Worst-case Values Research Articles

Water body and riparian buffer strip characteristics in a vineyard area to support aquatic pesticide exposure assessment

The implementation of a geodata-based probabilistic pesticide exposure assessment for surface waters in Germany offers the opportunity to base the exposure estimation on more differentiated assumptions including detailed landscape characteristics. Since these characteristics can only be estimated using field surveys, water body width and depth, hydrology, riparian buffer strip width, ground vegetation cover, existence of concentrated flow paths, and riparian vegetation were characterised at 104 water body segments in the vineyard region Palatinate (south-west Germany). Water body segments classified as permanent (n = 43) had median values of water body width and depth of 0.9 m and 0.06 m, respectively, and the determined median width:depth ratio was 15. Thus, the deterministic water body model (width = 1 m; depth = 0.3 m) assumed in regulatory exposure assessment seems unsuitable for small water bodies in the study area. Only 25% of investigated buffer strips had a dense vegetation cover (> 70%) and allow a laminar sheet flow as required to include them as an effective pesticide runoff reduction landscape characteristic. At 77 buffer strips, bordering field paths and erosion rills leading into the water body were present, concentrating pesticide runoff and consequently decreasing buffer strip efficiency. The vegetation type shrubbery (height > 1.5 m) was present at 57 (29%) investigated riparian buffer strips. According to their median optical vegetation density of 75%, shrubberies may provide a spray drift reduction of 72 ± 29%. Implementing detailed knowledge in an overall assessment revealed that exposure via drift might be 2.4 and via runoff up to 1.6 fold higher than assumed by the deterministic approach. Furthermore, considering vegetated buffer strips only by their width leads to an underestimation of exposure by a factor of as much as four. Our data highlight that the deterministic model assumptions neither represent worst-case nor median values and therefore cannot simply be adopted in a probabilistic approach.

Robust Spectrum Management for DMT-Based Systems

In recent years an increasing effort was made to reduce the energy consumption in digital subscriber line equipment. Dynamic spectrum management (DSM) has been identified as one promising method to achieve energy-efficiency in discrete multitone based systems. An open research question is how to ensure system robustness when applying highly optimized energy-efficient spectrum management. In this paper, we study the problem of uncertainty in crosstalk noise and parameters, the knowledge of which is indispensable for many DSM algorithms. We introduce robust optimization for spectrum balancing as a technique to achieve feasibility of the optimal power-allocation under a deterministic parameter uncertainty model. This can be seen as an extension of current schemes for spectrum balancing. As a special case we consider the simple strategy of scaling the crosstalk parameters to their worst-case values, which corresponds to a specific uncertainty model and entails no changes to current DSM algorithms. Finally, we quantify the benefit in worst-case performance and the price in terms of energy by simulations.

An Evaluation of the Double Exponentially Weighted Moving Average Control Chart

In this article we perform a careful investigation of the double exponentially weighted moving average (DEWMA) chart performance for monitoring the process mean. We compare the performance of this chart to the usual EWMA control chart based on zero-state and worst-case average run length (ARL) measures. We also evaluate the signal resistance measure of the DEWMA chart and compare its maximum value to that of the EWMA chart. We show that the superiority of the DEWMA chart over the simpler standard EWMA chart based on zero-state ARL performance disappears when the smoothing constant of the EWMA chart is chosen to give weights to past observations closer to those given by the DEWMA chart. Moreover, our results show that the standard EWMA chart has much better performance than the DEWMA chart in terms of worst-case ARL values, especially when small smoothing constants are used. We also demonstrate using an illustrative example that the DEWMA chart can build up an exceedingly large amount of inertia when used to monitor the process mean.

A Multivariate Adaptive Exponentially Weighted Moving Average Control Chart

A multivariate extension of the adaptive exponentially weighted moving average (AEWMA) control chart is proposed. The new multivariate scheme can detect small and large shifts in the process mean vector effectively. The proposed scheme can be viewed as a smooth combination of a multivariate exponentially weighted moving average (MEWMA) chart and a Shewhart χ2-chart. The optimal design of the proposed chart is given according to a pre-specified in-control average run length and two shift sizes; a small and large shift each measured in terms of the non centrality parameter. The signal resistance of the newly proposed multivariate chart is also given. Comparisons among the new chart, the MEWMA chart, and the combined Shewhart-MEWMA (S-MEWMA) chart in terms of the standard and worst-case average run length profiles are presented. In addition, the three charts are compared with respect to their worst-case signal resistance values. The proposed chart gives somewhat better worst-case ARL and signal resistance values than the competing charts. It also gives better standard ARL performance especially for moderate and large shifts. The effectiveness of our proposed chart is illustrated through an example with simulated data set.

