Double Mover–Stayer model on customer switching in telecommunications industry

Customer acquisition and customer retention are the most important challenges in the increasingly competitive telecommunications industry. Traditional studies of customer switching always assume that customers are homogeneous, and thus that model customer switching behavior follows a Markov formulation. However, this postulation is obviously inappropriate in most instances. Blumen et al. (Cornell Studies of Industrial and Labor Relations, Cornell University Press, Ithaca, NY, 1955) developed the Mover–Stayer (MS) model, a generalization of the Markov chain model, to relax the requirement of homogeneity and allow the presence of heterogeneity with two different types of individuals—“stayers,” who purchase the same kinds of products or services throughout the entire observation period; and “movers,” who look for variety in products or services over time. There are two purpose of this article. First, we extend the MS model to a Double Mover‐Stayer (DMS) model by assuming the existence of three types of individuals in the market: (1) stable and loyal customers, who have stable usage within the same company; (2) instable but loyal customers, whose usage varies within the same company over time; and (3) disloyal customers, who switch from one company to another to seek for new experiences or/and benefits. We also propose an estimation method for the DMS model. Second, we apply the DMS model to telecommunications data and demonstrate how it can be used for pattern identification, hidden knowledge discovery, and decision making. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

《连续系统建模》的计算机辅助学习与考核系统

本文介绍了一个用于《连续系统建模》学习与考核的计算机辅助教学课件的功能和设计原理,重点介绍了系统动力学模型图的知识表达和建模行为的有效性度量的方法。     

防空导弹武器系统抢修性时间参数模型

抢修性作为武器系统的一种特性,与武器系统本身的结构、损伤及抢修人员、工具等许多因素有关。防空导弹武器系统组成复杂,抢修性度量困难。在分析抢修性概念模型的基础上,从抢修性函数着手,推导并建立了防空导弹武器系统抢修性参数模型,最后通过算例对模型进行了验证,为解决防空导弹武器系统抢修性量化难题提供了一种有效的途径。     

磁性合金A1-xBx相图的理论计算

采用变分累积展开方法,研究了块体磁性合金的相图.利用随机键海森堡模型计算了面心立方、体心立方的居里点和耐尔点,给出了磁性合金临界点的计算公式,进而分别给出自旋1/2和3/2情况下Tc-x相图,并加以分析.     

浅析日历统筹图在制作资源优化计划中的应用

统筹法是一种科学的计划管理方法,绘制统筹图是掌握统筹法的基础。统筹图的优化,是在参数计算和分析的基础上,根据一定的约束条件对统筹图进行调整和改进,达到科学安排工作,合理分配人力、物力、财力等资源,从而提高工作效率的目的。日历统筹图能清楚地反映出工作进展的时间概念,并能直接提供一些有用的时间参数,便于优化、选择最佳方案。     

基于序列径向基函数的运载火箭蒙皮桁条结构轻质优化

以我国大型运载火箭薄壁加筋结构轻量化设计为研究背景,针对轴压下大直径大载荷蒙皮桁条结构开展后屈曲轻质优化研究。基于Python语言建立蒙皮桁条结构参数化模型,为确定结构的极限承载能力,开展蒙皮桁条结构后屈曲分析。针对蒙皮桁条结构轻质优化涉及离散的拓扑和连续的尺寸优化问题,提出基于近似模型和组合优化算法的序列近似优化方法,并利用近似最优解加速算法收敛,确定蒙皮桁条结构最优设计方案。工程算例结果表明,所提序列近似优化方法使大直径大载荷运载火箭蒙皮桁条结构减重效果明显,验证了方法的有效性。     

基于部件模型的无人机系统小样本车辆目标识别方法

对地目标检测与识别是无人机系统典型任务之一,但受限于任务特殊性,往往难以获取足够的目标样本数据以实现高可靠的目标识别。为此,结合人的认知特性,提出一种基于部件模型的小样本车辆目标识别方法,可有效提高无人机感知能力。采用视觉显著性检测与物体性检测相结合的检测方法,提取目标可能区域;采用基于图论的GrabCut方法与最大类间方差法相结合的分割方法,分割目标并提取目标内部件;采用基于概率图模型的部件识别方法,通过将部件轮廓稀疏表示为条件随机场,并进行概率推理实现部件识别;采用基于贝叶斯的目标识别方法完成目标是否为车辆的判断。通过无人机拍摄的车辆图像验证表明,算法可在样本较少、光照变化、存在遮挡等情况下,以较高准确率检测并识别出车辆目标,同时识别算法具有一定可解释性。     

结构主导目标检测中的纹理随机化

已有基于卷积神经网络的目标检测算法倾向于提取目标纹理特征,而非结构特征;因此,已有方法不能实现变纹理目标的可靠检测。针对此问题,提出基于纹理随机化的结构主导目标检测方法,采用仿真纹理随机化方法减弱网络模型对纹理特征的拟合,实现基于结构特征的变纹理目标可靠检测。利用目标的三维模型,借助Blender渲染引擎,完成纹理随机化仿真训练数据集的生成。仿真及真实图像实验测试结果表明:该方法能够实现基于目标结构特征的变纹理目标可靠检测。     

黏弹性基体中挠曲电Timoshenko纳米梁振动特性分析

以黏弹性基体中的Timoshenko纳米梁为研究对象,综合考虑非局部效应、压电效应和挠曲电效应的影响,基于哈密顿原理建立了系统的振动控制方程和相应的边界条件,给出了两端简支边界条件下挠曲电纳米梁控制方程的求解方法,系统地研究了非局部参数、挠曲电系数以及黏弹性基体对挠曲电纳米梁振动特性的影响规律。结果表明:横向挠曲电系数能显著增加挠曲电纳米梁的结构刚度,而非局部效应和切向挠曲电系数则会降低系统的结构刚度。此外,通过研究黏弹性基体的影响规律,可得到挠曲电纳米梁不再发生往复振动时对应的黏弹性基体临界阻尼系数。相关研究结果可为挠曲电纳米梁在俘能器中的推广应用提供理论基础。     

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive esti-mation algorithm is insensitive to initial conditions and of good robustness.