Features of the turbulent flow around symmetric elongated bluff bodies

The flow fields around three elongated bluff bodies with the same chord-to-thickness ratios but distinct leading and trailing edge details were measured at a Reynolds number of 3×10⁴. These models each represent a case where: leading edge shedding dominates, trailing edge shedding dominates and a case where there is a balance between the two. The results show that the vortex street parameters vary between the models, and in particular, the shedding frequencies are significantly altered by the geometry. However, contrary to the current understanding for shorter bluff bodies, the scale of the recirculation region is found to be similar for each model, even though the shedding frequency changes within the range from 0.15 to 0.24. Also, the base pressure does not follow trends with shedding frequency expected from shorter bluff bodies. A force balance of the recirculation region shows that the near wake of each body is significantly affected by the Reynolds shear stress distribution and the resultant force due to the pressure field in the mean recirculation region. These differences infer that the distinct vortex formation characteristics depend on the state of the trailing edge shear layers. The boundary layers at the trailing edge have been quantified, as have the leading edge separation bubbles, and the marked differences in the wake details are shown to depend on the leading edge separation.     

The bivariate noncentral chi-square distribution - A compound distribution approach

This paper proposes the bivariate noncentral chi-square (BNC) distribution by compounding the Poisson probabilities with the bivariate central chi-square distribution. The probability density and cumulative distribution functions of the joint distribution of the two noncentral chi-square variables are derived for arbitrary values of the correlation coefficient, degrees of freedom(s), and noncentrality parameters. Computational procedures to calculate the upper tail probabilities as well as the percentile points for selected values of the parameters, for both equal and unequal degrees of freedom, are discussed. The graphical representation of the distribution for different values of the parameters are provided. Some applications of the distribution are outlined.     

Exploring human rhythmic gait movement in the role of cerebral cortex signal

The rhythmic movement is a spontaneous behavior due to the central pattern generator (CPG). At present, the CPG model only shows the spontaneous behavior, but does not refer to the instruction regulation role of the cerebral cortex. In this paper, a modified model based on the Matsuoka neural oscillator theory is presented to better show the regulation role of the cerebral cortex signal to the CPG neuronal network. The complex interaction between the input signal and other parameters in the CPG network is established, making all parameters of the CPG vary with the input signal. In this way, the effect of the input signal to the CPG network is enhanced so that the CPG network can express the self-regulation movement state instead of being limited to the spontaneous behavior, and thus the regulation role of the cerebral cortex signal can be reflected. Numerical simulation shows that the modified model can generate various movement forms with different modes, frequencies, and interchanges between them. It is revealed in theories that the cerebral cortex signal can regulate the mode and frequency of the gait in the course of the gait movement.     

Empirical likelihood inference for the accelerated failure time model

Accelerated failure time (AFT) models are useful regression tools for studying the association between a survival time and covariates. Semiparametric inference procedures have been proposed in an extensive literature. Among these, use of an estimating equation which is monotone in the regression parameter and has some excellent properties was proposed by Fygenson and Ritov (1994). However, there is a serious under-coverage problem for small sample sizes. In this paper, we derive the limiting distribution of the empirical log-likelihood ratio for the regression parameter on the basis of the monotone estimating equations. Furthermore, the empirical likelihood (EL) confidence intervals/regions for the regression parameter are obtained. We conduct a simulation study in order to compare the proposed EL method with the normal approximation method. The simulation results suggest that the empirical likelihood based method outperforms the normal approximation based method in terms of coverage probability. Thus, the proposed EL method overcomes the under-coverage problem of the normal approximation method.     

