保守单流出非规则Markov链爆发后反复击中的若干性质

在Markov过程的研究中，首中与末离具有特殊重要的意义。我们将[1，2]性质进行了推广，对于保守单流出非规则的Narkov链，研究了该过程首次爆发后关于有界集合B的反复击中以及反复爆发的若干性质，特别地，该过程爆发后反复击中的性质可以通过爆发后首次击中的性质来表述。     

背越式跳高速度素质的训练手段

在总结对背越式跳高速度素质认识的基础上,通过对部分高校跳高教练员的调查与访谈,归纳出高校优秀背越式跳高运动员速度素质训练手段的选用特点,筛选出背越式跳高运动员速度素质效果较好的训练手段,以探讨训练实践中存在的问题,从而为广大教练员的训练实践提供有价值的参考.     

JDZ48C-A真空贴标机标签盒前后运动的运动规律设计

运动规律的设计，直接影响凸轮机构的运行精度。通过对凸轮机构运动规律的优选设计，提高机构的工作可靠性和工作精度。     

开环可重入排队网络的递阶增强型学习调度

对平均费用型马氏决策过程 ,研究了一种递阶增强型学习算法 ;并将算法应用于一个两台机器组成的开环可重入生产系统 ,计算机仿真结果表明 ,调度结果优于熟知的启发式调度策略.     

背越式跳高的大敌──过早倒体

过早倒体是背越式跳高教学训练中带有系普遍性而又难以改正的错误动作之一，它直接影响到技术的掌握和运动成绩的提高。对其错误动作产生的主要因素进行分析，以期对教学训练有所益处。     

A threshold stochastic volatility model

This article introduces a new model to capture simultaneously the mean and variance asymmetries in time series. Threshold non‐linearity is incorporated into the mean and variance specifications of a stochastic volatility model. Bayesian methods are adopted for parameter estimation. Forecasts of volatility and Value‐at‐Risk can also be obtained by sampling from suitable predictive distributions. Simulations demonstrate that the apparent variance asymmetry documented in the literature can be due to the neglect of mean asymmetry. Strong evidence of the mean and variance asymmetries was detected in US and Hong Kong data. Asymmetry in the variance persistence was also discovered in the Hong Kong stock market. Copyright © 2002 John Wiley & Sons, Ltd.     

一类脉冲控制模型的最小费用问题

研究了一类脉冲控制模型的最小费用问题，在一定条件下，给出了相应的最佳费用函数具体解析式。     

巡航导弹的被动声检测与分类技术

给出了一种基于被动声信号处理技术的巡航导弹被动声检测与分类技术。在检测目标时 ,综合利用目标声信号与环境信号的能量、过零率及线谱对频率的变化量构成多边界判决函数 ,在分类时提取Mel倒谱特征参数 ,利用隐马尔可夫模型进行分类。通过对实际的 4类目标噪声信号进行检测和分类实验 ,结果令人满意 ,证明了该方法的优越性和工程应用前景。     

A permutation-based algorithm for block clustering

Hartigan (1972) discusses the direct clustering of a matrix of data into homogeneous blocks. He introduces a stepwise divisive method for block clustering within a certain class of block structures which induce clustering trees for both row and column margins. While this class of structures is appealing, the stopping criterion for his method, which is based on asymptotic theory and the assumption that the individual elements of the data matrix are normally distributed, is quite restrictive. In this paper we propose a permutation-based algorithm for block clustering within the same class of block structures. By using permutation arguments to decide where to split and when to stop, our algorithm becomes applicable in a wide variety of cases, including matrices of categorical data and matrices of small-to-moderate size. In addition, our algorithm offers considerable flexibility in how block homogeneity is defined. The algorithm is studied in a series of simulation experiments on matrices of known structure, and illustrated in examples drawn from the fields of taxonomy, political science, and data architecture.