5 results about "Particle type" patented technology

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.

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.

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.

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.

The present application relates to a kind of particle identification and detection method based on deep learning improved algorithm, it includes the following steps: using electric sensitive area method obtains the electric signal of multiple particle;Extract the time domain, frequency domain and time-frequency domain characteristic parameters of electric signal;Characteristic parameters are combined to enhance the difference between characteristics;Screening characteristic parameters to reduce the amount of operation;Neural network and recurrent neural network model are weighted fusion;Using fusion model to test multiple repeat for the particle to be measured, using voting mechanism to determine the final classification result;The concentration, quantity proportion and particle size distribution of each kind of particle are output.The particle identification and detection method provided by the present application can realize the measurement of particle type, concentration and particle size distribution in water, improve the accuracy, accuracy and stability of water quality monitoring, provide more comprehensive information for water plant operation and environmental monitoring, so that the particle identification and detection method can be better applied to practical application.