基于证据理论的多传感器目标识别

为了解决证据理论在具体应用中存在的问题,对Dempster组合规则思想进行了具体的分析,讨论了该组合规则存在的问题及现行的一些改进措施;针对空中目标识别问题,提出了综合运用各种改进思想的融合方法,并给出了识别过程的流程图。     

Two-agent scheduling with linear resource-dependent processing times

This paper considers a two-agent scheduling problem with linear resource-dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is -hard in the ordinary sense, and there does not exist a polynomial-time approximation algorithm with performance ratio unless ; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.     

装备维修资源优化中的模拟退火遗传算法

在对模拟退火遗传算法改进的基础上,结合建立的通信装备维修资源分配模型,给出了该算法的实现过程,并利用此算法对模型进行了最优化求解。实例的计算结果表明该算法对于模型的求解具有较好的收敛性。     

蜂窝网络中基于干扰控制的D2D通信联合资源分配方案

在蜂窝网络中部署D2D(Device-to-Device)通信能够有效提升频谱利用率,降低基站负载,但D2D用户与蜂窝用户共享无线信道时会产生信号干扰。提出了一种联合资源分配算法,通过综合考虑信道分配对网络中已有的蜂窝用户和D2D用户的信号干扰,并在小区范围内寻找具有最小干扰值的信道资源分配给用户,以实现有效的干扰控制。仿真结果显示:联合资源分配能够提升D2D链路、蜂窝链路的信噪比及系统总吞吐量,使得蜂窝网络的整体性能优于独立资源分配。     

基于前景理论和证据推理的混合型多属性决策方法

针对属性权重完全未知的混合型多属性决策问题,提出一种基于前景理论和证据推理的决策方法。通过直觉模糊数对精确数、区间数和语言变量3种混合型属性的决策信息进行统一,根据前景理论对决策信息进行转化;提出基于直觉模糊熵与相似度的属性可靠性评估方法,结合属性重要度确定属性权重;采用证据推理算法集结属性信息,得到方案的综合前景值,并以此进行方案排序。算例分析结果表明,所提方法具有较强的区分能力,能够有效降低决策结果的不确定性,对混合型多属性决策问题具有较好的适用性。     

带有可信度因子的直觉模糊近似推理方法

针对基于相似度的直觉模糊近似推理问题,提出一种基于加权相似度量的直觉模糊推理方法。首先定义一种新的直觉模糊相似度度量公式,加入权重参数解决各维特征分配不均匀的问题,弥补了现有直觉模糊相似度量的缺陷。然后构建基于直觉模糊产生式规则的直觉模糊近似推理模型,加入可信度因子解决了随机性引起的信息不确定问题,同时给出模型的推理算法和计算步骤。最后通过实例验证了该方法的实用性和有效性,其在意图识别、目标识别等信息融合领域有良好的应用前景。     

光互连网络中资源预留的多级光互连仲裁机制

基于资源预留策略提出一种多级光互连仲裁机制,通过将网络分级实现快速、高效的仲裁。多优先级数据缓存队列的传输节点设计,提供了不同类型流量的差异化传输;通过预约式两级仲裁机制,实现网络的完全公平与100%的高吞吐率。设计并对快速仲裁通道进行了合理布局,极大地缩短了仲裁延迟。仿真结果表明:采用基于资源预留的分级仲裁策略,在多种流量模式下所有节点均获得公平的服务。与Feather Weight相比,分级仲裁策略吞吐率提高17%;与2-pass相比,仲裁延迟减少15%,同时,功耗减少5%。     

RCS特性辅助的CMIMO雷达功率资源分配方法

针对集中式多输入多输出(collocated multiple-input multiple-output, CMIMO)雷达在实际探测过程中,未有效结合目标雷达散射截面(radar cross section, RCS)高动态变化特性导致雷达跟踪精度不高甚至失跟的问题,提出一种基于目标RCS高动态特性的CMIMO雷达功率资源自适应分配方法。考虑到目标RCS特征的角度敏感性,利用目标运动状态的可预测性动态获取实际观测角度,从而完成跟踪帧的极化方式优选;在此基础上构建包含功率与RCS的多目标跟踪误差后验克拉美罗下界,将其作为目标函数进行优化,利用内点惩罚法求解该凸优化问题即可实现RCS高动态情况下的功率优化分配。实验结果表明:该方法可结合目标不同方向RCS动态分集特性实现功率的有效分配,相比于传统RCS模型分配方案,有效解决了分配方案与实际跟踪场景之间的失配问题,从而提升了CMIMO雷达的多目标跟踪性能。     

A bicriterion approach to common flow allowances due window assignment and scheduling with controllable processing times

We investigate a single‐machine scheduling problem for which both the job processing times and due windows are decision variables to be determined by the decision maker. The job processing times are controllable as a linear or convex function of the amount of a common continuously divisible resource allocated to the jobs, where the resource allocated to the jobs can be used in discrete or continuous quantities. We use the common flow allowances due window assignment method to assign due windows to the jobs. We consider two performance criteria: (i) the total weighted number of early and tardy jobs plus the weighted due window assignment cost, and (ii) the resource consumption cost. For each resource consumption function, the objective is to minimize the first criterion, while keeping the value of the second criterion no greater than a given limit. We analyze the computational complexity, devise pseudo‐polynomial dynamic programming solution algorithms, and provide fully polynomial‐time approximation schemes and an enhanced volume algorithm to find high‐quality solutions quickly for the considered problems. We conduct extensive numerical studies to assess the performance of the algorithms. The computational results show that the proposed algorithms are very efficient in finding optimal or near‐optimal solutions. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 41–63, 2017     

测试性增长中资源优化配置模型及求解

良好的测试性设计对系统维修性具有重要意义,测试性增长试验通过一系列测试性设计缺陷发现和纠正措施,可保证系统测试性指标达到设计要求。针对基于延缓纠正的测试性增长过程中的资源配置问题进行研究,基于增长试验目标是否明确和试验资源是否受限制问题构建资源优化配置模型,并提出一种基于拉格朗日松弛和本地搜索的快速优化算法。仿真结果表明:该模型能够有效指导测试性增长中的资源优化配置问题,所提混合优化方法能够高效、准确地求解整数规划问题。