38 results about "Particle type" patented technology

The invention discloses a radiation particle identification method based on an improved ResNet-18 network and a CIS transient response technology, and the method comprises the steps: collecting a CIS dark field image in a radiation environment, and generating a multi-modal data set according to neutron, proton and heavy ion radiation experiment sample images; a transient response image data set is obtained through theoretical calculation based on radiation analog simulation software; preprocessing the transient response image data set, and marking particle type, energy and angle information of each sample image as a label of multi-task learning; a pre-trained ResNet-18 model is used, an input layer is modified to adapt to the image size, and a channel attention module is added; and carrying out compression quantification on the model by adopting an optimizer in combination with gradient cutting through a self-adaptive category weight adjustment strategy. By constructing the intelligent feature extraction network, the system realizes automatic identification of transient response geometric features, and breaks through the bottleneck that a traditional method depends on artificially defined features.

The invention provides a cyclotron electrostatic deflection plate beam loss heat source reconstruction method, and relates to the technical field of particle accelerator thermal analysis and multi-physics field coupling calculation, and the method comprises the steps: 1, configuring beam dynamics simulation parameters, multi-particle tracking is carried out under a three-dimensional field graph containing a central area electromagnetic field, a harmonic coil magnetic field and an electrostatic deflection electric field, and a particle loss list is generated; step 2, transferring the particle loss list into a beam source file of a Monte Carlo radiation transport program, configuring Monte Carlo parameters including a deflection plate material, a boundary, a particle type and an energy spectrum, executing particle transport simulation, and obtaining a three-dimensional energy deposition matrix on an electrostatic deflection plate element; according to the method, high-resolution heat source reconstruction is realized, the peak temperature rise calculation error of the deflection plate can be reduced, a temperature field solving result can be fed back to optimize the particle source, and a reliable basis is provided for thermal structure design and cooling optimization of the electrostatic deflection plate.

The invention provides a method for improving accuracy of glass spontaneous explosion risk early warning, and relates to the technical field of glass processing.The particle type, the particle diameter and the depth of particles in the glass thickness direction are recognized through a high-power microscope, in addition, chemical components of the particles can be analyzed through an EDS, then heat treatment experiments are conducted on the particles, and the accuracy of glass spontaneous explosion risk early warning is improved. The relationship between glass self-explosion and particle type, size and depth direction is obtained, and the glass meeting the self-explosion condition can be selected before tempering, so that the situation that the glass is used on the wall after flowing to the next process is avoided, and the situation that the glass is split into sharp fragments during self-explosion after being used on the wall and possibly causes casualties or property losses is avoided; and particularly, the risk is obvious in scenes such as high-rise building curtain walls and bathroom partitions.

The present invention provides a method and system for determining the physical properties of arc discharge plasma in transformer oil, relating to the field of data processing technology. The implementation scheme comprises: performing a power-frequency arc discharge on the transformer oil to determine the initial particle concentration of each element when a plasma channel is formed in the transformer oil, and collecting the radiation light from the power-frequency arc discharge; performing spectral analysis on the radiation light, and determining the particle type, heavy particle temperature, electron temperature, and excited-state temperature of the plasma in the transformer oil based on the spectral analysis results; iteratively correcting the initial particle concentration of each element based on the particle type, heavy particle temperature, electron temperature, and excited-state temperature of the plasma in the transformer oil to obtain a target particle concentration for each element; and determining the physical properties of the plasma in the transformer oil based on the target particle concentration for each element. This method can accurately describe the physical properties of the plasma in the transformer oil.

The invention provides an adaptive multi-angle scattering ratio five-wavelength aerosol detection method. The adaptive multi-angle scattering ratio five-wavelength aerosol detection method comprises the following steps: S1, acquiring a multi-wavelength scattering light intensity signal; s2, scattering ratio parameter calculation; s3, classifying particle types; s4, angle-wavelength cooperative adjustment is carried out; and S5, fire alarm judgment. According to the method, different particle morphological characteristics are adapted through angle-wavelength dynamic cooperative adjustment, classification robustness is enhanced in combination with multi-algorithm fusion, the accuracy of fire early warning is finally improved, the false alarm rate is reduced, and the method is suitable for aerosol safety monitoring of scenes such as buildings and industrial places.

