相干信号源DOA估计改进ESPRIT算法研究

在目标方位估计(DOA)的众多算法中,ESPRIT是一种运算速度快、精度高的常用算法,但它不能解相干信号。提出一种基于观测数据直接空间平滑的改进型ESPRIT算法,解决了常规ESPRIT算法不能解相干、对信噪比要求高等问题。该方法适用于所有信号(非相干和相干信号)的目标方位估计。     

Tests for homogeneity of distributions of component lifetimes from system lifetime data with known system signatures

In this article, we discuss the problem of testing the homogeneity of distributions of component lifetimes based on system lifetime data when the system signatures are known. Both parametric and nonparametric procedures are developed for this problem. For nonparametric testing, the Mann–Whitney‐type statistic is used, and its performance and limitations are discussed. Next, we assume the component lifetimes to follow exponential distributions and then develop different parametric tests. Exact and asymptotic methods are developed based on the method of moments estimators. A Monte Carlo simulation study is used to compare the performance of different parametric procedures with that of the nonparametric procedure. Based on the results of the simulation study, discussions and practical recommendations are made and finally some concluding remarks are provided. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 550–563, 2015     

A note on the forecast performance of temporal aggregation

Earlier research on the effects of nonoverlapping temporal aggregation on demand forecasting showed the benefits associated with such an approach under a stationary AR(1) or MA(1) processes for decision making conducted at the disaggregate level. The first objective of this note is to extend those important results by considering a more general underlying demand process. The second objective is to assess the conditions under which aggregation may be a preferable approach for improving decision making at the aggregate level as well. We confirm the validity of previous results under more general conditions, and we show the increased benefit resulting from forecasting by temporal aggregation at lower frequency time units. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 489–500, 2014     

Designing acceptance sampling schemes for life testing with mixed censoring

Mixed censoring is useful extension of Type I and Type II censoring and combines some advantages of both types of censoring. This paper proposes a general Bayesian framework for designing a variable acceptance sampling scheme with mixed censoring. A general loss function which includes the sampling cost, the time‐consuming cost, the salvage value, and the decision loss is employed to determine the Bayes risk and the corresponding optimal sampling plan. An explicit expression of the Bayes risk is derived. The new model can easily be adapted to create life testing models for different distributions. Specifically, two commonly used distributions including the exponential distribution and the Weibull distribution are considered with a special decision loss function. We demonstrate that the proposed model is superior to models with Type I or Type II censoring. Numerical examples are reported to illustrate the effectiveness of the method proposed. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

离散型随机BAM神经网络时滞分布依赖全局指数稳定性

研究一类具有随机时滞与随机干扰的离散型BAM神经网络的全局指数稳定性,所建模型同时考虑离散时滞变化区间与分布概率对稳定性的影响．通过构造新的Lyapunov泛函并结合线性矩阵不等式（LMI）方法,得到了均方意义下依赖于时滞分布的全局指数稳定性条件．     

空间非平稳高斯噪声背景下混合信号的DOA估计

针对空间非平稳高斯噪声背景下混合信号的DOA估计问题,提出了基于四阶累积量对混合波源的DOA估计算法。该算法首先构造四阶累积量矩阵,利用类似ESPR IT方法估计出所有非相关信源的DOA,然后对四阶累积量进行修正空间差分平滑去相干,消除非相关源和空间非平稳高斯噪声的影响,从而实现了对相干源的DOA估计。此方法通过分别估计非相关和相关或者相干信号的DOA,能够以较少的阵元实现对多个信号DOA估计,最后仿真实验也验证该算法的有效性。     

装甲车辆使用与保障费用的指数回归模型

运用逐步回归和主成分分析的方法,选取了装甲车辆诸多战技性能指标集合中的自变量子集,建立了该子集与使用与保障费用之间指数回归方程组.该模型也可推广应用于装甲车辆综合性能的评估.     

GM(1，1)模型与指数回归模型的比较与研究

GM(1,1)模型与指数回归模型都是对具有指数或近似指数规律的系列数据建立的数学模型。通过对两种模型进行比较与研究,发现一方面,利用数据列的灰指数特性建立指数回归模型,可以简单地实现数据列的预测过程;另一方面,利用指数回归模型可以对灰色理论的GM(1,1)模型进行控制和优化。     

The impact of exponential smoothing forecasts on the bullwhip effect

An important phenomenon often observed in supply chain management, known as the bullwhip effect, implies that demand variability increases as one moves up the supply chain, i.e., as one moves away from customer demand. In this paper we quantify this effect for simple, two‐stage, supply chains consisting of a single retailer and a single manufacturer. We demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the use of a moving average forecast. We consider two types of demand processes, a correlated demand process and a demand process with a linear trend. We then discuss several important managerial insights that can be drawn from this research. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 269–286, 2000     

混合指数分布模型的Bayes分析

针对截尾试验数据的情况,给出了二元混合指数分布模型的平均寿命和可靠性函数的严格的Ｂａｙｅｓ点估计,并运用最大熵准则给出了可靠性函数的近似的Ｂａｙｅｓ置信下限估计。