基于小波Radon变换检测线性调频信号

对信号小波分析的结果进行Radon变换,简称小波Radon变换。通常情况下,线性调频信号小波变换的结果在时间尺度图上呈现曲线的形式,而在时频图上呈现直线的形式。通过Radon变换检测时频平面上的直线,可以精确地得到调频信号的参数。仿真结果表明,小波Radon变换是一种较好的线性调频信号检测方法。     

用多重网格TVD方法求解三维高超音速粘性流场

本文采用时间相关法求解了三维Ｎ－Ｓ方程。在计算中，对于压力和对流项采用Ｒｏｅ的通量差分分裂技术和Ｏｓｈｅｒ－Ｃｈａｋｒａｖａｒｔｈｙ的ＴＶＤ格式，对于粘性项，采用了中心差分格式。通过数值模拟得到了钝头双锥体高超音速层流流场在不同攻角下的数值解。计算的重点放在壁面热流率的预测。在攻角为４°和１２°的状态下，Ｓｔａｎｔｏｎ数的计算值与实验值进行了比较，二者吻合较好。为了加快收敛速率，计算时采用了多重网格方法。     

高超音速流场的隐式TVD格式并行计算

本文采用区域分割技术和拼接网格的并行策略，发展了一个适合于分布式存贮多机系统的ＴＶＤ隐式有限体积并行算法；并在ＰＶＭ并行环境下，对三维高超音速绕流流场实现了多机并行计算，通过负载平衡等方法得到了较高的加速比（在二处理机系统上加速比为１∶８４，在四处理机系统上为３∶４４）。     

基于IGAHP-IEW-GT-MCGC的装备体系方案质量评价模型

针对装备体系方案质量评价指标体系不够全面,通过运用鱼骨图分析法对各类影响因素进行综合分析,并从稳定度和有效度角度优化分析指标,以确定合理适用的装备体系方案质量评价指标体系;针对赋权过程中存在的不确定性,采用IGAHP(Improved Group Analytic Hierarchy Process,IGAHP)-IEW(Improved Entropy Weight,IEW)-GT(Game Theory,GT)相结合的方法,对评价指标体系各指标进行了赋权;针对原始数据处理和评价过程中的不确定性性,运用云重心评判法(Membership Cloud Gravity Center,MCGC)对装备体系方案质量的进行综合评价,以得出定性与定量评价结果。最后,以某装备体系方案为对象进行示例评价,验证了模型的合理性和适用性。     

基于节点综合效能的DTN路由算法

针对容忍延迟网络(DTN)高延迟、数据传输成功率低等问题,提出了一种基于节点综合效能的DTN路由算法SERA。该算法综合考虑移动节点的活跃度和剩余能量,使消息副本向综合效能高的节点扩散。SERA节点活跃度描述了节点的社会和动态特性,SERA尽量将消息副本传递给活跃度高的节点,以提高消息传输的成功率;在选择中继节点时,充分考虑节点的能量状态,以避免能量不足的节点承担更多的信息传输任务,从而提高网络节点的存活率。仿真结果表明,与典型的DTN路由算法相比,SERA能够更好地平衡节点的能耗,获得更高的消息递交成功率和更长的网络生存期。     

一种基于VMN的移动IP快速切换方法

为了满足用户对移动IP服务的安全性、低时延方面的要求,弥补现有移动IPv4协议中切换方案的不足,在移动IP/AAA模型的基础上,提出了一种基于VMN(虚拟移动节点)的移动IP快速切换方法。该方法增强了移动IP联合AAA的基本模型,并在MA(移动代理)上构建了一种新的数据结构;通过在NFA和OFA之间建立一个新的双向隧道,将VMN中的数据报文进行快速转移,在没有数据报文丢失的情况下,实现了快速低时延的切换;通过分发新的临时安全关联以及认证票据,有效地提高了移动IP在注册和切换过程中的安全,同时有效地降低了AAAH和AAAF之间的网络负载;通过对该方法进行安全性分析和仿真试验表明,我们提交的方法是安全有效的。     

采用PageRank和节点聚类系数的标签传播重叠社区发现算法

基于标签传播的社区发现算法可以检测出复杂网络的重叠社区结构,因此提出了一种基于PageRank和节点聚类系数的重叠社区发现算法。该算法使用PageRank算法对节点的影响力进行排序,可以稳定社区发现结果,节点的聚类系数是一个与节点相关的值,使用节点聚类系数修改算法的参数并限制每个节点拥有最多标签的数量值,可以提高社区挖掘的质量。在人工网络和真实世界的网络上测试,实验验证了该算法能够有效地检测出重叠社区,并具有可接受的时间效率和算法复杂度。     

Sensitivity analysis on responsive pricing and production under imperfect demand updating

This paper examines three types of sensitivity analysis on a firm's responsive pricing and responsive production strategies under imperfect demand updating. Demand has a multiplicative form where the market size updates according to a bivariate normal model. First, we show that both responsive production and responsive pricing resemble the classical pricing newsvendor with posterior demand uncertainty in terms of the optimal performance and first‐stage decision. Second, we show that the performance of responsive production is sensitive to the first‐stage decision, but responsive pricing is insensitive. This suggests that a “posterior rationale” (ie, using the optimal production decision from the classical pricing newsvendor with expected posterior uncertainty) allows a simple and near‐optimal first‐stage production heuristic for responsive pricing. However, responsive production obtains higher expected profits than responsive pricing under certain conditions. This implies that the firm's ability to calculate the first‐stage decision correctly can help determine which responsive strategy to use. Lastly, we find that the firm's performance is not sensitive to the parameter uncertainty coming from the market size, total uncertainty level and information quality, but is sensitive to uncertainty originating from the procurement cost and price‐elasticity.     

Stochastic production capacity: A bane or a boon for quick response supply chains?

The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold.     

When to commit in a serial supply chain with forecast updating

Negotiations between an end product manufacturer and a parts supplier often revolve around two main issues: the supplier's price and the length of time the manufacturer is contractually held to its order quantity, commonly termed the “commitment time frame.” Because actual demand is unknown, the specification of the commitment time frame determines how the demand risk is shared among the members of the supply chain. Casual observation indicates that most manufacturers prefer to delay commitments as long as possible while suppliers prefer early commitments. In this paper, we investigate whether these goals are always in the firm's best interest. In particular, we find that the manufacturer may sometimes be better off with a contract that requires an early commitment to its order quantity, before the supplier commits resources and the supplier may sometimes be better off with a delayed commitment. We also find that the preferred commitment time frame depends upon which member of the supply chain has the power to set their exchange price. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003