A system comprises a particle sensor for a vehicle comprising a light source that directs a beam to an interrogation region outside the vehicle; and an optical detector that receives scattered or reflected light from an aerosol particle in the interrogation region. A processor performs a method for particle classification comprising measuring an optical response from a single particle in the interrogation region by identifying an optical response peak for scattered or reflected light from the interrogation region; measuring an amplitude and duration of the optical response peak; correcting the duration of the optical response peak based on a vehicle airspeed; based on the measured amplitude of the optical response peak, generating an expected optical response duration for water droplet using a calibration table; analyzing the measured optical response duration and the expected optical response duration to determine particle classification; and generating classification probabilities for one or more particle types.

The invention relates to the technical field of visual detection of solid waste particles, in particular to an intelligent identification method and system for solid waste particles based on multi-modal fusion, and the method comprises the steps: firstly, carrying out the particle segmentation and particle size statistics of a solid waste infrared image, and calculating a particle adhesion evaluation value; dividing the solid wastes into low, medium and high adhesion grades; then, for different adhesion levels, channel weighted fusion guided by infrared images, or frequency domain fusion guided by RGB images, or instance level alignment fusion based on three-dimensional point cloud space anchor points is adopted to obtain multi-modal fusion features; and finally, the infrared image and the multi-modal fusion feature are mapped to a three-dimensional point cloud model to construct a three-dimensional solid waste particle model, accurate geometric dimensions and material features are obtained at the particle instance level, and the accuracy of solid waste particle type recognition and particle size measurement and the overall recognition efficiency are remarkably improved.

The application relates to a detection method and device for the concentration of particles in a cleaning device, a cleaning device and an apparatus. The method comprises the following steps: obtaining a target voltage value of a detection signal; when the target voltage value of the detection signal is less than a standard voltage value of a standard signal, determining a particle type corresponding to the target voltage value of the detection signal according to a difference between the standard voltage value of the standard signal and the target voltage value of the detection signal; obtaining cumulative values of at least one difference corresponding to the particle type in a preset period; and determining the concentration of the particle type according to the cumulative values, the maximum concentration and / or the minimum concentration of the corresponding type. The different particle concentrations can be accurately and stably obtained by analyzing and processing the data of different particle types in a certain period.

The invention relates to a simulation method for the influence of a radiation environment on the signal transmission characteristics of a microsystem device, and the method comprises the following steps: S1, constructing a three-dimensional model of the microsystem device, and splitting the three-dimensional model into independent grid files according to the types of materials; s2, setting a particle type, energy and an incident region in Geant4, simulating particle bombardment, and outputting single-particle energy deposition data, energy deposition distribution and linear energy transfer (LET) data; s3, establishing a material damage model in MATLAB, and calculating the post-radiation conductivity sigma (t) and relative dielectric constant epsilon r based on particle and substance action mechanism analysis; and S4, updating the material parameters calculated in the step S3 to a device model in the HFSS, simulating a signal transmission characteristic curve in a radiation environment, and analyzing an influence mechanism of radiation on signal transmission. The electrical performance of the micro-system device can be accurately evaluated in different radiation environments, a scientific basis is provided for design and optimization of the micro-system device in a high-radiation environment, and the anti-radiation capability and reliability of the device are effectively improved.

The application provides a simulation prediction method and system for slow positron source intensity in a research reactor, comprising the following steps: S1, reading arrangement information of components in a reactor core of the research reactor; S2, constructing a Monte Carlo model of the reactor core containing a slow positron source device, and obtaining neutron multi-group angular flux and photon multi-group angular flux information near the slow positron source generating device; S3, constructing a slow positron Monte Carlo model; S4, performing energy-angle double sampling to obtain an incident neutron energy probability density distribution function f n,ig,ia , an angle probability density distribution function f n,ia , an incident photon flux energy probability density distribution function f g,ig,ia , an angle probability density distribution function f g,ia , and a neutron-photon weight factor; S5, performing function sampling of particle types, energy and angle; and S6, obtaining slow positron distribution information generated in the slow positron generating device. The application accurately considers the whole life cycle of slow positron generation and transportation through multi-stage coupling, and realizes accurate prediction of slow positron source intensity.

