基于加窗非均匀采样周期信号重建改进方法

提出了一种基于窗函数正弦参数估计的重建非均匀采样周期信号迭代算法。该算法将非均匀采样值利用加窗正弦参数估计算法得到迭代初始值 ,然后利用最小化误差能量方法 ,求出迭代算法公式的自适应系数 ,最后重建原信号 ,可以在较少迭代次数下得到相当精确的结果。计算机仿真实验结果与已有算法比较证明了该方法的有效性和先进性     

美军引信靶场试验抽样方案研究

该文对美军引信抽样验收常用的(3,1)转移规则,应用母函数法推导了其复合特性函数的计算公式,找到了美军引信靶场试验抽样方案组一般不用MIL-STD-105D的原因,进而确立了引信靶场试验抽样方案组的一般设计原则。     

交流采样法监测电功率及电量的研究

本文以交流采样为出发点,论述了电网的微机监测系统测量电功率及电量的数学模型和实践方法。使系统的硬件结构大为简化,经济效益有所提高,同时还扩展了主机功能。故这一方法具有很好的推广价值。     

气溶胶线性吸收系数及其吸收可见辐射的研究

基于测量气溶胶吸收系数的粒子采样法,导出了表征气溶胶总体吸收性能的线性吸收系数E_l,从而简化了测量设备和手续,为研究气溶胶辐射吸收性能提供廉价而简便的测量工具。分析和实验表明,E_l实质上是Beer—Lambart定律中吸收系数Ka的合理近似。在纽约州奥尔巴尼和南京近郊外场观测表明,E_l值分别是10~(-6)-10~(-5)M~(-1)和10~(-5)M~(-1)。工业城市和排放源的下风方的E_l值普遍较大。E_l值与能见度呈反向趋势。     

Distributed genetic algorithm for optimal planar arrays of aperture synthesis telescope

Sparse arrays of telescopes have a limited (u, v)-plane coverage. In this paper, an optimization method for designing planar arrays of an aperture synthesis telescope is proposed that is based on distributed genetic algorithm. This distributed genetic algorithm is implemented on a network of workstations using community communication model. Such an aperture synthesis system performs with imperfection of (u, v) components caused by deviations and(or) some missing baselines. With the maximum (u, v)-plane coverage of this rotation-optimized array, the image of the source reconstructed by inverse Fourier transform is satisfactory.     

贝叶斯网络推理的一种仿真算法

贝叶斯网络是一种强有力的不确定性知识表达和推理工具。网络的推理是贝叶斯网络的重要内容之一。该文提出了一种近似仿真算法。由随机数发生器产生随机数，并按节点的先验概率，由赌轮对网络各个节点状态赋值，得到一个采样样本序列。当样本序列的数量足够大时，边缘统计量和条件统计量与节点的边缘概率和条件概率接近，从而得到网络的近似推理结果。仿真结果表明，该算法与精确解接近，有较好的适应性。基于该算法构造的贝叶斯诊断网络系统已成功应用于天津石化炼油厂。     

The generation of random ultrametric matrices representing dendrograms

Many methods and algorithms to generate random trees of many kinds have been proposed in the literature. No procedure exists however for the generation of dendrograms with randomized fusion levels. Randomized dendrograms can be obtained by randomizing the associated cophenetic matrix. Two algorithms are described. The first one generates completely random dendrograms, i.e., trees with a random topology, random fusion level values, and random assignment of the labels. The second algorithm uses a double-permutation procedure to randomize a given dendrogram; it proceeds by randomization of the fixed fusion levels, instead of using random fusion level values. A proof is presented that the double-permutation procedure is a Uniform Random Generation Algorithmsensu Furnas (1984), and a complete example is given. This work was supported by NSERC Grant No. A7738 to P. Legendre and by a NSERC scholarship to F.-J. Lapointe.     

成人服装号型系列数实际应用研究

本文结合服装产销的实际以及目前我国成人人体的特点．对服装号型系列展开研究，提出新的304服装号型系列，能以较少的号型量，满足较多的消费者体型，有利于消费者选购合体的服装，更有利于服装厂商拓展市场。     

Bayesian analysis of concurrent time series with application to regional IBM revenue data

Business data frequently arise in the form of concurrent time series. We present a general framework for simultaneous modeling and fitting of such series using the class of Box—Jenkins models. This framework is an exchangeable hierarchical Bayesian model incorporating dependence among the series. Our motivating data set consists of regional IBM revenue available monthly for several geographic regions. Stationary seasonal autoregressive models are simultaneously fit to the regional data series using various error covariance specifications for the strong interregional dependence. A modified Gibbs sampling algorithm is used to carry out the fitting and to enable all subsequent inference. Graphical techniques using predictive distributions are employed to assess model adequacy and to select among models. Outlier estimation and prediction under the chosen model are used for planning and to measure the effect of special promotional events.