Probabilistically Based Risk of Exposure to Power Line Magnetic Fields

Guidelines of exposure to power line magnetic fields are usually designed on a deterministic basis, making their validity and adequacy somewhat questionable. Based on an overall probabilistic approach, a method for risk assessment of exposure to overhead power line magnetic fields is presented. There are advantages to the probabilistic approach over conventional methods of developing the risk of exposure to overhead power line magnetic fields. In this article, a model is constructed to predict the randomness in power line magnetic fields, taking into account—in addition to line loading—the random nature of the parameters contributing to the temperature of the power line conductors, which in turn, influences the resulting conductor sag. Those parameters include line loading, ambient temperature, solar irradiation, and wind speed. The correlative nature of those parameters is also considered. The model accounts for the likelihood of the effects of those various operational and weather parameters (or variables), thus avoiding inflated risk estimates produced by compounding single-point worst-case values of the input variables and, consequently, leads to an improved risk assessment.

Multi-objective product configuration involving new components under uncertainty

Product configuration is a process of selecting suitable and compatible components for a product, arranging them, and then seeking one or more optimal options. To date, many researchers have studied its modelling, representing methods, and optimising approaches. However, uncertainty while involving new components, uncertain factors in supply chain and market, and imprecision and vagueness in customer requirements for configuration are always ignored. This paper identifies the uncertainty in configuration and proposes a fuzzy multi-objective optimisation algorithm to seek an optimal configuration scheme considering uncertainty. Multi-objective evaluation criteria are utilised to assess lifecycle cost, time to market, and serviceability of configuration options, Pareto optimality is applied to resolve conflicts between different configuration goals, a fuzzy set method is used to deal with imprecise estimated parameter values and a two-aspect judgment is proposed to balance expected values and worst case values in configuration. Compatibilities between components are also considered. The importance of optimisation objectives is determined according to different customers’ requirements, and the desired configuration option is obtained through comparing the results. Finally, the effectiveness of the proposed method is illustrated through a case of configuring laptop computers, and a sensitivity analysis.

2D Monte Carlo Analysis of Radiological Risk Assessment for the Food Intake In Korea

Most public health risk assessments assume and combine a series of average, conservative and worst-case values to derive an acceptable point estimate of risk. To improve quality of risk information, insight of uncertainty in the assessments is needed and more emphasis is put on the probabilistic risk assessment. Probabilistic risk assessment studies use probability distributions for one or more variables of the risk equation in order to quantitatively characterize variability and uncertainty. In this study, an advanced technique called the two-dimensional Monte Carlo analysis (2D MCA) is applied to estimation of internal doses from intake of radionuclides in foodstuffs and drinking water in Korea. The variables of the risk model along with the parameters of these variables are described in terms of probability density functions (PDFs). In addition, sensitivity analyses were performed to identify important factors to the radiation doses.

Mad Cows and Computer Models: The U.S. Response to BSE

The proportion of slaughtered cattle tested for BSE is much smaller in the U.S. than in Europe and Japan, leaving the U.S. heavily dependent on statistical models to estimate both the current prevalence and the spread of BSE. We examine the models relied on by USDA, finding that the prevalence model provides only a rough estimate, due to limited data availability. Reassuring forecasts from the model of the spread of BSE depend on the arbitrary constraint that worst-case values are assumed by only one of 17 key parameters at a time. In three of the six published scenarios with multiple worst-case parameter values, there is at least a 25% probability that BSE will spread rapidly. In public policy terms, reliance on potentially flawed models can be seen as a gamble that no serious BSE outbreak will occur. Statistical modeling at this level of abstraction, with its myriad, compound uncertainties, is no substitute for precautionary policies to protect public health against the threat of epidemics such as BSE.

