A sensitivity analysis for control systems having input time delays

The concept of singular sensitivity of a lumped linear system to small pure time delays in controls is introduced. From this, first order changes in a quadratic performance index can be calculated. A design method is proposed which calculates optimal linear feedback laws for the lumped system, but ensures that the effect of control delays is kept “small” in closed loop. Iterative computational algorithms are developed and simple examples presented. For stable plants, the computations always converge, while for unstable plants, the region of convergence limits the allowed delay to “non-dominant” values consistent with stabilisability under the given feedback structure.     

Optimal uncertainty reduction for multi-disciplinary multi-output systems using sensitivity analysis

We present a sensitivity analysis based uncertainty reduction approach, called Multi-dIsciplinary Multi-Output Sensitivity Analysis (MIMOSA), for the analysis model of a multi-disciplinary engineering system decomposed into multiple subsystems with each subsystem analysis having multiple inputs with reducible uncertainty and multiple outputs. MIMOSA can determine: (1) the sensitivity of system and subsystem outputs to input uncertainties at both system and subsystem levels, (2) the sensitivity of the system outputs to the variation from subsystem outputs, and (3) the optimal “investment” required to reduce uncertainty in inputs in order to obtain a maximum reduction in output variations at both the system and subsystem levels. A numerical and an engineering example with two and three subsystems, respectively, have been used to demonstrate the applicability of the MIMOSA approach.     

Insights into sensitivity analysis of Earth and environmental systems models: On the impact of parameter perturbation scale

This paper investigates the commonly overlooked “sensitivity” of sensitivity analysis (SA) to what we refer to as parameter “perturbation scale”, which can be defined as a prescribed size of the sensitivity-related neighbourhood around any point in the parameter space (analogous to step size Δx for numerical estimation of derivatives). We discuss that perturbation scale is inherent to any (local and global) SA approach, and explain how derivative-based SA approaches (e.g., method of Morris) focus on small-scale perturbations, while variance-based approaches (e.g., method of Sobol) focus on large-scale perturbations. We employ a novel variogram-based approach, called Variogram Analysis of Response Surfaces (VARS), which bridges derivative- and variance-based approaches. Our analyses with different real-world environmental models demonstrate significant implications of subjectivity in the perturbation-scale choice and the need for strategies to address these implications. It is further shown how VARS can uniquely characterize the perturbation-scale dependency and generate sensitivity measures that encompass all sensitivity-related information across the full spectrum of perturbation scales.     

A region analysis algorithm for the live variables problem

An algorithm to solve the “live variables” problem on reducible flow graphs is presented. It is based on the concept of a region of a flow graph. The algorithm is compared for time complexity with the well-known round-robin version of the iterative algorithm on “self-replicating” families of reducible flow graphs. The results of comparison are inconclusive in that the region analysis algorithm requires fewer bit-vector operations on some graphs and more on others.     

Extending a global sensitivity analysis technique to models with correlated parameters

The identification and representation of uncertainty is recognized as an essential component in model applications. One important approach in the identification of uncertainty is sensitivity analysis. Sensitivity analysis evaluates how the variations in the model output can be apportioned to variations in model parameters. One of the most popular sensitivity analysis techniques is Fourier amplitude sensitivity test (FAST). The main mechanism of FAST is to assign each parameter with a distinct integer frequency (characteristic frequency) through a periodic sampling function. Then, for a specific parameter, the variance contribution can be singled out of the model output by the characteristic frequency based on a Fourier transformation. One limitation of FAST is that it can only be applied for models with independent parameters. However, in many cases, the parameters are correlated with one another. In this study, we propose to extend FAST to models with correlated parameters. The extension is based on the reordering of the independent sample in the traditional FAST. We apply the improved FAST to linear, nonlinear, nonmonotonic and real application models. The results show that the sensitivity indices derived by FAST are in a good agreement with those from the correlation ratio sensitivity method, which is a nonparametric method for models with correlated parameters.     

