Publications

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Visual clutter metrics play an important role in both the design of information visualizations and in the continued theoretical development of visual search models. In visualization design, clutter metrics provide a mathematical prediction of the complexity of the display and the difficulty associated with locating and identifying key pieces of information. In visual search models, they offer a proxy to set size, which represents the number of objects in the search scene, but is difficult to estimate in real-world imagery. In this article, we first briefly review the literature on clutter metrics and then contribute our own results drawn from studies in two security-oriented visual search domains: airport X-ray imagery and radar imagery. We analyze our results with an eye toward bridging the gap between the scene features evaluated by current clutter metrics and the features that are relevant to our security tasks. The article concludes with a brief discussion of possible research steps to close this gap.

Characterizing the tails of probability distributions plays a key role in quantification of margins and uncertainties (QMU), where the goal is characterization of low probability, high consequence events based on continuous measures of performance. When data are collected using physical experimentation, probability distributions are typically fit using statistical methods based on the collected data, and these parametric distributional assumptions are often used to extrapolate about the extreme tail behavior of the underlying probability distribution. In this project, we character- ize the risk associated with such tail extrapolation. Specifically, we conducted a scaling study to demonstrate the large magnitude of the risk; then, we developed new methods for communicat- ing risk associated with tail extrapolation from unvalidated statistical models; lastly, we proposed a Bayesian data-integration framework to mitigate tail extrapolation risk through integrating ad- ditional information. We conclude that decision-making using QMU is a complex process that cannot be achieved using statistical analyses alone.

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The outputs available in the xLPR Version 2.0 code can be analyzed using statistical techniques that have been developed to compare sampling scheme selection, identify inputs for importance sampling, and assess result convergence and uncertainty. These techniques were developed and piloted for both the xLPR Scenario Analysis (SA) Report and the xLPR Sensitivity Analysis Template. This document provides a walk-through of the post-processing R code that was used to generate the results and figures presented in these documents. This page intentionally left blank.

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This document outlines a data-driven probabilistic approach to setting product acceptance testing limits. Product Specification (PS) limits are testing requirements for assuring that the product meets the product requirements. After identifying key manufacturing and performance parameters for acceptance testing, PS limits should be specified for these parameters, with the limits selected to assure that the unit will have a very high likelihood of meeting product requirements (barring any quality defects that would not be detected in acceptance testing). Because the settings for which the product requirements must be met is typically broader than the production acceptance testing space, PS limits should account for the difference between the acceptance testing setting relative to the worst-case setting. We propose an approach to setting PS limits that is based on demonstrating margin to the product requirement in the worst-case setting in which the requirement must be met. PS limits are then determined by considering the overall margin and uncertainty associated with a component requirement and then balancing this margin and uncertainty between the designer and producer. Specifically, after identifying parameters critical to component performance, we propose setting PS limits using a three step procedure: 1. Specify the acceptance testing and worst-case use-settings, the performance characteristic distributions in these two settings, and the mapping between these distributions. 2. Determine the PS limit in the worst-case use-setting by considering margin to the requirement and additional (epistemic) uncertainties. This step controls designer risk, namely the risk of producing product that violates requirements. 3. Define the PS limit for product acceptance testing by transforming the PS limit from the worst-case setting to the acceptance testing setting using the mapping between these distributions. Following this step, the producer risk is quantified by estimating the product scrap rate based on the projected acceptance testing distribution. The approach proposed here provides a framework for documenting the procedure and assumptions used to determine PS limits. This transparency in procedure will help inform what actions should occur when a unit violates a PS limit and how limits should change over time.

This report describes the methods, results, and conclusions of the analysis of 11 scenarios defined to exercise various options available in the xLPR (Extremely Low Probability of Rupture) Version 2 .0 code. The scope of the scenario analysis is three - fold: (i) exercise the various options and components comprising xLPR v2.0 and defining each scenario; (ii) develop and exercise methods for analyzing and interpreting xLPR v2.0 outputs ; and (iii) exercise the various sampling options available in xLPR v2.0. The simulation workflow template developed during the course of this effort helps to form a basis for the application of the xLPR code to problems with similar inputs and probabilistic requirements and address in a systematic manner the three points covered by the scope.

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