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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗
DOE OSTI2015

SU-E-J-199: A Software Tool for Quality Assurance of Online Replanning with MR-Linac

Purpose: To develop a quality assurance software tool, ArtQA, capable of automatically checking radiation treatment plan parameters, verifying plan data transfer from treatment planning system (TPS) to record and verify (R&V) system, performing a secondary MU calculation considering the effect of magnetic field from MR-Linac, and verifying the delivery and plan consistency, for online replanning. Methods: ArtQA was developed by creating interfaces to TPS (e.g., Monaco, Elekta), R&V system (Mosaiq, Elekta), and secondary MU calculation system. The tool obtains plan parameters from the TPS via direct file reading, and retrieves plan data both transferred from TPS and recorded during the actual delivery in the R&V system database via open database connectivity and structured query language. By comparing beam/plan datasets in different systems, ArtQA detects and outputs discrepancies between TPS, R&V system and secondary MU calculation system, and delivery. To consider the effect of 1.5T transverse magnetic field from MR-Linac in the secondary MU calculation, a method based on modified Clarkson integration algorithm was developed and tested for a series of clinical situations. Results: ArtQA is capable of automatically checking plan integrity and logic consistency, detecting plan data transfer errors, performing secondary MU calculations with or without a transverse magnetic field, and verifying treatment delivery. The tool is efficient and effective for pre- and post-treatment QA checks of all available treatment parameters that may be impractical with the commonly-used visual inspection. Conclusion: The software tool ArtQA can be used for quick and automatic pre- and post-treatment QA check, eliminating human error associated with visual inspection. While this tool is developed for online replanning to be used on MR-Linac, where the QA needs to be performed rapidly as the patient is lying on the table waiting for the treatment, ArtQA can be used as a general QA tool in radiation oncology practice. This work is partially supported by Elekta Inc.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Fuzzy logic, neural networks, and soft computing

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Zadeh, Lofti A.↗
NASA NTRS2007

Extravehicular Activity System Sizing Analysis Tool (EVAS_SAT)

An effort was initiated by NASA/JSC in 2001 to develop an Extravehicular Activity System Sizing Analysis Tool (EVAS_SAT) for the sizing of Extravehicular Activity System (EVAS) architecture and studies. Its intent was to support space suit development efforts and to aid in conceptual designs for future human exploration missions. Its basis was the Life Support Options Performance Program (LSOPP), a spacesuit and portable life support system (PLSS) sizing program developed for NASA/JSC circa 1990. EVAS_SAT estimates the mass, power, and volume characteristics for user-defined EVAS architectures, including Suit Systems, Airlock Systems, Tools and Translation Aids, and Vehicle Support equipment. The tool has undergone annual changes and has been updated as new data have become available. Certain sizing algorithms have been developed based on industry standards, while others are based on the LSOPP sizing routines. The sizing algorithms used by EVAS_SAT are preliminary. Because EVAS_SAT was designed for use by members of the EVA community, subsystem familiarity on the part of the intended user group and in the analysis of results is assumed. The current EVAS_SAT is operated within Microsoft Excel 2003 using a Visual Basic interface system.

Brown, Cheryl B.↗
NASA NTRS2024

Evolution of Spaceflight Renal Stone Risks and Update to the NASA Human Research Program (HRP) Renal Stone Evidence Report and Integrated Concept of Operations

BACKGROUND: The NASA HRP Evidence Report on the Risk of Renal Stone Formation is being updated based on new information obtained from 9 years of data from NASA’s astronaut renal stone surveillance program. In addition, the Concept of Operations for missions beyond Low Earth Orbit (LEO) was created to supplement the Evidence Report, and the NASA clinical practice guideline(CPG)for renal stone surveillance was updated. OVERVIEW: A literature review was performed to evaluate the state of literature regarding renal stone prevention, mitigation, and management both in terrestrial and spaceflight environments. Current renal stone treatment guidelines for the ISS, which were created by the Inflight Clinical Medicine Working Group and approved by the Multilateral Medical Operations Panel (MMOP)were reviewed, as well as recent ultrasound technology demonstrations onboard the ISS. This information was used in conjunction with emerging evidence from the surveillance program to update the Evidence Report and in-flight Concept of Operations. DISCUSSION: There are known and emerging renal stone prevention strategies including astronaut selection, the use of medications and resistive exercise, early diagnosis with regular urine studies and ultrasound imaging. In-flight treatment options include pharmaceuticals, use of lithotripsy and propulsive ultrasound, placement of percutaneous drains and other modalities. This information is useful to gauge future risks and update our strategies for prevention and management of renal stones in astronauts to reduce the risk of in-flight renal stone colic that could cause morbidity and mission impact.

