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Choosing arrival process models for service systems: Tests of a nonhomogeneous Poisson process
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Service systems such as call centers and hospital emergency rooms typically have strongly time‐varying arrival rates. Thus, a nonhomogeneous Poisson process (NHPP) is a natural model for the arrival process in a queueing model for performance analysis. Nevertheless, it is important to perform statistical tests with service system data to confirm that an NHPP is actually appropriate, as emphasized by Brown et al. [8]. They suggested a specific statistical test based on the Kolmogorov–Smirnov (KS) statistic after exploiting the conditional‐uniform (CU) property to transform the NHPP into a sequence of i.i.d. random variables uniformly distributed on [0,1] and then performing a logarithmic transformation of the data. We investigate why it is important to perform the final data transformation and consider what form it should take. We conduct extensive simulation experiments to study the power of these alternative statistical tests. We conclude that the general approach of Brown et al. [8] is excellent, but that an alternative data transformation proposed by Lewis [22], drawing upon Durbin [10], produces a test of an NHPP test with consistently greater power. We also conclude that the KS test after the CU transformation, without any additional data transformation, tends to be best to test against alternative hypotheses that primarily differ from an NHPP only through stochastic and time dependence. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 66–90, 2014 相似文献
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单站无源定位跟踪技术中,可观测性分析是一个关键问题,但是由于TOA测量方程的非线性程度太高,直接通过计算Jacobin矩阵得到的可观测矩阵形式非常复杂,很难得出明确的分析结果。采用新的思路推导了一种TOA测量方程的线性化方程,分析了测量TOA和DOA单站无源定位的可观测条件。最后给出了计算机仿真结果。 相似文献
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网络入侵检测的快速规则匹配算法 总被引:1,自引:0,他引:1
在分析入侵检测系统的基础上,指出现有规则匹配算法的不足.提出一种新的规则匹配算法,该算法主要利用非精确匹配技术,缩小入侵分类的检测范围,达到快速匹配的目的.根据不同的安全性要求设置不同的门限值,该算法可用于预测适合不同门限值的可疑入侵行为. 相似文献
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We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap. 相似文献
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讨论了自适应抗干扰滤波在最小频移键控的直接序列扩频(DS-MSK)数字化接收机中的应用问题,对非线性滤波加以改进,提出了部分解扩非线性滤波(PDNF)结构,将快速更新子带自适应滤波(FRSAF)算法用于PDNF结构以提高收敛速度。结合FRSAF算法的PDNF自适应抗干扰滤波方案在收敛速度、稳健性和输出信噪比等方面明显优于结合LMS算法的传统非线性抗干扰滤波方案,仿真结果验证了上述结论。 相似文献
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基于通信信号中的时延信息,构建了基本TDOA定位算法,详细推导出了其理论定位精度分布及理论上最优定位点精度,为其定位的主要误差影响因素分析,及基于时延信息的被动定位算法的设计和改进力度提供了参考依据。并结合通信信号实际环境,给出了较为全面的仿真案例验算。 相似文献
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主要对双声纳基阵二维目标联合测向交叉定位算法及精度进行了分析,给出了系统具体的定位公式和误差计算公式,并针对不同基线和不同基线误差、方位误差下目标GDOP的分布情况进行了仿真分析。仿真结果表明,定位误差随基线长度的增加而减小,随基线和方位误差的增大而增大,当基线增大到一定长度时.定位误差最终趋向稳定,达到最小。 相似文献