缺失数据下m相依样本的大样本性质

讨论了缺失数据在随机补充方式下均值的估计,证明了估计的相合性和渐进正态性,并给出了经验似然比统计量的渐进分布．     

滚齿加工中最大齿根圆角半径的简化计算

为快速验证所设计齿轮加工工艺性,本文提出了一种简化设计滚齿刀轴向剖面尺寸及一种简化计算滚齿刀所能加工齿轮最大齿根圆角的新方法。通过实践验证,本方法具有准确、实用和高效的优点,对齿轮优化设计具有一定指导意义。     

具有Rao简单结构的多元t-模型的异常点检验

异常点识别是统计诊断的主要研究问题之一.本文利用均值漂移模型和似然比检验,研究了具有Rao简单结构的多元t-模型的异常点识别问题,给出了判别异常点的方法.     

用PSO方法搜索基于MLE的ARMA模型参数

针对小样本条件下用矩估计(ME)方法获取ARMA模型参数粗略的缺点,将粒子群优化算法(PSO)用于小样本ARMA模型参数的极大似然估计(MLE),以获得概率上最优的数字解。在分析基于ARMA模型似然函数的基础上,详细分析了PSO的思想、方法和评价指标。以实际例证显示了联合PSO优化方法估计AMAR模型参数的优良特性,并从算法和似然函数角度分别阐释了形成利弊的原因。     

Computational fluid dynamics (CFD) modeling of spouted bed: Influence of frictional stress, maximum packing limit and coefficient of restitution of particles

Following the previous article [Du, W., Bao, X., Xu, J., Wei, W., 2006. Computational fluid dynamics (CFD) modeling of spouted bed: assessment of drag coefficient correlations. Chemical Engineering Science 61 (5), 1401-1420], this contribution describes the influences of the frictional stress, maximum packing limit and coefficient of restitution of particles on CFD simulation of spouted beds. Using the two-fluid method embedded in the commercial CFD simulation package Fluent 6.1, the spouting hydrodynamics of a cylindrical-conical spouted bed was simulated and verified with the experimental data of He et al. [He, Y.L., Lim, C.J., Grace, J.R., Zhu, J.X., Qin, S.Z., 1994a. Measurements of voidage profiles in spouted beds. Canadian Journal of Chemical Engineering 72 (4), 229-234; He, Y.L., Qin, S.Z., Lim, C.J., Grace, J.R., 1994b. Particle velocity profiles and solid flow patterns in spouted beds. Canadian Journal of Chemical Engineering 72(8), 561-568]. The results showed that, for coarse particles, the frictional stress is important only for the annulus computation and has no obvious effects on the hydrodynamics of the solids flow in the spout region. The specification of the maximum packing limit could significantly affect the properties of the pseudo-fluid phase of the particles by changing the radial distribution function. The strong dependence of the pseudo-fluid properties of the particle phase, such as pressure, bulk viscosity and shear viscosity, on the granular temperature accounts for the influence of the coefficient of restitution of particles on CFD modeling. The solids volume fraction at loose packing state is suitable for spouted bed simulations, and a pretest of the coefficient of restitution of particles must be conducted when no experimental datum is available.     

Adaptive detectors in the Krylov subspace

The validity of the application of the Krylov subspace techniques in adaptive filtering and detection is investigated. A new verification of the equivalence of two well-known methods in the Krylov subspace, namely the multistage Wiener filters （MWF） and the auxiliary-vector filtering （AVF）, is given in this paper. The MWF and AVF are incorporated into two well-known detectors, namely, the adaptive matched filter （AMF） and Kelly＇s generalized likelihood ratio test （CLRT） including their diagonally loaded versions, which form new detectors. Compared to the conventional AMF, CLRT, and their diagonally loaded versions as well as the reduced-rank AMF and GLRT, the probabilities of detection （PDs） of the new detectors are improved especially when the sample support is low. More importantly, the new detectors are robust of the rank selection of the clutter subspace compared to the reduced-rank AMF and GLRT. These new detectors all possess asymptotic constant false alarm rate （CFAR） property.     

