Robust sensor placement for pipeline monitoring: Mixed integer and greedy optimization

Intelligent water systems – aided by sensing technologies – have been identified as an important mechanism towards ensuring the resilience of urban systems. In this work, we study the problem of sensor placement that is robust to intermittent failures of sensors, i.e. sensor interruptions. We propose robust mixed integer optimization (RMIO) and robust greedy approximation (RGA) solution approaches. The underlying idea of both approaches is to promote solutions that achieve multiple detectability of events, such that these events remain detectable even when some sensors are interrupted. Additionally, we apply a previously proposed greedy approximation approach for solving the robust submodular function optimization (RSFO) problem. We compare scalability of these approaches and the quality of the solutions using a set of real water-networks. Our results demonstrate that considering sensor interruptions in the design stage improves sensor network performance. Importantly, we find that although the detection performances of RMIO and RGA approaches are comparable, RMIO generally has better performance than RGA, and is scalable to large-scale networks. Furthermore, the results demonstrate that RMIO and RGA approaches tend to outperform the RSFO approach.     

Adaptive pole placement pitch angle control of a flapping-wing flying robot

An adaptive pole placement control scheme for the adaptive pitch angle control of a bird-like flapping-wing flying robot is designed and implemented. The salient aims of this work are notably twofold: first, since the dynamics of bird-like flapping-wing robots are still not well understood and hence obfuscate the process of deriving a high-fidelity aerodynamical model, we instead elect to designate the system identification component of the control scheme to provide real-time estimates of the low level robot parameters. Input and output data are collated during flight and the recursive least squares method is applied to obtain real-time parameter estimates. Estimated parameters are subsequently used in designing the control law using adaptive pole placement via the polynomial method where we prescribe the desired closed-loop characteristic equation. Secondly, even if the dynamics of the robot varies over time, it is accounted by the adaptive controller without any need to perform tuning since proportional gain values are spontaneously generated. Numerical simulations are first used to assist the design and validate the correct operation of the control scheme. It is then implemented on a real bird-like flapping-wing flying robot; experimental results obtained exhibit close congruence with simulation results.     

混合云中面向数据中心的工作流数据布局方法

科学工作流是一种复杂的数据密集型应用程序.如何在混合云环境中对数据进行有效布局,是科学工作流所面临的重要问题,尤其是混合云的安全性要求给科学云工作流数据布局研究带来了新的挑战.传统数据布局方法大多采用基于负载均衡的划分模型布局数据集,该方法可以获得很好的负载平衡布局,然而传输时间并非最优.针对传统数据布局方法的不足,并结合混合云中数据布局的特点,首先设计一种基于数据依赖破坏度的矩阵划分模型,生成对数据依赖度破坏最小的划分;然后提出一种面向数据中心的数据布局方法,该方法依据划分模型将依赖度高的数据集尽量放在同一数据中心,从而减少数据集跨数据中心的传输时间.实验结果表明,该方法能够有效地缩短科学工作流运行时跨数据中心的数据传输时间.     

基于Delaunay三角网的简单面状要素自动注记

为使地图标注中简单面状要素的自动注记更加美观且高效,提出了一套简单面状要素的注记方案.该方案先用重心法试着将文本标注在重心附近,但当重心法不能将文本标注于多边形内部时,则用改进的Delaunay三角网骨架线法将文本顺着骨架线标注,以适应绝大多数多边形.重心法以O(n)的效率快速标注文本,而改进的三角网建网算法提高了建网效率,保证了骨架线方法的可行性.实验结果表明:该方案注记视觉效果良好,注记效率高.     

岛式FPGA线长驱动快速布局算法

传统的FPGA布局箅法需要花费大量时间,影响了FPGA物理设计效率.为了在保证布局质量的前提下缩短布局时间,提出一种岛式FPGA快速布局算法.首先考虑终端传输的迭代二划分,然后进行最小费用流初始布局和低温模拟退火的布局优化.在每一个划分层次中,考虑了线网的终端对线网权重的影响;对于每一个划分的区域,使用最小费用流来确定初始的布局;在布局的最后阶段使用低温模拟退火来提高初始布局的质量.实验结果表明,该算法布局结果的质量高、速度快.     

遗传算法在传感器优化配置中的应用

针对于传感器网络中传感器配置问题,在保证局部网络损坏的情况下仍能进行检测,提高检测网络的可靠性,提出了用遗传算法对其进行优化,并设计了该遗传算法的计算步骤和其相应的适应度函数程序。计算了一个实例,得出了较好的配置方式,满足了传感网络可靠性的要求,说明了用遗传算法进行传感器优化配置是可行的。     

分布式存储中数据放置策略的研究

分布式存储统系统的可靠性主要取决于系统采用何种数据放置策略来保证数据在网络中各结点的有效分配。总结了设计数据放置策略所需考虑的关键问题和衡量数据放置策略的标准,重点讨论了已有的几种放置策略,在Gossip副本更新算法思想的基础上,提出一种可适应动态网络环境的基于存储转发的随机放置策略,并通过测试验证了该策略对系统可靠性和性能的影响。     

基于对象分组的集群存储系统可靠性分析模型

可靠性问题是研究大规模集群存储系统的一个重要方面。借鉴RAID的方法,提出基于对象分组在算法一级实现数据冗余分布的高可靠数据对象布局算法。在数据对象和存储节点失效时,利用冗余数据重构数据对象和存储节点,有效保证存储系统的高可用性。采用马尔可夫激励模型对存储系统进行定量的可用性分析,计算结果表明该方法是有效的。     

Design of a nonlinear controller based on a piecewise-linear Hammerstein model

This paper presents a nonlinear controller, which can be used to control single-input single-output nonlinear systems that can be approximated in terms of Hammerstein models. The proposed nonlinear controller is based on the principles of a classic linear pole placement controller and the piecewise-linear Hammerstein model. The controller can be used to control processes with highly nonlinear or even discontinuous static functions, while keeping simple controller structure and a very low computational burden.     

Determination of optimum robot base location considering discrete end-effector positions by means of hybrid genetic algorithm

The performance of a robot manipulator during a process depends on its position relative to the corresponding path. An ill-placed manipulator risks inefficient operation as well as blocks due to singularities. The paper deals with an optimization algorithm to determine the base position and the joint angles of a spatial robot, when the end-effector poses are prescribed, avoiding the singular configurations. The optimization problem is solved through a hybrid heuristic method that combines the advantages of a genetic algorithm, a quasi-Newton algorithm and a constraints handling method. Six cases of a 6-DOF manipulator are studied to verify the feasibility of the proposed algorithm.