There is disclosed a system for duality modulation separation of charged particle wave packets comprising a magnet cascade including a plurality of magnets arranged coaxially along a length of a beam path, a beam source coaxially aligned with the magnet cascade at an initial end of the beam path, the beam source providing a selected particle beam projected along the beam path; a particle deflection means located at a point along the beam path beyond the terminal end of a final magnet of the magnet cascade; wherein a selected particle emitted from the beam source travels along the beam path; wherein a significant characteristic fraction of a particle wave packet of the selected particle is an empty wave packet longitudinally separated from a particle-occupied wave packet along the beam path when the system is tuned with characteristic magnetic gradients and a characteristic particle beam velocity for the selected particle type.

The invention discloses an adjustable-frequency steel rail vibration absorption structure, device and method based on the particle damping technology, and the adjustable-frequency steel rail vibration absorption structure based on the particle damping technology comprises a fastening piece which is fixed to a steel rail through a fixing piece, and the fastening piece is provided with a vibration absorption face making contact with the steel rail; the two foundation mass blocks are symmetrically arranged on the two sides of the fastening piece through cantilever pieces; and the two damping bins are arranged at the ends, away from the fasteners, of the basic mass blocks correspondingly, storage spaces are formed in the damping bins, and the storage spaces are used for containing vibration absorption particles. The inherent frequency of the steel rail vibration absorption device is adjusted by accurately regulating and controlling the mass, rigidity and damping parameters of the vibration absorption structure and adjusting the type and filling rate of particles in the cylinder, effective energy dissipation is carried out on steel rail vibration of a corresponding frequency band generated by interaction between wheel rails, and the effects of restraining steel rail corrugation and reducing wheel rail noise are achieved.

A method for dynamically estimating a biological effect of a variable combination of non-photon radiation in accordance with a relative biological effectiveness, RBE, model including at least one biological effect multiplier δ(T,E) which depends on particle type T and / or particle energy E, the method comprising:obtaining one or more non-photon radiation contributions D(i)(T,E), 1≤i≤N, at least one of said contributions including multiple particle types and / or multiple particle energies;storing per-contribution dose-weighted averages δ(i), 1≤i≤N, of said at least one biological effect multiplier with respect to each of the one or more contributions; andin response to obtaining an assignment Π of the combination, the assignment being in terms of non-negative coefficients k1, k2, . . . , kN≥0 to be applied to the one or more contributions, determining a biological effect of the combination, including computing a combined dose-weighted average δΠ of said at least one biological effect multiplier on the basis of the stored per-contribution dose-weighted averages.

The present invention provides a method for real-time calculating the radiation effect of a satellite in its on-orbit operating environment, belonging to the technical field of satellite space environment analysis. The method includes: S1. Import an orbit environment file into an environment solver to obtain corresponding parameters; S2. Select a time period N, a particle type, and a vehicle in the orbit environment file, set a minimum cumulative radiation simulation calculation time k, integrate the particle flux within the k time to generate an energy spectrum file, and the number of energy spectrum files is the ratio of N to k; S3. Set analysis parameters and the number of running particles, select a target structure, generate a corresponding script file according to the energy spectrum file, and input the script file into the solver for sequential solution; S4. Perform summation and normalization processing on the solution results to generate a final result. The present invention can real-time simulate and calculate the radiation damage degree of a spacecraft caused by different types of radiation particles during any period of operation in different orbit environments, and supports simultaneous calculation by multiple solvers, greatly improving the calculation efficiency.

The invention belongs to the technical field of aero-engines, particularly relates to an oil abrasive particle detection device and method for a fuel oil system of a marine gas turbine, and aims to solve the problems of insufficient abrasive particle detection information and easy secondary pollution in the prior art. The device comprises a backwashing system and an abrasive particle separation and detection system, wherein the backwashing system realizes deep cleaning of a filter element through an ultrasonic massage box and a heat exchanger, and prevents secondary pollution of particles at wrinkles by adopting silicone oil paper; the abrasive particle separation system adsorbs ferromagnetic abrasive particles through a high-gradient magnetic field, and the image acquisition system shoots abrasive particle images through a transflective double-color light source and an automatic focusing objective lens. The method comprises the following three steps: firstly, separating abrasive particles through ultrasonic massage vibration and spray backwashing, then depositing the abrasive particles through a magnetic field, and finally, identifying the type of the abrasive particles through image processing and judging the wear degree. The method has the advantages of simplicity and convenience in operation, high detection precision and capability of qualitatively and quantitatively analyzing the abrasive particles, and is suitable for monitoring the wear state of a fuel system.

