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PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)
The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.
A New Reflected Target Optical Assessment System: Stage 1 Development Results
NREL has completed stage 1 development of an indoor optical measurement tool for fully assembled heliostats and single facets. This tool began as an indoor version of NREL’s outdoor Non-Intrusive Optical (NIO) measurement technique [1]. It uses similar techniques to other available tools (deflectometry, photogrammetry, etc.), but is designed to require very little infrastructure, labor, and time to set up and collect surface slope and canting measurements, making it a valuable tool for quality assurance and laboratory measurement of heliostat optics. It accomplishes this by using computer vision, photogrammetry, and multiple images stitched together to minimize the printed target size and required setup precision. This adaptable setup is useful for taking measurements at a variety of heliostat pointing angles, and for measuring fully assembled heliostats on the assembly line. In this paper, we describe the methodology behind the measurement system, present an initial analysis of its uncertainty and sensitivity, and compare it with established optical measurement systems.
Intelligent fault-tolerant controllers
A system with fault tolerant controls is one that can detect, isolate, and estimate failures and perform necessary control reconfiguration based on this new information. Artificial intelligence (AI) is concerned with semantic processing, and it has evolved to include the topics of expert systems and machine learning. This research represents an attempt to apply AI to fault tolerant controls, hence, the name intelligent fault tolerant control (IFTC). A generic solution to the problem is sought, providing a system based on logic in addition to analytical tools, and offering machine learning capabilities. The advantages are that redundant system specific algorithms are no longer needed, that reasonableness is used to quickly choose the correct control strategy, and that the system can adapt to new situations by learning about its effects on system dynamics.
The History Of The Kalman Filter
Paper presents historical view of adaptation of Kalman filtering techniques to aerospace applications and eventually to fields as diverse as exploration for oil and control of powerplants. Describes scientific breakthroughs and reformulations that transformed Kalman filtering techniques into fundamental tool for analyzing and solving broad class of estimation problems.
Proposed Facility Modifications to Support Propulsion Systems Testing Under Simulated Space Conditions at Plum Brook Station's Spacecraft Propulsion Research Facility (B-2)
Preparing NASA's Plum Brook Station's Spacecraft Propulsion Research Facility (B-2) to support NASA's new generation of launch vehicles has raised many challenges for B-2 s support staff. The facility provides a unique capability to test chemical propulsion systems/vehicles while simulating space thermal and vacuum environments. Designed and constructed 4 decades ago to support upper stage cryogenic engine/vehicle system development, the Plum Brook Station B-2 facility will require modifications to support the larger, more powerful, and more advanced engine systems for the next generation of vehicles leaving earth's orbit. Engine design improvements over the years have included large area expansion ratio nozzles, greater combustion chamber pressures, and advanced materials. Consequently, it has become necessary to determine what facility changes are required and how the facility can be adapted to support varying customers and their specific test needs. Instrumental in this task is understanding the present facility capabilities and identifying what reasonable changes can be implemented. A variety of approaches and analytical tools are being employed to gain this understanding. This paper discusses some of the challenges in applying these tools to this project and expected facility configuration to support the varying customer needs.
Space Launch System Spacecraft and Payload Elements: Making Progress Toward First Launch
Significant and substantial progress continues to be accomplished in the design, development, and testing of the Space Launch System (SLS), the most powerful human-rated launch vehicle the United States has ever undertaken. Designed to support human missions into deep space, SLS is one of three programs being managed by the National Aeronautics and Space Administration's (NASA's) Exploration Systems Development directorate. The Orion spacecraft program is developing a new crew vehicle that will support human missions beyond low Earth orbit, and the Ground Systems Development and Operations program is transforming Kennedy Space Center into next-generation spaceport capable of supporting not only SLS but also multiple commercial users. Together, these systems will support human exploration missions into the proving ground of cislunar space and ultimately to Mars. SLS will deliver a near-term heavy-lift capability for the nation with its 70 metric ton (t) Block 1 configuration, and will then evolve to an ultimate capability of 130 t. The SLS program marked a major milestone with the successful completion of the Critical Design Review in which detailed designs were reviewed and subsequently approved for proceeding with full-scale production. This marks the first time an exploration class vehicle has passed that major milestone since the Saturn V vehicle launched astronauts in the 1960s during the Apollo program. Each element of the vehicle now has flight hardware in production in support of the initial flight of the SLS -- Exploration Mission-1 (EM-1), an un-crewed mission to orbit the moon and return. Encompassing hardware qualification, structural testing to validate hardware compliance and analytical modeling, progress in on track to meet the initial targeted launch date in 2018. In Utah and Mississippi, booster and engine testing are verifying upgrades made to proven shuttle hardware. At Michoud Assembly Facility in Louisiana, the world's largest spacecraft welding tool is producing tanks for the SLS core stage. This paper will particularly focus on work taking place at Marshall Space Flight Center (MSFC) and United Launch Alliance in Alabama, where upper stage and adapter elements of the vehicle are being constructed and tested. Providing the Orion crew capsule/launch vehicle interface and in-space propulsion via a cryogenic upper stage, the Spacecraft/Payload Integration and Evolution (SPIE) Element serves a key role in achieving SLS goals and objectives. The SPIE element marked a major milestone in 2014 with the first flight of original SLS hardware, the Orion Stage Adapter (OSA) which was used on Exploration Flight Test-1 with a design that will be used again on EM-1. Construction is already underway on the EM-1 Interim Cryogenic Propulsion Stage (ICPS), an in-space stage derived from the Delta Cryogenic Second Stage. Manufacture of the Orion Stage Adapter and the Launch Vehicle Stage Adapter is set to begin at the Friction Stir Facility located at MSFC while structural test articles are either completed (OSA) or nearing completion (Launch Vehicle Stage Adapter). An overview is provided of the launch vehicle capabilities, with a specific focus on SPIE Element qualification/testing progress, as well as efforts to provide access to deep space regions currently not available to the science community through a secondary payload capability utilizing CubeSat-class satellites.
