57 results about "Linear regression function" patented technology

The invention relates to a lithium ion battery, and further relates to a lithium ion battery service life prediction method. The method comprises the following steps of collecting the running data ofthe in-service or retired battery of the same model, and establishing a database which comprises the battery operating temperature, the battery discharge rate, the battery internal resistance and thetotal service life parameter; establishing a battery service life prediction linear regression function model: h (x) = h theta (x) = theta 0+theta 1*1+theta 2*2 + theta 3*3, and the operating temperature, the discharge rate, the internal resistance of the battery in a specified model are substituted into the regression model, and the total service life of the battery is obtained. The operating temperature, the discharge rate and the internal resistance of the battery are key factors affecting the service life of the battery, and the operating temperature, and by introducing the discharge rateand the internal resistance of the battery to be used as the influence parameters of the battery service life, the modeling prediction is effective.

The invention relates to a sea ice information extraction method, which comprises the following steps of: preprocessing acquired photon data, further classifying sea ice information in combination with a calculation result, calculating a sea ice freeboard height value according to a classification condition, optimizing an existing accumulated snow depth measurement model result, and according to other sea ice information parameters, calculating the sea ice thickness value by combining the Archimedes law, a linear regression function and other calculation formulas. Compared with the prior art, after the satellite initial data are corrected, subsequent processing and screening are carried out by using relatively accurate data, the data are further limited, and the utilization rate of the data is improved.

The invention relates to a single change point detection method, system and device and a storage medium. The method comprises the steps of setting a time series data set, and generating a corresponding linear regression function expression; utilizing a least square method to obtain a linear regression parameter value of the linear regression function expression; obtaining a variable point T statistic expression and a variable point D statistic expression according to the linear regression parameter value; according to the change point T statistical magnitude expression and the change point D statistical magnitude expression, obtaining a change point maximum statistical magnitude; and obtaining single change point detection information according to the distribution area of the change pointmaximum statistics in hypothesis testing. According to the invention, the change point of the time sequence with a long-term trend can be detected, and the change point detection accuracy is higher than that of the existing detection method.

The invention discloses a weighing-free AI intelligent identification truck overload estimation method, device and system. The method comprises the steps: firstly, obtaining the average deformation coefficients of rear wheel truck tires of the same type of vehicles under different load capacities, and carrying out fitting to obtain a statistical data linear regression function model that the corresponding average deformation coefficients of the tires change along with the change of the load capacities; determining a truck load upper limit value and a multiple load value according to the trucktype, and substituting the truck load upper limit value and the multiple load value into the function model to obtain a corresponding truck overload tire deformation coefficient value and a multiple overload deformation coefficient value; and when the vehicle to be detected passes through the video detection point, calculating the average deformation coefficient of the rear wheel truck tire according to the deformation horizon length of the rear wheel tire and the radius of the truck tire, comparing the average deformation coefficient with a correlation numerical value, and judging whether thevehicle is an overload or multiple overload suspected vehicle. The invention aims to solve the problems of low cost, no lane guidance, no-dead-corner deploying and controlling intelligent monitoringoverload of various levels of highway networks, especially severe overload vehicles, in the aspects of technicality and economy.

The invention discloses a soil volume moisture content test method based on electromagnetic wave time-domain reflection method, which prepares 3-5 soil samples with different dry densities through sample preparation, and then passes a series of mass moisture content standard values, The mass water content conversion value wi of the electromagnetic wave time domain reflectometry and the test of the dry density are then fitted to obtain the relative error ηi of the mass water content and the linear regression function ηi=aρdi+b of the corresponding soil sample dry density ρdi; and draw The standard value of moisture content of the soil and the calibration coefficient ζ=1-aρd-b of the measured value of the electromagnetic wave time-domain reflectometry. Then use the electromagnetic wave time domain reflectometry to measure the volume moisture content θ of the soil to be tested, and then obtain the corrected value θ′ of the soil volume moisture content measured by the electromagnetic wave time domain reflectometry, θ′=ζθ. At the same time, the method eliminates the influence of different soil types and dry density changes on the volumetric moisture content measured by the electromagnetic wave time domain reflection method, significantly reduces the error of the test results, and the test results are accurate and reliable. So as to provide more accurate and reliable test parameters for the design and construction of geotechnical engineering.

Method for the estimation of at least a variable (β; νx, νy; ψ, μ) affecting a vehicle dynamics (10), including measuring dynamic variables (MQ) of the vehicle (10) during its motion, calculating in real time an estimate (Formula (I)) of said variable (β; νx, νy; ψ, μ), on the basis of said measured dynamic variables (MQ), The method includes: calculating (230) said estimate of said at least a variable (β; νx, νy; ψ, μ) by an estimation procedure (DVSβ; DVSβν,-DVSβνμ) comprising taking in account a set of dynamic variables (MQ) measured during the motion of the vehicle (10) over respective time intervals (ny, nw, nψ, nx, nα) and applying on said set of measured dynamic variables (MQ) at least an optimal nonlinear regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒψ2) calculated with respect to said variable (β; νx, νy; ψ, μ) to estimate to obtain said estimate of said variable (β; νx, νy; ψ, μ), said optimal non linear regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒ*ψ2) being obtained by an optimal calculation procedure (220) including: on the basis of an acquired set of reference data (Dd) and of said set of dynamic variables (MQ) measured during the motion of the vehicle (10), finding, for a desired accuracy level (ε), a regression function (ƒ*β; ƒ*x, ƒ*y; ƒ*β1, ƒ*β2, ƒ*ψ1, ƒ*ψ2) giving an estimation error lower or equal than said desired accuracy level (ε) in a given set of operative conditions (OC), said acquired set of reference data (Dd) being obtained by acquiring (210) in said given set of operative conditions (OC) a set of reference data (Dd) of variables including variables corresponding to said measured dynamic variables (MQ) of the vehicle (10) and a lateral (vy) and a longitudinal velocity (vx) of the vehicle (10).