Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms

An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measurements can not be fully resolved due to finite resolution. The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem. Computer simulation results demonstrate the effectiveness and feasibility of this method.     

Comparison between several satellite constellation schemes for MEO-TDRSS of China

The satellite constellation classes, which are suitable for the medium earth orbit tracking and data relay satellite system （MEO-TDRSS） of China, are investigated. On the basis of the functionality and the traffic distribution characteristic of MEO-TDRSS, the coverage performance and inter-satellite link properties of four different constellation schemes are compared by simulations. Simulation results indicate that the rosette and common-track constellations, whose satellites are distributed on the celestial sphere more uniformly, are appropriate for the implementation of MEO-TDRSS of China.     

单向S-粗集对偶与数据筛选-过滤的扩展应用

S-粗集(singular rough sets)是对Z.Pawlak粗集的改进,单向S-粗集对偶(dual of one direction sin-gular rough sets)是S-粗集的基本形式之一。利用单向S-粗集对偶,给出数据属性,数据筛选-过滤概念,数据筛选-过滤序定理,合成数据筛选-过滤定理,及数据筛选-过滤准则。利用这些结果,给出应用。单向S-粗集对偶是动态数据筛选-过滤研究的一个新工具。     

一种油藏储层三维可视化散乱数据插值方法

提出一种快捷有效的确定待估点的有效样本点集的辐射吸收插值模型,该模型应用到散乱数据网格化过程中,它不但具有加权平均算法的优点,而且具有插值效率高、局部性好、易于控制、易于对数据增加约束条件等特点.辐射吸收插值模型由辐射过程、吸收过程和合成过程组成.讨论了辐射吸收插值模型的实现原理、设计过程、在油藏储层三维可视化散乱数据插值中的应用和模型特点的分析.     

嵌套二叉树实现大规模地形实时绘制

实时调入视点可见数据,快速动态地建立连续细节层次(LOD)模型是提高大规模地形绘制速度的关键技术。在数据分块组织的基础上构建了两层嵌套二叉树。通过索引数据二叉树和可见性判断,实现可见数据的实时调入;对可见数据块,通过LOD建模三角形二叉树和设计的基于包围球的节点误差评价函数,实现连续LOD模型的快速动态构建,提高大规模地形的绘制速度。实验表明文中的方法可以取得较高的帧速率和较好的绘制效果,实现了大规模地形的实时绘制。     

Participatory Sustainability Impact Assessment: Scientific Policy Advice as a Social Learning Process

This paper deals with the development of scientific policy advice by providing a methodology to foster a social learning process. The methodology, called participatory Sustainability Impact Assessment (pSIA), aims to structure complex problem situations, to clarify interests and basic assumptions, to interpret scientific studies, to develop impact assessment, and to explore sensitivity of uncertainty and lack of information. In pSIA workshops the participants are supposed to build conceptual models with different modelling methods, like System Dynamics, Value Chain Analysis, and Morphological Analysis. A case study is presented that describes a workshop series with political as well as academic actors, applying the pSIA approach to an impact assessment of Economic Partnership Agreements between the European Union and the Eastern and Southern Africa Region.

一种新的多关系朴素贝叶斯分类器

针对现有多关系朴素贝叶斯分类器中存在的统计偏斜问题,扩展了语义关系图的定义,给出了一种新的统计计数方法,构建了相应得多关系朴素贝叶斯分类公式,形成了一种基于关系数据库技术的新的多关系朴素贝叶斯分类器。为高效进行关系表连接,采用元组ID传播方法对关系表进行虚拟连接。进一步提高分类准确率,基于互信息标准对属性进行剪枝。实验显示新的分类器具有良好的分类性能。     

基于目标导向的多属性决策属性价值公度方法

MACBETH方法在多个属性维度上确定出的一组属性Good值(或Neutral值)并不能真地具有保证属性价值公度的完全相同的偏好涵义.为克服该方法缺陷,基于包容性属性价值函数,通过引入数据包络分析技术和马尔可夫理论给出了以目标参照方案为导向的目标导向属性价值函数和多属性决策属性价值公度的方法程序.相比于具有较大随意性确定属性Good值和Neutral值的MACBETH方法,依赖目标导向属性价值函数的多属性决策属性价值公度程序具有更明显的科学合理性,因此具有发展MACBETH方法,推动实际决策应用该方法及相关多属性偏好关联决策模型的创新价值.基于决策实验的应用分析直接验证了多属性决策属性价值公度方法程序的科学有效性,间接验证了包容性属性价值函数和目标导向属性价值函数的科学理性.     

平顺移动模式下考虑加工时间与调整时间可分离的多目标流水车间批量调度

对平行顺序移动模式下考虑加工时间与调整时间可分离的多目标流水车间批量调度问题展开研究.构建以加工制造设备总停机次数、批量工件生产周期以及搬运批量工件的总次数为决策目标的基于分层序列法的多目标决策模型,利用该模型可确定批量工件的最优加工排序方案.建立平行顺序移动模式的加工与调整时间模型,该模型是求解生产周期的基础,也是为批量工件的最优调度方案制定生产作业计划的依据.提出并设计平行顺序移动模式下考虑加工时间与调整时间可分离的禁忌搜索算法对问题进行求解.研究结果表明:本研究可为平顺移动模式下考虑加工时间与调整时间可分离的批量生产流水车间选出批量工件的最优调度方案,同时可为批量工件的加工和加工制造设备的调整制定精确的生产作业计划.     

Evaluating Efficiency of Decision Making Units with Network Structures

This paper considers the problem of evaluating efficiency of Decision Making Units （DMUs） with network structures of divisions by the Data Envelopment Analysis （DEA） model. All divisions in the network are under a decentralized authority organiza- tion. That is, each division in a decision making unit has its own authority to adjust its input and output. By incorporating the division operations in the DEA model, we discuss the sufficient and necessary conditions for a DMU to be network efficient in series structure and general structure respectively.