DATAWorks Sessions Archive

The NASA Big Data, Big Think team jump-starts coordination, strategy, and progress for NASA applications of Big Data Analytics techniques, fosters collaboration and teamwork among centers and improves agency-wide understanding of Big Data research techniques & technologies and their application to NASA mission domains. The effort brings the Agency’s Big Data community together and helps define near term projects and leverages expertise throughout the agency. This presentation will share examples of Big Data activities from the Agency and discuss knowledge areas and experiences, including data management, data analytics and visualization.

Data within a project, investigation or test series are often seen as a bunch of numbers that were produced. While this is part of the story, it forgets the most important part: the data’s users. This more powerful process begins with early focus on planning, executing and managing data flow within a test or project as a system, treating each handoff between internal and external stakeholders a system interface. This presentation will persuade you why data production should be replaced by the idea of a data supply chain focused on goals and customers. The presenter will outline how this could be achieved in your team. The talk is aimed at not only project and data managers, but also team members who produce or use data. Retooling team thinking and processes along these lines will help communication, facilitate availability, display and understanding of data by any stakeholder, make data verification, validation and analysis easier, and help keep team members focused on what is necessary and important: solving the problem at hand.

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.

Autonomous robotic systems (hereafter referred to simply as autonomous systems) have attracted interest in recent years as capabilities improve to operate in unstructured, dynamic environments without continuous human guidance. Acquisition of autonomous systems potentially decrease personnel costs and provide a capability to operate in dirty, dull, or dangerous mission segments or achieve greater operational performance. Autonomy enables a particular action of a system to be automatic or, within programmed boundaries, self-governing. For our purposes, autonomy is defined as the system having a set of intelligence-based capabilities (i.e., learned behaviors) that allows it to respond to situations that were not pre-programmed or anticipated (i.e. learning-based responses) prior to system deployment. Autonomous systems have a degree of self-governance and self-directed behavior, possibly with a human’s proxy for decisions. Because of these intelligence-based capabilities, autonomous systems pose new challenges in conducting test and evaluation that assures adequate performance, safety, and cybersecurity outcomes. We propose an autonomous systems architecture concept and map the elements of a decision theoretic view of a generic decision problem to the components of this architecture. These models offer a foundation for developing a decision-based, common framework for autonomous systems. We also identify some of the various challenges faced by the Department of Defense (DoD) test and evaluation community in assuring the behavior of autonomous systems as well as test and evaluation requirements, processes, and methods needed to address these challenges.

Small screening designs are frequently used in the initial stages of experimentation with the goal of identifying important main effects as well as to gain insight on potentially important two-factor interactions. Commonly utilized experimental designs for screening (e.g., resolution III or IV two-level fractional factorials, Plackett-Burman designs, etc.) are unreplicated and as such, provide no unbiased estimate of experimental error. However, if statistical inference is considered an integral part of the experimental analysis, one view is that inferential procedures should be performed using the unbiased pure error estimate. As full replication of an experiment may be quite costly, partial replication offers an alternative for obtaining a model independent error estimate. Gilmour and Trinca (2012, Applied Statistics) introduce criteria for the design of optimal experiments for statistical inference (providing for the optimal selection of replicated design points). We begin with an extension of their work by proposing a Bayesian criterion for the construction of partially replicated screening designs with less dependence on an assumed model. We then consider the use of the proposed criterion within the context of multi-criteria design selection where estimation and protection against model misspecification are considered. Insights for analysis and model selection in light of partial replication will be provided.

In an experiment with a single factor with three levels, treatments A, B, and C, a single treatment is to be applied to each of several experimental units selected from some set of units. The response variable is continuous, and differences in its value show the relative effectiveness of the treatments. An experimental design will dictate which treatment is applied to what units. Since differences in the response variable are used to judge differences between treatments, the most important goal of the design is to prevent the treatment effect being masked by some unrelated property of the experimental units. Second important function of the design is to ensure power, that is, that if the treatments are not equally effective, the differences in the response variable are likely to be larger than background noise. Classical experimental design theory uses three principles: replication, randomization, and blocking, to produce an experimental design. Replication refers to how many units are used, blocking is a possible grouping of the units to reduce between unit heterogeneity, and randomization governs the assignment of units to treatment. Classical experimental designs are balanced as much as possible, that is, the three treatments are applied the same number of times, in each potential block of units. Bayesian experimental design aims to make use of additional related information, often called prior information, to produce a design. The information may be in the form of related experimental results, for example, treatments A and B may have been previously studied. It could be additional information about the experimental units, or about the response variable. This additional information could be used to change the usual blocking, to reduce the number of units assigned to treatments A and B compared to C, and/or reduce the total number of units needed to ensure power. This talk aims to explain Bayesian design concepts and illustrate them on realistic examples.

