DATAWorks Sessions Archive

Systems with autonomy are beginning to permeate civilian, industrial, and military sectors. Though these technologies have the potential to revolutionize our world, they also bring a host of new challenges in evaluating whether these tools are safe, effective, and reliable. The Institute for Defense Analyses is developing methodologies to enable testing systems that can, to some extent, think for themselves. In this talk, we share how we think about this problem and how this framing can help you develop a test strategy for your own domain.

An overview of Deep Reinforcement Learning and it’s recent successes in creating high performing agents. Covering it’s application in “easy” environments up to massively complex multi-agent strategic environments. Will analyze the behaviors learned, discuss research challenges, and imagine future possibilities.

The performance and reliability of additively manufactured (AM) metals is limited by the ubiquitous presence of void- and crack-like defects that form during processing. Many applications require non-destructive evaluation of AM metals to detect potentially critical flaws. To this end, we propose a deep learning approach that can help with the interpretation of inspection reports. Convolutional neural networks (CNN) are developed to predict the elastic stress fields in images of defect-containing metal microstructures, and therefore directly identify critical defects. A large dataset consisting of the stress response of 100,000 random microstructure images is generated using high-resolution Fast Fourier Transform-based finite element (FFT-FE) calculations, which is then used to train a modified U-Net style CNN model. The trained U-Net model more accurately predicted the stress response compared to previous CNN architectures, exceeded the accuracy of low-resolution FFT-FE calculations, and were evaluated more than 100 times faster than conventional FE techniques. The model was applied to images of real AM microstructures with severe lack of fusion defects, and predicted a strong linear increase of maximum stress as a function of pore fraction. This work shows that CNNs can aid the rapid and accurate inspection of defect-containing AM material.

Collaborative autonomous sensor networks have recently been used in many applications including inspection, law enforcement, search and rescue, and national security. They offer scalable, low cost solutions which are robust to the loss of multiple sensors in hostile or dangerous environments. While often comprised of less capable sensors, the performance of a large network can approach the performance of far more capable and expensive platforms if nodes are effectively coordinating their sensing actions and data processing. This talk will summarize work to date at LLNL on distributed signal processing and decentralized optimization algorithms for collaborative autonomous sensor networks, focusing on ADMM-based solutions for detection/estimation problems and sequential greedy optimization solutions which maximize submodular functions, e.g. mutual information.

As statisticians, we are always working on new ways to explain statistical methodologies to non-statisticians. It is in this realm that we never underestimate the value of graphics and patience! In this presentation, we present a case study that involves stress rupture data where a Weibull regression is needed to estimate the parameters. The context of the case study results from a multi-stage project supported by NASA’s Engineering Safety Center (NESC) where the objective was to assess the safety of composite overwrapped pressure vessels (COPVs). The analytical team was tasked with devising a test plan to model stress rupture failure risk in carbon fiber strands that encase the COPVs with the goal of understanding the reliability of the strands at use conditions for the expected mission life. While analyzing the data, we found that the proper analysis contradicts accepted theories about the stress rupture phenomena. In this talk, we will introduce ways to graph the stress rupture data to better explain the proper analysis and also explore assumptions.

Teams of people with many different talents and skills work together at NASA to improve our understanding of our planet Earth, our Sun and solar system, and the Universe. The Earth System is made up of complex interactions and dependencies of the solar, oceanic, terrestrial, atmospheric, and living components. Solar storms have been recognized as a cause of technological problems on Earth since the invention of the telegraph in the 19th century. Solar flares, coronal holes, and coronal mass ejections (CME’s) can emit large bursts of radiation, high speed electrons and protons, and other highly energetic particles that are released from the sun, and are sometimes directed at Earth. These particles and radiation can damage satellites in space, shutdown power grids on earth, cause GPS outages, and have serious health concerns to humans flying at high altitudes on earth, as well as astronauts in space. NASA builds and operates a fleet of satellites to study the sun and a fleet of satellites and aircraft to observe the Earth system. NASA’s Computer Models combine the observations with numerical models, to understand how these systems work. Using satellite observations alongside computer models we can combine many pieces of information to form a coherent view of Earth and the Sun. NASA research helps us understand how processes combine to affect life on Earth: this includes severe weather, health, changes in climate, and space weather. The Scientific Visualization Studio wants you to learn about NASA programs through visualization. The SVS works closely with scientists in the creation of data visualizations, animations, and images in order to promote a greater understanding of Earth and Space Science research activities at NASA and within the academic research community supported by NASA.