The invention discloses a particle classification and size measurement method and device based on interference imaging and improved UNet + + +, and belongs to the field of particle measurement and image processing. The method comprises the following steps: acquiring a spherical particle interference fringe pattern and an irregular particle interference speckle pattern, and constructing a data set containing a mask pattern and a category label; dividing into a training set, a verification set and a test set; the training set is used for training a pre-constructed neural network model, the pre-constructed neural network model takes an original UNet + + + network as a basis, a space and channel attention module, a frequency domain dynamic convolution unit and a multi-axis Hadamard convolution attention module are introduced, and the network is trained to output a reconstructed image and a category label at the same time; and inputting a to-be-measured particle image into the trained network, outputting a particle category and a reconstructed image, and measuring the reconstructed image to obtain size information. According to the method, high-precision, real-time and end-to-end prediction of particle types and sizes is realized, and the method is suitable for a mixed scene of spherical droplets and irregular ice crystals in cloud.

A system comprises a particle sensor for a vehicle comprising a light source that directs a beam to an interrogation region outside the vehicle; and an optical detector that receives scattered or reflected light from an aerosol particle in the interrogation region. A processor performs a method for particle classification comprising measuring an optical response from a single particle in the interrogation region by identifying an optical response peak for scattered or reflected light from the interrogation region; measuring an amplitude and duration of the optical response peak; correcting the duration of the optical response peak based on a vehicle airspeed; based on the measured amplitude of the optical response peak, generating an expected optical response duration for water droplet using a calibration table; analyzing the measured optical response duration and the expected optical response duration to determine particle classification; and generating classification probabilities for one or more particle types.

The invention discloses a radiation detection method and device, and belongs to the technical field of nuclear radiation measurement. According to the radiation detection method, pulse signals output by a plurality of photomultipliers can be converted into digital signals; multiple paths of first signals can be processed to extract pulse amplitude information, first integral processing is performed on the pulse amplitude information to determine a first integral value, and second integral processing is performed on the pulse amplitude information to obtain a second integral value; generating a two-dimensional distribution diagram according to the first integral value, the second integral value and the pulse amplitude information; and classifying the data points in the two-dimensional distribution diagram based on the identification curve so as to distinguish at least two particle types. According to the invention, the charge integration method is adopted to discriminate multiple particles, the waveform is subjected to two kinds of integration processing to obtain the first integration value and the second integration value, the peak value of the waveform is extracted, the integration mode is adopted to extract the characteristics of the whole waveform, the waveform acquisition error is reduced, and the particle separation precision is improved.

The invention discloses a transformer oil pollution degree evaluation method based on lensless imaging and machine learning, relates to the technical field of transformer oil detection, and is used for solving the problems that in the existing transformer oil particle pollution detection technology, the three-dimensional morphology of particles cannot be obtained, and a detection result and the pollution degree are lack of quantitative correlation. The method comprises the following steps: S1, obtaining a lens-free diffraction image of particles in a transformer oil sample to be detected; S2, reconstructing the amplitude of the diffraction image; S3, reconstructing the phase of the diffraction image and inverting the thickness; S4, segmenting and extracting a particle region; particle diffraction information is acquired in a lens-free imaging mode, synchronous acquisition of two-dimensional morphology and three-dimensional thickness information of particles is realized by combining amplitude reconstruction and thickness inversion, and automatic identification of particle types is realized by combining structured feature extraction with a machine learning model. And intelligent and quantitative evaluation of the pollution level of the transformer oil is realized.