Analysis of Worst-Case Fiber Splice Loss Associated with Mechanical Tolerance

An analysis is presented of optical fiber splice loss associated with mechanical tolerance for coated and uncoated avionic fibers coupled in a loose sleeve (glass capillary). A simple prescription is provided for calculating worst-case loss, combining simultaneous contributions from core offset, core diameter mismatch, numerical aperture mismatch, and core ellipticity. Worst-case values are interpreted with the aid of a Monte Carlo analysis, simulating 10,000 splices. The analysis produces a simple rule of thumb relating the worst-case splice loss to the value exceeded by 1% of the simulated splices: “divide the worst-case loss by 3 to get the 1% value.”

Robust Control Design for Resonance Damping of a Directional Microphone

Abstract This work applies µ analysis and synthesis techniques to develop a robust strategy for damping resonant modes in micromachined directional microphones of the parallel capacitive type. By properly selecting a weighting function, a robust controller is successfully designed via the D-K algorithm. Linear simulations show that the resonant mode of a directional microphone can be flattened out, and pushed to a frequency beyond the sensitivity of the human ear. The closed loop responses are free of ringing effects observed in open loop even for the worst-case parameter values.

Limitations to a Geostationary Infrared Sounder due to Diffraction: The Meteosat Third Generation Infrared Sounder (MTG IRS)

Abstract Geostationary infrared sounding missions offer good temporal coverage; however, because of the large distance to the observed earth targets, the effect of diffraction is increased compared to sounding from a low earth orbit (LEO). Because of the wavelength dependence of diffraction, the spectral channels do not sample the same volume of air, as is generally assumed by retrieval algorithms for LEO infrared (IR) sounder data. This additional error introduced in the retrieval by diffraction-limited instruments is called pseudonoise throughout the paper. One such diffraction-limited geostationary system is the candidate Infrared Sounder (IRS) mission on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellites with a planned need date in 2015, due to the expected lifetime of the current Meteosat Second Generation (MSG) satellites. A simplified point spread function (PSF) is applied. To represent the channels within natural conditions, measured spectra from the LEO Atmospheric Infrared Sounder (AIRS) are used as an underlying scene when integrating over the PSF. As the AIRS spatial resolution is 15 km, the basic assumption made was that the meteorological and surface features can be regarded as fractal to the extent that they can be downscaled to at least 4 km, making them useful for these investigations. The results show that the pseudonoise is highly dependent on wavelength, and highest in the window and CO2 regions (within the broader 700–1200 cm−1, or 8.3–14.3-μm, region). The worst-case pseudonoise values are approximately 1 K for these regions. A case study of the impact of the obtained pseudonoise values on temperature and water vapor retrievals shows that only with the worst-case assumptions (uncorrelated noise) is there a quantifiable impact on the results. For temperature retrievals, this ranged from 0.1 to 0.3 K in the lower and upper troposphere, respectively.

Fatigue Bearing Capacity of Anchors exposed to Shear Loading

Tests on M12 chemical anchors were conducted to determine the effect of concrete properties on the fatigue bearing capacity of anchors under shear loading. The properties of the concrete analysed included compression strength and a crack of 0.3 mm width compared with the non-cracked concrete. The results obtained produced a Wohler curve for each of the four test series. Furthermore, a function was developed for cracked and non-cracked concrete, which allows the estimation of the fatigue bearing capacity of bonded anchors in concrete of any compression strength. In the technical approvals issued so far, only worst-case values of fatigue bearing capacity have been given. It was also possible to recognise many characteristics of the fracture and recorded parameters, which allow conclusions about the type of failure. This makes the planning of future tests easier.

