Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Polarized hyperspectral imaging,which has been widely studied worldwide,can obtain four-dimensional data including polarization,spectral,and spatial domains.To simplify data acquisition,compressive sensing theory is utilized in each domain.The polarization information represented by the four Stokes parameters currently requires at least two compressions.This work achieves full-Stokes single compression by introducing deep learning reconstruction.The four Stokes parameters are modulated by a quarter-wave plate(QWP)and a liquid crystal tunable filter(LCTF)and then compressed into a single light intensity detected by a complementary metal oxide semiconductor(CMOS).Data processing involves model training and polarization reconstruction.The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework.Unknown Stokes parameters can be reconstructed from a single compression using the trained model.Benefiting from the acquisition simplicity and reconstruction efficiency,this work well facilitates the development and application of polarized hyperspectral imaging.

Relationship between single and bulk mechanical properties for zeolite ZSM5 spray-dried particles

In this work typical mechanical properties for a catalyst support material, ZSM5 （a spray-dried granular zeolite）, have been measured in order to relate the bulk behaviour of the powder material to the single particle mechanical properties. Particle shape and size distribution of the powders, determined by laser diffraction and scanning electron microscopy （SEM）, confirmed the spherical shape of the spray-dried particles. The excellent flowability of the material was assessed by typical methods such as the Hausner ratio and the Cart index, This was confirmed by bulk measurements of the particle-particle internal friction parameter and flow function using a Schulze shear cell, which also illustrated the low compressibility of the material. Single particle compression was used to characterize single particle mechanical properties such as reduced elastic modulus and strength from Hertz contact mechanics theory. Comparison with surface properties obtained from nanoindentation suggests heterogeneity, the surface being harder than the core. In order to evaluate the relationship between single particle mechanical properties and bulk compression behaviour, uniaxial confined compression was carried out. It was determined that the Adams model was suitable for describing the bulk compression and furthermore that the Adams model parameter, apparent strength of single particles, was in good agreement with the single particle strength determined from single particle compression test.

Adjoint based state estimation of compressible flow in porous media

In this paper,we study the state estimation of compressible single phase flow in compressible porous media.The initial pressure distribution is estimated according to discrete adjoint approach based on the collected well pressure data.The first-order Tykhonov regularization method is used to obtain reasonable estimation.By analyzing the optimality condition of estimation problem,the discrete adjoint state equation and discrete adjoint gradient are derived based on the numerical scheme of the continuous equations.A quasi-Newton numerical optimization method related to adjoint gradient is proposed to solve the estimation problem.The estimation results with different regularization coefficients are compared and analyzed by numerical experiments.The deviation between the estimated pressure obtained without regularization and the real pressure is large.Estimation result with smaller deviation and higher smoothness can be obtained through appropriate regularization coefficient.When the observation error is large,the observed values generated by the estimated pressure fit well with the real pressure.