城市建筑点云的自适应分割方法

自动地提取城市建筑点云数据中的高层次结构是城市数字化等应用的重要基础步骤.通过提出"自适应分割"的概念来自动地获取三维城市建筑点云的一种灵活、层次化的结构表示.由于城市建筑的单元分布大都呈一种级联或者交错的形式,与之前采用全局规则格点进行分析的方法不同,文中采用一种自适应的方法对建筑点云进行分析;也可将自适应分割的概念用于建筑图像的分析上.最后给出了在不同类型的建筑点云和图像进行分析的结果以及一些相关的应用.     

多站虚拟量测变换均值无源定位算法

在多站无源均值定位算法中,为了解决部分传感器间夹角过大或过小所导致的定位精度下降问题,提出一种基于虚拟量测变换的多传感器管理无源定位算法.首先在全局坐标系下分析了传感器间夹角对误差几何稀释度(GDOP)的影响,进而得到双站获得较好定位精度的夹角约束关系;其次针对不满足该约束关系的传感器组合提出一种虚拟量测变换定位算法,通过空间管理的方法达到对传感器的优化布站,并结合算法的实施步骤对其原理及特点进行了理论分析,尤其对变换前后的交点精度进行了比较.仿真结果表明虚拟量测算法的定位精度要明显优于均值算法,进而说明该算法的有效性及传感器管理在多站无源定位中的重要作用.     

全局优化的一类新的F-C函数

针对求解全局优化问题,有很多种求解方法。文中提出了一种快速求解一般无约束最优化问题的辅助函数方法。即F-C函数方法。该方法与填充函数法和跨越函数法相比较,既有相同点又有不同点。F-C函数法最大的优点就是在极小化F-C函数阶段中只需要进行一次局部极小化算法就能得到比当前极小值更低的目标函数局部极小点。文中在无Lipschitz连续的条件下,给出了一类新的求解全局优化问题的F-C函数。文中讨论了该F-C函数的优良性质并对该函数设计了相应的算法。最后,通过数值试验表明该F-C函数方法具有有效性和可行性。     

多分辨率点状地标实时渲染研究

大规模点状地标的高性能实时渲染是地理信息系统和计算机图形技术面临的一项具有挑战性的难题。采用多分辨率的技术是解决上述问题的有效方法,其本质是通过动态控制显示的规模获得最佳的显示效果和性能。根据不同层次分辨率的点状地标的空间分布特点,提出一种基于空间分割和视点高度的显示规模控制方法,推导了帧时间计算公式,由此确定最佳空间分割尺寸。通过两个不同层次点状地标的空间分割和性能测试,给出了最佳空间分割尺寸,验证了基于空间分割的多分辨率点状地标渲染方法的正确性和有效性。     

砷化镓材料中EPR“As_(Ga)”的退火行为

本文比较了中子辐照。压缩变形和原生LEC砷化镓等三种不同来源样品的EPR“As_(Ga)”的Hamiltonian参数。并系统地研究了EPR“As_(Ga)”的浓度和低温光猝灭行为随退火温度的变化,从而进一步验证了EPR“As_(Ga)”的本性,即除孤立As_(Ga)反位原子外,还可能包括As_(Ga)的一些空位络合物。这些不同本性的EPR“As_(Ga)”缺陷及其它有关的缺陷在样品热处理过程中可能相互转化。按照物理化学中Le Chatlier原理,缺陷的原始浓度和晶体内部应变能似应是引起这些转化反应的重要因素。     

基于桌面云的统一身份认证架构研究

桌面云是云计算的一种典型应用,能够有效提高资源利用率和维护效率,使人们应用桌面系统的方式发生了深刻变革。但是随着桌面云的广泛应用,如何集中管理和统一认证用户身份成为了桌面云面临的挑战。文章通过分析传统单点登录和桌面云认证技术,提出了一种基于桌面云的统一身份认证模式．并对该架构的优势进行了分析。     

Experiments with a hybrid interior point/combinatorial approach for network flow problems

Interior point (IP) algorithms for Min Cost Flow (MCF) problems have been shown to be competitive with combinatorial approaches, at least on some problem classes and for very large instances. This is in part due to availability of specialized crossover routines for MCF; these allow early termination of the IP approach, sparing it with the final iterations where the Karush Kuhn-Tucker (KKT) systems become more difficult to solve. As the crossover procedures are nothing but combinatorial approaches to MCF that are only allowed to perform few iterations, the IP algorithm can be seen as a complex ‘multiple crash start’ routine for the combinatorial ones. We report our experiments of allowing one primal-dual combinatorial algorithm to MCF to perform as many iterations as required to solve the problem after being warm-started by an IP approach. Our results show that the efficiency of the combined approach critically depends on the accurate selection of a set of parameters among very many possible ones, for which designing accurate guidelines appears not to be an easy task; however, they also show that the combined approach can be competitive with the original combinatorial algorithm, at least on some ‘difficult’ instances.     

An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch

Economic dispatch is carried out at the energy control center to find out the optimal output of thermal generating units such that power balance criterion is met, unit operating limits are satisfied and the fuel cost is minimized. With growing environmental awareness and strict government regulations throughout the world, it has become essential to optimize not only the total fuel cost but also the harmful emissions, both, under static as well as dynamic conditions. The static environment economic dispatch finds the optimal output of generating units for a fixed load demand at a given time, while the dynamic environmental economic dispatch schedules the output of online generators with changing power demands over a certain time period (normally one day) so as to minimize these two conflicting objectives, simultaneously. In this paper, the price penalty factor approach is employed for simultaneous minimization of cost and emission. The generator ramp rate constraints, non-convex and discontinuous nature of cost function and the large number of generators in practical power plants, make this problem very difficult to solve. Here, a fuzzy ranking approach is employed to identify the solution which offers the best compromise between cost and emission objectives.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.