DATAWorks Sessions Archive

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.

We have developed a new sensor fusion approach called Doppler Assisted Sensor Fusion (DASF), which pairs a range rate profile from one moving sensor with location accuracy with another range rate profile from another sensor with high location accuracy. This paring provides accurate identification, location, and tracking of moving emitters, with low association latency. The approach we use for data fusion is distinct from previous approaches. In the conventional approach, post detection data from the each sensor is overlaid with data from another sensor in an attempt to associate the data outputs. For the DASF approach the fusion is at the sensor level, the first sensor collects data and provides the standard identification in addition a unique emitter range rate profile. This profile is used to associate the emitter signature to a range-rate signature obtained by the geolocation sensor. The geolocation sensor then provides the desired location accuracy. We will provide results using real tracking data scenarios.

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.

The use of statistically rigorous methods to support testing at Arnold Engineering Development Complex (AEDC) has been an area of focus in recent years. As part of this effort, the use of Design of Experiments (DOE) has been introduced for calibration of AEDC wind tunnels. Historical calibration efforts used One- Factor-at-a-Time (OFAT) test matrices, with a concentration on conditions of interest to test customers. With the introduction of DOE, the number of test points collected during the calibration decreased, and were not necessary located at historical calibration points. To validate the use of DOE for calibration purposes, the 4-ft Aerodynamic Wind Tunnel 4T was calibrated using both DOE and OFAT methods. The results from the OFAT calibration were compared to model developed from the DOE data points and it was determined that the DOE model sufficiently captured the tunnel behavior within the desired levels of uncertainty. DOE analysis also showed that within Tunnel 4T, systematic errors are insignificant as indicated by agreement noted between the two methods. Based on the results of this calibration, a decision was made to apply DOE methods to future tunnel calibrations, as appropriate. The development of the DOE matrix in Tunnel 4T required the consideration of operational limitations, measurement uncertainties, and differing tunnel behavior over the performance map. Traditional OFAT methods allowed tunnel operators to set conditions efficiently while minimizing time consuming plant configuration changes. DOE methods, however, require the use of randomization which had the potential to add significant operation time to the calibration. Additionally, certain tunnel parameters, such as variable porosity, are only of interest in a specific region of the performance map. In addition to operational concerns, measurement uncertainty was an important consideration for the DOE matrix. At low tunnel total pressures, the uncertainty in the Mach number measurements increase significantly. Aside from introducing non-constant variance into the calibration model, the large uncertainties at low pressures can increase overall uncertainty in the calibration in high pressure regions where the uncertainty would otherwise be lower. At high pressures and transonic Mach numbers, low Mach number uncertainties are required to meet drag count uncertainty requirements. To satisfy both the operational and calibration requirements, the DOE matrix was divided into multiple independent models over the tunnel performance map. Following the Tunnel 4T calibration, AEDC calibrated the Propulsion Wind Tunnel 16T, Hypersonic Wind Tunnels B and C, and the National Full-Scale Aerodynamics Complex (NFAC). DOE techniques were successfully applied to the calibration of Tunnel B and NFAC, while a combination of DOE and OFAT test methods were used in Tunnel 16T because of operational and uncertainty requirements over a portion of the performance map. Tunnel C was calibrated using OFAT because of operational constraints. The cost of calibrating these tunnels has not been significantly reduced through the use of DOE, but the characterization of test condition uncertainties is firmly based in statistical methods.

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.

Principal Component Analysis is a standard tool in an analyst’s toolbox. The standard practice of rescaling each column can be reframed as a copula-based decomposition in which the marginal distributions are fit with a univariate Gaussian distribution and the joint distribution is modeled with a Gaussian copula. In this light, we present an alternative to traditional PCA we call XPCA by relaxing the marginal Gaussian assumption and instead fit each marginal distribution with the empirical distribution function. Interval-censoring methods are used to account for the discrete nature of the empirical distribution function when fitting the Gaussian copula model. In this talk, we derive the XPCA estimator and inspect the differences in fits on both simulated and real data applications.

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.

For the past several years, researchers at NASA Langley have been engaged in a series of projects to study the degree to which existing facilities and capabilities, originally created for work on full-scale aircraft, are extensible to smaller scales – those of the small unmanned aerial systems (sUAS, also UAVs and, colloquially, `drones’) that have been showing up in the nation’s airspace. This paper follows an effort that has led to an initial human-subject psychoacoustic test regarding the annoyance generated by sUAS noise. This effort spans three phases: 1. the collection of the sounds through field recordings, 2. the formulation and execution of a psychoacoustic test using those recordings, 3. the analysis of the data from that test. The data suggests a lack of parity between the noise of the recorded sUAS and that of a set of road vehicles that were also recorded and included in the test, as measured by a set of contemporary noise metrics.

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.

Combining physical measurements with computational models is key to many investigations involving validation and uncertainty quantification (UQ). This talk surveys some of the many approaches taken for validation and UQ, with large-scale computational models. Experience with such applications suggests classifications of different types of problems with common features (e.g. data size, amount of empiricism in the model, computational demands, availability of data, extent of extrapolation required, etc.). More recently, social and social-technical systems are being considered for similar analyses, bringing new challenges to this area. This talk will approaches for such problems and will highlight what might be new research directions for application and methodological development in UQ.