A multidimensional central limit theorem with speed of convergence for axiom a diffeomorphisms

Let T:X → X be an Axiom A diffeomorphism,m the Gibbs state for a Hlder continuous function ɡ. Assume that f:X → R^d is a Hlder continuous function with ∫_X^fdm = 0.If the components of f are cohomologously independent, then there exists a positive definite symmetric matrix σ²:=σ² （f ） such that S^fn √ n converges in distribution with respect to m to a Gaussian random variable with expectation 0 and covariance matrix σ² . Moreover, there exists a real number A > 0 such that, for any integer n ≥ 1,Π( m*（ 1√ nS f n ）,N （0,σ² ） ≤A√n, where m*（1√ n S^fn）denotes the distribution of 1√ n S^fn with respect to m, and Π is the Prokhorov metric.     

一类马尔可夫骨架过程的强大数定律和中心极限定理

基于马尔可夫骨架过程极限分布的已有研究结果,本文运用波莱尔-康特立引理、更新理论、科尔莫哥洛夫的强大数定律以及独立同分布情形的中心极限定理等重要理论,分别给出了一类马尔可夫骨架过程对应的累积过程满足强大数定律和中心极限定理的充分条件.     

广义超特殊p-群的自同构群(Ⅱ)

重新确定了广义超特殊p-群G的自同构群的结构.设|G|=p~(2n+m),|ζG|=p~m,其中n≥1,m≥2,Aut_cG是AutG中平凡地作用在ζG上的元素形成的正规子群,则(i)若p是奇素数,则AutG=〈θ〉×Aut_cG,其中θ的阶是(p-1)p~(m-1);若p=2,则AutG=〈θ_1,θ_2〉×Aut_cG,其中〈θ_1,θ_2〉=〈θ_1〉×〈θ_2〉≌Z_(2m-2)×Z_2.(ii)如果G的幂指数是p~m,那么Aut_cG/InnG≌Sp(2n,p).(iii)如果G的幂指数是p~(m+1),那么Aut_cG/InnG≌K×Sp(2n-2,p),其中K是p~(2n-1)阶超特殊p-群(若p是奇素数)或者初等Abel 2-群.特别地,当n=1时,Aut_cG/InnG≌Z_p.     

单位上三角矩阵群的注记

记Tr_1(n,Z)是整数环Z上对角线元素全是1的全体上三角矩阵组成的群,k_(ij)(1≤i相似文献   

Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation

Attribute reduction is one of the key issues in rough set theory. Many heuristic attribute reduction algorithms such as positive-region reduction, information entropy reduction and discernibility matrix reduction have been proposed. However, these methods are usually computationally time-consuming for large data. Moreover, a single attribute significance measure is not good for more attributes with the same greatest value. To overcome these shortcomings, we first introduce a counting sort algorithm with time complexity O(∣C∣ ∣U∣) for dealing with redundant and inconsistent data in a decision table and computing positive regions and core attributes (∣C∣ and ∣U∣ denote the cardinalities of condition attributes and objects set, respectively). Then, hybrid attribute measures are constructed which reflect the significance of an attribute in positive regions and boundary regions. Finally, hybrid approaches to attribute reduction based on indiscernibility and discernibility relation are proposed with time complexity no more than max(O(∣C∣²∣U/C∣), O(∣C∣∣U∣)), in which ∣U/C∣ denotes the cardinality of the equivalence classes set U/C. The experimental results show that these proposed hybrid algorithms are effective and feasible for large data.     

基于微粒群区域搜索和小波评价的差分式自动聚焦

为进一步提高图像法自动聚焦的性能,提出了一种差分式提取图像边缘的方法,并构造了图像清晰度的小波评价函数,同时利用微粒群(PSO)算法对聚焦区域进行快速搜索。首先,介绍了差分式边缘提取方法及其优势,给出了一种评价区域的选取判据以及基于PSO的高效搜索方法;然后,对小波评价函数参数进行了比较分析和优选;最后,与传统方法进行了对比实验。结果表明,由于采用了差分式提取方法以及新的自适应聚焦窗口和评价函数,聚焦曲线较传统方法具有更高的调焦分辨率,PSO算法的使用使聚焦速度提高了约170 ms,聚焦精度约为2.3μm,同时调焦效果不受初始位置的影响。