Comparison of Liquid Water Content Retrievals for Airborne Millimeter-Wave Radar with Different Particle Parameter Schemes

In the application of the physical iterative method to retrieve millimeter-wave radar liquid water content(LWC)and liquid water path(LWP),particle parameter scheme is the main factor affecting retrieval performance.In this paper,synchronous measurements of an airborne millimeter-wave radar and a hot-wire probe in stratus cloud are used to compare the LWC retrievals of the oceanic and continental particle parameter scheme with diameter less than 50μm and the particle parameter scheme with diameter less than 500μm and 1500μm(scheme 1,scheme 2,scheme 3,and scheme4,respectively).The results show that the particle parameter scheme needs to be selected according to the reflectivity factor when using the physical iterative method to retrieve the LWC and LWP.When the reflectivity factor is less than-30 d BZ,the retrieval error of scheme 1 is the minimum.When the reflectivity factor is greater than-30 d BZ,the retrieval error of scheme 4 is the minimum.Based on the reflectance factor value,the LWP retrievals of scheme 4 are closer to the measurements,the average relative bias is 5.2%,and the minimum relative bias is 4.4%.Compared with other schemes,scheme 4 seems to be more useful for the LWC and LWP retrieval of stratus cloud in China.

Fast determination of meso-level mechanical parameters of PFC models

To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

Parameter Estimation of S-Shaped Growth Model:A Modified Particle Swarm Algorithm

Parameter estimation plays a critical role for the application and development of S-shaped growth model in the agricultural sciences and others.In this paper,a modified particle swarm optimization algorithm based on the diffusion phenomenon（DPPSO） was employed to estimate the parameters for this model.Under the sense of least squares,the parameter estimation problem of S-shaped growth model,taking the Gompertz and Logistic models for example,is transformed into a multi-dimensional function optimization problem.The results show that the DPPSO algorithm can effectively estimate the parameters of the S-shaped growth model.

A NUMERICAL MODEL OF MIXED CONVECTIVESTRATIFORM CLOUD

A 2-D slab-symmetric model of mixed convective-stratiform cloud is developed by superimposing convective cloud-size field on the convergence field,in order to simulate and study the mixed clouds consisting of stratiform cloud and convective cloud.A deep convective,anelastic and conservative system of equations with basic variables(V,θ,π')is solved by a new method to calculate dynamic field.The water substance in the cloud is divided into 6 categories and the microphysical processes are described in spectrum with two variable parameters and more reasonable particle number/size distributions.To compare with measured radar echo intensity and structure,the model may calculate echo intensity of the model cloud observed by radar.

Chiral geometry in multiple chiral doublet bands

The chiral geometry of multiple chiral doublet bands with identical configuration is discussed for different triaxial deformation parameters γ in the particle rotor model with πh11/2×γh11/2^-1.The energy spectra,electromagnetic transition probabilities B（M1） and B（E2）,angular momenta,and K-distributions are studied.It is demonstrated that the chirality still remains not only in the yrast and yrare bands,but also in the two higher excited bands whenγ deviates from 30°.The chiral geometry relies significantly on γ,and the chiral geometry of the two higher excited partner bands is not as good as that of the yrast and yrare doublet bands.