16 results about "Particle number" patented technology

Embodiments of the present application disclose a method for determining the number of effective particles, the method comprising: obtaining the total number of particles of a detection sample and pulse signals of each particle; performing waveform feature analysis based on the pulse signals to obtain waveform feature parameters of each particle, the waveform feature parameters at least including: a waveform feature type of the particle and a waveform feature value corresponding to the waveform feature type; generating a feature parameter distribution map of the particles in the detection sample according to the waveform feature type of each particle and the waveform feature value corresponding to the waveform feature type; determining an effective area of the feature parameter distribution map and the number of effective particles in the effective area. The waveform feature parameters of all the particles in the detection sample are counted to obtain the feature parameter distribution map, and the number of effective particles in the effective area of the feature parameter distribution map is further obtained, so that the number of particles that are misdetected as effective particles and not in the effective area is excluded, and a more accurate number of effective particles is obtained.

The invention discloses a moving abrasive particle detection and tracking method based on fusion of a Gaussian mixture model (GMM) and a watershed algorithm, which is characterized in that on the basis of acquiring abrasive particle moving targets based on the Gaussian mixture model (GMM), each extracted target is segmented by using the watershed algorithm based on distance transformation, so that the adhered abrasive particle targets can be accurately detected, and the detection accuracy is improved. Algorithm verification is carried out on abrasive particle videos in different stages, the detection accuracy of the abrasive particle adhesion target can reach 98.86%, the number, size and other characteristics of abrasive particles can be accurately extracted, and abrasion faults can be analyzed easily. In addition, on the basis of accurate detection of the adhered abrasive particle target, the SORT algorithm is further applied to track the multi-target abrasive particles in real time, and the result shows that the tracking effect of the adhered abrasive particle target is good.

According to the discrete solution method for particle number balance calculation in the particle coalescence and crushing process, the total extinction item and the correction new item on the feature points of the discrete interval are obtained, then the particle number distribution of the updated discrete interval is obtained, and finally the particle number density and the order particle number moment at the moment are obtained. And discrete solution for particle number balance calculation in the particle coalescence and crushing process is completed. The particle size is dispersed into a plurality of intervals, and each interval is provided with a plurality of feature points to reconstruct particle size distribution and a particle number moment, namely, the particle size distribution and the particle number moment are synchronously predicted; new items of all feature points are distributed according to new particle moments, and conservation of high-order moments is guaranteed; the number of particles is updated through the relative distance and the threshold value in the discrete interval, and the physical effectiveness of the number of particles is ensured.

The invention discloses a method for calibrating the conversion relation between the particle number and the mass concentration, and the method comprises the following steps: for aspheric particles, introducing a voltage correction coefficient through employing the concept of equivalent diameter, and obtaining a calculation formula of the mass concentration of the aspheric particles; in a test environment, the number of voltage pulses of the particulate matter sensor within a period of time is collected, the average value of peak voltage of each pulse is solved, and meanwhile, the PM2.5 mass concentration is collected by using a standard instrument; after the calibration coefficient and the proportionality coefficient are calculated and obtained by combining the mass concentration measured by the standard instrument and the quantity concentration detected by the particulate matter sensor, the calibration coefficient and the proportionality coefficient are substituted into a formula to calculate the mass concentration corresponding to the quantity concentration detected by the particulate matter sensor, and meanwhile, the measurement precision of the particulate matter sensor is guaranteed when the particulate matter concentration changes suddenly.

The application relates to a target detection pre-tracking method and system based on fuzzy control optimized particle filtering, and belongs to the field of target tracking. Particle states are predicted, and particle weights are calculated, the particle with the largest weight is regarded as an optimal particle, and the space near the optimal particle is regarded as an optimal region; the distance between the particle and the optimal particle and the particle ratio in the optimal region are calculated and taken as inputs of a fuzzy controller, a moving coefficient is output after a process of fuzzification, fuzzy reasoning and defuzzification; the particle state is updated according to the moving coefficient, and the particle with a lower weight is moved to a high-likelihood region; the iteration is performed for multiple times until the number of particles in the optimal region exceeds a threshold value; and the state of the target is estimated according to the particle state. The application can effectively alleviate the particle impoverishment problem, improve tracking accuracy, control particle movement through the fuzzy controller, be adaptively applied to different environments and scenes, and has good robustness and interpretability, and the rules can be expressed in natural language.

