95 resultados para Radial distribution function
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This paper presents exact density, velocity and temperature solutions for two problems of collisionless gas flows around a flat plate or a spherical object. At any point off the object, the local velocity distribution function consists of two pieces of Maxwellian distributions: one for the free stream which is characterized by free stream density, temperature and average velocity, n0, T0, U0; and the other is for the wall and it is characterized by density at wall and wall temperature, nw,Tw. Directly integrating the distribution functions leads to complex but exact flowfield solutions. To validate these solutions, we perform numerical simulations with the direct simulation Monte Carlo (DSMC) method. In general, the analytical and numerical results are virtually identical. The evaluation of these analytical solutions only requires less than one minute while the DSMC simulations require several days.
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We propose an integrated algorithm named low dimensional simplex evolution extension (LDSEE) for expensive global optimization in which only a very limited number of function evaluations is allowed. The new algorithm accelerates an existing global optimization, low dimensional simplex evolution (LDSE), by using radial basis function (RBF) interpolation and tabu search. Different from other expensive global optimization methods, LDSEE integrates the RBF interpolation and tabu search with the LDSE algorithm rather than just calling existing global optimization algorithms as subroutines. As a result, it can keep a good balance between the model approximation and the global search. Meanwhile it is self-contained. It does not rely on other GO algorithms and is very easy to use. Numerical results show that it is a competitive alternative for expensive global optimization.
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完全电离等离子体中,当试探粒子分布函数fα是关于试探粒子速度vα的偶函数时,导出了一个新的动力学方程的碰撞算子.该碰撞算子同时包括了大角散射(库仑近碰撞)和小角散射(库仑远碰撞)的二体碰撞的贡献,因此,该碰撞算子同时适用于弱耦合(库仑对数ln∧≥10)和中等耦合(库仑对数2≤ln∧≤10)等离子体.而且经过修改的碰撞算子和Rosenbluth势有直接的联系,当试探粒子和场粒子满足条件mα<mβ(如电子-离子碰撞或Lorentz气体模型)和|vα|〉|vβ|时,经约化的电子-离子碰撞算子同最初的Fokker
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Dynamic properties of proteins have crucial roles in understanding protein function and molecular mechanism within cells. In this paper, we combined total internal reflection fluorescence microscopy with oblique illumination fluorescence microscopy to observe directly the movement and localization of membrane-anchored green fluorescence proteins in living cells. Total internal reflect illumination allowed the observation of proteins in the cell membrane of living cells since the penetrate depth could be adjusted to about 80 nm, and oblique illumination allowed the observation of proteins both in the cytoplasm and apical membrane, which made this combination a promising tool to investigate the dynamics of proteins through the whole cell. Not only individual protein molecule tracks have been analyzed quantitatively but also cumulative probability distribution function analysis of ensemble trajectories has been done to reveal the mobility of proteins. Finally, single particle tracking has acted as a compensation for single molecule tracking. All the results exhibited green fluorescence protein dynamics within cytoplasm, on the membrane and from cytoplasm to plasma membrane.
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随着研究工作的逐步深入,目前已经利用经典热光源实现了关联衍射成像,使得该技术有望在X射线以及中子衍射成像等方面得到广泛应用。在实验利用非相干光得到物体无透镜傅里叶变换频谱的基础上,采用误差消除与输入输出恢复算法,并结合过采样理论,实现了实验所用物体透射率函数的恢复。分别得到了纯振幅物体的振幅分布函数与纯相位物体的相位分布函数。此外,还讨论了实验所得傅里叶变换频谱的噪声等因素对图像恢复结果的影响。
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The ambiguity function was employed as a merit function to design an optical system with a high depth of focus. The ambiguity function with the desired enlarged-depth-of-focus characteristics was obtained by using a properly designed joint filter to modify the ambiguity function of the original pupil in the phase-space domain. From the viewpoint of the filter theory, we roughly propose that the constraints of the spatial filters that are used to enlarge the focal depth must be satisfied. These constraints coincide with those that appeared in the previous literature on this topic. Following our design procedure, several sets of apodizers were synthesized, and their performances in the defocused imagery were compared with each other and with other previous designs. (c) 2005 Optical Society of America.