Rigorous, efficient and effective test science techniques are individually taking hold in many software centric DoD acquisition programs, both in developmental and operational test regimes. These techniques include agile software development, cybersecurity test and evaluation (T&E), design and analysis of experiments and automated software testing. Many software centric programs must also be tested together with other systems to demonstrate they can be successfully integrated into a more complex systems of systems. This presentation focuses on the two test science disciplines of designed experiments (DOE) and automated software testing (AST) and describes how they can be used effectively and leverage one another in planning for and executing a system of systems test strategy. We use the Navy’s Distributed Common Ground System as an example.

Helicopters serve a number of useful roles within the community; however, community acceptance of helicopter operations is often limited by the resulting noise. Because the noise characteristics of helicopters depend strongly on the operating condition of the vehicle, effective noise abatement procedures can be developed for a particular helicopter type, but only when the noisy regions of the operating envelope are identified. NASA Langley Research Center—often in collaboration with other US Government agencies, industry, and academia—has conducted noise measurements for a wide variety of helicopter types, from light commercial helicopters to heavy military utility helicopters. While this database is expansive, it covers only a fraction of helicopter types in current commercial and military service and was measured under a limited set of ambient conditions and vehicle configurations. This talk will describe a new “universal” helicopter noise model suitable for planning helicopter noise abatement procedures. Modern machine learning techniques will be combined with the principle of nondimensionalization and applied to NASA’s helicopter noise data in order to develop a model capable of estimating the noisy operating states of any conventional helicopter under any specific ambient conditions and vehicle configurations.

The goal of the Crew State Monitoring (CSM) project is to use machine learning models trained with physiological data to predict unsafe cognitive states in pilots such as Channelized Attention (CA) and Startle/Surprise (SS). These models will be used in a real-time system that predicts a pilot’s mental state every second, a tool that can be used to help pilots recognize and recover from these mental states. Pilots wore different sensors that collected physiological data such as a 20-channel electroencephalography (EEG), respiration, and galvanic skin response (GSR). Pilots performed non-flight benchmark tasks designed to induce these states, and a flight simulation with “surprising” or “channelizing” events. The team created a pipeline to generate pilot-dependent models that trains on benchmark data, tune on a portion of a flight task, and be deployed onto the remaining flight task. The model is a series of anomaly-detection based ensembles, where each ensemble focuses on predicting a single state. Ensembles were comprised of several anomaly detectors such as One Class SVMs, each focusing on a different subset of sensor data. We will discuss the performance of these models, as well as the ongoing research generalizing models across pilots and improving accuracy.

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.

This presentation discusses a methodology to use knowledge of cyber attacks and defender responses from operational assessments to gain insights into the defensive posture and to inform a strategy for improvement. The concept is to use the attack thread as the instrument to probe and measure the detection capability of the cyber defenses. The data enable a logistic regression approach to provide a quantitative basis for the analysis and recommendations.

Over the past decade, uncertainty quantification has become an integral part of engineering design and analysis. Both NASA and the DoD are making significant investments to advance the science of uncertainty quantification, increase the knowledge base, and strategically expanding its use. This increased use of uncertainty based results improves investment strategies and decision making. However, in complex systems, many challenges still exist when dealing with uncertainty in cases that have sparse, unreliable, poorly understood, and/or conflicting data. Often times, assumptions are made regarding the statistical nature of data that may not be well grounded and the impact of those assumptions is not well understood, which can lead to ill-informed decision making. This talk will focus on the quantification of uncertainty when both well characterized, aleatory, and not well known, epistemic, uncertainty sources exist. Particular focus is given to the treatment and management of epistemic uncertainty. A summary of non-probabilistic methods will be presented along with the propagation of mixed uncertainty and optimization under uncertainty. A discussion of decision making under uncertainty is also included to illustrate the use of uncertainty quantification.