Computer analysis of filament-reinforced metallic-spherical pressure vessels

A computer analysis of filament-reinforced-metallic spherical vessels is presented. The design method uses the load-bearing liner approach and leak-before-rupture as a design criterion. The specific thicknesses of liner and filament composite required to sustain the pressure load are based on the equation of load compatibility and controlled by (1) buckling strength of the metal liner, (2) the maximum strain range for the required operating cycles and (3) the maximum strain capability remaining after cycling. The desirable filament winding pattern is such that the composite has, to the greatest extent possible, uniform thickness and isotropic elastic constants. To account for discontinuity effects, and the slippage between the liner and the composite, the stress and strain levels are best determined using finite element techniques. Aerojet's “AB5U” computer program for axisymmetric bodies having anisotropic material properties is used for the elastic-plastic stress (strain) analysis. Three pre-processor computer programs, “EPCS”, “FIBER” and “REVNOD”, developed by the authors, calculate (from specified filament properties, resin properties, winding pattern, and other geometric data) the necessary input to “AB5U” which includes the geometric data, and elastic constants. A post-processor “POST” was developed to calculate the residual stresses following the proof cycle, the operating strain ranges, and the burst pressure prediction. This capability provides accurate results for use in optimization of the design and enables the analyst to converge quickly to the lightest weight tank possible consistent with specification requirements.     

Simulation-based evaluation of evacuation effectiveness using driving behavior sensitivity analysis

Worldwide, man-made or natural phenomena occasionally occur that create emergency conditions and require the evacuation of areas of different sizes and characteristics. Drivers’ behavior becomes a very important factor for the evacuation operations. This paper provides an analytical study of the effectiveness of evacuation according to drivers’ behavior, using the sensitivity analysis method. Collecting real-time data about this factor is a difficult to impossible task for large scale cases; therefore, traffic simulation is the most appropriate method for analysis. Our goal is to investigate how drivers’ aggressiveness affects the evacuation effectiveness. In this case, we used the AIMSUN traffic simulation model; the parameters of the driver behavior models are chosen through all-at-once sensitivity analysis of the parameters. This model is applied to different demand scenarios for well-defined parameters’ value ranges. This investigation produces estimated ranges of the evacuation duration and the number of evacuated people, both for a baseline “do-nothing” scenario, as well as the outcome of improvement actions. The sensitivity analysis results suggest that evacuation time can be significantly reduced by reversing the most congested links; furthermore, the use of a bus fleet would allow many more people to evacuate the danger zone timely, albeit with a small increase in minimum evacuation time. This methodology could be applicable to other emergency response scenarios, as it obviates the need for real-time data.     

Design and sensitivity analysis of dynamical systems subjected to stochastic loading

The paper presents an efficient procedure which allows to carry out reliability-based optimization of linear systems subjected to stochastic loading. The optimization problem is replaced by a sequence of approximate explicit sub-optimization problems that are solved in an efficient manner. Approximation concepts are used to construct high quality approximations of dynamic responses during the optimization process. The approximations are combined with efficient simulation methods to generate explicit approximations of reliability measures in terms of the design variables. The number of dynamic analyses required for the convergence of the design process is reduced dramatically. An efficient sensitivity analysis with respect to the optimization variables and general system parameters becomes possible with the proposed formulation. The sensitivity is evaluated by considering the behavior of the design when the parameters vary within a bounded region. The analysis can identify the degree of robustness of the final design with respect to variations of selected system parameters. A numerical example in terms of a 26-story reinforced concrete building under stochastic earthquake excitation exemplifies the proposed methodology.     

Knowledge transfer in pair programming: An in-depth analysis

Whilst knowledge transfer is one of the most widely-claimed benefits of pair programming, little is known about how knowledge transfer is achieved in this setting. This is particularly pertinent for novice−expert constellations, but knowledge transfer takes place to some degree in all constellations. We ask “what does it take to be a good “expert” and how can a “novice” best learn from a more experienced developer?”. An in-depth investigation of video and audio excerpts of professional pair programming sessions using Interaction Analysis reveals: six teaching strategies, ranging from “giving direct instructions” to “subtle hints”; and challenges and benefits for both partners. These strategies are instantiations of some but not all teaching methods promoted in cognitive apprenticeship; novice articulation, reflection and exploration are not seen in the data. The context of pair programming influences the strategies, challenges and benefits, in particular the roles of driver and navigator and agile prioritisation which considers business value rather than educational progression. Utilising these strategies more widely and recognizing the challenges and benefits for both partners will help developers to maximise the benefits from pairing sessions.     

