DEA models for two‐stage processes: Game approach and efficiency decomposition

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). This tool has been utilized by a number of authors to examine two‐stage processes, where all the outputs from the first stage are the only inputs to the second stage. The current article examines and extends these models using game theory concepts. The resulting models are linear, and imply an efficiency decomposition where the overall efficiency of the two‐stage process is a product of the efficiencies of the two individual stages. When there is only one intermediate measure connecting the two stages, both the noncooperative and centralized models yield the same results as applying the standard DEA model to the two stages separately. As a result, the efficiency decomposition is unique. While the noncooperative approach yields a unique efficiency decomposition under multiple intermediate measures, the centralized approach is likely to yield multiple decompositions. Models are developed to test whether the efficiency decomposition arising from the centralized approach is unique. The relations among the noncooperative, centralized, and standard DEA approaches are investigated. Two real world data sets and a randomly generated data set are used to demonstrate the models and verify our findings. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

JPEG2000遥感图像实时压缩系统中基于并行机制的“零时间”数据搬移策略

为了满足JPEG2000遥感图像实时压缩设备的实时性要求,提出了一种基于并行机制的“零时间”数据搬移策略。通过对EBC算法结构进行拆分,并采用EDMA方式进行数据搬移,使得算法运算与数据搬移过程完全并行,搬移时间等效为零。实验结果表明,采用新的数据搬移策略后,编码效率可以提升到100％,EBC算法实现的总时间可缩短46％以上。该搬移机制已成功应用于JPEG2000星载遥感图像实时压缩系统中,系统的实时性达到了设计要求。     

Dynamic pricing and inventory control with learning

Optimal operating policies and corresponding managerial insight are developed for the decision problem of coordinating supply and demand when (i) both supply and demand can be influenced by the decision maker and (ii) learning is pursued. In particular, we determine optimal stocking and pricing policies over time when a given market parameter of the demand process, though fixed, initially is unknown. Because of the initially unknown market parameter, the decision maker begins the problem horizon with a subjective probability distribution associated with demand. Learning occurs as the firm monitors the market's response to its decisions and then updates its characterization of the demand function. Of primary interest is the effect of censored data since a firm's observations often are restricted to sales. We find that the first‐period optimal selling price increases with the length of the problem horizon. However, for a given problem horizon, prices can rise or fall over time, depending on how the scale parameter influences demand. Further results include the characterization of the optimal stocking quantity decision and a computationally viable algorithm. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 303–325, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10013     

雷达与红外数据融合评述

雷达和红外数据融合由于能够实现信息互补 ,改善对目标的跟踪、识别以及提高系统的生存能力 ,因而引起了广泛的注意。对基于雷达和红外传感器的数据融合算法进行了综述 ,并介绍了一些关键课题。     

多传感器多目标定位与跟踪技术研究

针对炮兵打击目标的特性和获取目标信息所采用的侦察设施 ,研究了多传感器多目标定位和跟踪问题。在闭圆和聚类概念的基础上 ,提出了一种多传感器多目标融合算法 ,并给出了状态估计的最优解。仿真结果证明了这种算法的有效性。这一模型的物理实现正在进一步研究之中。     

单平台多传感器数据融合系统的设计与实现

从工程应用观点出发研究单平台多传感器数据融合系统 (Data Fusion System ,DFS)设计和实现。首先通过分析此类型 DFS的功能需求 ,提出一种具有一定可伸缩性的系统结构 ;然后运用面向对象的方法 ,进行系统设计 ,以确保系统本身的可扩充性及系统中所采用的算法和模块的可移植性 ;同时也给出了一种确保系统实时性的多线程实现方案。     

多目标数据关联的神经网络方法

数据关联是多目标跟踪的关键问题。基于 Hopfield神经网络的 JPDA是解决这一关键问题的有效方法之一 ,但此方法的难点在于优化系数的整定。提出一种改进算法 ,用于解决优化系数在线自适应整定问题。首先重新构造了李雅普诺夫能量函数 ,接着引入变化的优化系数因子 ,并给出了优化系数求解的迭代公式 ;最后对已有和改进的算法进行了仿真研究。结果表明改进的方法和原有的方法相比 ,一方面具有在线整定优化系数的功能 ,另一方面可以获得和原有算法非常接近的估计误差。     

被动传感器融合检测系统的优化分布密度

被动传感器由于采用无源探测,而且制造成本低,体积小,在战场环境中将采用并配置大量的被动传感器来检测目标.不论是主动还是被动式传感器,在实际应用中均存在资源优化配置问题.针对被动传感器的具体应用模型,采取了若干合理近似,得到了在"OR"融合检测条件下,基于最大检测概率的被动传感器优化配置密度计算公式.     

基于B样条小波的异常数据辨识

为解决靶场测量数据中系统误差引起的大量连续奇异数据的辨识难题,同时针对传统和目前国内外较先进的异常值识别方法的不足,提出了基于B样条小波的异常数据辨识。首先描述了异常值,然后分析了小波异常值识别原理和物理意义,构造了用于异常数据辨识的B样条小波分析方法。通过对不同情况仿真数据的计算分析,证明了该方法科学、合理,实现了测量数据异常值的辨识和重构。     

对空观察哨情报融合

在得到对空观察哨直接观测信息的基础上,对目标可能航向进行二次外推,并利用效用函数综合评估法对目标相关性进行检验,完成对目标信息的融合,解决了观察哨情报数据关联和航迹外推问题.仿真及试验结果表明该方法用于防空观察哨情报融合是可行的,其结果分析的应用能指导观察哨训练和战时观察哨的配置.