E Stratton↗
NASA NTRSDate not supplied

HUMAN FACTORS AND BEHAVIORAL PERFORMANCE EXPLORATION MEASURES: ASSESSING ASTRONAUT RISK

INTRODUCTION: The Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures to assess behavioral health and performance risk related to future exploration class missions, and to support reduction of the Human Research Program’s (HRP) Behavioral Medicine (BMed), Team, Sleep, and Human Systems Integration Architecture (HSIA) risks. This presentation will provide an overview of the HFBP-EM program, describe its implementation across spaceflight analogs and the international space station (ISS), and discuss its applicability to audience members. TOPIC: HFBP-EM is a research program designed to develop a standard set of measures that can be used in space and space-analog research to characterize BMed, Team, Sleep, and HSIA risks. It is an ongoing research project that is used examine the validity and reliability of HFBP measures, as well as their shorter forms. It also serves as a test bed for HFBP measures being considered for the spaceflight standard measures. The suite of measures is used to test the efficacy of countermeasures. To date, HFBP-EM has been collected in Human Exploration Research Analogs campaigns 4 and 5, and the SIRIUS 19 mission in the Russian Ground Based Experiment Complex. A subset of the HFBP-EM suite was collected during spaceflight as part of HRP’s Standard Measures in Spaceflight Project. Data was collected from a total of 55 multinational astronaut and astronaut-like crewmembers (mean age: 39.5, SD = 7.6; 31% female; 91% with advanced degrees). Three broad categories of HFBP-EM measures and their relevance to HRP risks will be discussed: 1) surveys that assess team functioning (Teams risk) as well as mood and affect (Bmed risk), 2) performance-based tasks of cognitive functioning and operationally relevant performance (Bmed risk), and 3) physiological biomarkers of sleep (sleep risk) and heart rate (Bmed risk). We will provide an overview of the background of the HFBP-EM program, what the suite currently includes, and next steps in its future development. We will also discuss the application to aerospace practitioners and researchers. APPLICATION: Astronaut teams selected for future space exploration missions will face several challenges that pose significant yet still unknown risks to the behavioral health and performance of astronauts. The HFBP-EM suite provides a comprehensive assessment of behavioral health and performance in space analog and spaceflight settings. This suite can be applied to both operational and research settings to advance risk reduction research for long duration space exploration missions.

S T Bell↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
NASA NTRS1999

Time course of sleep inertia dissipation in human performance and alertness

Alertness and performance on a wide variety of tasks are impaired immediately upon waking from sleep due to sleep inertia, which has been found to dissipate in an asymptotic manner following waketime. It has been suggested that behavioural or environmental factors, as well as sleep stage at awakening, may affect the severity of sleep inertia. In order to determine the time course of sleep inertia dissipation under normal entrained conditions, subjective alertness and cognitive throughput were measured during the first 4 h after habitual waketime from a full 8-h sleep episode on 3 consecutive days. We investigated whether this time course was affected by either sleep stage at awakening or behavioural/environmental factors. Sleep inertia dissipated in an asymptotic manner and took 2-4 h to near the asymptote. Saturating exponential functions fitted the sleep inertia data well, with time constants of 0.67 h for subjective alertness and 1.17 h for cognitive performance. Most awakenings occurred out of stage rapid eye movement (REM), 2 or 1 sleep, and no effect of sleep stage at awakening on either the severity of sleep inertia or the time course of its dissipation could be detected. Subjective alertness and cognitive throughput were significantly impaired upon awakening regardless of whether subjects got out of bed, ate breakfast, showered and were exposed to ordinary indoor room light (approximately 150 lux) or whether subjects participated in a constant routine (CR) protocol in which they remained in bed, ate small hourly snacks and were exposed to very dim light (10-15 lux). These findings allow for the refinement of models of alertness and performance, and have important implications for the scheduling of work immediately upon awakening in many occupational settings.

Non-NASA Center↗
DOE OSTI2021

Performance Evaluation of Comparative Vacuum Monitoring and Piezoelectric Sensors for Structural Health Monitoring of Rotorcraft Components

The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.