Over the past decade, uncertainty quantification has become an integral part of engineering design and analysis. Both NASA and the DoD are making significant investments to advance the science of uncertainty quantification, increase the knowledge base, and strategically expanding its use. This increased use of uncertainty based results improves investment strategies and decision making. However, in complex systems, many challenges still exist when dealing with uncertainty in cases that have sparse, unreliable, poorly understood, and/or conflicting data. Often times, assumptions are made regarding the statistical nature of data that may not be well grounded and the impact of those assumptions is not well understood, which can lead to ill-informed decision making. This talk will focus on the quantification of uncertainty when both well characterized, aleatory, and not well known, epistemic, uncertainty sources exist. Particular focus is given to the treatment and management of epistemic uncertainty. A summary of non-probabilistic methods will be presented along with the propagation of mixed uncertainty and optimization under uncertainty. A discussion of decision making under uncertainty is also included to illustrate the use of uncertainty quantification.

Under current FAA regulations, civilian aircraft may not operate at supersonic speeds over land. However, over the past few decades, there have been renewed efforts to invest in technologies to mitigate sonic boom from supersonic aircraft through advances in both vehicle design and sonic boom prediction. NASA has heavily invested in tools and technologies to enable commercial supersonic flight and currently has several technical challenges related to sonic boom reduction. One specific technical challenge relates to the development of tools and methods to predict, under uncertainty, the noise on the ground generated by an aircraft flying at supersonic speeds. In attempting to predict ground noise, many factors from multiple disciplines must be considered. Further, classification and treatment of uncertainties in coupled systems, mutlifidelity simulations, experimental data, and community responses are all concerns in system level analysis of sonic boom prediction. This presentation will introduce the various methodologies and techniques utilized for uncertainty quantification with a focus on the build up to system level analysis. An overview of recent research activities and case studies investigating the impact of various disciplines and factors on variance in ground noise will be discussed.

The Boeing Company is assessing uncertainty quantification methodologies across many phases of aircraft design in order to establish confidence in computational fluid dynamics-based simulations of aircraft performance. This presentation provides an overview of several of these efforts. First, the uncertainty in aerodynamic performance metrics of a commercial aircraft at transonic cruise due to turbulence model and flight condition variability is assessed using 3D CFD with non-intrusive polynomial chaos and second order probability. Second, a sample computation of uncertainty in increments is performed for an engineering trade study, leading to the development of a new method for propagating input-uncontrolled uncertainties as well as input-controlled uncertainties. This type of consideration is necessary to account for variability associated with grid convergence on different configurations, for example. Finally, progress toward applying the computed uncertainties in forces and moments into an aerodynamic database used for flight simulation will be discussed. This approach uses a combination of Gaussian processes and multiple-fidelity Kriging meta-modeling to synthesize the required data.

Increasingly humans will rely on the outputs of our computational partners to make sense of the complex systems in our world. To be employed, the statistical and algorithmic analysis tools that are deployed to analysts’ toolboxes must afford their proper use and interpretation. Interface design for these tool users should provide decision support appropriate for the current stage of sensemaking. Understanding how users build, test, and elaborate their mental models of complex systems can guide the development of robust interfaces.

During the summer of 2001, Microsoft launched Windows XP, which was lauded by many users as the most reliable and usable operating system at the time. Miami Herald columnist, Dave Berry, responded to this praise by stating that “this is like saying asparagus is the most articulate vegetable ever.” Whether you agree or disagree with Dave Berry, these users’ reactions are relative (to other operating systems and to other past and future versions). This is due to an array of technological factors that have facilitated human-system improvements. Automation is often cited as improving human-system performance across many domains. It is true that when the human and automation are aligned, performance improves. But, what about the times that this is not the case? This presentation will describe the myths and facts about human-system performance and increasing levels of automation through examples of human-system R&D conducted on a satellite ground system. Factors that affect human-system performance and a method to characterize mission performance as it relates to increasing levels of automation will also be discussed.

Loss of control in flight has been a leading cause of accidents and incidents in commercial aviation worldwide. The Commercial Aviation Safety Team (CAST) requested studies on virtual day-visual meteorological conditions displays, such as synthetic vision, in order to combat loss of control. Over the last four years NASA has conducted a series of experiments evaluating the efficacy of synthetic vision displays for increased spatial awareness. Commercial pilots with various levels of experience from both domestic and international airlines were used as subjects. This presentation describes the synthetic vision research and how pilot subjects affected experiment design and statistical analyses.