Equipment Failure Reports (EFRs) describe equipment failures and the steps taken as a result of these failures. EFRs contain both structured and unstructured data. Currently, analysts manually read through EFRs to understand failure modes and make recommendations to reduce future failures. This is a tedious process where important trends and information can get lost. This motivated the creation of an interactive dashboard that extracts relevant information from the unstructured (i.e. free-form text) data and combines it with structured data like failure date, corrective action and part number. The dashboard is an RShiny application that utilizes numerous text mining and visualization packages, including tm, plotly, edgebundler, and topicmodels. It allows the end-user to filter to the EFRs that they care about and visualize meta-data, such as geographic region where the failure occurred, over time allowing previously unknown trends to be seen. The dashboard also applies topic modeling to the unstructured data to identify key themes. Analysts are now able to quickly identify frequent failure modes and look at time and region-based trends in these common equipment failures.

Cybersecurity Metrics and Quantification is a fundamental but notoriously hard problem. It is one of the pillars underlying the emerging Science of Cybersecurity. In this talk, I will describe a number of cybersecurity metrics quantification research problems that are encountered in evaluating the effectiveness of a range of cyber defense tools. I will review the research results we have obtained over the past years. I will also discuss future research directions, including the ones that are undertaken in my research group.

Reconnaissance, footprinting, and enumeration are critical steps in the CYBER penetration testing process because if these steps are not fully and extensively executed, the information available for finding a system’s vulnerabilities may be limited. During the CYBER testing process, penetration testers often find themselves doing the same initial enumeration scans over and over for each system under test. Because of this, automated scripts have been developed that take these mundane and repetitive manual steps and perform them automatically with little user input. Once automation is present in the penetration testing process, Scientific Test and Analysis Techniques (STAT) can be incorporated. By combining automation and STAT in the CYBER penetration testing process, Mr. Tim McLean at Marine Corps Tactical Systems Support Activity (MCTSSA) coined a new term called CYBERSTAT. CYBERSTAT is applying scientific test and analysis techniques to offensive CYBER penetration testing tools with an important realization that CYBERSTAT assumes the system under test is the offensive penetration test tool itself. By applying combinatorial testing techniques to the CYBER tool, the CYBER tool’s scope is expanded beyond “one at a time” uses as the combinations of the CYBER tool’s capabilities and options are explored and executed as test cases against the target system. In CYBERSTAT, the additional test cases produced by STAT can be run automatically using scripts. This talk will show how MCTSSA is preparing to use CYBERSTAT in the Developmental Test and Evaluation process of USMC Command and Control systems.

Abstract. While fault testing a system with many factors each appearing at some number of levels, it may not be possible to test all combinations of factor levels. Most faults are caused by interactions of only a few factors, so testing interactions up to size t will often find all faults in the system without executing an exhaustive test suite. Call an assignment of levels to t of the factors a t-way interaction. A covering array is a collection of tests that ensures that every t-way interaction is covered by at least one test in the test suite. Locating arrays extend covering arrays with the additional feature that they not only indicate the presence of faults but locate the faulty interactions when there are no more than d faults in the system. If an array is (d, t)-locating, for every pair of sets of t-way interactions of size d, the interactions do not appear in exactly the same tests. This ensures that the faulty interactions can be differentiated from non-faulty interactions by the results of some test in which interactions from one set or the other but not both are tested. When the property holds for t-way interaction sets of size up to d, the notation (d, t ¯ ) is used. In addition to fault location, locating arrays have also been used to identify significant effects in screening experiments. Locating arrays are fairly new and few techniques have been explored for their construction. Most of the available work is limited to finding only one fault (d = 1). Known general methods require a covering array of strength t + d and produce many more tests than are needed. In this talk, we present Partitioned Search with Column Resampling (PSCR), a computational search algorithm to verify if an array is (d, t ¯ )-locating by partitioning the search space to decrease the number of comparisons. If a candidate array is not locating, random resampling is performed until a locating array is constructed or an iteration limit is reached. Algorithmic parameters determine which factor columns to resample and when to add additional tests to the candidate array. We use a 5 × 5 × 3 × 2 × 2 full factorial design to analyze the performance of the algorithmic parameters and provide guidance on how to tune parameters to prioritize speed, accuracy, or a combination of both. Last, we compare our results to the number of tests in locating arrays constructed for the factors and levels of real-world systems produced by other methods.