The invention relates to the technical field of ionosphere electromagnetic environment detection, and discloses a method for measuring ionosphere electron density, which comprises the following steps: constructing an ionosphere model associated with an ionosphere D region and configuring initial conditions of the model; obtaining physical parameters associated with the ionosphere physical process under the initial condition of the model; wherein the physical parameters comprise an ion generation rate corresponding to a photochemical process and an ion generation rate and an ion loss rate corresponding to an ion chemical process; obtaining particle densities of different particle types according to the ion generation rate, the ion generation rate and the ion loss rate; and determining the electron density of the D region of the ionized layer according to the particle densities of different particle types. The method can accurately calculate the electron density of the D region of the ionosphere, and the accurate detection capability of the ionosphere electromagnetic environment is improved. The invention also discloses a device for measuring the electron density of the ionized layer, detection equipment and a storage medium.

The invention relates to the technical field of particle physical detection and high-energy density physical diagnosis, in particular to an electron positron ion spectrometer, which comprises a collimation unit, an aiming unit and a shell, and is characterized in that a magnetic field dispersion unit, an electric field separation unit and a detection unit are arranged in the shell; a magnetic field area is formed on one side, opposite to the collimation unit, of the magnetic field dispersion unit so as to perform energy expansion on incident charged particles along a first direction; the electric field separation unit is orthogonally arranged with the first direction and distinguishes particle types along the second direction; the detection unit adopts a curved surface structure conformal with dispersion geometry, so that particles are incident approximately vertically and aberration is suppressed, and a two-dimensional pattern representing energy in an arc length direction and representing particle types in an arc height direction is obtained. Through the synergistic effect of the magnetic field dispersion unit and the electric field separation unit, clear spatial separation of positive electrons, protons and electrons in a wide energy region can be realized; and the detection unit adopts a curved surface structure conformal with dispersion geometry, so that the accuracy of wide-energy-region energy spectrum data can be ensured.

This invention discloses a method, system, and storage medium for identifying the morphological features of lubricating oil abrasive particles, relating to the field of abrasive particle morphology recognition technology. The steps are as follows: acquiring a high-resolution abrasive particle image; performing preliminary feature extraction on the abrasive particle image using a pre-processing multi-channel encoding component to obtain preliminary features; further extracting the preliminary features using deep learning to obtain deep features; labeling the abrasive particle type and marking the saliency of the abrasive particle features; inputting the deep features with saliency markings into a multi-head self-attention mechanism for processing to obtain key features; performing multi-scale feature fusion on the key features using a BiFPN network, and combining it with the abrasive particle type feature map to achieve online detection of multi-scale abrasive particles. This invention can achieve high-precision automatic identification of various abrasive particles with good real-time performance, meeting the needs of intelligent operation and maintenance of mechanical equipment.

The application provides a cyclotron electrostatic deflection plate beam loss heat source reconstruction method, and relates to the technical field of particle accelerator heat analysis and multi-physical field coupling calculation, and the method comprises the following steps: step 1, configuring beam dynamics simulation parameters, performing multi-particle tracking under a three-dimensional field diagram containing a central area electromagnetic field, a harmonic coil magnetic field and an electrostatic deflection electric field, and generating a particle loss list; step 2, converting the particle loss list into a beam source file of a Monte Carlo radiation transport program, configuring Monte Carlo parameters, including deflection plate materials, boundaries, particle types and energy spectra, performing particle transport simulation, and obtaining a three-dimensional energy deposition matrix on the electrostatic deflection plate element. The application realizes high-resolution heat source reconstruction, can reduce the calculation error of the peak temperature rise of the deflection plate, and can feed back the temperature field solving result to optimize the particle source, thereby providing a reliable basis for the thermal structure design and cooling optimization of the electrostatic deflection plate.

The application relates to a transformer oil pollution degree evaluation method based on lens-free imaging and machine learning, relates to the technical field of transformer oil detection, and is used for solving the problems that the existing transformer oil particle pollution detection technology cannot obtain particle three-dimensional morphology and the detection result lacks quantitative correlation with the pollution degree. The application comprises the following steps: S1, acquiring a lens-free diffraction image of particles in a transformer oil sample to be detected; S2, amplitude reconstruction of the diffraction image; S3, phase reconstruction and thickness inversion of the diffraction image; S4, particle region segmentation and extraction; S5, structured feature extraction; S6, particle classification; and S7, pollution degree grade evaluation. The application realizes synchronous acquisition of particle two-dimensional morphology and three-dimensional thickness information by acquiring particle diffraction information in a lens-free imaging mode, combining amplitude reconstruction and thickness inversion, realizes automatic identification of particle types by combining structured feature extraction with a machine learning model, and realizes intelligent and quantitative evaluation of the pollution degree grade of transformer oil.