A discrete solution method for particle number balance in the process of particle coalescence and breakage is provided, total extinction items and modified new generation items on the characteristic points of discrete intervals are obtained, then the particle number distribution of the updated discrete interval is obtained, finally the particle number density and order particle number moment at the moment are obtained, and the discrete solution for particle number balance in the process of particle coalescence and breakage is completed. By discretizing the particle size into several intervals, multiple characteristic points are set in each interval to reconstruct the particle size distribution and particle number moment, that is, to simultaneously predict the particle size distribution and particle number moment; the new generation items of each characteristic point are distributed according to the new generation particle moment, so as to ensure the conservation of high-order moment; the particle number is updated through the relative distance and threshold value in the discrete interval, so as to ensure the physical validity of the particle number.

This invention discloses a machine learning-based method and system for predicting particle migration and blockage in fractures. The method includes the following steps: First, preset data on fracture roughness coefficient (JRC), flow velocity, equivalent particle size, and particle number; construct a fracture model with realistic shape and conduct a visualization migration experiment; record fracture blockage label data; input the parameters and labels into a neural network binary classification probability model with a multilayer perceptron as its core, and obtain a blockage probability prediction function through training; based on the trained model, perform sensitivity analysis and feature importance evaluation, and output the ranking of the impact of blockage probability on JRC, flow velocity, equivalent particle size, and particle number, thereby achieving the prediction of particle blockage events within fractures. This invention studies the particle blockage mechanism inside fractures under the coupling effect of multiple factors, providing a scientific basis for the design and construction of geotechnical engineering.

The invention discloses a particle number state prediction method and device, equipment and a medium. The method comprises the steps of performing data prediction on current state information based on a target system model corresponding to a target particle counter and target noise parameter information to determine a basic state prediction value and a basic measurement prediction value corresponding to a target sample; performing covariance calculation on the basic state prediction value based on a preset singular value decomposition method to determine a prediction covariance error, and performing residual calculation on the basic measurement prediction value based on a preset residual rule to determine a measurement prediction error; and performing gain calculation on the prediction covariance error based on a preset adjustment fusion strategy to determine a target gain, and performing state updating on the basic state prediction value based on the target gain and the measurement prediction error to obtain a target state prediction value corresponding to the target sample. According to the technical scheme, the influence caused by multiplicative noise can be effectively reduced, and the accuracy of a particle number state result is improved.

The embodiment of the invention discloses a high-pressure gas switch arc plasma particle component prediction method, system, medium and equipment, and the method comprises the steps: constructing a thermochemical database, and storing the thermodynamic parameters of SF6, air, fluorocarbon and dissociation / ionization decomposition products thereof, and metal steam; the method comprises the following steps: performing atomic-scale decomposition on a detected arc extinguishing medium to identify basic elements, extracting metal vapor, neutral particles containing the elements and charged ions generated by ionization of the neutral particles in combination with a database, and forming a candidate particle list; constructing a reaction equation library (reactants and products are from candidate lists) on the basis of mass conservation, and determining dominant reaction paths under different temperatures and pressures; determining particle number density distribution during balance according to the dominant path, constructing a neural network model taking temperature pressure as input and number density as output, defining a loss function and completing training; and finally, inputting the current arc temperature pressure to quickly obtain the corresponding population density distribution so as to provide support for the optimization of the arc extinguishing performance of the high-voltage switch.

The invention provides a steady-state quantum turbulence generation method and a steady-state quantum turbulence generation system capable of guaranteeing particle number conservation. The method comprises the following steps: constructing an evolution model of the quantum fluid system, wherein the evolution model is obtained by introducing an external driving item and a particle number conservation dissipation item based on a Gross-Pitaevskii equation; the external driving item is an additional external potential of spatio-temporal variation and is used for injecting energy into the system; the particle number conservation dissipation item depends on the time change rate of the particle number density and is used for dissipating the energy of the system; the external driving item and the particle number conservation dissipation item are configured to keep the total particle number conservation of the system; and performing time evolution on the quantum fluid system based on the evolution model until the system reaches a steady-state quantum turbulence state of total kinetic energy dynamic balance. According to the method, the loss of chemical potential compensation particles does not need to be adjusted, quantum turbulence structures of different scales can be stably generated and regulated, and the physical self-consistency and long-term stability of the quantum fluid evolution process are guaranteed.

The application relates to an aerogel microstructure reconstruction method and system based on potential energy driving, and belongs to the technical field of aerogel materials, solving the problems of low aerogel microstructure reconstruction precision and difficult porosity control in the prior art. The method comprises the following steps: generating a target particle based on aerogel particle characteristics, generating current Brownian motion parameters based on a random diffusion model, calculating aggregation potential when the target particle after displacement based on the current Brownian motion parameters contacts an existing aggregated particle cluster, and determining the target particle and the existing aggregated particle cluster as a current aggregated particle cluster when the potential energy threshold is reached; calculating the number of particles in the current aggregated particle cluster, calculating the current porosity of the current aggregated particle cluster when the contact particle number requirement is met, and outputting the microstructure of the current aggregated particle cluster as the microstructure of the reconstructed target aerogel if the target porosity is reached. The effective control and accurate reconstruction of the porosity of the aerogel microstructure are realized.

