1IntroductionPolymer bonded explosives (PBXs) [1][2][3][4] have usually been considered as ap articulate composite material containing the energetic materials hexahydro-1,3,5-trinitro-1,3,5-s-triazine (RDX) embedded in av iscoelastic polymer binder. PBXs are widely used in av ariety of conventional engineering and aerospace fields. As ah ighly-filled composite material, PBX exhibits complex physical and mechanical properties that are dependent on an umber of variables, such as temperature, strain rate, grains weight, and size distribution of grains, etc. [5,6].The Los Alamos National Laboratory (LANL) performed al ot of research on dynamic mechanical properties of PBXs. These results suggested that dynamic mechanical properties of PBX are strongly influenced by strain rate and temperature [7][8][9][10].A lso, the compressive strength is enhanced when increasing strain rate or decreasing temperature. PBX9501i sw idely researched at present,a nd the result illustrated that PBX9501 duringh igh-rate loading continue straining after the maximum flows tress have been achieved. Figure 1s hows the published compressive stress-strain data on PBX9501 currently known to the authors. When the peak strengths of PBX9501 are achieved, the flow strength beginst od ecline sharply because of damage accumulation. Graye tal. [11] revealed that at high-rate PBX 9501 fails via predominantly transgranular cleavage through the HMX crystals. Strains to failure remain almost constant throughout,a talevel of approximately 1-3 %. In fact, these damagep rocesses give rise to the enhanced sensitivity of PBX9501 followinga ni mpact.In the presented work, the micromechanical modelh as profited greatly from the theoretical analysis of Weng and co-workers [12-17] and considerable referencet ot heir work is madet hroughout this article. Their work is based on Eshelby's celebrateds olution of the stressa nd strain fields arising from the presence of an ellipsoidal inhomogeneity in an elastic matrix and Mori and Ta naka'se ffective medium theory,w hich was used to extendE shelby's analysis to larger valueso ff iller concentration [18,19].C lements and Mas have put forth am ethod that is ah ybrid of two Abstract:T he dynamic mechanical properties of PBX1314 and its binder are systematicallyi nvestigated. Based on split-Hopkinson pressure bar technique, the experimental results of PBX1314 and its binder are obtainedu nder high strain rate. Ac onstitutive theory is developed for modeling the mechanical response of dynamically loaded PBX1314 binder.T oa ccomplish this aim, the PBX1314 binderi sa ssayed by relaxation tests at different temperatures, in order to apply the time-temperature superposition principle (TTSP) and raise the masterc urves, based on WLF equation.The rate dependence of mechanical response of the polymer binder is accounted for by ag eneralized Maxwell viscoelasticity model. The basis for this work is Mori and Ta naka's effectivem ediumt heory.T he grains in this analysis are assumed to be spherical and uniformly distributed in the b...

Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the key technologies to provide valuable information for path planning in autonomous driving. As opposed to traditional radar processing, where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene characterization. Hence, clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, we proposed a method of automatic segmentation of automotive radar images based on two main steps: unsupervised image pre-segmentation using marker-based watershed transformation, followed by the supervised segmentation and classification of regions containing objects and surfaces based on the use of statistical distribution parameters. Several distributions were considered to characterize returns from specific region types of interest within the scene (denoted as classes) in calibrated radar imagery-the extracted distribution parameters were assessed for their ability to distinguish each class. These parameters were then used as features in a multivariate Gaussian distribution model classifier. Both the performances of the proposed supervised classification algorithm and the automatically segmented results were investigated using F1-score and Jaccard similarity coefficients, respectively.

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In medical image processing, robust segmentation of inhomogeneous targets is a challenging problem. Because of the complexity and diversity in medical images, the commonly used semiautomatic segmentation algorithms usually fail in the segmentation of inhomogeneous objects. In this study, we propose a novel algorithm imbedded with a seed point autogeneration for random walks segmentation enhancement, namely SPARSE, for better segmentation of inhomogeneous objects. With a few user‐labeled points, SPARSE is able to generate extended seed points by estimating the probability of each voxel with respect to the labels. The random walks algorithm is then applied upon the extended seed points to achieve improved segmentation result. SPARSE is implemented under the compute unified device architecture (CUDA) programming environment on graphic processing unit (GPU) hardware platform. Quantitative evaluations are performed using clinical homogeneous and inhomogeneous cases. It is found that the SPARSE can greatly decrease the sensitiveness to initial seed points in terms of location and quantity, as well as the freedom of selecting parameters in edge weighting function. The evaluation results of SPARSE also demonstrate substantial improvements in accuracy and robustness to inhomogeneous target segmentation over the original random walks algorithm.PACS number: 87.57.nm

DNA microarray technique can detect tens of thousands of genes activity in cells and has been widely used in clinical diagnosis. However, microarray data has characteristics of high dimension and small samples, moreover many irrelevant and redundant genes also decrease performance of classification algorithm .Mutual information is very effective method and has widely been used in feature gene selection, but it cannot directly deal with continuous features. Therefore, this paper proposes a novel feature gene selection method to resolve this problem. Firstly, a lot of irrelevant genes are eliminated from original data by using reliefF algorithm , and the candidate subset of genes is obtained; Secondly, a algorithm based on neighborhood mutual information and forward greedy search strategy which deals with directly continuous features is proposed to select feature genes in above genes subset. Here, because radius of neighborhood greatly affects reduction performance, differential evolution algorithm is applied to optimize radius before reduction. The simulation results on six benchmark microarray datasets show that our method can obtain higher classification accuracy using as few genes as possible, especially neighborhood mutual information can directly continuous features. Feature genes selected has an important meaning for understanding microarray data and finding pathogenic genes of cancer. It is an effective and efficient method for feature genes selection.