Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is generated.Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor.The experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.

Nonlinear corrections for the nuclear gluon distribution in eA processes

An analytical study with respect to the nonlinear corrections for the nuclear gluon distribution function in the next-to-leading order approximation at small x is presented.We consider the nonlinear corrections to the nuclear gluon distribution functions at low values of x and Q^(2) using the parametrization F_(2)(x,Q^(2))and the nuclear modification factors obtained from the Khanpour-Soleymaninia-Atashbar-Spiesberger-Guzey model.The CT18 gluon distribution is used for the baseline proton gluon density at Q^(2)_(0)=1.69GeV2.We discuss the behavior of the gluon densities in the next-to-leading order and the next-to-next-to-leading order approximations at the initial scale Q^(2)_(0),as well as the modifications due to the nonlinear corrections.We find that the QCD nonlinear corrections are more significant for the next-to-leading order accuracy than the next-to-next-to-leading order for light and heavy nuclei.The results of the nonlinear GLR-MQ evolution equation are similar to those obtained with the Rausch-Guzey-Klasen gluon upward and downward evolutions within the uncertainties.The magnitude of the gluon distribution with the nonlinear corrections increases with a decrease in x and an increase in atomic number A.

Prediction of separation flows around a 6:1 prolate spheroid using RANS/LES hybrid approaches

This paper presents hybrid Reynolds-averaged Navier-Stokes （RANS） and large-eddy-simulation （LES） methods for the separated flows at high angles of attack around a 6：1 prolate spheroid. The RANS/LES hybrid meth- ods studied in this work include the detached eddy simulation （DES） based on Spalart-Allmaras （S-A）, Menter＇s k-ω shear-stress-transport （SST） and k-o9 with weakly nonlinear eddy viscosity formulation （Wilcox-Durbin＋, WD＋） models and the zonalANS/LES methods based on the SST and WD＋ models. The switch from RANS near the wall to LES in the core flow region is smooth through the implementation of a flow-dependent blending function for the zonal hybrid method. All the hybrid methods are designed to have a RANS mode for the attached flows and have a LES behavior for the separated flows. The main objective of this paper is to apply the hybrid methods for the high Reynolds number separated flows around prolate spheroid at high-incidences. A fourth-order central scheme with fourth-order artificial viscosity is applied for spatial differencing. The fully implicit lower-upper symmetric-Gauss-Seidel with pseudo time sub-iteration is taken as the temporal differentiation. Comparisons with available measurements are carried out for pressure distribution, skin friction, and profiles of velocity, etc. Reasonable agreement with the experiments, accounting for the effect on grids and fundamental turbulence models, is obtained for the separation flows.

A Lightweight Model of VGG-U-Net for Remote Sensing Image Classification

Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other aspects.In this paper,we propose a lightweight model combining vgg-16 and u-net network.By combining two convolutional neural networks,we classify scenes of remote sensing images.While ensuring the accuracy of the model,try to reduce the memory of themodel.According to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 MB.At the same time,The classification and convergence speed of the model are greatly improved.We simultaneously take the remote sensing scene image of 64×64 as input into the designed model.As the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low accuracy.Therefore,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.

Comments on chevron bend specimen for determining fracture toughness of rock

Based on a number of tests on different rocks, Suggested Methods for Determining the Fracture Toughness of Rock (SMs) was reviewed. The advantages of SMs are obvious, but some problems are also discovered. A serious one is that the nonlinear corrected fracture toughness of chevron bend specimens, K C CB , is less than the uncorrected one, K CB , for hard rock like granite, marble and others. The reason is discussed and the proposal is given.

Application of BP neural networks in non-linearity correction of optical tweezers

The back-propagation(BP)neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer sys-tem.Considering the low convergence rate of the BP algo-rithm,the Levenberg-Marquardt(LM)algorithm is used to improve the BP network.The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics.The result shows that with the nonlinear correction using BP net-works,the range of force measurement of an optical tweezer system is enlarged by 30%and the precision is also improved compared with the polynomial fitting method.It is demonstrated that nonlinear correction by the neural network method effectively improves the per-formance of optical tweezers without adding or changing the measuring system.

Using modified Mach-Zehnder interferometer to get better nonlinear correction term of an isotropic nonlinear material

Nonlinear materials have been well established as photo refractive switching material. Important applica- tions of isotropic nonlinear materials are seen in self-focusing, defocusing phenomena, switching systems, etc. The nonlinear correction term is basically responsible for the optical switches. Mach-Zehnder inter- ferometer （MZI） is a well-known arrangement for determining the above correction term, but there are some major problems for finding out the term by MZI. We propose a new method of finding the nonlinear correction term as well as the second order nonlinear susceptibility of the materials by using a modified MZI system. This method may be used to find out the above parameters for any unknown nonlinear material.

A novel pressure sensor calibration system based on a neural network

According to the specific input-output characteristics of a pressure sensor, a novel calibration algorithm is presented and a calibration system is developed to correct the nonlinear error caused by temperature. In contrast to the routine BP and RBF, curve fitting based on RBF is first used to get the slope and intercept, and then the voltage-pressure curve is described. Test results show that the algorithm features fast convergence speed, strong robustness and minimum SSE （sum of squares for error）. It is proven by practical applications that this calibration system works well and the measurement precision is better than the design demands. Furthermore, this calibration system has a good real-time capability.

A Nonlinear Finite Volume Element Method Satisfying Maximum Principle for Anisotropic Diffusion Problems on Arbitrary Triangular Meshes

A nonlinear finite volume element scheme for anisotropic diffusion problems on general triangular meshes is proposed.Starting with a standard linear conforming finite volume element approximation,a corrective term with respect to the flux jumps across element boundaries is added to make the scheme satisfy the discrete maximum principle.The new scheme is free of the anisotropic non-obtuse angle condition which is a severe restriction on the grids for problems with anisotropic diffusion.Moreover,this manipulation can nearly keep the same accuracy as the original scheme.We prove the existence of the numerical solution for this nonlinear scheme theoretically.Numerical results and a grid convergence study are presented for both continuous and discontinuous anisotropic diffusion problems.