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NASA NTRS2014

NASA Tech Briefs, February 2014

Topics include: JWST Integrated Simulation and Test (JIST) Core; Software for Non-Contact Measurement of an Individual's Heart Rate Using a Common Camera; Rapid Infrared Pixel Grating Response Testbed; Temperature Measurement and Stabilization in a Birefringent Whispering Gallery Resonator; JWST IV and V Simulation and Test (JIST) Solid State Recorder (SSR) Simulator; Development of a Precision Thermal Doubler for Deep Space; Improving Friction Stir Welds Using Laser Peening; Methodology of Evaluating Margins of Safety in Critical Brazed Joints; Interactive Inventory Monitoring; Sensor for Spatial Detection of Single-Event Effects in Semiconductor-Based Electronics; Reworked CCGA-624 Interconnect Package Reliability for Extreme Thermal Environments; Current-Controlled Output Driver for Directly Coupled Loads; Bulk Metallic Glasses and Matrix Composites as Spacecraft Shielding; Touch Temperature Coating for Electrical Equipment on Spacecraft; Li-Ion Electrolytes Containing Flame-Retardant Additives; Autonomous Robotic Manipulation (ARM); CARVE Log; Platform Perspective Toolkit; Convex Hull-Based Plume and Anomaly Detection; Pre-Filtration of GOSAT Data Using Only Level 1 Data and an Intelligent Filter to Remove Low Clouds; Affordability Comparison Tool - ACT; "Ascent - Commemorating Shuttle" for iPad; Cassini Mission App; Light-Weight Workflow Engine: A Server for Executing Generic Workflows; Model for System Engineering of the CheMin Instrument; Timeline Central Concepts; Parallel Particle Filter Toolkit; Particle Filter Simulation and Analysis Enabling Non-Traditional Navigation; Quasi-Terminator Orbits for Mapping Small Primitive Bodies; The Subgrid-Scale Scalar Variance Under Supercritical Pressure Conditions; Sliding Gait for ATHLETE Mobility; and Automated Generation of Adaptive Filter Using a Genetic Algorithm and Cyclic Rule Reduction.

Source record↗

DOE OSTI2022

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Real-time GIS Programming and Geocomputation

Streaming data generated continuously from sensor networks, mobile devices, social media platforms and other edge devices have posed significant challenges to existing computing platforms for achieving both high throughput and low latency data processing in addition to scalable computing. This entry introduces a real-time computing and programming platform for time-critical GIS (Geographic Information System) applications. In this platform, advanced streaming data processing software, such as Apache Kafka and Spark Streaming, are integrated to enable data analytics in real-time. This computing platform can also be extended to integrate GeoAI (Geospatial Artificial Intelligence) based machine learning models to leverage both historical and streaming data to achieve real-time prediction and intelligent geospatial analytics. Two real-time geospatial applications in terms of flood simulation and climate data visualization are introduced to demonstrate how real-time programming and computing can help tackle real-world problems with important societal impacts.

Liu, Yan↗

DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

The smart highway project: Smart highways, smart vehicles, smart engineering

The Smart Highway project is a six mile, limited access roadway being built between Interstate 81 and Blacksburg, Virginia. The initial construction segment will be two miles long and is designed to serve as a test bed and test track for Intelligent Transportation Systems (ITS) research. The Center for Transportation Research (CTR) at Virginia Tech is developing three evaluation tools for its ITS research including DYNAVIMTS (a software framework), and the FLASH Lab (a 1/15th scale model highway and vehicle system). The Smart Highway rounds out the Center's evaluation methodology by allowing full scale operational tests, evaluations, and research under both experimental and conventional traffic conditions. Currently under development is a concept for a fully automated highway using a 'Cooperative Infrastructure Managed System' which involves ultra wide band communication beacons installed in the infrastructure with appropriate sensors, receivers and processors on board the vehicles. The project is part of the research program funded by the National Automated Highway System Consortium. The CTR hopes to develop the automated concept to prototype status by 1997. Other smart transportation and smart engineering concepts are proposed. This presentation will address the goals and objectives of the Smart Highway project, overview its status and importance to the region, and identify some of the transportation technology now under development and planned in the future.

