101 results about "Quantitative measure" patented technology

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.

The invention discloses a method for establishing a dimensionality reduction training model of a high-dimensional raman spectrum in noninvasive detection, which is a method for performing dimensionality reduction training on high-dimensional spectral data to low-dimensional characteristic peak data, and comprises the following steps of: acquiring a Raman spectrum and a test result while acquiring venous blood of a human body for test to obtain an original data set, and eliminating abnormal spectrums through t test and Z test to obtain a dimensionality reduction training model of the high-dimensional raman spectrum in noninvasive detection. The method comprises the following steps: performing baseline correction on spectral data through a least square method, eliminating noise in a spectrum through discrete wavelet transform, and performing screening, wavelet method, recursive method and verification on a pre-characteristic peak to realize dynamic dimension reduction so as to generate a pre-training data set; according to the method, the problems that traditional characteristic peak selection is inaccurate, equipment system errors are uncertain, mixed substance characteristic peaks are superposed, dynamic characteristic peak optimization is not supported and the like are solved, continuous optimization of the accuracy of the characteristic peaks in iteration can be realized, and an innovative result and a basis are provided for later quantitative measurement of the mixed substances.

Devices, methods, and kits are provided for measuring exophthalmos or enophthalmos. In particular, devices are provided for making either unilateral or bilateral measurements of exophthalmos or enophthalmos. The devices are designed for use during surgery with a patient in a supine position and are composed of a biocompatible metal, resin, or plastic that can withstand sterilization procedures. In addition, the devices can be produced inexpensively with a single-use design, which reduces the burden on sterile processing departments at hospitals, as a device may be discarded once a surgery is completed. The use of these devices will augment surgeons' qualitative visual estimations with quantitative measurements, which should make surgical procedures easier for surgeons to conduct and outcomes more precise and satisfactory for patients.

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 present disclosure relates to a method. The method may be performed by accessing data derived from one or more routine clinical medical imaging scans including a lesion in which the lesion and associated vasculature are segmented in a three-dimensional segmentation. At least two features are extracted from the three-dimensional segmentation of the associated vasculature. The at least two features include at least one feature indicative of a morphology of the associated vasculature or a portion thereof, and at least one feature indicative of a function of the associated vasculature or a portion thereof. The at least two features, and / or one or more statistics of the at least two features, are provided to a machine learning model trained to make a prediction concerning the lesion. The prediction concerning the lesion is received from the machine learning model.

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 spatial point target luminosity space-based quantitative measurement method and a spatial point target luminosity space-based quantitative measurement device, which solve the problem of how to improve the efficiency and the precision of spatial point target luminosity space-based quantitative measurement. Belongs to the photoelectric detection field. Comprising the following steps: performing non-uniform correction and bilateral filtering denoising on an original image to obtain a preprocessed image; estimating a dark field value of the preprocessed image as a background gray value by adopting a mode method; if a point target is detected in the preprocessed image, performing photometric aperture calculation to obtain a photometric aperture when the optical signal-to-noise ratio is maximum, otherwise, directly feeding back invalid information; inputting a photometric aperture parameter into the calibration response relation function, and calculating an inversion photometric value of the point target; and according to the point target luminosity value error model or the point target luminosity value error transmission model, calculating a star magnitude error or a star magnitude standard deviation, and verifying the space point target luminosity space-based quantitative measurement precision. According to the invention, the spatial point target photometric measurement efficiency and precision are improved.

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.

A dynamic bubble quantitative measuring device in a hydraulic model test long tunnel is characterized by comprising a base, a telescopic rod and measuring ropes, the telescopic rod is vertically fixed to the top face of the base, scales are arranged on the outer wall face of the telescopic rod, the scale 0 is calculated from the bottom of the base, four spring reels are installed in the base, one measuring rope is wound around each spring reel, and the measuring ropes are connected with the telescopic rod. The measuring rope penetrates through the inside of the telescopic rod and extends out of an opening in the top of the telescopic rod, scales are arranged on the measuring rope, the zero scale starts from the end of the end extending out of the telescopic rod, a blocking piece is arranged at the end, and the blocking piece is larger than the opening in the top of the telescopic rod. According to the device, a device used on a long tunnel model and used for measuring the dynamic bubbles is provided for measuring personnel, and the evolution process of the bubbles is described after calculation by recording bubble form parameters at different moments.

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.