A challenging aspect of a system reliability assessment is integrating multiple sources of information, including component, subsystem, and full-system data, previous test data, or subject matter expert opinion. A powerful feature of Bayesian analyses is the ability to combine these multiple sources of data and variability in an informed way to perform statistical inference. This feature is particularly valuable in assessing system reliability where testing is limited and only a small number (or no failures at all) are observed. The F-35 is DoD’s largest program; approximately one-third of the operations and sustainment cost is attributed to the cost of spare parts and the removal, replacement, and repair of components. The failure rate of those components is the driving parameter for a significant portion of the sustainment cost, and yet for many of these components, poor estimates of the failure rate exist. For many programs, the contractor produces estimates of component failure rates, based on engineering analysis and legacy systems with similar parts. While these are useful, the actual removal rates can provide a more accurate estimate of the removal and replacement rates the program anticipates to experience in future years. In this presentation, we show how we applied a Bayesian analysis to combine the engineering reliability estimates with the actual failure data to overcome the problems of cases where few data exist. Our technique is broadly applicable to any program where multiple sources of reliability information need be combined for the best estimation of component failure rates and ultimately sustainment costs.

Sensors that record sequences of measurements are now embedded in many products from wearable exercise watches to chemical and semiconductor manufacturing equipment. There is information in the shapes of the sensor stream curves that is highly predictive of a variety of outcomes such as the likelihood of a product failure event or batch yield. Despite this data now being common and readily available, it is often being used either inefficiently or not at all due to lack of knowledge and tools for how to properly leverage it. In this presentation, we will propose fitting splines to sensor streams and extracting features called functional principal component scores that offer a highly efficient low dimensional compression of the signal data. Then, we use these features as inputs into machine learning models like neural networks and LASSO regression models. Once one sees sensor data in this light, answering a wide variety of applied questions becomes a straightforward two stage process of data cleanup/functional feature extraction followed by modeling using those features as inputs.

The application opportunities for behavioral analytics in the cybersecurity space are based upon simple realities. 1. The great majority of breaches across all cybersecurity venues is due to human choices and human error. 2. With communication and information technologies making for rapid availability of data, as well as behavioral strategies of bad actors getting cleverer, there is need for expanded perspectives in cybersecurity prevention. 3. Internally-focused paradigms must now be explored that place endogenous protection from security threats as an important focus and integral dimension of cybersecurity prevention. The development of cybersecurity monitoring metrics and tools as well as the creation of intrusion prevention standards and policies should always include an understanding of the underlying drivers of human behavior. As temptation follows available paths, cyber-attacks follow technology, business models, and behavioral habits. The human element will always be the most significant part in the anatomy of any final decision. Choice options – from input, to judgement, to prediction, to action – need to be better understood for their relevance to cybersecurity work. Behavioral Performance Indexes harness data about aggregate human participation in an active system, helping to capture some of the detail and nuances of this critically important dimension of cybersecurity.

Technology advances are accelerating at a rapid pace, with the potential to enable greater capability and power to the Warfighter. However, if human capabilities and limitations are not central to concepts, requirements, design, and development then new/upgraded weapons and systems will be difficult to train, operate, and maintain, may not result in the skills, job, grade, and manpower mix as projected, and may result in serious human error, injury or Soldier loss. The Army Human Systems Integration (HSI) program seeks to overcome these challenges by ensuring appropriate consideration and integration of seven technical domains: Human Factors Engineering (e.g., usability), Manpower, Personnel, Training, Safety and Occupational Health, Habitability, Force Protection and Survivability. The tradeoffs, constraints, and limitations occurring among and between these technical domains allows HSI to execute a coordinated, systematic process for putting the warfighter at the center of the design process – equipping the warfighter rather than manning equipment. To that end, the Army HSI Headquarters, currently as a directorate within the Army Headquarters Deputy Chief of Staff (DCS), G-1 develops strategies and ensures human systems factors are early key drivers in concepts, strategy, and requirements, and are fully integrated throughout system design, development, testing and evaluation, and sustainment The need to consider HSI factors early in the development cycle is critical. Too often, man-machine interface issues are not addressed until late in the development cycle (i.e. production and deployment phase) after the configuration of a particular weapon or system has been set. What results is a degraded combat capability, suboptimal system and system-of-systems integration, increased training and sustainment requirements, or fielded systems not in use. Acquisition test data are also good sources to glean HSI return on investment (ROI) metrics. Defense acquisition reports such as test and evaluation operational assessments identifies HSI factors as root causes when Army programs experience increase cost, schedule overruns, or low performance. This is identifiable by the number and type of systems that require follow-on test and evaluation (FOT&E), over reliance on field service representatives (FSRs), costly and time consuming engineering change requests (ECRs), or failures in achieving reliability, availability, and maintainability (RAM) key performance parameters (KPPs) and key system attributes (KSAs). In this presentation, we will present these data and submit several return on investment (ROI) metrics, closely aligned to the defense acquisition process, to emphasize and illustrate the value of HSI. Optimizing Warfighter-System performance and reducing human errors, minimizing risk of Soldier loss or injury, and reducing personnel and materiel life cycle costs produces data that are inextricably linked to early, iterative, and measurable HSI processes within the defense acquisition system.