Pethtel, Ray D.↗

NASA NTRSDate not supplied

Thinking outside the box: The human role in increasingly automated aviation systems

Rapid advances in artificial intelligence are enabling automated systems to operate in an increasingly autonomous manner in domains that previously required the involvement of human operators. Examples are rail transport systems, self-driving cars, and warehouse delivery systems. From time to time, such automation encounters operational conditions that fall outside a “competency box” within which the system has been designed to operate. Human operators add resilience because they can see and act outside the competency box of scenarios and environments for which the system was designed. The system’s competencies can be expanded over time with modifications to software, sensors, etc.; however, it is unclear at what point the competency box becomes large enough to safely eliminate the role of the human operator. One area where advanced automation may be applied is Urban Air Mobility (UAM). Current UAM concepts envision fleets of highly automated air vehicles providing on-demand transport for people and goods. A phased development of UAM has been proposed, beginning with on-board pilots and transitioning to a future state where automated vehicles operate with minimal human involvement. Proponents of UAM note that this final state reduces cost as well as eliminating pilot error, identified as a contributing factor in many aircraft accidents. However, eliminating human involvement also risks eliminating their positive contributions to system resilience. Here we examine Concepts of Operation proposed for future UAM systems and explore how humans can best be incorporated to maintain resilience while minimizing cost and risk. A human-autonomy teaming approach is suggested.

Advanced Air Mobility↗

NASA NTRS2008

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 3 - Design of the Concept Demonstrator

This report provides an outline of the essential features of a Structural Health Monitoring Concept Demonstrator (CD) that will be constructed during the next eight months. It is emphasized that the design cannot be considered to be complete, and that design work will continue in parallel with construction and testing. A major advantage of the modular design is that small modules of the system can be developed, tested and modified before a commitment is made to full system development. The CD is expected to develop and evolve for a number of years after its initial construction. This first stage will, of necessity, be relatively simple and have limited capabilities. Later developments will improve all aspects of the functionality of the system, including sensing, processing, communications, intelligence and response. The report indicates the directions this later development will take.

Abbott, David↗

NASA NTRS1994

An active antenna for ELF magnetic fields

The work of Nikola Tesla, especially that directed toward world-wide electrical energy distribution via excitation of the earth-ionosphere cavity resonances, has stimulated interest in the study of these resonances. Not only are they important for their potential use in the transmission of intelligence and electrical power, they are important because they are an integral part of our natural environment. This paper describes the design of a sensitive, untuned, low noise active antenna which is uniquely suited to modern earth-ionosphere cavity resonance measurements employing fast-Fourier transform techniques for near-real-time data analysis. It capitalizes on a little known field-antenna interaction mechanism. Recently, the authors made preliminary measurements of the magnetic fields in the earth-ionosphere cavity. During the course of this study, the problem of designing an optimized ELF magnetic field sensor presented itself. The sensor would have to be small, light weight (for portable use), and capable of detecting the 5-50 Hz picoTesla-level signals generated by the natural excitations of the earth-ionosphere cavity resonances. A review of the literature revealed that past researchers had employed very large search coils, both tuned and untuned. Hill and Bostick, for example, used coils of 30,000 turns wound on high permeability cores of 1.83 m length, weighing 40 kg. Tuned coils are unsuitable for modern fast-Fourier transform data analysis techniques which require a broad spectrum input. 'Untuned' coils connected to high input impedance voltage amplifiers exhibit resonant responses at the resonant frequency determined by the coil inductance and the coil distributed winding capacitance. Also, considered as antennas, they have effective areas equal only to their geometrical areas.