42 ENGINEERING↗
NASA NTRS2005

Effect of contrast on the active control of a moving line

In many passive visual tasks, human perceptual judgments are contrast dependent. To explore whether these contrast dependencies of visual perception also affect closed-loop manual control tasks, we examined visuomotor performance as humans actively controlled a moving luminance-defined line over a range of contrasts. Four subjects were asked to use a joystick to keep a horizontal line centered on a display as its vertical position was perturbed by a sum of sinusoids under two control regimes. The total root mean square (RMS) position error decreased quasi-linearly with increasing log contrast across the tested range (mean slope across subjects: -8.0 and -7.7% per log2 contrast, for the two control regimes, respectively). Frequency-response (Bode) plots showed a systematic increase in open-loop gain (mean slope: 1.44 and 1.30 dB per log2 contrast, respectively), and decrease in phase lag with increasing contrast, which can be accounted for by a decrease in response time delay (mean slope: 32 and 40 ms per log2 contrast, respectively). The performance data are well fit by a Crossover Model proposed by McRuer and Krendel, which allowed us to identify both visual position and motion cues driving performance. This analysis revealed that the position and motion cues used to support manual control under both control regimes appear equally sensitive to changes in stimulus contrast. In conclusion, our data show that active control of a moving visual stimulus is as dependent on contrast as passive perception and suggest that this effect is attributed to a shared contrast sensitivity early in the visual pathway, before any specialization for motion processing.

Contrast Sensitivity/physiology↗
DOE OSTI2022

IDAC-Bio, A Software for Internal Dosimetry Based on the New ICRP Biokinetic Models and Specific Absorbed Fractions

Radiation dosimetry is central to virtually all radiation safety applications, optimization, and research. It relates to various individuals and population groups and to miscellaneous exposure situations—including planned, existing, and emergency situations. The International Commission on Radiological Protection (ICRP) has developed a new computational framework for internal dose estimations. Important components are more detailed and improved anatomical models and more realistic biokinetic models than before. The ICRP is currently producing new organ dose and effective dose coefficients for occupational intakes of radionuclides (OIR) and environmental intakes of radionuclides (EIR), which supersede the earlier dose coefficients in Publication 68 and the Publication 72 series, respectively. However, the ICRP only publishes dose coefficients for a single acute intake of a radionuclide and for an integration period of 50 years for intake by adults and to age 70 years for intakes by pre-adults. The new software, IDAC-Bio, performs committed absorbed dose and effective dose calculations for a selectable intake scenario, e.g., for a continuous intake or an intake during x hours per day and y days per week, and for any selected integration time. The software uses the primary data and models of the ICRP biokinetic models and numerically solves the biokinetic model and calculates the absorbed doses to organs and tissues in the ICRP reference human phantoms. The software calculates absorbed dose using the nuclear decay data in ICRP publication 107. IDAC-Bio is a further development and an important addition to the internal dosimetry program IDAC-Dose2.1. Here, the results generated by the software were validated against published ICRP dose coefficients. The potential of the software is illustrated by dose calculations for a nuclear power plant worker who had been exposed to varying levels of 60 Co and who had undergone repeated whole-body measurements, and for a hypothetical member of the public subject to future releases of 148 Gd from neutron spallation in tungsten at the European Spallation Source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking

"Detect the expected, discover the unexpected" was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sensemaking process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.

Sensemaking, human-machine teaming, agency, artifi↗
DOE OSTI2015

Insights from Smart Meters. Ramp-up, dependability, and short-term persistence of savings from Home Energy Reports

Smart meters, smart thermostats, and other new technologies provide previously unavailable high-frequency and location-specific energy usage data. Many utilities are now able to capture real-time, customer specific hourly interval usage data for a large proportion of their residential and small commercial customers. These vast, constantly growing streams of rich data (or, “big data”) have the potential to provide novel insights into key policy questions about how people make energy decisions. The richness and granularity of these data enable many types of creative and cutting-edge analytics. Technically sophisticated and rigorous statistical techniques can be used to pull useful insights out of this high-frequency, human-focused data. In this series, we call this “behavior analytics.” This kind of analytics has the potential to provide tremendous value to a wide range of energy programs. For example, disaggregated and heterogeneous information about actual energy use allows energy efficiency (EE) and/or demand response (DR) program implementers to target specific programs to specific households; enables evaluation, measurement and verification (EM&V) of energy efficiency programs to be performed on a much shorter time horizon than was previously possible; and may provide better insights into the energy and peak hour savings associated with EE and DR programs (e.g., behavior-based (BB) programs). The goal of this series is to enable evidence-based and data-driven decision making by policy makers and industry stakeholders, including program planners, program administrators, utilities, state regulatory agencies, and evaluators. We focus on research findings that are immediately relevant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Mars Observer as a precursor to intensive exploration of Mars