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针对非合作小目标激光测距系统,目标表面的反射特征对激光回波信号有很大的影响。建立测量表面双向反射分布函数(BRDF)的装置,对常用的两种热控材料——白漆涂层和F36多包层,测量了其在1064 nm波长下的双向反射分布函数。得出了白漆涂层镜面反射很小,散射角较大,利于各方向接收回波信号;而F36多包层镜面反射很强,散射角-2°~2°,不利于探测。通过由表面BRDF与由朗伯散射计算得到的最小接收功率的比较,得出了入射角大于45°入射白漆涂层时,回波信号较小;大于2°入射F36多包层时,没有回波信号。
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采用提拉法生长了质量优异的Yb:Ca5(PO4)2F(Yb:FAP)晶体。运用化学腐蚀,光学显微镜、扫描电子显微镜以及能量散射光谱仪观察了该晶体中的生长条纹和包裹物等宏观缺陷,以及晶体的位错腐蚀形貌、位错密度及其分布情况,同时观察了晶体中亚晶界的形态。由晶体中位错的径向变化以及生长条纹可知:晶体在生长过程中为微凸界面生长。高温下CaF2的挥发造成了在晶体生长后期熔体中组分偏离化学计量比,出现组分过冷,形成包裹物。且位错密度显著增加。Yb:FAP晶体的各向异性使得晶体在(10 10)面的位错蚀坑形状、大小以
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The paper demonstrates the nonstationarity of algal population behaviors by analyzing the historical populations of Nostocales spp. in the River Darling, Australia. Freshwater ecosystems are more likely to be nonstationary, instead of stationary. Nonstationarity implies that only the near past behaviors could forecast the near future for the system. However, nonstionarity was not considered seriously in previous research efforts for modeling and predicting algal population behaviors. Therefore the moving window technique was incorporated with radial basis function neural network (RBFNN) approach to deal with nonstationarity when modeling and forecasting the population behaviors of Nostocales spp. in the River Darling. The results showed that the RBFNN model could predict the timing and magnitude of algal blooms of Nostocales spp. with high accuracy. Moreover, a combined model based on individual RBFNN models was implemented, which showed superiority over the individual RBFNN models. Hence, the combined model was recommended for the modeling and forecasting of the phytoplankton populations, especially for the forecasting.
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This paper reports the mechanical properties and fracture behavior of silicon carbide (3C-SiC) thin films grown on silicon substrates. Using bulge testing combined with a refined load-deflection model of long rectangular membranes, which takes into account the bending stiffness and prestress of the membrane material, the Young's modulus, prestress, and fracture strength for the 3C-SiC thin films with thicknesses of 0.40 and 1.42 mu m were extracted. The stress distribution in the membranes under a load was calculated analytically. The prestresses for the two films were 322 +/- 47 and 201 +/- 34 MPa, respectively. The thinner 3C-SiC film with a strong (111) orientation has a plane-gstrain moduli of 415 +/- 61 GPa, whereas the thicker film with a mixture of both (111) and (110) orientations exhibited a plane-strain moduli of 329 +/- 49 GPa. The corresponding fracture strengths for the two kinds of SiC films were 6.49 +/- 0.88 and 3.16 +/- 0.38 GPa, respectively. The reference stresses were computed by integrating the local stress of the membrane at the fracture over edge, surface, and volume of the specimens and were fitted with Weibull distribution function. For the 0.40-mu m-thick membranes, the surface integration has a better agreement between the data and the model, implying that the surface flaws are the dominant fracture origin. For the 1.42-mu m-thick membranes, the surface integration presented only a slightly better fitting quality than the other two, and therefore, it is difficult to rule out unambiguously the effects of the volume and edge flaws.
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The mechanical properties and fracture behavior of silicon nitride (SiNx) thin film fabricated by plasma-enhanced chemical vapor deposition is reported. Plane-strain moduli, prestresses, and fracture strengths of silicon nitride thin film; deposited both oil a bare Si substrate and oil a thermally oxidized Si substrate were extracted using bulge testing combined with a refined load-deflection model of long rectangular membranes. The plane-strain modu i and prestresses of SiNx thin films have little dependence on the substrates, that is, for the bare Si substrate, they are 133 +/- 19 GPa and 178 +/- 22 MPa, respectively, while for the thermally oxidized substrate, they are 140 +/- 26 Gila and 194 +/- 34 MPa, respectively. However, the fracture strength values of SiNx films grown on the two substrates are quite different, i.e., 1.53 +/- 0.33 Gila and 3.08 +/- 0.79 GPa for the bare Si substrate a A the oxidized Si substrate, respectively. The reference stresses were computed by integrating the local stress of the membrane at the fracture over the edge, Surface, and volume of the specimens and fitted with the Weibull distribution function. For SiNx thin film produced oil the bare Si Substrate, the Volume integration gave a significantly better agreement between data and model, implying that the volume flaws re the dominant fracture origin. For SiNx thin film grown on the oxidized Si substrate, the fit quality of surface and edge integration was significantly better than the Volume integration, and the dominant surface and edge flaws could be caused by buffered HF attacking the SiNx layer during SiO2 removal. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.
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In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.
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Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.
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Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.
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Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.