Principles from Two Small SRM-Based Launcher Design/Developments
Two solid rocket motor based launch vehicles are in design and development at MSFC, a 4-stage Earth-to-Orbit vehicle cube-satellite launcher, and the 2-stage Mars Ascent Vehicle for the Mars sample return mission. Trade studies that refined these designs have benefited from developments in understanding sensitivities and approximate modeling. The development and practice of that has demonstrated 3 principles: PRINCIPLE I: Identify mission-specific key design drivers — particularly interactions (and focus knowledge-development on those). PRINCIPLE II: Agility — don’t lock down your derived requirements early — keep trading, and characterizing sensitivities. PRINCIPLE III: Use multi-fidelity tools to improve “understanding value generated per modeling effort cost.” The designer should expect different design solution techniques to derive from different mission needs. This paper shows how two launch vehicle designs developed and adapted to those needs.
Sketch-To-Solution: An Exploration of Viscous CFD with Automatic Grids
Numerical simulation of the Reynolds-averaged Navier–Stokes (RANS) equations has become a critical tool for the design of aerospace vehicles. However, the issues that affect the grid convergence of three dimensional RANS solutions are not completely understood, as documented in the AIAA Drag Prediction Workshop series. Grid adaption methods have the potential for increasing the automation and discretization error control of RANS solutions to impact the aerospace design and certification process. The realization of the CFD Vision 2030 Study includes automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification by using analysis without demonstration in flight test (i.e., certification or qualification by analysis). For example, the Cart3D inviscid analysis package has automated Cartesian cut-cell gridding with output-based error control. Fueled by recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a similar work flow is explored for viscous CFD simulations; where a CFD application engineer provides geometry, boundary conditions, and flow parameters, and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation.
Current Capabilities of AFRL’s Spacecraft Simulation Tool
Assessment of spacecraft integration issues is typically accomplished with a combination of numerical tools developed to simulate different regions with different key physics. For instance, a detailed study of spacecraft with an electric propulsion device requires a device model, a plume model, and a spacecraft charging model. AFRL’s in-house spacecraft simulation tool, TURF, is now capable of performing all of these calculations in one simulation. In addition, the plume simulation capability has been expanded significantly to improve speed and accuracy. Other upgrades in TURF include adaptive mesh refinement (AMR) and dynamic load balancing. All of these upgrades are covered in this paper by providing detailed descriptions or pointing to relevant references. This paper also presents three example simulations demonstrating its multi-physics multi-scale capability.
Real-Time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-Negative Matrix Factorization
Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems because the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate or sacrifice detection sensitivity to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) is a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation, it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. Here, we have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.
An operational approach to long-duration mission behavioral health and performance factors
NASA's participation in nearly 10 yr of long-duration mission (LDM) training and flight confirms that these missions remain a difficult challenge for astronauts and their medical care providers. The role of the astronaut's crew surgeon is to maximize the astronaut's health throughout all phases of the LDM: preflight, in flight, and postflight. In support of the crew surgeon, the NASA-Johnson Space Center Behavioral Health and Performance Group (JSC-BHPG) has focused on four key factors that can reduce the astronaut's behavioral health and performance. These factors are defined as: sleep and circadian factors; behavioral health factors; psychological adaptation factors; and human-to-system interface (the interface between the astronaut and the mission workplace) factors. Both the crew surgeon and the JSC-BHPG must earn the crewmember's trust preflight to encourage problem identification and problem solving in these four areas. Once on orbit, the crew medical officer becomes a valuable extension of the crew surgeon and BHPG on the ground due to the crew medical officer's constant interaction with crewmembers and preflight training in these four factors. However, the crew surgeon, BHPG, and the crew medical officer need tools that will help predict, prevent, monitor, and respond to developing problems. Objective data become essential when difficult mission termination decisions must be made. The need for behavioral health and performance tool development creates an environment rich for collaboration between operational healthcare providers and researchers. These tools are also a necessary step to safely complete future, more autonomous exploration-class space missions.
