7results about How to "Accurate inference" patented technology

The present application relates to a method and system for inferring the age of an individual from semen or a semen stain, and a methylation detection kit, which comprises the following steps: firstly, extracting the genomic DNA of the semen or semen stain of the individual; secondly, performing bisulfite treatment on the genomic DNA; thirdly, selecting a plurality of sites in the CpG sites in the genomic DNA and amplifying the selected CpG sites to obtain the methylation rate of the selected CpG sites; and finally, performing regression analysis on the methylation rate of the selected CpG sites and the age of the individual to construct a regression model for inferring the age of an unknown individual from semen or a semen stain. The average absolute error of the present application reaches 1.68 to 4.44 years.

The application discloses a limb action disassembly capturing system based on multi-modal analysis and relates to the technical field of limb action disassembly capturing, and solves the technical problem that in the prior art, the limb action disassembly capturing cannot be selected through multi-modal acquisition and analysis to improve the capturing efficiency of the limb action, specifically, a multi-modal analysis module records the whole limb action and selects the action through multi-modal analysis to obtain a multi-modal analysis period; after the multi-modal analysis period is obtained, an action disassembly recognition unit analyzes the action execution subject of the multi-modal analysis period; an action execution track is obtained according to the analysis; after the action track is set, the action track is sent to an action track distinguishing unit; after receiving, the action track distinguishing unit distinguishes the action track of the action execution subject.

The application provides a target two-dimensional spatial orientation relationship identification method based on a spatial orientation vector field, relates to the technical field of visual relationship identification, and comprises the following steps: constructing a target two-dimensional spatial orientation relationship identification network model based on a spatial orientation vector field; constructing a supervision label and a loss function of the target two-dimensional spatial orientation relationship identification network based on the spatial orientation vector field; presetting four types of two-dimensional spatial orientation basic relationships, collecting image data sets, completing labeling and data set division, iteratively optimizing network model parameters through the labeled training set and the composite loss function, adjusting hyperparameters with a verification set, and completing identification model training; inputting a to-be-identified image into the trained identification model, respectively analyzing output data of a target center point detection head and a spatial orientation detection head, calculating a vector field score between any two targets, determining a two-dimensional spatial orientation relationship between the targets through a preset threshold, and outputting a final identification result in a triple format.

This invention discloses a method and system for joint prediction of meteorological, wind, solar, and energy storage power based on ultra-long context, belonging to the field of power system prediction technology. The method constructs a multi-agent collaborative architecture for data cleaning, time alignment, context generation, and joint prediction, achieving automatic processing and efficient organization of historical data on wind power, solar power, and energy storage. By introducing an ultra-long context input mechanism spanning seasons and years, it overcomes the limitations of traditional model context length, achieving unified modeling and collaborative representation of multi-source data from different frequencies. Based on this, it utilizes a large time-series model to conduct zero-sample joint prediction, and combines physical constraints such as wind power curves, solar irradiance, and energy storage state of charge to construct a wind-solar-storage coupling consistency correction mechanism to optimize the prediction results. This invention effectively improves the multi-source covariate fusion capability and prediction accuracy, possessing good generalization performance and engineering adaptability.

The application provides a kind of based on physical drive deep learning's non-diffraction computing ghost imaging optimization method, using the deep learning method of cyclic consistent generative adversarial network model based on physical drive inhibits the image quality degradation caused by quasi-non-diffraction speckle point spread function multi-stage side lobe, improves the imaging quality and depth of field of computing ghost imaging, comprising: using annular phase template generates quasi-non-diffraction speckle;The binary image data set is reconstructed to obtain the initial reconstructed image;The real physical imaging degradation process is embedded into the network as an explicit physical priori, a physical driven double cycle generative adversarial network model is constructed and trained;The transmission light intensity of the actual target is collected by using a single-pixel detection system and is correlated and reconstructed, and the reconstructed image with high-order oscillation is input into the trained network model, and finally a high-quality target image is reconstructed.The application significantly removes the high-order oscillation caused by the side lobe of quasi-non-diffraction speckle point spread function, while maintaining the advantage of large depth of field, realizes high-resolution and high-quality image reconstruction.This also provides a new idea for the subsequent research of long-distance imaging of computing ghost imaging.

The present application belongs to the technical field of fatigue fracture quantitative analysis, and relates to a single crystal high-temperature alloy low-cycle fatigue expansion stress fracture quantitative backstepping method based on fatigue strip features. First, the low-cycle fatigue test of a sample is carried out, and the change of stress data is monitored during the fatigue test process. Through fracture feature analysis, the fatigue initiation and expansion stages are divided. The fatigue expansion life fracture quantitative backstepping of the modeling sample is based on the modeling of the fatigue strip spacing. The fatigue total life corresponding to different crack lengths of the modeling sample is analyzed. The stress range corresponding to different crack lengths of the modeling sample is determined. The low-cycle fatigue expansion stress fracture quantitative backstepping model is established. The low-cycle fatigue expansion stress fracture quantitative analysis model is applied. The method solves the problem that the direct use of the stress intensity factor range K to characterize the low-cycle fatigue fracture feature may not be suitable or have a large error, and provides an effective new method for realizing the low-cycle fatigue fracture quantitative backstepping of the fatigue expansion stress.

This application discloses a method, apparatus, and equipment for acquiring the vertical input force at the wheel center of an air suspension. The method includes: constructing a load transfer inverse model and a full-condition error compensation parameter library through bench calibration tests; acquiring the vertical displacement parameters, vertical load parameters, and vehicle condition identification parameters of the air spring; classifying the operating conditions according to the vertical displacement parameters, vertical load parameters, and vehicle condition identification parameters to obtain the current operating condition; obtaining the current compensation parameters corresponding to the current operating condition according to the full-condition error compensation parameter library; inputting the vertical displacement parameters and vertical load parameters into the load transfer inverse model, and obtaining the wheel center vertical input force corresponding to the current operating condition according to the current compensation parameters. This application can acquire the vertical input force at the wheel center of an air suspension without additional hardware costs, improves the accuracy of wheel center vertical input force acquisition, and can cover the entire working range of the air suspension, which can be widely used in the field of automotive testing technology.