To provide a method for measuring the number of fine particles capable of accurately measuring the number of fine particles in a liquid by eliminating the influence of air bubbles by a simple means.SOLUTION: The method for measuring the number of fine particles in a liquid includes the following steps: (1) A step of filling a vessel with a liquid such that a length from a spout for pouring the liquid into the vessel to an inner face of the vessel (drop length of the liquid) is in a range of 30 to 90mm and a Reynold's number (Re) satisfies 2200 ≤ Re ≤ 3200. (2) A step of allowing the container filled with the liquid to stand for 1 hour or more. (3) A step of passing water through the pipe from the liquid injection port to the particle size detection unit to wash the 750ml or more in the fine particles counter. (4) A step of sending the liquid allowed to stand in the step (2) from the liquid injection port of the fine particle counter to the particle detection unit, and measuring the number of fine particles in the liquid in the particle detection unit.SELECTED DRAWING: None

This invention belongs to the field of molecular dynamics, specifically relating to a method for calculating short-range interactions between particles based on a GPU. The method includes: designing Map and Reduce operators; dividing the particle space into grids and preprocessing them; dividing the particles into regions; assigning particles to each thread of the GPU according to their particle number and position; selecting all particles in the S-sphere and calculating the short-range forces in the S-sphere using Map and Reduce operations; randomly and uniformly selecting a portion of particles in the CS-sphere and calculating the short-range forces in the CS-sphere using Map and Reduce operations; and combining the short-range forces in the S-sphere and CS-sphere, along with a correction term, to calculate the actual short-range forces of the particles. This invention efficiently calculates short-range interactions between particles using a mainstream GPU architecture. By introducing random forces through sphere partitioning, it significantly reduces the number of nearest-neighbor particles, thereby reducing computational complexity and greatly improving computational efficiency.

A computing device including one or more processing devices configured to receive classical shadow measurement results associated with a fermion wavefunction. The fermion wavefunction may describe a particle-number-conserving fermion system. The one or more processing devices may compute an estimated k-reduced density matrix (k-RDM), where k is a number of ladder operator pairs. Computing the estimated k-RDM may include computing, as terms of the estimated k-RDM, traces of respective products of a number operator of the particle-number-conserving fermion system, a single-particle basis rotation matrix dependent upon the classical shadow measurement result, elementary symmetric polynomials of Majorana bivectors, and a conjugate transpose of the single-particle basis rotation matrix. Computing each trace may include iteratively computing derivatives of a Pfaffian. The one or more processing devices may output the estimated k-RDM.

The invention discloses a soot breaking modeling and predicting method based on a bivariate moment projection algorithm, and relates to the field of particle system dynamics modeling. According to the method, a bivariate group balance equation containing starting, growing, shrinking, condensing and crushing source items is constructed, an improved crushing model containing the particle size effect and dimension conservation is designed, the equation is converted into a bivariate moment evolution equation, and closed solving is achieved through a bivariate moment projection method (BVMPM). The method is characterized in that a weighted particle system is constructed through a two-dimensional Blumstein-Wheeler algorithm, coordinates of particles with the minimum size are fixed to track the number of the particles with the minimum size, and the problem that a moment equation is not closed is solved. The method breaks through the limitation of single-variable description, accurately captures the characteristics of the soot fractal aggregate, gives consideration to the calculation efficiency and the prediction precision, and is suitable for scenes such as engine emission control and fire radiation simulation.

The invention relates to a radiation field acceleration calculation method and system based on a denoising neural network, processing equipment and a storage medium, and the method comprises the steps: obtaining a low particle number MC simulation result in a to-be-detected scene, and carrying out the preprocessing; the preprocessed low-particle-number MC simulation result is input into a pre-trained and optimized deep radiation field denoising neural network model, a high-particle-number MC simulation result in the scene to be measured is obtained, radiation field acceleration calculation is completed, the deep radiation field denoising neural network model is obtained by adopting an improved UNet convolutional neural network, and the depth radiation field denoising neural network model is obtained by adopting an improved UNet convolutional neural network. The method has breakthrough improvement in calculation efficiency, the prediction result completely meets the precision requirement of engineering application, and the method has excellent generalization performance and can be widely applied to the technical field of nuclear application.