patients with improved health monitoring by aggregating multiple interconnected nodes on a human body for sensorimotor measurements and provides patients with quantitative measurements of their progress. The data is obtained in a way that allows for the number of transmissions to be reduced thereby conserving the energy of the wearable devices. This is made possible by each sensor reducing the number of samples by eliminating predictable samples and configuring the sensors to pack the data efficiently. A neural network can determine whether a sample can be skipped or needs to be reported. A long short term memory architecture creates a waveform for a given snapshot of samples based on the previous samples regardless of whether these samples were reported or predicted.

The invention discloses a stereoscopic photogrammetry method for the dusting characteristic of lunar dust under the plume effect, and relates to the technical field of spaceflight exploration, a measuring system composed of a high-speed camera, a pitching adjusting mechanism, a rotating mechanism and the like is built in a vacuum cabin, and reliable work of a camera under the vacuum environment is achieved through a camera protection cover; the view field of the binocular camera is overlapped by adjusting the pitch angle and the rotation angle, and an internal reference matrix and a conversion matrix are solved based on standard checkerboard calibration; binocular images under the plume effect are synchronously collected, the three-dimensional coordinates of feature points are inversely calculated based on the triangulation principle, and three-dimensional reconstruction of the contour and depth of the corrosion pit and dust particle motion trail and speed calculation are completed. The method realizes quantitative measurement of corrosion pit morphology and particle motion in a vacuum environment, provides high-precision data support for a lunar dust dusting model, is suitable for plume effect evaluation and protection technology research and development in lunar exploration, and has the advantages of accurate measurement, non-contact type, complete spatio-temporal data and the like.

The invention relates to the technical field of optical imaging, in particular to an endoscopic imaging device based on a forward probe, a medical host system and an imaging method.The endoscopic imaging device comprises a forward scanning probe, an in-vitro system, an excitation unit and a signal processing unit, receiving an optical signal carrying tissue scattering information; the in-vitro system is used for realizing light interference, converting an interference light signal into digital OCT original data, outputting a synchronous trigger signal to the excitation unit, collecting an OCT scattering signal in an elastic wave propagation process, converting the OCT scattering signal into an OCT complex value signal, and packaging the OCT complex value signal into an original data stream; and the signal processing unit performs phase extraction, displacement calculation or wave velocity estimation and the like on the original data stream, performs elastic inversion, and outputs tissue elastic information in all directions, so that the problems of limited imaging view field, insufficient lesion recognition sensitivity, limited clinical application, elastic quantitative measurement and the like in related technologies are solved, and the accuracy and the reliability of the system are improved. And the accuracy and the sensitivity of early lesion diagnosis are improved.

The invention discloses a combustion chamber flow field multi-parameter three-dimensional quantitative reconstruction method and device based on multi-source data fusion and PINN. Comprising the following steps: acquiring measurement data of a flow field in a combustion chamber and a three-dimensional luminous field structure of a flow field flame under the same space coordinate system by adopting a one-dimensional Raman scattering technology, a two-dimensional plane laser-induced fluorescence technology, a two-dimensional particle image velocity measurement technology and a three-dimensional chemical self-luminous chromatography technology respectively; combining time and space coordinates of the combustion chamber flow field to construct training data; constructing a combustion chamber flow field parameter reconstruction model based on a physical information neural network, and training the combustion chamber flow field parameter reconstruction model by adopting the training data to obtain a trained combustion chamber flow field parameter reconstruction model; and inputting the time and space coordinates of the combustion chamber flow field to be reconstructed into the trained combustion chamber flow field parameter reconstruction model to obtain corresponding flow field parameters and predicted values of luminous intensity, thereby solving the problem that the existing combustion chamber flow field multi-parameter three-dimensional quantitative measurement is difficult.

This application relates to a method and computer device for geometric measurement of defects in product appearance. The method involves inputting 3D point cloud data to be measured into a target semantic segmentation network and outputting point-by-point prediction results. The target semantic segmentation network includes an encoder and a decoder. The encoder performs multi-scale feature extraction on the 3D point cloud data. At each scale, the feature enhancement module in the encoder adaptively adjusts the neighborhood of the points based on the spatial distribution of the point set at the current scale, and aggregates local features based on the adjusted neighborhood to obtain enhanced features of the point set at the current scale. The decoder generates point-by-point prediction results based on the multi-scale enhanced features. Based on the point-by-point prediction results, the geometric parameters of the appearance defects are measured to obtain quantitative measurement results. This method solves the problem of insufficient performance of 2D visual inspection in detecting appearance defects in product appearance, and can completely and accurately characterize the spatial geometric morphology of defects, obtaining precise measurement results.

