WSN中层次性簇聚路由选择算法中的一种优化策略

对层次性簇聚路由算法,提出了一种优化策略。具体实施是在已选出的多个簇首中,以能量均衡损耗和节能为依据,设计一个优化路由机制．选出一个二级簇首,由该二级簇首与汇点通信。通过分析,该策略能够有效的降低网络的能量损耗,大大延长网络的生命周期。     

大气层外弹道目标温度变化研究

针对弹道目标在飞行过程中的表面温度是导弹攻防两端关注的重点,简要介绍了弹道目标在大气层外飞行时的表面温度及红外辐射源;提出了大气层外弹道目标在飞行过程中的表面温度计算方法;重点针对弹头与诱饵的典型热物参数和大气层外不同的表面初始温度,分别计算了在阴影区与日照区弹头和诱饵表面温度随飞行时间的变化情况,发现弹头热惯量大,基本保持初始温度,诱饵热惯量小,迅速就会达到平衡温度,并采用实例对其进行验证。     

基于改进蚁群算法的铁路乘务交路计划的编制

针对提高铁路乘务交路计划编制质量和效率的问题,将乘务交路计划编制问题抽象为单基地、均衡行驶路程的多旅行商问题（MTSP）,引入均衡因子,建立了以乘务交路用时少和子乘务交路间任务均衡为目标的数学模型。针对该模型提出了一种双重策略蚁群优化算法,该算法首先构建满足时空约束的解空间,分别对乘务区段节点和接续路径设置信息素浓度,然后采用双重策略状态的转移概率,使蚂蚁遍历所有乘务区段,最终找到符合乘务约束规则的子乘务交路。最后运用广深线城际铁路数据对设计的模型及算法进行检验,经与遗传算法的实验结果对比分析表明：在相同的模型条件下,运用双重策略蚁群优化算法编制的乘务交路计划乘务交路个数减少了约21.74%、乘务交路总时长降低了约5.76%、交路超劳率为0。运用所设计的模型和算法编制乘务交路计划能够减少乘务计划交路时长,均衡工作量,避免产生超劳交路。     

Dantzig—Wolfe Decomposition of Variational Inequalities

The creation and ongoing management of a large economic model can be greatly simplified if the model is managed in separate smaller pieces defined, e.g. by region or commodity. For this purpose, we define an extension of Dantzig–Wolfe decomposition for the variational inequality (VI) problem, a modeling framework that is widely used for models of competitive or oligopolistic markets. The subproblem, a collection of independent smaller models, is a relaxed VI missing some “difficult” constraints. The subproblem is modified at each iteration by information passed from the last solution of the master problem in a manner analogous to Dantzig–Wolfe decomposition for optimization models. The master problem is a VI which forms convex combinations of proposals from the subproblem, and enforces the difficult constraints. A valid stopping condition is derived in which a scalar quantity, called the “convergence gap,” is monitored. The convergence gap is a generalization of the primal-dual gap that is commonly monitored in implementations of Dantzig–Wolfe decomposition for optimization models. Convergence is proved under conditions general enough to be applicable to many models. An illustration is provided for a two-region competitive model of Canadian energy markets.     

复杂大跨空间钢结构非线性有限元稳定分析

由于工程应用中空间钢结构跨度增大导致结构整体失稳问题越来越突出,且现有规范缺少专门的大跨空间钢结构设计方法,因此,针对大跨空间钢结构设计中需要对具体结构进行非线性有限元整体稳定分析来保证结构安全性的问题,简单介绍大跨钢结构稳定分析的概念,对某具体工程实例的大跨钢结构屋盖进行完善结构和带缺陷结构的大位移弹性整体稳定和弹塑性稳定分析.通过比较数值仿真结果,总结此类大跨空间钢结构的整体稳定特点,为类似工程的设计提供有价值的参考资料.     

Collaborative distribution between two logistics service providers

Mathematical models are described for collaborative distribution between two logistics service providers (LSPs). We find that (a) collaborative distribution is conditionally profitable for LSPs, and the conditions for cooperation are given; (b) LSPs save more costs primarily by shortening the delivery distance and decreasing delivery speed; (c) collaborative distribution makes the delivery time either shorter or longer; (d) if one LSP changes its equilibrium price, the total profit conditionally increases, and this condition is related to demand structure and delivery cost structure; and (e) collaborative distribution can solve the personalization of delivery time and reduce pollution to some extent. For the companies sharing the incremental profit, we characterize a transfer payment contract that includes fixed and variable fees. Our findings suggest that LSPs should adopt collaborative distribution to formulate an optimal distribution plan to save costs and shorten the delivery time, and should select partners with smaller cost parameters and both different and moderate speeds. In addition, warehouse and vehicle sharing are beneficial to the implementation of collaborative distribution.     

带温度补偿的双余度舵机伺服控制算法研究

针对双余度舵机不同控制通道间速度环输出指令存在偏差、电流冲击和输出转矩波动等问题,本文提出了一种带温度补偿的双余度舵机伺服控制算法。首先,采用积分均衡的方法抑制传感器和双通道控制器解算误差引起的速度环输出指令的差异,然后采用电流截止负反馈的方法减小负载扰动和电机过渡过程带来的电流冲击,最后通过对电机进行温度补偿来减小环境温度和本体升温对输出转矩的影响。仿真结果表明,该算法能够有效抑制速度环输出指令误差和减小电流冲击,使电机在不同的工作温度下保持良好的性能。     

溢油应急决策中的物资调度方法

针对多出救点多物资多目标应急调度问题的特点,并结合溢油敏感区等对响应时间的限制,提出了基于不同响应时间段的动态优化模型．以应急救援时间最早和出救点数目最少为优化目标,对不同响应时间段分别运用理想点法和构造剔除集合确定最优调度方案．最后通过算例验证了所提出调度方法的可行性．     

基于Q学习的DDoS攻防博弈模型研究

新形势下的DDoS攻防博弈过程和以往不同,因此利用现有的方法无法有效地评估量化攻防双方的收益以及动态调整博弈策略以实现收益最大化。针对这一问题,设计了一种基于Q学习的DDoS攻防博弈模型,并在此基础上提出了模型算法。首先,通过网络熵评估量化方法计算攻防双方收益;其次,利用矩阵博弈研究单个DDoS攻击阶段的攻防博弈过程;最后,将Q学习引入博弈过程,提出了模型算法,用以根据学习效果动态调整攻防策略从而实现收益最大化。实验结果表明,采用模型算法的防御方能够获得更高的收益,从而证明了算法的可用性和有效性。     

模糊控制系统的全局稳定性

利用合同变换的方法讨论了模糊控制系统的全局稳定性,给出了当初始状态为任意的正则模糊集时,模糊控制系统的状态都收敛于其平衡态的充要条件.