The invention discloses a deep thrombus quantitative measurement method and device based on a photoacoustic imaging technology, signals of a deep thrombus lesion area and signals of adjacent blood vessels are directly measured in the same area, invasive detection is not needed, and the deep thrombus quantitative measurement method is a nondestructive detection method and is convenient for clinical use; according to the method, near blood vessel signals are used as endogenous calibration factors, so that measurement errors caused by direct measurement of a deep thrombus area due to a spectrum coloring effect can be effectively calibrated, and a more accurate quantitative measurement result is obtained; the light absorption property of the overlying tissue of the lesion area does not need to be considered, so that the detection process is simplified, the calculation complexity is reduced, and the method is simpler and easier to use; a reconstruction algorithm and a calibration method based on simple calculation are used, priori knowledge and complex simulation calculation of a tissue environment are not needed, calculation resources are saved, the method has the advantages of being simple and easy to use, the accurate light absorption spectrum of the lesion thrombus in the deep tissue can be rapidly obtained, and the method is suitable for large-scale popularization and application. Therefore, a valuable reference is provided for clinical diagnosis and judgment of thrombus.

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 invention discloses a visual quantitative measurement system and method for an air flow angle at an outlet of a reverse thrust cascade, and belongs to the field of pneumatic technologies of engines. The device comprises a binocular photogrammetry system, a digital pulse delay signal generator, a computer, a square guide rail and a wrench sliding block, the binocular photogrammetry system is installed on the square guide rail in a sliding mode through the wrench sliding block, and the binocular photogrammetry system is connected with the digital pulse delay signal generator. An image acquired by the binocular photogrammetry system is input into the computer for storage, and the digital pulse delay signal generator is connected with the computer. In order to overcome the problem that the existing silk thread technology can only display visually and qualitatively, the quantitative measurement capability of the airflow angle at the outlet of the reverse thrust cascade of the whole machine of the reverse thrust full-ring performance test piece ground table is realized.

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.

A method and system for MRI may be provided. A plurality of signals of a subject generated using an MRI device may be acquired. Each signal may correspond to a set of values in a plurality of signal dimensions of signal acquisition. A primary signal dimension associated with a signal representation of the subject among the plurality of signal dimensions may be determined. A first value and a second value relating to the signal representation may be determined based on the plurality of signals. The first value may represent the signal representation along a first direction of the primary signal dimension, the second value may represent the signal representation along a second direction of the primary signal dimension, and the first direction may be opposite to the second direction. A value of a quantitative parameter of the subject may be determined based on the first value and the second value.

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.

The utility model discloses a coating water-adding proportioning device which comprises a blending and mixing barrel, a supporting frame is arranged above one side of the blending and mixing barrel, the top of the supporting frame is rotatably connected with a quantitative measuring barrel, scale marks are arranged on the outer wall of the quantitative measuring barrel and the outer wall of the blending and mixing barrel in the vertical direction, and the scale marks are arranged on the outer wall of the quantitative measuring barrel and the outer wall of the blending and mixing barrel. The quantitative measuring barrel and the blending and mixing barrel are both movably connected with liquid level detection pieces, and the positions of the liquid level detection pieces correspond to the positions of the scale marks. The water adding device is mainly used for adding water in the coating proportioning process, accurate proportioning can be achieved, and proportioning accuracy is improved.

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.

A device includes a holder. The holder has a body defining a longitudinal axis extending through a distal end and a proximal end. A rod has a stem with a distal tip and a proximal head. The stem is in slidable engagement with the holder. The rod is slidable relative to the holder toward the distal end to enable the distal tip to extend beyond the distal end of the holder. A pressure sensing unit is disposed at the proximal end of the holder. An intermediate component is elastically deformable between a first component end and a second component end. A pocket is defined between a membrane and the second component end. The pocket may include a pocket of air. The resistive sensor is disposed on the membrane and configured to output a signal responsive to a membrane deformation.

The present invention discloses ultra-high field T1, T2 and T based on magnetic resonance fingerprint 1ρ Quantitative measurement method, which can achieve rapid quantitative analysis of tissue T1, T2 and T under ultra-high field conditions 1ρ The present invention introduces T1, T2 and T in the signal evolution by adding a preparation module containing adiabatic pulses to the traditional MRF sequence. 1ρ Weight. Combined with the EPG method, the evolution of the magnetization vector was simulated, and a magnetic resonance fingerprint dictionary with different relaxation time value combinations was established. The reconstructed image acquired and reconstructed was matched with the magnetic resonance fingerprint dictionary to obtain T1, T2 and T 1ρ Quantitative graph. Compared with the traditional method, the method of the present invention does not need to collect multiple groups of signals separately for exponential fitting, which greatly shortens the scanning time.