The invention provides a left atrium wall thickness evaluation and intraoperative navigation method and system based on artificial intelligence. Comprising the steps of collection and preprocessing of multi-modal medical image data, artificial intelligence image segmentation under topological constraints, three-dimensional structure reconstruction and inner and outer surface separation, wall thickness calculation and risk assessment, visual mapping interaction of wall thickness and risk levels, and intraoperative navigation and dynamic adjustment. The system automatically identifies a left atrium structure through a deep learning model, introduces topological consistency constraint to improve segmentation precision, and realizes quantitative measurement of wall thickness and high-risk region identification through three-dimensional reconstruction and geometric analysis. The method supports intra-operative real-time image synchronization and path dynamic updating, and has closed-loop navigation and risk early warning capabilities. According to the invention, accurate evaluation of the thickness of the left atrial wall and intelligent navigation in the operation can be realized, and the safety and individualized level of treatment of arrhythmia such as atrial fibrillation and the like are improved.

The invention provides a frequency spectrum data processing method and ultrasonic imaging equipment, and the method comprises the steps: setting a plurality of sampling gates at different positions on the same target blood vessel in a blood flow image, and obtaining the frequency spectrum data corresponding to each sampling gate; performing quantitative measurement on the frequency spectrum data corresponding to the at least two sampling gates to obtain a parameter value of at least one type of frequency spectrum quantitative parameter corresponding to each sampling gate in the at least two sampling gates; and determining a target parameter according to the parameter value of the same type of frequency spectrum quantitative parameter corresponding to each sampling gate in the at least two sampling gates at the same time. According to the scheme, the target parameter is determined according to the parameter values of the same type of frequency spectrum quantitative parameters corresponding to the at least two sampling gates at different positions on the same target blood vessel in the blood flow image at the same time, and the hemodynamic change conditions of the blood flow of the target area at different positions of the same target blood vessel are provided through the target parameter. Therefore, more diversified and valuable information is provided for clinical diagnosis.

The invention relates to the technical field of concrete dam safety detection, in particular to a concrete dam underwater defect quantitative measurement method based on image three-dimensional reconstruction. The method comprises the steps that 1, an ROV is operated on the underwater surface of a concrete dam, and dam body surface images are collected point by point; and step 2, correcting geometric distortion of the image by using ROV attitude information and a perspective transformation algorithm, generating an image after transparentizing the shelter by combining an image restoration adversarial network model GAN, and identifying texture features. And step 3, according to the texture richness, respectively generating point clouds for different shooting points, and constructing a dam body three-dimensional model by using a Poisson surface reconstruction algorithm. And 4, extracting defect features, analyzing the difference between the defect features and the historical model to position a defect change area, and calculating the defect size. And 5, based on the report and the three-dimensional model, generating a construction guidance file containing defect coordinates and a repair range. The method is used for overcoming the interference problem of impurities on the surface of the dam body, and the underwater surface defects of the dam body of the concrete dam can be accurately detected.

The invention discloses a rapid quantitative sample introduction device and method in an electronic nose air chamber, VOC quantitative measurement is completed outside the electronic nose air chamber, the measured VOC is sealed in one or more sample introduction cabins, the sample introduction cabins are placed in the electronic nose air chamber, when sample introduction is needed, an opening command is sent out from the outside of the electronic nose air chamber, and when sample introduction is needed, the electronic nose air chamber is opened. The starting task of the sample introduction device is completed through the execution mechanism, so that VOC can be volatilized into the electronic nose gas chamber on the premise that the sealing performance of the electronic nose gas chamber is not damaged. The sampling device with high sealing performance is designed, and the high-precision VOC measuring device is used in cooperation, so that the precision of the VOC sampling amount is guaranteed.

The present disclosure relates to a near-field electric field quantitative measurement method and device, electronic equipment and storage medium. The method comprises: obtaining a first linear relationship between a first incident electric field and a first near-field electric field; obtaining an instrument constant of a scanning near-field optical microscope; obtaining a first linear coefficient of any to-be-measured point on a to-be-measured sample according to the instrument constant and the first linear relationship; obtaining a reconstructed near-field electric field of the to-be-measured point under an actual incident electric field incident to the to-be-measured sample as a near-field electric field quantitative measurement result of the to-be-measured point according to the actual incident electric field and the first linear coefficient of the to-be-measured point. The process combines the first linear relationship reflecting the incident electric field and the near-field electric field relationship of the first measurement area of the to-be-measured sample with the instrument constant reflecting the influence of the tip-sample coupling on the near-field electric field of the surface of the to-be-measured sample, realizes the reconstruction of the near-field electric field of the surface of the to-be-measured sample, and improves the accuracy of the quantitative measurement of the near-field electric field of the surface of the to-be-measured sample.

The present invention discloses a magnetic susceptibility tensor imaging method and system that separates microstructure-related frequency shifts, including: using a three-dimensional flow-compensated gradient echo sequence to scan and collect 16-channel multi-echo complex data S in six head directions, and preprocessing the 16-channel multi-echo complex data S collected in each head direction to obtain multi-echo phase data in six head directions and multi-echo amplitude data in six directions; the six head directions are specifically head anteroposterior, head posterior, head left, head right, head prone, and head supine; based on the multi-echo amplitude data in the head anteroposterior direction, a template M of the brain region of interest is obtained, and the multi-echo amplitude data in other directions are aligned with the echo amplitude data in the head anteroposterior direction to obtain a registration matrix. The method and system of the present application can better describe the magnetic susceptibility distribution of white matter regions and improve the accuracy of quantitative measurement of magnetic susceptibility.