Iterative methods in image analysis

This paper reviews recent developments in the use of iterative (or “relaxation”) methods in image analysis. Applications of these methods include histogram modification, noise cleaning, edge and curve detection, thinning, angle detection, template matching, and region labelling. These applications are briefly described, and references are given to papers and reports in which more detailed discussions and examples can be found.     

Application of the homotopy analysis method to determine the analytical limit state functions and reliability index for large deflection of a cantilever beam subjected to static co-planar loading

In this paper, the Homotopy Analysis Method (HAM) is applied to obtain the limit state function, probability of failure and reliability index based on all stochastic and deterministic variables for a cantilever beam subjected to co-planar loading for the first time. First, it is established that a few iterations in the series expansion are sufficient to obtain highly accurate results and a substantial convergence region. After showing the effectiveness of HAM, two limit state functions are introduced as the maximum deflection in the y direction and maximum allowable stress, respectively. Then the first order reliability method (FORM) is employed to obtain reliability index, and omission sensitivity factor analytically. It is shown that HAM is a promising tool to obtain limit state function, probability of failure and reliability index analytically for nonlinear problems. Finally, a sensitivity analysis is done to show that which parameters could be considered deterministic or stochastic variables.     

Reliability analysis for an apparel manufacturing system applying fuzzy multistate network

This paper addresses the reliability analysis for a real-world apparel manufacturing system by using fuzzy mathematics. The studied apparel manufacturing system is a precise handicraft profession which involves a great amount of labor-intensive processes. To consider human performance, the apparel manufacturing system is constructed as a fuzzy multistate network, termed apparel manufacturing network (AMN). The workload state of a workstation in the AMN is defined by three fuzzy membership functions: “under-normal-workload”, “normal-workload”, and “over-normal-workload”. Hence, the workload of a workstation is fuzzy multistate and the workstation-reliability is measured by three fuzzy membership functions. Subsequently, the system reliability is evaluated in terms of all workstation-reliabilities, and is derived by fuzzy intersection. The reliability analysis can help the production manager to understand the demand satisfaction of the AMN.     

A volume-preserving approach for modeling and animating water flows generated by metaballs

This paper presents a volume-preserving approach for animating liquid flows modeled by metaballs. A volume of liquid can be adjusted to a previous volume by using the influence radius and the maximum density of metaballs as volume-controlling parameters. Recursive subdivision is used to efficiently calculate the volume of implicit surfaces. The criterion for subdivision is obtained by using the concept of interval analysis and the common property of metaball density functions. Providing a sequence of parameters, the volume-compensation region can be controlled according to the substance making up the object, resulting in local preservation of the volume. Set partition is used for determining isolated surfaces in order to apply local-volume preservation. Published online: 23 July 2002 Correspondence to: K. Kaneda     

高维数据聚类可视分析方法综述

数据聚类的可视分析方法利用可视化与交互技术帮助用户对聚类过程与结果进行 多角度分析,从而发现数据内部隐藏的结构和关系。但由于高维数据自身的“维度诅咒”问题 使得聚类分析面临着许多挑战,例如模型参数设定、数据特征捕捉、结果解释以及可视化展现 等。本文从高维数据聚类过程中遇到的问题出发,首先总结了高维数据聚类过程中常用的数据 处理方法并对其性能进行了比较,这些方法能够较好地解决“维度诅咒”问题,帮助用户挖掘 数据中存在的聚类模式。在分析和理解不同聚类结果中包含的数据内部结构和规律时,由于前 期采取的数据处理方法不同,因此需要采取不同的探索分析策略,所以本文将近10 年来高维数 据聚类的可视分析方法分为2 大类进行总结,即基于降维的聚类可视分析方法和基于子空间聚 类的可视分析方法。最后对该领域目前存在的机遇与挑战进行了讨论。     

TOPO III: a FORTRAN program for terrain analysis

TOPO III is a FORTRAN program that operates with rectangular arrays of equally spaced elevations, producing lists of terrain parameters (coordinates, elevation, average and maximum relief, average and maximum slope, azimuth of maximum slope, and topographic shape: slope, ridge, valley, knob, sink, saddle), point-frequency distributions of maximum and average slope, symbolic slope maps, frequency distributions of areas comprising contiguous points with the same topographic shape, and topographic shape maps with point-frequency distributions of shape types. The program compares a central elevation with interpolated elevations around the perimeter of a circular “window”, and the radius of the window may be increased in successive passes through the array. The results have been used for slope studies, for analysis of terrain factors in environmental studies, and for terrain classification.     

