A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LP_τ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones.     

Decentralized control: Status and outlook

This paper reviews state of the art in the area of decentralized networked control systems with an emphasis on event-triggered approach. The models or agents with the dynamics of linear continuous-time time-invariant state-space systems are considered. They serve for the framework for network phenomena within two basic structures. The I/O-oriented systems as well as the interaction-oriented systems with disjoint subsystems are distinguished. The focus is laid on the presentation of recent decentralized control design and co-design methods which offer effective tools to overcome specific difficulties caused mainly by network imperfections. Such side-effects include communication constraints, variable sampling, time-varying transmission delays, packet dropouts, and quantizations. Decentralized time-triggered methods are briefly discussed. The review is deals mainly with decentralized event-triggered methods. Particularly, the stabilizing controller–observer event-based controller design as well as the decentralized state controller co-design are presented within the I/O-oriented structures of large scale complex systems. The sampling instants depend in this case only on a local information offered by the local feedback loops. Minimum sampling time conditions are discussed. Special attention is focused on interaction-oriented system architecture. Model-based approach combined with event-based state feedback controller design is presented, where the event thresholds are fully decentralized. Finally, several selected open decentralized control problems are briefly offered as recent research challenges.     

高速网络流量测量方法

高速网络流量测量是目前实施实时准确地监测、管理和控制网络的基础.基于网络流量测量的应用,将网络流量测量分为抽样方法和数据流方法.从不同的层次,将抽样方法分为分组抽样和流抽样,分别介绍了两类抽样方法;从测度角度介绍了数据流方法.详细介绍了高速网络流量测量的常用数据结构,以及抽样、数据流方法在高速网络流量测量中的应用,比较了各种方法的优劣.概述了高速网络流量测量技术的研究进展.最后,就现有的网络流量测量方法的不足,对网络流量测量的发展趋势和进一步的研究方向进行了讨论.     

具有随机采样特性的网络化系统H∞ 滤波

研究一类具有随机采样特性的网络化系统H∞滤波问题.通过将传感器的随机采样过程建模成马尔可夫链,将数据量化作用转化为模型的参数不确定性,并用二值随机变量描述丢包过程,从而用一个多随机变量的马尔可夫不确定性模型来描述滤波误差系统.应用Lyapunov稳定性理论和随机系统分析方法,导出了滤波误差系统随机稳定且具有给定H∞性能的充分条件,并给出了滤波器的设计方法.仿真结果验证了所提出方法的有效性.     

基于噪声缩放的自适应UKF-SLAM算法

针对扩展卡尔曼滤波(EKF)算法在移动机器人同时定位和环境建模(SLAM)中的缺点,即非线性系统简单线性化所导致的系统状态方程的不准确性、雅克比矩阵的计算所导致的计算复杂化以及噪声模型不确定性所导致的滤波稳定性降低等问题,提出一种对噪声自适应的UKF-SLAM算法。该算法通过对噪声缩放进而改变噪声模型,利用观测残差序列准确估计观测噪声模型协方差,运用预测的新息协方差和IAE开窗法求其系统状态噪声缩放因子,从而准确估计系统状态噪声模型协方差,实现对不确定的噪声模型能够自适应UKF-SLAM算法。UKF的Sigma点采样策略是比例对称采样。实验结果证明,该方法相对EKF算法和UKF算法具有较高的定位精度和自适应能力。     

一种32位浮点型处理器的励磁控制器设计

针对同步发电机励磁控制系统在控制精度和控制稳定性方面存在的不足,通过对国内外励磁控制系统发展状况的分析,提出了一种基于32位浮点型处理器的数字式励磁控制系统.该系统通过将先进的交流采样算法和DSP的优异性能相结合,实现了励磁控制器的深度数字化;通过对同步发电机系统关键模拟量的采集与计算,得出适用于当前工况下触发脉冲角度α.试验验证了该设计能很好地解决发电机励磁控制系统中的稳定性问题.     

基于图像空间的三维动态场景远程可视化方法

对复杂三维动态场景进行远程可视化,基于顶点运动轨迹的压缩、传输方法需要计算的顶点数量巨大,速度慢.针对这些问题,提出一种基于图像空间的三维动态场景远程可视化方法:首先在服务器端进行先空间后时间的自适应采样,获得多幅动画深度图像.通过使用动画深度图像的采样点代替原始动态场景中的顶点,极大减少需要参与计算的三维点数量;然后在每幅动画深度图像中并行压缩采样点的运动轨迹,有效减少压缩时间;最后将压缩后的动态数据传输到客户端并重构一定时间内的三维动态场景.实验结果表明,算法可以极大提高服务器端数据压缩速度,减少需要传输的数据量,有效降低网络带宽对数据传输的限制.     

求解高维函数优化问题的交叉熵蝙蝠算法

为改善蝙蝠算法求解高维函数优化问题的全局搜索能力,提高其搜索精度,将交叉熵方法和蝙蝠算法相结合,提出一种交叉熵蝙蝠算法。该算法将基于重要度抽样和Kullback-Leibler距离的交叉熵全局随机优化算法应用于蝙蝠算法中,采用自适应平滑技术提高算法的收敛速度,利用交叉熵方法的遍历性、自适应性和鲁棒性,有效抑制蝙蝠算法的早熟收敛现象。对经典测试函数和CEC2005测试函数的仿真结果表明,该算法具有全局搜索能力强、求解精度高和鲁棒性好等特性。     

基于Walker星座的导航卫星自主导航的GDOP最小值分析

卫星自主定轨时选择测量卫星的几何构型对于定轨精度有着重要的影响.目前地面接收机与导航卫星的几何构型对定位精度的影响主要由GDOP（几何衰减因子）来衡量,GDOP值的下限限制了在一定的测量精度下用户定位的精度范围.通过构建Walker构型的导航星座将地面接收机GDOP取最小值时的边界确定,推广应用到自主导航环境下,利用均匀采样和遗传算法分别独立得到GDOP的最小值,同时用仿真数据验证了这个值的正确性.     

海洋数据质量检验方案中残差优化选择算法磁

随着信息化的快速发展,海洋数据的获取手段变得多种多样,海洋数据的“量”急剧增大,如何保证海洋大数据的质量问题成为热点。抽样检验是一个较成熟的课题,但适用于海洋数据特性的抽样方案研究较少。结合海洋数据的特点,利用 Skyline 的思想,提出了分批排序的优化抽样检验方案选择算法。通过各抽样检验方案残差的 Skyline 集合,选择优化的质量检验方案,提高海洋数据质量检验精度的同时保证海洋数据的检验费用。