Sutton, John F.↗

NASA NTRS2008

Fast Plasma Instrument for MMS: Data Compression Simulation Results

Magnetospheric Multiscale (MMS) mission will study small-scale reconnection structures and their rapid motions from closely spaced platforms using instruments capable of high angular, energy, and time resolution measurements. To meet these requirements, the Fast Plasma Instrument (FPI) consists of eight (8) identical half top-hat electron sensors and eights (8) identical ion sensors and an Instrument Data Processing Unit (IDPU). The sensors (electron or ion) are grouped into pairs whose 6 deg x 180 deg fields-of-view (FOV) are set 90 deg apart. Each sensor is equipped with electrostatic aperture steering to allow the sensor to scan a 45 deg x 180 deg fan about its nominal viewing (0 deg deflection) direction. Each pair of sensors, known as the Dual Electron Spectrometer (DES) and the Dual Ion Spectrometer (DIS), occupies a quadrant on the MMS spacecraft and the combination of the eight electron/ion sensors, employing aperture steering, image the full-sky every 30-ms (electrons) and 150-ms (ions), respectively. To probe the results in the DES complement of a given spacecraft generating 6.5-Mbs(exp -1) of electron data while the DIS generates 1.1-Mbs(exp -1) of ion data yielding an FPI total data rate of 6.6-MBs(exp -1). The FPI electron/ion data is collected by the IDPU then transmitted to the Central Data Instrument Processor (CIDP) on the spacecraft for science interest ranking. Only data sequences that contain the greatest amount of temporal/spatial structure will be intelligently down-linked by the spacecraft. Currently, the FPI data rate allocation to the CIDP is 1.5-Mbs(exp -1). Consequently, the FPI-IDPU must employ data/image compression to meet this CIDP telemetry allocation. Here, we present simulations of the CCSDS 122.0-B-1 algorithm-based compression of the FPI-DES electron data. Compression analysis is based upon a seed of re-processed Cluster/PEACE electron measurements. Topics to be discussed include: review of compression algorithm; data quality; data formatting/organization; and, implications for data/matrix pruning. To conclude a presentation of the base-lined FPI data compression approach is provided.

Barrie, A.↗

NASA NTRS1992

US Department of Energy's Efforts in Intelligent Processing Equipment

The Department of Energy (DOE) uses intelligent processing equipment (IPE) technologies to conduct research and development and manufacturing for energy and nuclear weapons programs. This paper highlights several significant IPE efforts underway in DOE. IPE technologies are essential to the accomplishment of DOE's missions, because of the need for small lot production, precision, and accuracy in manufacturing, hazardous waste management, and protection of the environment and the safety and health of the workforce and public. Applications of IPE technologies include environmental remediation and waste handling, advanced manufacturing, and automation of tasks carried out in hazardous areas. DOE laboratories have several key programs that integrate robotics, sensor, and control technologies. These programs embody a considerable technical capability that also may be used to enhance U.S. industrial competitiveness. DOE encourages closer cooperation with U.S. industrial partners based on mutual benefits. This paper briefly describes technology transfer mechanisms available for industrial involvement.

Peavy, Richard D.↗

NASA NTRS1996

The human power amplifier technology at the University of California, Berkeley

A human's ability to perform physical tasks is limited by physical strength, not by intelligence. We define "extenders" as a class of robot manipulators worn by humans to augment human mechanical strength, while the wearer's intellect remains the central control system for manipulating the extender. Our research objective is to determine the ground rules for the design and control of robotic systems worn by humans through the design, construction, and control of several prototype experimental direct-drive/non-direct-drive multi-degree-of-freedom hydraulic/electric extenders. The design of extenders is different from the design of conventional robots because the extender interfaces with the human on a physical level. Two sets of force sensors measure the forces imposed on the extender by the human and by the environment (i.e., the load). The extender's compliances in response to such contact forces were designed by selecting appropriate force compensators. This paper gives a summary of some of the selected research efforts related to Extender Technology, carried out during 1980s. The references, at the end of this article, give detailed description of the research efforts.

