异构资源类型下多无人机任务分配

针对多无人机在执行侦察、打击任务的过程中携带任务资源的异构性,以及任务对于异构资源的要求,设计了一种改进的基于共识的捆绑算法(consensus-based bundle algorithm, CBBA)。考虑任务价值、任务执行时间窗以及航程代价等条件建立了多无人机对地目标侦察、打击任务分配模型。利用K-medoids聚类分析方法对多无人机进行基于距离和携带资源平衡的聚类,以解决多无人机对于异构资源类型的要求。对打击任务进行子任务生成,并利用改进后的CBBA求解所建立的任务分配模型,通过对比仿真实验验证了算法的可行性和有效性。     

大功率柔性互联设备的有功互济协调控制方法

针对以常规母联开关互联的交流电网线路变压器容量无法合理利用的问题,提出了依靠由背靠背电压源变流器组成的新型大功率柔性互联装置实现两侧交流电网有功功率互济的协调控制方法。介绍了新型柔性互联装置的拓扑结构与基本的控制原理,为实现两侧线路变压器容量合理的目标制定了一种生成有功功率参考的方法,并基于PSCAD/EMTDC搭建了一套仿真算例,对新型柔性互联装置的基本控制与所提出的有功互济的协调控制方法进行了验证。仿真结果表明,在柔性互联装置采用所提方法时能够实现两侧交流线路的有功功率互济,解决了线路变压器容量无法合理分配的问题。     

潜艇上浮运动数值计算方法的研究与验证

预报潜艇上浮运动是探索研究潜艇上浮运动控制规律以及潜艇安全上浮的前提。就潜艇上浮运动的数值计算方法和计算流程进行了详细介绍,对数值计算方法中的湍流模型及离散格式等进行了优选。基于模型上浮试验结果,对数值计算的不确定度进行了分析且对数值计算结果进行了验证。证明了该数值计算的可靠性,并发现数值计算结果与试验结果很接近,验证了该数值计算方法的可行性,说明该数值计算方法和试验可为潜艇上浮运动的研究提供参考和借鉴。     

一种安全的组签字方案

基于离散对数给出了一种组签字方案。该方案保留了已有方案的主要优点 ,并且解决了“匿名”性问题。在该方案中 ,既使组权威公布了一些附加信息以供验证者确认签字者的身份 ,但可确保签字者其它的组签字的“匿名”性 ,组权威也不必更新签字者的任何密钥。因为组权威公布的信息只是为了识别指定的组签字。     

正弦波型直流无刷电动机位置伺服系统的变结构控制

在分析正弦波型直流无刷电动机数学模型的基础上,采用比例等速变速控制策略(PCV)设计控制器,应用于正弦波型直流无刷电动机位置伺服系统.仿真结果表明:与传统PI控制器比较,PCV控制策略可以明显改善系统的品质,并增强系统的鲁棒性.     

焊接工艺对熔敷金属韧性的影响

探讨了改变焊接工艺对焊条熔敷金属冲击韧性的影响．研究表明：改变焊接工艺,将通过改变熔敷金属的化学成分,影响熔敷金属的强度和韧性;降低焊接线能量时,将降低熔敷金属中的氧含量及其氧化夹杂物的数量和大小,从而提高熔敷金属的韧性     

Periodic review inventory management with contingent use of two freight modes with fixed costs

We study a stochastic inventory model of a firm that periodically orders a product from a make‐to‐order manufacturer. Orders can be shipped by a combination of two freight modes that differ in lead‐times and costs, although orders are not allowed to cross. Placing an order as well as each use of each freight mode has a fixed and a quantity proportional cost. The decision of how to allocate units between the two freight modes utilizes information about demand during the completion of manufacturing. We derive the optimal freight mode allocation policy, and show that the optimal policy for placing orders is not an (s,S) policy in general. We provide tight bounds for the optimal policy that can be calculated by solving single period problems. Our analysis enables insights into the structure of the optimal policy specifying the conditions under which it simplifies to an (s,S) policy. We characterize the best (s,S) policy for our model, and through extensive numerical investigation show that its performance is comparable with the optimal policy in most cases. Our numerical study also sheds light on the benefits of the dual freight model over the single freight models. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

基于聚类算法的多无人机系统任务分配

针对现有任务分配方法在任务点较多时不易解算,且计算量大的问题,提出了基于模糊C-均值聚类算法的多无人机系统任务分配方法.首先,利用模糊C-均值聚类算法得到的隶属度矩阵对任务点进行初始分配;其次,针对基于空间划分聚类可能造成各UAV任务不均衡的问题,设计任务的局部优化调整规则;最后,结合单旅行商问题,利用Tabu Sea...     

Quantifying the performance effects of idle time utilization in multiserver systems

In many practical multiserver queueing systems, servers not only serve randomly arriving customers but also work on the secondary jobs with infinite backlog during their idle time. In this paper, we propose a c‐server model with a two‐threshold policy, denoted by (e d), to evaluate the performance of this class of systems. With such a policy, when the number of idle servers has reached d (<c), then e (<d) idle agents will process secondary jobs. These e servers keep working on the secondary jobs until they find waiting customers exist in the system at a secondary job completion instant. Using the matrix analytic method, we obtain the stationary performance measures for evaluating different (e, d) policies. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.     

Technical note: Find a hidden “treasure”

This paper deals with a two searchers game and it investigates the problem of how the possibility of finding a hidden object simultaneously by players influences their behavior. Namely, we consider the following two‐sided allocation non‐zero‐sum game on an integer interval [1,n]. Two teams (Player 1 and 2) want to find an immobile object (say, a treasure) hidden at one of n points. Each point i ∈ [1,n] is characterized by a detection parameter λ_i (μ_i) for Player 1 (Player 2) such that p_i(1 ? exp(?λ_ix_i)) (p_i(1 ? exp(?μ_iy_i))) is the probability that Player 1 (Player 2) discovers the hidden object with amount of search effort x_i (y_i) applied at point i where p_i ∈ (0,1) is the probability that the object is hidden at point i. Player 1 (Player 2) undertakes the search by allocating the total amount of effort X(Y). The payoff for Player 1 (Player 2) is 1 if he detects the object but his opponent does not. If both players detect the object they can share it proportionally and even can pay some share to an umpire who takes care that the players do not cheat each other, namely Player 1 gets q₁ and Player 2 gets q₂ where q₁ + q₂ ≤ 1. The Nash equilibrium of this game is found and numerical examples are given. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007