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).     

Log-Sigmoid Multipliers Method in Constrained Optimization

In this paper we introduced and analyzed the Log-Sigmoid (LS) multipliers method for constrained optimization. The LS method is to the recently developed smoothing technique as augmented Lagrangian to the penalty method or modified barrier to classical barrier methods. At the same time the LS method has some specific properties, which make it substantially different from other nonquadratic augmented Lagrangian techniques.We established convergence of the LS type penalty method under very mild assumptions on the input data and estimated the rate of convergence of the LS multipliers method under the standard second order optimality condition for both exact and nonexact minimization.Some important properties of the dual function and the dual problem, which are based on the LS Lagrangian, were discovered and the primal–dual LS method was introduced.     

An Algorithm Based on Active Sets and Smoothing for Discretized Semi-Infinite Minimax Problems

We present a new active-set strategy which can be used in conjunction with exponential (entropic) smoothing for solving large-scale minimax problems arising from the discretization of semi-infinite minimax problems. The main effect of the active-set strategy is to dramatically reduce the number of gradient calculations needed in the optimization. Discretization of multidimensional domains gives rise to minimax problems with thousands of component functions. We present an application to minimizing the sum of squares of the Lagrange polynomials to find good points for polynomial interpolation on the unit sphere in ℝ³. Our numerical results show that the active-set strategy results in a modified Armijo gradient or Gauss-Newton like methods requiring less than a quarter of the gradients, as compared to the use of these methods without our active-set strategy. Finally, we show how this strategy can be incorporated in an algorithm for solving semi-infinite minimax problems.     

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.     

Average coherence with respect to complementary measurements

We investigate the average coherence with respect to a complete set of complementary measurements.By using a Wigner-Yanase skew information-based coherence measure introduced in Luo and Sun(2017 Phys.Rev.A 96,022130),we evaluate the average coherence of a state with respect to any complete set of mutually unbiased measurements and general symmetric informationally complete measurements,respectively.We also establish analytically the relations among these average coherences.     

气体团簇离子束两步能量修形法的平坦化效应

本文提出采用气体团簇离子束的两步能量修形法来改善4H-SiC(1000)晶片表面形貌.先用15 keV的高能Ar团簇离子进行整体修形,再用5 keV的低能团簇离子优化表面.结果表明,在相同的团簇离子剂量下,与单一15 keV的高能团簇处理相比,两步法修形后的表面具有更低的均方根粗糙度,两者分别为1.05 nm和0.78 nm.本文还以原子级平坦表面为研究对象,揭示了载能团簇引起的半球形离子损伤(弧坑)与团簇能量的关系,及两步能量修形法在弧坑修复中的优势.在原子力显微镜表征的基础上,引入了二维功率谱密度函数,以直观全面地给出材料的表面形貌特征及其随波长(频率)的分布.结果表明,经任何能量的团簇离子轰击的表面,在0.05—0.20μm波长范围内,团簇轰击都能有效地降低粗糙度,而在0.02—0.05μm范围内,则出现了粗化效应,这是由于形成了半球形离子损伤,但第二步更低能量的团簇离子处理可以削弱这种粗化效应.