梯级水电枢纽群巨灾风险分析与防控研究综述

伴随超标准巨震、极端暴雨洪水、巨型滑坡等灾害频繁发生,极端载荷作用下梯级水电枢纽群的灾害风险分析与防控等问题成为当前水利工程领域的研究热点。为分析梯级水电枢纽群巨灾风险研究现状,绘制了国内外水库大坝溃坝事件的时间序列图,分析了梯级水电枢纽群的风险特征,总结评述了梯级水电枢纽群巨灾风险分析和巨灾防控研究进展,主要结论如下：1)梯级水电枢纽群巨灾风险是我国水利水电工程风险防控面临的主要问题;2)梯级水电枢纽群风险分析方面主要聚焦于梯级水库连溃概率的分析和计算,对于溃决可能产生的巨灾损失的量化研究不足,缺乏对巨灾因子及其相关影响作用下巨灾风险的评估;3)缺乏对梯级枢纽群灾害链的阻断技术研究和应急避险方案设计研究。为此提出了梯级水电枢纽群可能最大灾难(Probable Maximum Disaster, PMD)的科学内涵,考虑可能遭遇的多种致灾因子和承灾体特征,分析相互因果关系和极端荷载组合情况,初步建立了PMD评估的理论模型,为绘制梯级水电枢纽群在巨灾情景下的灾难空间外包线和估算PMD损失上限值提供科学依据,为梯级水电枢纽群巨灾风险分析和防控提供理论基础和技术支持。     

基于双重权重偏差建模的无监督域适应

无监督域适应(Unsupervised Domain Adaptation,UDA)是一类新兴的机器学习范式,其通过对源域知识在无标记目标域上的迁移利用,来促进目标域模型的训练。为建模源域与目标域之间的域分布差异,最大均值差异(Maximum Mean Discrepancy,MMD)建模被广泛应用,其对UDA的性能提升起到了有效的促进作用。然而,这些方法通常忽视了领域之间对应类规模与类分布等结构信息,因为目标域与源域的数据类规模与数据分布通常并非一致。为此,文中提出了一种基于跨域类和数据样本双重加权的无监督域适应模型(Sample weighted and Class weighted based Unsupervised Domain Adaptation Network,SCUDAN)。具体而言,一方面,通过源域类层面的适应性加权来调整源域类权重,以实现源域与目标域之间的类分布对齐;另一方面,通过目标域样本层面的适应性加权来调整目标域样本权重,以实现目标域与源域类中心的对齐。此外,文中还提出了一种CEM(Classification Expectation Maximization)优化算法,以实现对SCUDAN的优化求解。最后,通过对比实验和分析,验证了所提模型和算法的有效性。     

极值-I型风速预测的Bayes方法

为提高极值-I型风速预测精度,在Jeffreys准则的基础上,采用Bayes估计中的Lindley近似方法推导极值-I型风速预测表达式.采用Monte Carlo法产生服从极值-I型分布的伪风速母样,基于伪风速母样分别采用基于Bayes理论和最大似然估计理论的极值-I型风速预测方法进行风速预测,并与伪风速母样的理论值进行对比分析.结果表明:与最大似然估计法相比,采用基于Bayes理论建立的极值-I型风速预测模型进行风速预测的精度更高,且精度随着伪风速母样样本量的增加而提高,位置参数先验样本数量的增加以及先验方差的增大对计算精度没有影响.     

Registration based compensation method based on adaptive subspace tracking

The registration based compensation (RBC) method is an effective method to compensate the range-dependence of the main-lobe clutter and side-lobe clutter in the same time. However, the compensation performance of the RBC degrades because of the mismatch of prior information and the loss of degree of system freedom. Moreover, the RBC is not very suited for real-time implementation because of the enormous computational complexity and memory usage of eigenvalue decomposition. In this paper, a novel clutter range-dependence compensation method using the modified maximum likelihood adaptive subspace estimation algorithm, which is named the MRBC method for short, is proposed. The eigenvectors matrix and eigenvalues matrix of the clutter covariance matrix are estimated by iterative tracking instead of temporal and spatial smoothing, spectrum calculation and eigenvalue decomposition. Compared with the traditional RBC method, the proposed method can reduce the computational complexity significantly and maintain the performance of clutter range-dependence compensation. In addition, the proposed method can also achieve good performance when the system error exists because of no use of prior information. Experimental simulations demonstrate the validity of this method.