A polynomial time algorithm for Zero-Clairvoyant scheduling

In the design of real-time systems, it is often the case that certain process parameters, such as the execution time of a job are not known precisely. The challenge in real-time system design then, is to develop techniques that efficiently meet the requirements of impreciseness, while simultaneously guaranteeing safety. In a traditional scheduling model, such as the one discussed in [M. Pinedo, Scheduling: Theory, Algorithms, and Systems, Prentice-Hall, Englewood Cliffs, 1995. [23]]; [P. Brucker, Scheduling Algorithms, second ed., Springer, 1998. [3]], the tendency is to either overlook the effects of impreciseness or to simplify the issue of impreciseness by assuming worst-case values. This assumption is unrealistic and at the same time, may cause certain timing constraints to be violated at run-time. Further, depending on the nature of the constraints involved, it is not immediately apparent, what the worst-case value for a given parameter is. Whereas, in traditional scheduling, constraints among jobs are no more complex than those that can be represented by a precedence graph, in case of real-time scheduling, complicated constraints such as relative timing constraints are commonplace. Additionally, the purpose of scheduling is to achieve a schedule that optimizes some performance metric, whereas in real-time scheduling the goal is to ensure that the imposed constraints are met at run-time. In this paper, we study the problem of scheduling a set of ordered, non-preemptive jobs under non-constant execution times. Typical applications for variable execution time scheduling include process scheduling in Real-Time Operating Systems such as Maruti, compiler scheduling, database transaction scheduling and automated machine control. An important feature of application areas such as robotics is the interaction between execution times of various processes. We explicitly model this interaction through the representation of execution time vectors as points in convex sets. This modeling vastly extends previous models of execution times as either single points or range-bound intervals. Our algorithms do not assume any knowledge of the distributions of execution times, i.e. they are Zero-Clairvoyant. We present both sequential and parallel algorithms for determining the existence of a Zero-Clairvoyant schedule. To the best of our knowledge, our techniques are the first of their kind.

An abdominal aortic aneurysm segmentation method: Level set with region and statistical information

We present a system for segmenting the human aortic aneurysm in CT angiograms (CTA), which, in turn, allows measurements of volume and morphological aspects useful for treatment planning. The system estimates a rough "initial surface," and then refines it using a level set segmentation scheme augmented with two external analyzers: The global region analyzer, which incorporates a priori knowledge of the intensity, volume, and shape of the aorta and other structures, and the local feature analyzer, which uses voxel location, intensity, and texture features to train and drive a support vector machine classifier. Each analyzer outputs a value that corresponds to the likelihood that a given voxel is part of the aneurysm, which is used during level set iteration to control the evolution of the surface. We tested our system using a database of 20 CTA scans of patients with aortic aneurysms. The mean and worst case values of volume overlap, volume error, mean distance error, and maximum distance error relative to human tracing were 95.3% +/- 1.4% (s.d.); worst case = 92.9%, 3.5% +/- 2.5% (s.d.); worst case = 7.0%, 0.6 +/- 0.2 mm (s.d.); worst case = 1.0 mm, and 5.2 +/- 2.3 mm (s.d.); worst case = 9.6 mm, respectively. When implemented on a 2.8 GHz Pentium IV personal computer, the mean time required for segmentation was 7.4 +/- 3.6 min (s.d.). We also performed experiments that suggest that our method is insensitive to parameter changes within 10% of their experimentally determined values. This preliminary study proves feasibility for an accurate, precise, and robust system for segmentation of the abdominal aneurysm from CTA data, and may be of benefit to patients with aortic aneurysms.

DISTRIBUTIONAL UNCERTAINTY ANALYSIS OF A BATCH CRYSTALLIZATION PROCESS USING POWER SERIES AND POLYNOMIAL CHAOS EXPANSIONS

Computationally efficient approaches are presented that quantify the influence of parameter uncertainties upon the states and outputs of finite-time control trajectories for nonlinear systems. In the first approach, the worst-case values of the states and outputs due to model parameter uncertainties are computed as a function of time along the control trajectories. The approach uses an efficient contour mapping technique to provide an estimate of the distribution of the states and outputs as a function of time. To increase the estimation accuracy of the shape of the distribution, an approach that uses second order power series expansion in combination with Monte Carlo simulations is proposed. Another approach presented here is based on the approximate representation of the model via polynomial chaos expansion. A quantitative and qualitative assessment of the approaches is performed in comparison to the Monte Carlo simulation technique that uses the nonlinear model. It is shown that the power series and polynomial chaos expansion based approaches require a significantly lower computational burden compared to Monte Carlo approaches, while give good approximation of the shape of the distribution. The techniques are applied to the crystallization of an inorganic chemical with uncertainties in the nucleation and growth parameters.