NASA Discipline Space Human Factors↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

NASA NTRS1986

Optical processing and Space Station automation

Optical information processing research aimed at Space Station automation applications is reviewed. The emphasis of the NASA Ames Research Center program is on intelligent optical pattern recognition and optical control processing. Attention is given to the primary functions of an overall scene understanding system: distortion-invariant optical feature generation, dimensionality reduction, object classification, and contextual information processing. A method of using synthetic discriminant functions to facilitate learning in a high-speed optical correlator is described. A discussion is presented of candidate analog and digital architectures for the optical implementation of state-estimation algorithms needed for the control of high-dimension dynamic systems. The multivariate system chosen for the optical control technology demonstration - a segmented, adaptive mirror and interferometrically based wavefront sensor - is also described.

Ennis, D. J.↗

NASA NTRS1990

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.

Kim, Hakil↗

NASA NTRS1996

The NIST Real-Time Control System (RCS): A Reference Model Architecture for Computational Intelligence

The Real-time Control System (RCS) developed at NIST and elsewhere over the past two decades defines a reference model architecture for design and analysis of complex intelligent control systems. The RCS architecture consists of a hierarchically layered set of functional processing modules connected by a network of communication pathways. The primary distinguishing feature of the layers is the bandwidth of the control loops. The characteristic bandwidth of each level is determined by the spatial and temporal integration window of filters, the temporal frequency of signals and events, the spatial frequency of patterns, and the planning horizon and granularity of the planners that operate at each level. At each level, tasks are decomposed into sequential subtasks, to be performed by cooperating sets of subordinate agents. At each level, signals from sensors are filtered and correlated with spatial and temporal features that are relevant to the control function being implemented at that level.

Albus, James S.↗

NASA NTRS1989

A situated reasoning architecture for space-based repair and replace tasks

Space-based robots need low level control for collision detection and avoidance, short-term load management, fine-grained motion, and other physical tasks. In addition, higher level control is required to focus strategic decision making as missions are assigned and carried out. Reasoning and control must be responsive to ongoing changes in the environment. Research aimed at bridging the gap between high level artificial intelligence (AI) planning techniques and task-level robot programming for telerobotic systems is described. Situated reasoning is incorporated into AI and Robotics systems in order to coordinate a robot's activity within its environment. An integrated system under development in a component maintenance domain is described. It is geared towards replacing worn and/or failed Orbital Replacement Units (ORUs) designed for use aboard NASA's Space Station Freedom based on the collection of components available at a given time. High level control reasons in component space in order to maximize the number operational component-cells over time, while the task-level controls sensors and effectors, detects collisions, and carries out pick and place tasks in physical space. Situated reasoning is used throughout the system to cope with component failures, imperfect information, and unexpected events.

Bloom, Ben↗

NASA NTRS1990

Technology for an intelligent, free-flying robot for crew and equipment retrieval in space

Crew rescue and equipment retrieval is a Space Station Freedom requirement. During Freedom's lifetime, there is a high probability that a number of objects will accidently become separated. Members of the crew, replacement units, and key tools are examples. Retrieval of these objects within a short time is essential. Systems engineering studies were conducted to identify system requirements and candidate approaches. One such approach, based on a voice-supervised, intelligent, free-flying robot was selected for further analysis. A ground-based technology demonstration, now in its second phase, was designed to provide an integrated robotic hardware and software testbed supporting design of a space-borne system. The ground system, known as the EVA Retriever, is examining the problem of autonomously planning and executing a target rendezvous, grapple, and return to base while avoiding stationary and moving obstacles. The current prototype is an anthropomorphic manipulator unit with dexterous arms and hands attached to a robot body and latched in a manned maneuvering unit. A precision air-bearing floor is used to simulate space. Sensor data include two vision systems and force/proximity/tactile sensors on the hands and arms. Planning for a shuttle file experiment is underway. A set of scenarios and strawman requirements were defined to support conceptual development. Initial design activities are expected to begin in late 1989 with the flight occurring in 1994. The flight hardware and software will be based on lessons learned from both the ground prototype and computer simulations.

Erickson, J. D.↗

DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