The Mars Observer Mission, to be launched by the United States in September 1992, is designed to perform an extended orbital study of the Martian surface, atmosphere, climate, and gravitational and magnetic fields using a spacecraft in Mars orbit for one Martian year. This information will add to the global data base that is necessary for increasing scientific understanding of Mars, as well as for planning the next stages of intensive exploration of the planet. Future missions will progress from more specific robotic exploration to eventual human exploration, and will require progressively more detailed engineering information. Mars Observer will provide key basic data including planet shape, gravity field, geodetic framework, topography, characteristics of the surface, atmospheric profiles, dust content of the atmosphere, and wind field. Special data products for engineering purposes could be obtained from the currently planned data return, and additional engineering data could be returned by augmentation to the basic Mars Observer's operation.

Cunningham, Glenn E.↗
NASA NTRS1999

Mars Surface Ionizing Radiation Environment: Need for Validation

Protection against the hazards from exposure to ionizing radiation remains an unresolved issue in the Human Exploration and Development of Space (HEDS) enterprise [1]. The major uncertainty is the lack of data on biological response to galactic cosmic ray (GCR) exposures but even a full understanding of the physical interaction of GCR with shielding and body tissues is not yet available and has a potentially large impact on mission costs. "The general opinion is that the initial flights should be short-stay missions performed as fast as possible (so-called 'Sprint' missions) to minimize crew exposure to the zero-g and space radiation environment, to ease requirements on system reliability, and to enhance the probability of mission success." The short-stay missions tend to have long transit times and may not be the best option due to the relatively long exposure to zero-g and ionizing radiation. On the other hand the short-transit missions tend to have long stays on the surface requiring an adequate knowledge of the surface radiation environment to estimate risks and to design shield configurations. Our knowledge of the surface environment is theoretically based and suffers from an incomplete understanding of the physical interactions of GCR with the Martian atmosphere, Martian surface, and intervening shield materials. An important component of Mars surface robotic exploration is the opportunity to test our understanding of the Mars surface environment. The Mars surface environment is generated by the interaction of Galactic Cosmic Rays (GCR) and Solar Particle Events (SPEs) with the Mars atmosphere and Mars surface materials. In these interactions, multiple charged ions are reduced in size and secondary particles are generated, including neutrons. Upon impact with the Martian surface, the character of the interactions changes as a result of the differing nuclear constituents of the surface materials. Among the surface environment are many neutrons diffusing from the Martian surface and especially prominent are energetic neutrons with energies up to a few hundred MeV. Testing of these computational results is first supported by ongoing experiments at the Brookhaven National Laboratory but equally important is the validation to the extent possible by measurements on the Martian surface. Such measurements are limited by power and weight requirements of the specific mission and simplified instrumentation by necessity lacks the full discernment of particle type and spectra as is possible with laboratory experimental equipment. Yet, the surface measurements are precise and a necessary requisite to validate our understanding of the surface environment. At the very minimum the surface measurements need to provide some spectral information on the neutron environment. Of absolute necessity is the precise knowledge of the detector response functions for absolute comparisons between the computational model of the surface environment and the detector measurements on the surface.

Wilson, J. W.↗
NASA NTRS2005

Effective Team Support: From Task and Cognitive Modeling to Software Agents for Time-Critical Complex Work Environments

The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in completing a system for empirical data collection, cognitive modeling, and the building of software agents to support a team's tasks, and in running experiments for the collection of baseline data.

Remington, Roger W.↗
NASA NTRS2008

Proving Correctness for Pointer Programs in a Verifying Compiler

This research describes a component-based approach to proving the correctness of programs involving pointer behavior. The approach supports modular reasoning and is designed to be used within the larger context of a verifying compiler. The approach consists of two parts. When a system component requires the direct manipulation of pointer operations in its implementation, we implement it using a built-in component specifically designed to capture the functional and performance behavior of pointers. When a system component requires pointer behavior via a linked data structure, we ensure that the complexities of the pointer operations are encapsulated within the data structure and are hidden to the client component. In this way, programs that rely on pointers can be verified modularly, without requiring special rules for pointers. The ultimate objective of a verifying compiler is to prove-with as little human intervention as possible-that proposed program code is correct with respect to a full behavioral specification. Full verification for software is especially important for an agency like NASA that is routinely involved in the development of mission critical systems.

Kulczycki, Gregory↗