A framework for the response time analysis of fixed-priority tasks with stochastic inter-arrival times

Real-time scheduling usually considers worst-case values for the parameters of task (or message stream) sets, in order to provide safe schedulability tests for hard real-time systems. However, worst-case conditions introduce a level of pessimism that is often inadequate for a certain class of (soft) real-time systems. In this paper we provide an approach for computing the stochastic response time of tasks where tasks have inter-arrival times described by discrete probabilistic distribution functions, instead of minimum inter-arrival (MIT) values.

Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumor perfusion, microvascular vessel wall permeability and extravascular-extracellular volume fraction. Analysis of DCE-MRI data is usually based on indicator dilution theory that requires knowledge of the concentration of the contrast agent in the blood plasma, the arterial input function (AIF). A method is presented that compares the tissues of interest (TOI) curve shape to that of a reference region (RR), thereby eliminating the need for direct AIF measurement. By assigning literature values for Ktrans (the blood perfusion-vessel permeability product) and v(e) (extravascular-extracellular volume fraction) in a reference tissue, it is possible to extract the Ktrans and v(e) values for a TOI without knowledge of the AIF. The operational RR equation for DCE-MRI analysis is derived, and its sensitivity to noise and incorrect assignment of the RR parameters is tested via simulations. The method is robust at noise levels of 10%, returning accurate (+/-20% in the worst case) and precise (+/-15% in the worst case) values. Errors in the TOI Ktrans and v(e) values scale approximately linearly with the errors in the assigned RR Ktrans and v(e) values. The methodology is then applied to a Lewis Lung Carcinoma mouse tumor model. A slowly enhancing TOI yielded Ktrans=0.039+/-0.002 min-1 and v(e)=0.46+/-0.01, while a rapidly enhancing region yielded Ktrans=0.35+/-0.05 min-1 and v(e)=0.31+/-0.01. Parametric Ktrans and v(e) mappings manifested a tumor periphery with elevated Ktrans (>0.30 min-1) and v(e) (>0.30) values. The main advantage of the RR approach is that it allows for quantitative assessment of tissue properties without having to obtain high temporal resolution images to characterize an AIF. This allows for acquiring images with higher spatial resolution and/or SNR, and therefore, increased ability to probe tissue heterogeneity.

The Zen of overclocking

The author spotlights "better-than-worst-case" design. With normal worst-case design, any computing system is a conglomeration of components, operating within frequencies, power supply voltages, and temperature ranges that were set to simultaneously accommodate worst-case values of every single component. These stacked margins result in noticeable headroom under nominal conditions. It is this headroom that lets the overclockers ply their trade and provides ammunition to the conspiracy theory-minded among them. Overclockers can demonstrate computers apparently running stably at substantially higher clock rates than those that the chip vendors guarantee. The author looks at whether they have a point.

A modem in CMOS technology for data communication on the low-voltage power line

This paper presents a CMOS 0.8 μm mixed-signal half-duplex Modem ASIC for data transmission on the low-voltage power line. It includes all the analog circuitry needed for input interfacing and modulation/demodulation (low-noise amplifier, PLL-based frequency synthesis, tunable filter banks, and decision circuitry), logic circuitry for control purposes, and an output amplifier used as front-end for an off-chip line driver. The chip demodulates signals down to 283μV rms (these are worst case values among 30 randomly-selected samples used as vehicles for detailed electrical characterization; most of the samples featured 200 μV rms sensitivity; bit error rate (BER) is below 0.5×10 −5) at 10 kbps, and operates correctly in the whole industrial temperature range, from −45°C to 80°C, under 5% variations of the 5 V supply voltage. This ASIC is now in commercial production.

Worst-case and distributional robustness analysis of finite-time control trajectories for nonlinear distributed parameter systems

A novel approach is proposed that quantifies the influence of parameter and control implementation uncertainties upon the states and outputs of finite-time control trajectories for nonlinear lumped and distributed parameter systems. The worst-case values of the states and outputs due to model parameter uncertainties are computed as a function of time along the control trajectories. The algorithm can also compute the part of the optimal control trajectory for which implementation inaccuracies are of increased importance. An analytical expression is derived that provides an estimate of the distribution of the states and outputs as a function of time, based on simulation results. The approaches require a relatively low computational burden to perform the analysis, compared to Monte Carlo approaches for robustness analysis. The technique is applied to the crystallization of an inorganic chemical with uncertainties in the nucleation and growth parameters and in the implementation of the control trajectory.