时间跨度对GPS坐标序列噪声模型及速度估计影响分析

选取全球范围内72个基准站的坐标序列,采用改进的赤池信息量准则、贝叶斯信息量准则对不同噪声模型组合进行噪声分析,得到基准站坐标序列的最优噪声模型及速度参数,探讨时间序列跨度对噪声模型及速度估计的影响。结果表明,基准站坐标序列噪声模型不能由单一的噪声模型表述,其呈现出多样性特征,主要表现为幂律噪声、高斯马尔科夫噪声、闪烁噪声+白噪声特征,且三坐标分量表现出不同的噪声特性;随着时间跨度的增加,坐标时间序列的最优噪声模型、GPS站速度及其不确定度逐渐由发散趋于收敛,随机游走噪声模型的比重有所增加。结果表明10 a以上的时间跨度是较为理想的噪声模型估计尺度。     

Newsvendor problems with sequentially revealed demand information

This article analyzes a capacity/inventory planning problem with a one‐time uncertain demand. There is a long procurement leadtime, but as some partial demand information is revealed, the firm is allowed to cancel some of the original capacity reservation at a certain fee or sell off some inventory at a lower price. The problem can be viewed as a generalization of the classic newsvendor problem and can be found in many applications. One key observation of the analysis is that the dynamic programming formulation of the problem is closely related to a recursion that arises in the study of a far more complex system, a series inventory system with stochastic demand over an infinite horizon. Using this equivalence, we characterize the optimal policy and assess the value of the additional demand information. We also extend the analysis to a richer model of information. Here, demand is driven by an underlying Markov process, representing economic conditions, weather, market competition, and other environmental factors. Interestingly, under this more general model, the connection to the series inventory system is different. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 2012     

群时延傅里叶分解模型及其估计方法

建立了基于三角函数级数的群时延模型,并理论证明了仅采用有限阶数的三角函数群时延即可表征任意群时延对信号相关峰的影响。在此基础上,通过测量信号相关峰,并与不同三角函数群时延组合下的相关峰进行匹配搜索即可估计群时延的三角函数分解级数,从而估计得到相关峰影响等效的群时延特性。仿真中,采用该方法估计得到的群时延特性对滤波器进行修正后,信号相关峰与经过滤波器之前的信号相关峰高度吻合,时延估计偏差在0.1 ns之内。     

高铁大风预警模式挖掘

高铁大风预警的传统方法基于风速预测,当瞬时值高于限速阈值时触发报警,存在大量的误报警,不必要的限速控制影响了高铁行车效率。创新地提出了基于序列模式的预警方法,旨在挖掘报警事件前序数据中的频繁模式,找出报警事件的变化规律,通过滤除与非预警序列共有的频繁模式,得到预警序列独有的序列特征,构建了预警模式库。经兰新高铁沿线的监测数据验证,该方法在提高预测准确率的基础上降低了漏报率,同时有效地减少了模式匹配所需的时间,为提前预警预留充分的时间窗口,更加符合实际应用的需求。     

Optimal allocation of minimal repairs in parallel and series systems

In reliability engineering, the concept of minimal repair describes that the repair brings the failed unit (eg, system or component) to the situation which is same as it was just before the failure. With the help of the well‐known Gamma‐Poisson relationship, this paper investigates optimal allocation strategies of minimal repairs for parallel and series systems through implementing stochastic comparisons of various allocation policies in terms of the hazard rate, the reversed hazard rate, and the likelihood ratio orderings. Numerical examples are presented to illustrate these findings as well. These results not only strengthen and generalize some known ones in the seminal work of Shaked and Shanthikumar, but also solve the open problems proposed by Chahkandi et al.'s study and Arriaza et al.'s study.     

基于连分式的简化拉普拉斯函数递推算法

根据简化拉普拉斯函数级数展开和将级数展开为连分式的方法,将简化拉普拉斯函数化为连分式形式。推导出基于连分式的简化拉普拉斯函数渐近分数的递推计算公式,讨论了递推计算中误差控制的方法。计算结果表明,该算法简单实用,在炮兵及防空兵火力毁伤概率计算中有应用价值。     

Volterra级数模型的非线性压缩测量辨识算法

针对非线性系统的辨识问题,提出了非线性压缩测量辨识算法,且推导出了一种符合压缩感知测量准则的测量模型。相比递归最小二乘法,该方法极大地减少了所需的测量数,使得高阶Volterra级数辨识成为可能。此外,还分析了实际应用中的各项因素对辨识准确性的影响,如信号稀疏度、测量噪声、测量矩阵形式等。     

量测误差的时间序列分析方法

针对工程实践中实际量测误差大多并非纯白噪声特点,运用概率统计学的一个分支——时间序列分析中提供的动态数据处理的方法,采用多种的参数估计算法来对量测误差进行拟合和仿真,给出了实验室仿真过程中需叠加的量测误差的新生成办法。所用方法可以推广到对其它数据序列的处理。     

基于模糊时间序列的时变动态系统模糊建模

要想建立结构不确定、参数时变的动态系统的模型是很困难的。改进了Song Chisson的模糊时间序列方法,并成功应用于时变动态系统的辨识与建模和自适应控制。仿真结果表明该方法计算量小,实时性好,能很快地跟踪系统的变化。     

超声速混合层发展情况的非线性时间序列分析

采用大涡模拟数值方法,模拟了具有工程应用背景的二维超声速湍流混合层,通过非线性时间序列分析伪相图、关联维数和Lyapunov指数,得到了混合层发展情况的混沌特性.结果表明,混合层中心线沿流向位置的压力伪相图可定性地表示混合层的稳定性,关联维数分布可定量描述混合层经历的线性失稳、非线性失稳和涡合并阶段等发展情况,最大Lyapunov指数分布作为关联维数分析结果的验证.对于相应的超声速混合层实验,采用非线性时间序列分析方法研究混合层发展情况具有通用性.