系统评估效度分析的定量化方法

系统评估效度的定量化分析是评估后检验的有效方法,对于判断评估方法的相容性、评估结果的合理性具有重要意义。研究了等级相关系数法、兼容度优化法、肯德尔和谐系数法和模糊聚类法等4种效度分析方法的原理和步骤,分析了各种效度分析方法的优缺点,并通过实例验证了4种方法分析结果的一致性。     

基于公平谓词法的JMS消息过滤改进算法

研究了JMS发布／订阅消息传递模式的工作机制,分析了JMS在对事件进行匹配时的过滤算法,针对该算法的不足提出了采用公平谓词法来改进JMS的消息过滤速度,改进方法对基于JMS的发布／订阅应用程序的开发具有一定的指导意义。     

基于主成分法的侦察雷达系统效能评价*

为评估雷达系统的效能优劣,应用主成分分析法建立雷达系统效能评估模型,引入施密特正交法对原始数据进行预处理,解决了由于指标间的多重相关性导致的结果失真问题,并以某型雷达及其三种改进型的系统效能评估为例,进行了实例仿真,为评估和改进现有侦察雷达的效能提供了参照。     

基于改进AHP与集对分析法的防空兵群阵地选择

防空兵群阵地选择是防空兵力部署的前提条件,阵地选择与配置的正确与否是防空兵部队能否有效进行抗击敌空袭兵器的一个重要因素.在分析以往防空阵地优选方法不足的基础上,提出了基于改进的层次分析与集对分析的多因素优选方法,建立了防空兵群配置地域方案优选的模型.通过实例分析了影响防空兵群阵地选择的相关因素,确定了配置地域优选的指标体系,用三标度法确定指标因素的权重,以相对贴近度的大小对待选阵地进行优选和排序.该方法能够定性与定量相结合,模型简单规范,为防空兵群阵地优选提供了一种有效的方法和途径.     

MIMO-OFDM最优训练序列设计

推导得到了宽带 MIMO-OFDM 信道模型。在空间相关情况下推导了非盲信道估计的 Bayesian CRB及 Bayesian MMSE。利用 Bayesian CRB 准则对发送的训练序列进行了功率分配,而后利用一种线性规划的方法对带有保护带宽 MIMO-OFDM 训练符号进行子载波功率分配,并利用发端酉矩阵进行最优相位设计。     

一种面向频域的通信信号盲干扰检测算法

针对低干信比和复杂动态情况下传统的干扰检测算法效率不高的问题,提出一种基于盲通信信号频域处理的未知干扰自动检测算法。该方法首先借助快速独立成份分析(FICA)从频谱差分数据中分离干扰和通信信号成份分量,然后利用各分量的峰度作为干扰与通信信号的识别特征检测干扰。实验结果表明,此算法能有效地在干信比小于5 dB的情况下从盲通信信号背景中自动提取未知干扰的信息,实现高精度地盲干扰检测。     

基于S变换的跳频信号参数估计

对跳频信号的时频域进行分析和参数估计是识别、跟踪及干扰跳频信号的重要基础。针对接收到的未知任何先验参数的跳频信号,提出了一种基于S变换(S-transform)的跳频信号参数估计算法,通过S变换,能正确估计出跳频信号的跳频周期、跳变时刻及跳频图案。仿真结果表明,与平滑伪Wigner-Ville分布(SPWVD)进行比较,该算法能够在低信噪比条件下,在时频域中有较好的解析能力,提高了跳频信号参数估计的准确性。     

多输入多输出雷达恒模稳健波形联合优化算法

针对目标冲激响应及杂波冲激响应分布特性先验知识不准确导致的多输入多输出雷达检测性能下降的问题,提出恒模稳健波形与接收机滤波器联合优化算法。将目标冲激响应及杂波冲激响应分布特性先验知识不准确时的优化问题建模为一个极大极小化问题。运用迭代优化算法将联合优化问题分解为两个子优化步骤:将波形固定时的接收机滤波器权值优化问题建模为广义瑞利商模型,求解得到相应的接收机滤波器权值矢量;利用半正定松弛技术对权值固定时的波形优化问题进行求解,获得对应的波形矩阵,并根据得到的波形矩阵,通过高斯随机化的方法获得所需的恒模波形。对所提算法的收敛性进行了证明,仿真结果表明所提算法有效。     

Steady‐state diffusion approximations for discrete‐time queue in hospital inpatient flow management

In this article, we analyze a discrete‐time queue that is motivated from studying hospital inpatient flow management, where the customer count process captures the midnight inpatient census. The stationary distribution of the customer count has no explicit form and is difficult to compute in certain parameter regimes. Using the Stein's method framework, we identify a continuous random variable to approximate the steady‐state customer count. The continuous random variable corresponds to the stationary distribution of a diffusion process with state‐dependent diffusion coefficients. We characterize the error bounds of this approximation under a variety of system load conditions—from lightly loaded to heavily loaded. We also identify the critical role that the service rate plays in the convergence rate of the error bounds. We perform extensive numerical experiments to support the theoretical findings and to demonstrate the approximation quality. In particular, we show that our approximation performs better than those based on constant diffusion coefficients when the number of servers is small, which is relevant to decision making in a single hospital ward.     

Aggregating courier deliveries

We consider the problem of efficiently scheduling deliveries by an uncapacitated courier from a central location under online arrivals. We consider both adversary‐controlled and Poisson arrival processes. In the adversarial setting we provide a randomized (3βΔ/2δ ? 1) ‐competitive algorithm, where β is the approximation ratio of the traveling salesman problem, δ is the minimum distance between the central location and any customer, and Δ is the length of the optimal traveling salesman tour overall customer locations and the central location. We provide instances showing that this analysis is tight. We also prove a 1 + 0.271Δ/δ lower‐bound on the competitive ratio of any algorithm in this setting. In the Poisson setting, we relax our assumption of deterministic travel times by assuming that travel times are distributed with a mean equal to the excursion length. We prove that optimal policies in this setting follow a threshold structure and describe this structure. For the half‐line metric space we bound the performance of the randomized algorithm in the Poisson setting, and show through numerical experiments that the performance of the algorithm is often much better than this bound.