The Stratospheric Aerosol and Gas Experiment (SAGE III) aboard the International Space Station (ISS) was experiencing a series of anomalies called Single Event Upsets (SEUs). Booz Allen Hamilton was tasked with conducting a statistical analysis to model the incidence of SEUs in the SAGE III equipment aboard the ISS. The team identified factors correlated with SEU incidences, set up a model to track degradation of Sage III, and showed current and past probabilities as a function of the space environment. The space environment of SAGE III was studied to identify possible causes of SEUs. The analysis revealed variables most correlated with the anomalies, including solar wind strength, solar and geomagnetic field behavior, and location/orientation of the ISS, sun, and moon. The data was gathered from a variety of sources including US government agencies, foreign and domestic academic centers, and state-of-the-art simulation algorithms and programs. Logistic regression was used to analyze SEUs and gain preliminary results. The data was divided into small time intervals to approximate independence and allow logistic regression. Due to the rarity of events the initial model results were based on few SEUs. The team set up a Graphical User Interface (GUI) program to automatically analyze new data as it became available to the SAGE III team. A GUI was built to allow the addition of more data over the life of the SAGE III mission. As more SEU incidents occur and are entered into the model, its predictive power will grow significantly. The GUI enables the user to easily rerun the regression analysis and visualize its results to inform operational decision making.

Automated systems are technologies that actively select data, transform information, make decisions, and control processes. The U.S. military uses automated systems to perform search and rescue and reconnaissance missions, and to assume control of aircraft to avoid ground collision. Facilitating appropriate trust in automated systems is essential to improving the safety and performance of human-system interactions. In two studies, we developed and validated an instrument to measure trust in automated systems. In study 1, we demonstrated that the scale has a 2-factor structure and demonstrates concurrent validity. We replicated these results using an independent sample in study 2.

Traffic Aware Strategic Aircrew Requests (TASAR) is a NASA-developed operational concept for flight efficiency and route optimization for the near-term airline flight deck. TASAR provides the aircrew with a cockpit automation tool that leverages a growing number of information sources on the flight deck to make fuel- and time-saving route optimization recommendations while in route. In partnership with a commercial airline, a research prototype software that implements TASAR has been installed on three aircraft to enable the evaluation of this software in operational use. During the flight trials, data are being collected to quantify operational performance, which will enable NASA to improve algorithms and enhance functionality in the software based on real-world user experience. This presentation highlights statistical challenges and discusses lessons learned during the initial stages of the operational evaluation.

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.

Shelley Cazares served as a crewmember of the 14th mission of NASA’s Human Exploration Research Analog (HERA). In August 2017, Dr. Cazares and her three crewmates were enclosed in an approximately 600-sq. ft. simulated spacecraft for an anticipated 45 days of confined isolation at Johnson Space Center, Houston, TX. In preparation for long-duration missions to Mars in the 2030s and beyond, NASA seeks to understand what types of diets, habitats, and activities can keep astronauts healthy and happy on deep space voyages. To collect this information, NASA is conducting several analog missions simulating the conditions astronauts face in space. HERA is a set of experiments to investigate the effects of isolation, confinement, and remote conditions in space exploration scenarios. Dr. Cazares will discuss the application procedure, the pre-mission training process, the life and times inside the habitat during the mission, and her crew’s emergency evacuation from the habitat due to the risk of rising floodwaters in Hurricane Harvey.

Although reliability analysis is a part of Operational Test and Evaluation, it is uncommon for analysts to have a background in reliability theory or experience applying it. This presentation highlights some lessons learned from reliability analysis conducted on several AFOTEC test programs. Topics include issues related to censored data, limitations and alternatives to using the exponential distribution, and failure rate analysis using test data.

The Mars Rover 2020 team commonly faces nonlinear behavior across the test program that is often closely related to the underlying physics. Classical and newer response surface designs do well with quadratic approximations while space filling designs have proven useful for modeling & simulation of complex surfaces. This talk specifically covers fitting nonlinear equations based on engineering functional forms as well as sigmoid and exponential decay curves. We demonstrate best practices on how to design and augment nonlinear designs using the Bayesian D-Optimal Criteria. Several examples, to include drill bit degradation, illustrate the relative ease of implementation with popular software and the utility of these methods.