Hybrid data-driven and model-informed online tool wear detection in milling machines
Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.
Z-checker: A framework for assessing lossy compression of scientific data
Because of the vast volume of data being produced by today’s scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific data sets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data analysis components for data compression. On the other hand, Z-checker is implemented as an open-source community tool to which users and developers can contribute and add new analysis components based on their additional analysis demands. In this article, we present a survey of existing lossy compressors. Then, we describe the design framework of Z-checker, in which we integrated evaluation metrics proposed in prior work as well as other analysis tools. Specifically, for lossy compressor developers, Z-checker can be used to characterize critical properties (such as entropy, distribution, power spectrum, principal component analysis, and autocorrelation) of any data set to improve compression strategies. For lossy compression users, Z-checker can detect the compression quality (compression ratio and bit rate) and provide various global distortion analysis comparing the original data with the decompressed data (peak signal-to-noise ratio, normalized mean squared error, rate–distortion, rate-compression error, spectral, distribution, and derivatives) and statistical analysis of the compression error (maximum, minimum, and average error; autocorrelation; and distribution of errors). Z-checker can perform the analysis with either coarse granularity (throughout the whole data set) or fine granularity (by user-defined blocks), such that the users and developers can select the best fit, adaptive compressors for different parts of the data set. Z-checker features a visualization interface displaying all analysis results in addition to some basic views of the data sets such as time series. To the best of our knowledge, Z-checker is the first tool designed to assess lossy compression comprehensively for scientific data sets.
Risk management integration into complex project organizations
This paper describes the approach used in designing and adapting the SIRTF prototype, discusses some of the lessons learned in developing the SIRTF prototype, and explains the adaptability of the risk management database to varying levels project complexity.
Hybrid data-driven and model-informed online tool wear detection in milling machines
Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.
Operating advanced scientific instruments with AI agents that learn on the job
Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.
ISRU-Based Robotic Construction Technologies For Lunar And Martian Infrastructures
Economically viable and reliable building systems and tool sets are being sought, examined and tested for extraterrestrial infrastructure buildup. This project utilizes a unique architecture weaving the robotic building construction technology with designs for assisting rapid buildup of initial operational capability Lunar and Martian bases. The project intends to develop and test methodologies to construct certain crucial infrastructure elements in order to evaluate the merits, limitations and feasibility of adapting and using such technologies for extraterrestrial application. High priority infrastructure elements suggested by our NASA advisors to be considered include landing pads and aprons, roads, blast walls and shade walls, thermal and micrometeorite protection shields and dust-free platforms utilizing the well-known insitu resource utilization (ISRU) strategy. Current extraterrestrial settlement buildup philosophy holds that in order to minimize the materials needed to be flown in, at great transportation costs, strategies that maximize the use of locally available resources must be adopted. Tools and heavy equipment flown as cargo from Earth are proposed to build required infrastructure to support future missions and settlements on the Moon and Mars. Several unique systems including the Lunar Electric Rover, the unpressurized Chariot rover, the versatile light-weight crane and Tri-Athlete cargo transporter as well as the habitat module mockups and a new generation of spacesuits are undergoing coordinated tests at NASAs D-RATS. This project intends to draw up a detailed synergetic plan to utilize these maturing systems coupled with modern robotic fabrication technologies based primarily on 3D Printing, tailored for swift and reliable Lunar and Martian infrastructure development. This project also intends to increase astronaut safety, improve buildup performance, ameliorate dust interference and concerns, and reduce time-to-commission, all in an economic manner. The goal stated in our Phase I proposal was a high fidelity demonstration at D-RATS to be conducted at the conclusion of the Phase II study. In the course of the Phase I study, however, it became clear that such demonstration was neither possible (due to the maximum Phase II budget limitation and the cost of NASA assets and related overhead expenses to support such demonstrations), nor necessary (due to NASA's low TRL expectation of Phase II results). These important facts were revealed to us only after interacting with the NIAC administrators and meetings with potential future partners at JPL and KSC. Accordingly, it was decided by the team that in order to make best use of resources we should investigate novel directions in the adaptation of our fabrication technologies by using in-house laboratories and to produce truly useful technologies and data, and then proceed with high fidelity demonstration at a later opportunity when sufficient resources become available. Furthermore, we have recognized that in addition to our building scale 3D printing technology called Contour Crafting, variations of some of our other fabrication technologies under development are suitable for construction of infrastructure elements such as regolith based ceramic tiles and hence we have decided to include some related preliminary research in this Phase II proposal.