DATAWorks Sessions Archive

The DoD’s challenge to provide test results at the “Speed of Relevance” has generated many new strategies to accelerate data collection, adjudication, and analysis. As a result, the Air Force Operational Test and Evaluation Center (AFOTEC), in conjunction with the Air Force Chief Data Office’s Visible, Accessible, Understandable, Linked and Trusted Data Platform (VAULT), is developing a Survey Application. This new cloud-based application will be deployable on any AFNET-connected computer or tablet and merges a variety of tools for collection, storage, analytics, and decision-making into one easy-to-use platform. By placing cloud-computing power in the hands of operators and testers, authorized users can view report-quality visuals and statistical analyses the moment a survey is submitted. Because the data is stored in the cloud, demanding computations such as machine learning are run at the data source to provide even more insight into both quantitative and qualitative metrics. The T-7A Red Hawk will be the first operational test (OT) program to utilize the Survey Application. Over 1000 flying and simulator test points have been loaded into the application, with many more coming from developmental test partners. The Survey app development will continue as USAF testing commences. Future efforts will focus on making the Survey Application configurable to other research and test programs to enhance their analytic and reporting capabilities.

The Icing Research Tunnel (IRT) at NASA Glenn Research Center follows the recommended practice for icing tunnel calibration outlined in SAE’s ARP5905 document. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within in the specifications presented to customers that the variation is within +/- 10% of the calibrated, target measurement. With recent measurements and instrumentation errors, a more thorough assessment of error source was desired. By constructing statistical process control charts, the ability to determine how the instrument varies in the short term, mid term, and long term was gained. The control charts offer a view of instrument error, facility error, or installation changes. It was discovered that there was a shift from target to mean baseline thus leading to the study of the overall capability indices of the liquid water content measuring instrument to perform within specifications defined in the IRT. This presentation describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, Chauvenet’s Criterion for rejection of data, distribution check of data, and mean, median and mode for construction of control charts. Further data is presented to describe the repeatability of the IRT with the Multi-Element Sensor and the ability to maintain a stable process for the defined calibration schedule.

The U.S. Army Research Laboratory’s (ARL) Essential Research Program (ERP) on Artificial Intelligence & Machine Learning (AI & ML) seeks to research, develop and employ a suite of AI-inspired and ML techniques and systems to assist teams of soldiers and autonomous agents in dynamic, uncertain, complex operational conditions. Systems will be robust, scalable, and capable of learning and acting with varying levels of autonomy, to become integral components of networked sensors, knowledge bases, autonomous agents, and human teams. Three specific research gaps will be examined: (i) Learning in Complex Data Environments, (ii) Resource-constrained AI Processing at the Point-of-Need and (iii) Generalizable & Predictable AI. The talk will highlight ARL’s internal research efforts over the next 3-5 years that are connected, cumulative and converging to produce tactically-sensible AI-enabled capabilities for decision making at the tactical edge, specifically addressing topics in: (1) adversarial distributed machine learning, (2) robust inference & machine learning over heterogeneous sources, (3) adversarial reasoning integrating learned information, (4) adaptive online learning and (5) resource-constrained adaptive computing. The talk will also highlight collaborative research opportunities in AI & ML via ARL’s Army AI Innovation Institute (A2I2) which will harness the distributed research enterprise via the ARL Open Campus & Regional Campus initiatives.

A case study will be presented where patients wearing continuous glycemic monitoring systems provide sensor stream data of their glucose levels before and after consuming 1 of 5 different types of snacks. The goal is to be able to better predict a new patient’s glycemic-response-over-time trace after being given a particular type of snack. Functional Data Analysis (FDA) is used to extract eigenfunctions that capture the longitudinal shape information of the traces and principal component scores that capture the patient-to-patient variation. FDA is used twice. First it is used on the “before” baseline glycemic-response-over-time traces. Then a separate analysis is done on the snack-induced “after” response traces. The before FPC scores and the type of snack are then used to model the after FPC scores. This final FDA model will then be used to predict the glycemic response of new patients given a particular snack and their existing baseline response history. Although the case study is for medical sensor data, the methodology employed would work for any sensor stream where an event perturbs the system thus affecting the shape of the sensor stream post event.

The Air Force (AF) Human Systems Integration (HSI) program is led by the 711th Human Performance Wing’s Human Systems Integration Directorate (711 HPW/HP). 711 HPW HP provides direct support to system program offices and AF Major Commands (MAJCOMs) across the acquisition lifecycle from requirements development to fielding and sustainment in addition to providing home office support. With an ever-increasing demand signal for support, HSI practitioners within 711 HPW/HP assess HSI domain areas for human-centered risks and strive to ensure systems are designed and developed to safely, effectively, and affordably integrate with human capabilities and limitations. In addition to system program offices and MAJCOMs, 711 HPW/HP provides HSI support to AF Centers (e.g., AF Sustainment Center, AF Test Center), the AF Medical Service, and special cases as needed. The AF Global Strike Command (AFGSC) is the largest MAJCOM with several Programs of Record (POR), such as the B-1, B-2, and B-52 bombers, Intercontinental Ballistic Missiles (ICBM), Ground-Based Strategic Deterrent (GBSD), Airborne Launch Control System (ALCS), and other support programs/vehicles like the UH-1N. Mr. Anthony Thomas (711 HPW/HP), the AFGSC HSI representative, will discuss how 711 HPW/HP supports these programs at the MAJCOM headquarters level and in the system program offices.