A high-performance parallel implementation of the certified reduced basis method

The certified reduced basis method (herein RB method) is a popular approach for model reduction of parametrized partial differential equations. In this paper we introduce new techniques that are required to efficiently implement the Offline “Construction stage” of the RB method on high-performance parallel supercomputers. This enables us to generate certified RB models for large-scale three-dimensional problems that can be evaluated on standard workstations and other “thin” computing resources with speedup of many orders of magnitude compared to the corresponding full order model. We use our implementation to perform detailed numerical studies for two computationally expensive model problems: a natural convection fluid flow problem and a “many parameter” heat transfer problem. In the heat transfer problem, we exploit the computational efficiency of the RB method to perform a detailed study of “snapshot” selection in the Greedy algorithm, and we also examine statistics of the output sensitivity derivatives to obtain a “global” view of the relative importance of the parameters.     

翼伞系统最优归航轨迹设计的敏感度分析方法

本文对三自由度翼伞系统归航轨迹优化问题进行了研究,采用控制变量参数化与时间尺度变换相结合的优化算法对翼伞系统的最优控制问题进行数值求解.该方法是基于灵敏度分析的优化算法,将控制量以及控制量转换时间转化为一系列参数优化问题同时进行求解.仿真结果表明,相对于基于两端边值优化算法而言,灵敏度分析法只需要正向积分进行求解,因而具有计算简单、耗时短等优点,其控制效果良好,距离偏差和方向偏差均满足实际需求,有效地提高了翼伞系统的着陆精度,验证了该优化算法的可行性.     

Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model

Global Sensitivity Analysis (GSA) is an essential technique to support the calibration of environmental models by identifying the influential parameters (screening) and ranking them.In this paper, the widely-used variance-based method (Sobol') and the recently proposed moment-independent PAWN method for GSA are applied to the Soil and Water Assessment Tool (SWAT), and compared in terms of ranking and screening results of 26 SWAT parameters. In order to set a threshold for parameter screening, we propose the use of a “dummy parameter”, which has no influence on the model output. The sensitivity index of the dummy parameter is calculated from sampled data, without changing the model equations. We find that Sobol' and PAWN identify the same 12 influential parameters but rank them differently, and discuss how this result may be related to the limitations of the Sobol' method when the output distribution is asymmetric.     

图像数据挖掘在SARS辅助诊断中的应用

严重急性呼吸道综合症(SARS)，又称“非典型肺炎”，是目前人类面临的一种严重危害生命和健康的新发传染病。利用PACS系统中的胸部数字X光(DX)正位图像，采用图像数据挖掘技术，设计并实现了SARS计算机辅助诊断系统。经过数据清理定位DX肺部图像的感兴趣区域，分割出双肺区域，提取特征参数，构造决策树，实现对SARS患者和一般肺炎胸部DX正位图像的分类。实验结果表明，检测SARS图像正确率达到70%以上。     

Yield improvement analysis with parameter-screening factorials

This paper presents a technique for the critical parameter analysis of the disk drive manufacturing process. The objective of the work is to improve the manufacturing yield by tuning the parameters that significantly affect the yield. Several techniques were studied including the sensitivity analysis framework, which is currently used at several disk drive plants. From our initial experiments, we found that the sensitivity analysis results were not sufficiently good and the interactions between parameters were not identified. We then designed a new technique based on factorial designs, the parameter-screening factorials algorithm. Our method can work with a large number of inputs within reasonable computing time, and can identify both the parameter and the interaction effects. The results can be obtained more quickly and are better in comparison with the currently used technique. Moreover, by applying the technique to the full list instead of the pre-selected list of the manufacturing parameters, we discovered that the parameters watch list previously identified by the experts should be adjusted to include some extra parameters. After the results were validated by the experts, we designed software that automates the critical parameter analysis process. The software should greatly benefit the daily yield analysis at the disk drive manufacturing plant greatly.