Statistical tolerance intervals of the form (1−α, P) provide bounds to capture at least a specified proportion P of the sampled population with a given confidence level 1−α. The quantity P is called the content of the tolerance interval and the confidence level 1−α reflects the sampling variability. Statistical tolerance intervals are ubiquitous in regulatory documents, especially regarding design verification and process validation. Examples of such regulations are those published by the Food and Drug Administration (FDA), the Environmental Protection Agency (EPA), the International Atomic Energy Agency (IAEA), and the standard 16269-6 of the International Organization for Standardization (ISO). Research and development in the area of statistical tolerance intervals has undoubtedly been guided by the needs and demands of industry experts. Some of the broad applications of tolerance intervals include their use in quality control of drug products, setting process validation acceptance criteria, establishing sample sizes for process validation, assessing biosimilarity, and establishing statistically-based design limits. While tolerance intervals are available for numerous parametric distributions, procedures are also available for regression models, mixed-effects models, and multivariate settings (i.e., tolerance regions). Alternatively, nonparametric procedures can be employed when assumptions of a particular parametric model are not met. Tools for computing such tolerance intervals and regions are a necessity for researchers and practitioners alike. This was the motivation for designing the R package ‘tolerance,’ which not only has the capability of computing a wide range of tolerance intervals and regions for both standard and non-standard settings, but also includes some supplementary visualization tools. This session will provide a high-level introduction to the ‘tolerance’ package and its many features. Relevant data examples will be integrated with the computing demonstration, and specifically designed to engage researchers and practitioners from industry and government. A recently-launched Shiny app corresponding to the package will also be highlighted.

Goodness-of-fit (GOF) testing is used in many applications, including statistical hypothesis testing to determine if a set of data come from a hypothesized distribution. In addition, combined probability tests are extensively used in meta-analysis to combine results from several independent tests to asses an overall null hypothesis. This paper summarizes a study conducted to determine which GOF and/or combined probability test(s) can be used to determine if a set of data with relative small sample size comes from the standard uniform distribution, U(0,1). The power against different alternative hypothesis of several GOF tests and combined probability methods were examined. The GOF methods included: Anderson-Darling, Chi-Square, Kolmogorov-Smirnov, Cramér-Von Mises, Neyman-Barton, Dudewicz-van der Meulen, Sherman, Quesenberry-Miller, Frosini, and Hegazy-Green; while thecombined probability test methods included: Fisher’s Combined Probability Test, Mean Z, Mean P, Maximum P, Minimum P, Logit P, and Sum Z. While no one method was determined to provide the best power in all situations, several useful methods to support model validation were identified.

In the operational testing of DoD weapons systems, modeling and simulation (M&S) is often used to supplement live test data in order to support a more complete and rigorous evaluation. Before the output of the M&S is included in reports to decision makers, it must first be thoroughly verified and validated to show that it adequately represents the real world for the purposes of the intended use. Part of the validation process should include a statistical comparison of live data to M&S output. This presentation includes an example of one such validation analysis for a tactical missile system. In this case, the goal is to validate a lethality model that predicts the likelihood of destroying a particular enemy target. Using design of experiments, along with basic analysis techniques such as the Kolmogorov-Smirnov test and Poisson regression, we can explore differences between the M&S and live data across multiple operational conditions and quantify the associated uncertainties.

This tutorial will show how to compare and choose experimental designs based on multiple criteria. Answers to questions like “Which Design of Experiments (DOE) is better/best?” will be answered by looking at both data and graphics that show the relative performance of the designs based on multiple criteria, including; power of the designs for different model terms, how well the designs minimize predictive variance across the design space, to what level are model terms confounded or correlated, what are the relative efficiencies that measure how well coefficients are estimated or how well predictive variance is minimized. Many different case studies of screening, response surface, and screening augmented to response surface designs will be compared. Designs with both continuous and categorical factors, and with constraints on the experimental region will also be compared.