Multivariate meta analysis with potentially correlated marketing study results

A univariate meta analysis is often used to summarize various study results on the same research hypothesis concerning an effect of interest. When several marketing studies produce sets of more than one effect, multivariate meta analysis can be conducted. Problems one might have with such a multivariate meta analysis are: (1) Several effects estimated in one model could be correlated to each other but their correlation is seldom published and (2) an estimated effect in one model could be correlated to the corresponding effect in the other model due to similar model specification or the data set partly shared, but their correlation is not known. Situations like (2) happen often in military recruiting studies. We employ a Monte‐Carlo simulation to evaluate how neglecting such potential correlation affects the result of a multivariate meta analysis in terms of Type I, Type II errors, and MSE. Simulation results indicate that such effect is not significant. What matters is rather the size of the variance component due to random error in multivariate effects. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 500–510, 2000.     

Estimating distribution functions from partially observed samples

Suppose that some components are initially operated in a certain condition and then switched to operating in a different condition. Working hours of the components in condition 1 and condition 2 are respectively observed. Of interest is the lifetime distribution F of the component in the second condition only, i.e., the distribution without the prior exposure to the first condition. In this paper, we propose a method to transform the lifetime obtained in condition 1 to an equivalent lifetime in condition 2 and then use the transformed data to estimate F. Both parametric and nonparametric approaches each with complete and censored data are discussed. Numerical studies are presented to investigate the performance of the method. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 521–530, 2000     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.     

GSVM:一种支持Gather/Scatter的向量存储器

宽单指令多数据流(Single Instruction Multiple Data, SIMD)架构数字信号处理器一般都能高效支持地址连续或等距跨步等规则应用的向量访存,但对于科学与工程计算中广泛存在的不规则应用的数据访存则带宽利用率往往较低,从而大幅降低了其整体运算能效。为了提高不规则应用的向量访存性能,基于某SIMD数字信号处理器的体系结构,设计了一种支持Gather/Scatter访存的向量存储器GSVM。通过设计与SIMD宽度相匹配的向量地址计算单元和合适深度的冲突缓冲器阵列,实现了Gather/Scatter指令向量地址计算、仲裁与缓存的全流水访存操作。实验结果表明,相比以前不支持Gather/Scatter访存的存储器,GSVM在增加22%的硬件代价基础上,基于稀疏矩阵向量乘的测试程序集获得了2～8的性能加速比。     

随机-区间混合不确定性单输出模型确认指标

针对既有随机变量又有区间变量的模型预测结果与实验数据之间的一致性度量问题,对随机-区间混合不确定性模型确认指标进行研究。根据工程数学模型和实验过程中的不确定性来源,分析随机-区间混合不确定性模型确认的特点;运用概率方法和区间理论,提出新的随机-区间混合不确定性模型确认指标,讨论所提指标的性质,给出指标的计算方法和步骤。通过数字算例和工程算例,验证了所提指标的可行性和有效性。     

随机-非概率模型下的船体结构屈服强度可靠性分析

针对在役舰船船体载荷、材料属性的随机特性和实船勘验得出的腐蚀损伤下船体抗力区间特性,提出一种基于随机概率理论和区间非概率理论的随机-非概率可靠性分析模型,分别从区间变量随机化和随机变量区间化两个角度给出该模型的可靠性指标求解方法。基于该模型对某舰船船体结构屈服强度进行可靠性分析,结果表明:该模型既体现了结构变量的客观不确定性又减弱了评估结果的盲目保守性,对于既含有随机变量又含有区间变量的在役舰船的可靠性评估具有很强的适用性。     

动态疏散逃生系统研究及其应用

保证人员疏散安全是建筑防火安全设计的重要目标之一,而疏散逃生设施是疏散设施不可或缺的组成部分。分别从理念、技术和应用层面介绍动态疏散逃生系统在机场环境中的应用,该系统实现了信息化控制管理,可根据火灾实时情况动态引导人员疏散,具有语音、调整疏散路径、光流等特点,实现＂安全、准确、迅速＂地引导人员避烟逃生,较以往应急疏散指示灯具具有更高的可靠性和安全性。     

多维度把握中国特色社会主义的科学定位

所谓“中国特色社会主义”,主要强调中国社会主义发展道路的独特性,可以从四个角度对其内涵进行界定：经济文化落后的基本国情决定了其与科学社会主义的区别;一系列鲜明的时代特征体现了其与传统社会主义模式的迥异;社会主义现代化的道路又有别于资本主义现代化的发展道路;两种截然不同的政治道路则划清了其与民主社会主义的界限。     

考虑细观结构变化的原状Q3黄土变形和强度特性

为研究结构性对非饱和原状Q3黄土变形和强度特性的影响,做了12个非饱和原状Q3黄土的CT-三轴侧向卸荷试验。在试验剪切过程中,利用CT机对试样进行跟踪扫描,共得到168张CT图像。基于试验数据建立了细观结构参数,并利用该参数来反映非饱和原状Q3黄土的变形和强度特性,从而得到能反映细观结构性影响的非线性应力-应变关系以及摩尔-库伦强度公式。     

浅谈教师对数学学业不良生的影响

本文通过教师的信念、课堂教学、教师的问题意识三个方面的探讨,来论述教师对数学学业不良生的影响,希望通过教师的作用有效促进数学学业不良生的转化。