Model Validation for Simulations of CVN-78 Sortie Generation As part of the test planning process, IDA is examining flight operations on the Navy’s newest carrier, CVN-78. The analysis uses a model, the IDA Virtual Carrier Model (IVCM), to examine sortie generation rates and whether aircraft can complete missions on time. Before using IVCM, it must be validated. However, CVN-78 has not been delivered to the Navy, and data from actual operations are to validate the model. Consequently, we will validate IVCM by comparing it to another model. This is a reasonable approach when a model is used in general analyses such as test planning, but is not acceptable when a model is used in the assessment of system effectiveness and suitability. The presentation examines the use of various statistical tools – Wilcoxon Rank Sum Test, Kolmogorov-Smirnov Test, and lognormal regression – to examine whether the results from two models provide similar results and to quantify the magnitude of any differences. From the analysis, IDA concluded that locations and distribution shapes are consistent, and that the differences between the models are less than 15 percent, which is acceptable for test planning.

Binary response experiments are common in epidemiology, biostatistics as well as in military applications. The Up and Down method, Langlie’s Method, Neyer’s method, K in a Row method and 3 Phase Optimal Design are methods used for sequential experimental design when there is a single continuous variable and a binary response. During this talk, we will discuss a new sequential experimental design approach called the Break Separation Method (BSM). BSM provides an algorithm for determining sequential experimental trials that will be used to find a median quantile and fit a logistic regression model using Maximum Likelihood estimation. BSM results in a small sample size and is designed to efficiently compute the median quantile.

We describe a model-based framework for increasing effectiveness of simulation experiments in the presence of uncertainty. Unlike conventionally designed simulation experiments, it adaptively chooses where to sample, based on the value of information obtained. A Bayesian perspective is taken to formulate and update the framework’s four models. A simulation experiment is conducted to answer some question. In order to define precisely how informative a run is for answering the question, the answer must be defined as a random variable. This random variable is called a query and has the general form of p(theta | y), where theta is the query parameter and y is the available data. Examples of each of the four models employed in the framework are briefly described below: 1. The continuous correlated beta process model (CCBP) estimates the proportions of successes and failures using beta-distributed uncertainty at every point in the input space. It combines results using an exponentially decaying correlation function. The output of the CCBP is used to estimate value of a candidate run. 2. The mutual information model quantifies uncertainty in one random variable that is reduced by observing the other one. The model quantifies the mutual information between any candidate runs and the query, thereby scoring the value of running each candidate. 3. The cost model estimates how long future runs will take, based upon past runs using, e.g., a generalized linear model. A given simulation might have multiple fidelity options that require different run times. It may be desirable to balance information with the cost of a mixture of runs using these multi-fidelity options. 4. The grid state model, together with the mutual information model, are used to select the next collection of runs for optimal information per cost, accounting for current grid load. The framework has been applied to several use cases, including model verification and validation with uncertainty quantification (VVUQ). Given a mathematically precise query, an 80 percent reduction in total runs has been observed.

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.

Several optimization models are described for allocating resources to different testing activities in a system’s reliability growth program. These models assume availability of an underlying reliability growth model for the system, and capture the tradeoffs associated with focusing testing resources at various levels (e.g., system, subsystem, component) and/or how to divide resources within a given level. In order to demonstrate insights generated by solving the model, we apply the optimization models to an example series-parallel system in which reliability growth is assumed to follow the Crow/AMSAA reliability growth model. We then demonstrate how the optimization models can be extended to incorporate uncertainty in Crow/AMSAA parameters.

Military grade energetics are, by design, required to operate under extreme conditions. As such, warheads in a munition must demonstrate a high level of structural integrity in order to ensure safe and reliable operation by the Warfighter. In this example which involved an artillery munition, a systematic analytics-driven approach was executed which synthesized physical test data results with probabilistic analysis, non-destructive evaluation, modeling and simulation, and comprehensive risk analysis tools in order to determine the probability of a catastrophic event. Once the severity, probability of detection, occurrence, were synthesized, a model was built to determine the risk of a catastrophic event during firing which then accounts for defect growth occurring as a result of rough-handling. This comprehensive analysis provided a defensible, credible, and dynamic snapshot of risk while allowing for a transparent assessment of contribution to risk of the various inputs through sensitivity analyses. This paper will illustrate intersection of product safety, reliability, systems-safety policy, and analytics, and highlight the impact of a holistic multidisciplinary approach. The benefits of this rigorous assessment included quantifying risk to the user, supporting effective decision-making, improving resultant safety and reliability of the munition, and supporting triage and prioritization of future Non-Destructive Evaluation (NDE) screening efforts by identifying at-risk subpopulations.

This presentation will provide a brief overview of sensitivity testing, and emphasize applications to several products and system of importance to the Defense as well as private industry, including Insensitive Energetics, Ballistic testing of protective armor, testing of munition fuzes and Microelectromechanical Systems (MEMS) components, and safety testing of high-pressure test ammunition, and packaging for high-value materials.

The process for testing military systems which are largely software intensive involves techniques and procedures often different from those for hardware-based systems. Much of the testing can be performed in laboratories at many of the acquisition stages, up to operational testing. Testing software systems is not different from testing hardware-based systems in that testing earlier and more intensively benefits the acquisition program in the long run. Automated testing of software systems enables more frequent and more extensive testing, allowing for earlier discovery of errors and faults in the code. Automated testing is beneficial for unit, integrated, functional and performance testing, but there are costs associated with automation tool license fees, specialized manpower, and the time to prepare and maintain the automation scripts. This presentation discusses some of the features unique to automated software testing and offers a framework organizations can implement to make the business case for, to organize for, and to execute and benefit from automating the right aspects of their testing needs. Automation has many benefits in saving time and money, but is most valuable in freeing test resources to perform higher value tasks.