Bergman型空间的Ces(α)ro算子

设g1,g2为正规函数,对所有的0相似文献   

核实数据下响应变量缺失的线性EV模型经验似然推断

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双参数半线性高阶椭圆型方程的奇摄动解

讨论了一类具有双参数的半线性高阶椭圆型方程边值问题.利用微分不等式理论,研究了边值问题解的存在性和渐近性态.     

航天器空间静电效应研究进展

航天器在轨运行过程中面临的空间环境复杂多变,高能电子、等离子体环境、低气压、大温差等环境因素会引起航天器发生静电带电和放电效应,对航天器的安全运行造成严重影响。基于国内外试验数据和案例分析了空间环境引起的航天器故障,从数值仿真软件、地面模拟技术、强场诱发放电以及防护技术等方面介绍了空间环境作用下航天器充放电效应研究进展,对我国目前研究差距和未来研究方向进行了展望。研究表明：我国航天器充放电效应防护技术研究取得了进步,下一步重点针对空间站、深空探测、探月工程等新任务,进一步拓展空间环境作用下航天器充放电效应机理和防护新技术研究,为提升我国航天器的安全性和可靠性提供技术支撑。     

利用完全复数极限学习机增强近场声全息空间分辨率

针对稀疏测量阵列条件下近场声全息重建结果空间分辨率不足的问题,提出了一种基于完全复数极限学习机的全息声压插值方法。该方法首先将已测量的全息面复声压和对应的测点坐标组成训练样本输入完全复数极限学习机,接着把插值点的坐标代入训练好的极限学习机,得到相应位置的复声压,实现全息数据的插值。利用插值后的全息数据进行重建,并与不做插值处理的重建结果和传统插值处理后的重建结果比较。仿真和实验结果均表明:与不做插值相比,该方法在不增加传声器的条件下显著提高了重建结果的空间分辨率。与基于支持向量机或传统极限学习机的插值方法相比,该方法速度更快,插值后重建结果精度更高。同时,通过添加噪声干扰验证了该方法的稳健性。      

基于深度约束的声速估算方法*

高精度的声速剖面数据是提高水下定位精度的关键。在无法精确获得声速剖面数据的情况下,该文提出了一种基于深度约束,同时根据浮标与水下目标的空间几何距离进行有效声速估算的方法。通过仿真模拟,将该文提出的方法与加权平均声速法、泰勒级数展开法和等效声速剖面法的定位结果进行对比分析。结果表明,新方法不需要依靠声速剖面数据,通过深度约束可有效地估算出声速值,其定位精度与等效声速结果相当,极大地提高了无声速剖面数据下的水下定位精度。     

Efficient estimation for semiparametric varying- coefficient partially linear regression models with current status data

This article considers a semiparametric varying-coefficient partially linear regression model with current status data. The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are conducted to examine the small-sample properties of the proposed estimates and a real dataset is used to illustrate our approach.     

Local linear regression for data with AR errors

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.     

非紧超凸度量空间中的极大元定理及其对极大极小问题和鞍点问题的应用

在非紧超凸度量空间中的非紧次允许子集中建立了一个极大元定理．作为应用,研究了Fan-Browder型不动点定理、KyFan极大极小不等式和鞍点定理．     

单圈图的最大特征值的上界的改进

Let G（V, E） be a unicyclic graph, Cm be a cycle of length m and Cm G, and ui ∈ V（Cm）. The G - E（Cm） are m trees, denoted by Ti, i = 1, 2,..., m. For i = 1, 2,..., m, let eui be the excentricity of ui in Ti and ec = max{eui ： i = 1, 2 , m}. Let κ = ec＋1. Forj = 1,2,...,k- 1, let δij = max{dv ： dist（v, ui） = j,v ∈ Ti}, δj = max{δij ： i = 1, 2,..., m}, δ0 = max{dui ： ui ∈ V（Cm）}. Then λ1（G）≤max{max 2≤j≤k-2 （√δj-1-1＋√δj-1）,2＋√δ0-2,√δ0-2＋√δ1-1}. If G ≌ Cn, then the equality holds, where λ1 （G） is the largest eigenvalue of the adjacency matrix of G.