高平均功率电光开关晶体热畸变分析

 深入分析了方板构型的电光开关晶体在高功率载荷条件下的热畸变行为，讨论了光强分布对热效应的影响。以KDP晶体为例，分别计算了激光束光强为高斯分布和均匀分布时晶体的温升、相应的热应力分布、波前畸变以及热退偏。结果表明，光强的分布形式对波前畸变和热退偏的影响是不同的。相对于光强均匀分布的激光束，高斯光束减缓了光斑边沿处的温度梯度，产生的热应力较小，因此可以减弱热退偏效应；另一方面，在光束口径范围内，高斯光束产生了附加的温度分布非均匀性，因而波前畸变会大一些。     

Evaluation of elastic modulus of cantilever beam by TA-ESPI

The paper proposes an evaluation technique for the elastic modulus of a cantilever beam by vibration analysis based on time average electronic speckle pattern interferometry (TA-ESPI) and Euler-Bernoulli equation. General approaches for the measurement of elastic modulus of a thin film are the Nano indentation test, Buldge test, Micro-tensile test, and so on. They each have strength and weakness in the preparation of the test specimen and the analysis of experimental results. ESPI is a type of laser speckle interferometry technique offering non-contact, high-resolution and whole-field measurement. The technique is a common measurement method for vibration mode visualization and surface displacement. Whole-field vibration mode shape (surface displacement distribution) at resonance frequency can be visualized by ESPI. And the maximum surface displacement distribution from ESPI can be used to find the resonance frequency for each vibration mode shape. And the elastic modules of a test material can be easily estimated from the measured resonance frequency and Euler-Bernoulli equation. The TA-ESPI vibration analysis technique can be used to find the elastic modulus of a material requiring simple preparation process and analysis.     

Direct numerical simulation of stationary particles in homogeneous turbulence decay: Application of the k–ε model

This study focuses on understanding how the presence of particles, in homogeneous turbulence decay, affects the dissipation of dissipation coefficient within the volume averaged dissipation transport equation. In developing this equation, the coefficient for dissipation of dissipation was assumed to be the sum of the single phase coefficient and an additional coefficient that is related to the effects of the dispersed phase. Direct numerical simulation was used to isolate the effect of stationary particles in homogeneous turbulent decay at low Reynolds numbers (Re_L = 3.3 and 12.5). The particles were positioned at each grid point and modeled as point forces and a comparison was made between a 64³ and 128³ domain. The results show that the dissipation of dissipation coefficient correlates well with a dimensionless parameter called the momentum coupling factor.     

Optimal Conditional Estimation: Average Case Setting

We consider an estimation problem which appears in the identification of systems by means of restricted complexity models: find the optimal approximation to an element of a linear normed space (a system) based on noisy information, subject to the restriction that approximations (models) can be selected from a prescribed subspace M of the problem element space. In contrast to the worst-case optimization criterion, which may be pessimistic, in this paper the quality of an identification algorithm is measured by its local average performance. Two types of local average errors are considered: for a given information (measurement) y and for a given unknown element x, the latter in two versions. For a wide spectrum of norms in the measurement space, we define an optimal algorithm and give expressions for its average errors which show the dependence on information, information errors, unmodelled dynamics, and norm in the measurement space.     

Lower bounds on the minimum average distance of binary codes

Let β(n,M) denote the minimum average Hamming distance of a binary code of length n and cardinality M. In this paper we consider lower bounds on β(n,M). All the known lower bounds on β(n,M) are useful when M is at least of size about 2ⁿ⁻¹/n. We derive new lower bounds which give good estimations when size of M is about n. These bounds are obtained using a linear programming approach. In particular, it is proved that lim_n→∞β(n,2n)=5/2. We also give a new recursive inequality for β(n,M).     

Ito过程在免费邮箱硬盘容量动态分配模型中的应用

分析了目前电子邮箱市场的情况,运用微分方程建立了邮箱用户增长模型,预测某邮箱未来几年内的用户数量及企业需要提供的硬盘容量。从减少成本的角度分析,运用Ito随机过程,对风险进行评估,建立硬盘容量动态分配模型,指导企业如何安排合适的硬盘容量,既能满足市场需要,又达到规避风险和降低成本的目的,提高市场竞争力。     

原子的共振激发—速率方程与半经典理论的比较

本文由半经典理论推得了原子电离率随脉冲空间传输出现周期性起伏这一新的相干作用结果,这点与速率方程的预计相差甚远.文中定量说明了利用速率方程对同位素分离过程进行分析时产生的系统误差,为实验分析工作提供了依据.     

Inference on superimposed subcritical Galton-Watson processes with immigration

Summary In this paper we consider the superimposed processZ generated by two independent subcritical Galton-Watson processesX ₁ andX ₂, with immigration, by the relationZ=X ₁ +X ₂. The seemingly second order autoregressive relation, that is identified inZ, is exploted towards proposing CAN estimators for the parameters ofZ,X ₁ andX ₂, based on only a partial realisation ofZ, using time series techniques. The results of this paper are motivated by a time series approach for studying specific branching processes due to Venkataraman (1982,Adv. Appl. Prob.,14, 1–20).     

Sample average approximation of expected value constrained stochastic programs

We propose a sample average approximation (SAA) method for stochastic programming problems with expected value constraints. Such problems arise, for example, in portfolio selection with constraints on conditional value-at-risk (CVaR). We provide a convergence analysis and a statistical validation scheme for the proposed method.     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.