The invention provides a method and a system for simulating and predicting the intensity of a slow positron source in a research reactor. The method comprises the following steps: S1, reading arrangement information of components in a reactor core of the research reactor; s2, constructing a research reactor core Monte Carlo model containing a slow positron source device, and obtaining neutron multi-group angle flux and photon multi-group angle flux information near a slow positron source generation device; s3, constructing a slow positron source Monte Carlo model; s4, performing energy and angle double sampling to obtain incident neutron energy probability density distribution functions fn, ig and ia, angle probability density distribution functions fn and ia, incident photon flux energy probability density distribution functions fg, ig and ia, angle probability density distribution functions fg and ia and neutron and photon weight factors; s5, function sampling of particle types, energy and angles is carried out; and S6, acquiring slow positron distribution information generated in the slow positron generation device. According to the method, the generation of the slow positron and the full life cycle of transportation are accurately considered through multi-stage coupling, and accurate prediction of the intensity of the slow positron source is realized.

The invention discloses a suspension particle size monitoring and dynamic regulation and control method, which adopts a polarizing microscope to monitor the dissolution state of an auxiliary material in a suspension, combines with a laser particle analyzer to obtain particle size distribution data, and adjusts production process parameters. The method specifically comprises the following steps: step 1, observing the particle size and form of each original sample by a polarizing microscope; step 2, preparing a suspension according to a process formula, sampling in the preparation process, and observing the particle size, shape, distribution condition, agglomeration and caking phenomenon and polarization state of particles in the suspension by using a polarizing microscope; step 3, judging the type of the particles in the sample by comparing the particle size and form of the original sample in the step 1; observing the dissolution state of the auxiliary materials; and 4, testing the particle size and particle size distribution of the sample. The combined method can help to more scientifically adjust process parameters, eliminate the influence of incomplete dissolution of auxiliary materials on the particle size of the finished product, and significantly reduce the particle size of the finished product.

This invention discloses a method and apparatus for determining particle types, a smoke detector, and an electronic device. The method includes: emitting red and blue light towards a target area, and acquiring a first signal value corresponding to the red light and a second signal value corresponding to the blue light; determining the target particle type of the air in the target area using the first and second signal values. This technical solution solves the problem of inaccurately detecting the particle type of airborne particles.

The application discloses a kind of based on multi-view feature's moving abrasive particle identification method and related device, method includes obtaining moving abrasive particle video, and obtains abrasive particle multi-view image in moving abrasive particle video;Area change curve, length-width ratio change curve and roundness change curve are determined based on multi-view image;Based on the feature change curve of each acquisition identifies the abrasive particle type of moving abrasive particle.This application obtains the video of moving abrasive particle, then determines multi-angle image according to moving abrasive particle video, can obtain the multi-view information of abrasive particle by multi-angle image, can quickly determine the abrasive particle quantity and abrasive particle type contained in wear tissue fluid, so as to can improve abrasive particle analysis efficiency.Meanwhile, the feature change curve of area, length-width ratio and circularity of each view image is obtained in the application, and the abrasive particle type is identified by fusing each feature change curve, so that the accuracy and identification speed of abrasive particle type identified can be guaranteed.

A system and procedure are provided for collecting and analyzing airborne particles at a user's site. The system includes a blower module for mobilizing dust from surfaces, a sampling system with an integrated vacuum pump for collecting particles on a slide for microscopic examination, and a mobile microscope for analyzing the collected particles. The system also includes an artificial intelligence (AI) module for classifying and counting particle types, and an app connectivity module for wireless communication with a mobile device. The procedure involves using the powerful blower to remove dust from surfaces, collecting particles on a slide, and analyzing the collected particles using the mobile microscope and the AI ​​modules.The system enables the analysis of airborne particles and allows users to detect and monitor particle concentrations in their environment. The system is delivered in a shipping / packaging box.