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NASA NTRS1997

Short latency vestibular evoked potentials in the Japanese quail (Coturnix coturnix japonica)

Short-latency vestibular-evoked potentials to pulsed linear acceleration were characterized in the quail. Responses occurred within 8 ms following the onset of stimuli and were composed of a series of positive and negative peaks. The latencies and amplitudes of the first four peaks were quantitatively characterized. Mean latencies at 1.0 g ms-1 ranged from 1265 +/- 208 microseconds (P1, N = 18) to 4802 +/- 441 microseconds (N4, N = 13). Amplitudes ranged from 3.72 +/- 1.51 microV (P1/N1, N = 18) to 1.49 +/- 0.77 microV (P3/N3, N = 16). Latency-intensity (LI) slopes ranged from -38.7 +/- 7.3 microseconds dB-1 (P1, N = 18) to -71.6 +/- 21.9 microseconds dB-1 (N3, N = 15) and amplitude-intensity (AI) slopes ranged from 0.20 +/- 0.08 microV dB-1 (P1/N1, N = 18) to 0.07 +/- 0.04 microV dB-1 (P3/N3, N = 11). The mean response threshold across all animals was -21.83 +/- 3.34 dB re: 1.0 g ms-1 (N = 18). Responses remained after cochlear extirpation showing that they could not depend critically on cochlear activity. Responses were eliminated by destruction of the vestibular end organs, thus showing that responses depended critically and specifically on the vestibular system. The results demonstrate that the responses are vestibular and the findings provide a scientific basis for using vestibular responses to evaluate vestibular function through ontogeny and senescence in the quail.

NASA Discipline Neuroscience↗

NASA NTRS1991

Robotic control and inspection verification

Three areas of possible commercialization involving robots at the Kennedy Space Center (KSC) are discussed: a six degree-of-freedom target tracking system for remote umbilical operations; an intelligent torque sensing end effector for operating hand valves in hazardous locations; and an automatic radiator inspection device, a 13 by 65 foot robotic mechanism involving completely redundant motors, drives, and controls. Aspects concerning the first two innovations can be integrated to enable robots or teleoperators to perform tasks involving orientation and panal actuation operations that can be done with existing technology rather than waiting for telerobots to incorporate artificial intelligence (AI) to perform 'smart' autonomous operations. The third robot involves the application of complete control hardware redundancy to enable performance of work over and near expensive Space Shuttle hardware. The consumer marketplace may wish to explore commercialization of similiar component redundancy techniques for applications when a robot would not normally be used because of reliability concerns.

Davis, Virgil Leon↗

NASA NTRS1994

A statistical inference approach for the retrieval of the atmospheric ozone profile from simulated satellite measurements of solar backscattered ultraviolet radiation

NASA's Mission to Planet Earth (MTPE) will address important interdisciplinary and environmental issues such as global warming, ozone depletion, deforestation, acid rain, and the like with its long term satellite observations of the Earth and with its comprehensive Data and Information System. Extensive sets of satellite observations supporting MTPE will be provided by the Earth Observing System (EOS), while more specific process related observations will be provided by smaller Earth Probes. MTPE will use data from ground and airborne scientific investigations to supplement and validate the global observations obtained from satellite imagery, while the EOS satellites will support interdisciplinary research and model development. This is important for understanding the processes that control the global environment and for improving the prediction of events. In this paper we illustrate the potential for powerful artificial intelligence (AI) techniques when used in the analysis of the formidable problems that exist in the NASA Earth Science programs and of those to be encountered in the future MTPE and EOS programs. These techniques, based on the logical and probabilistic reasoning aspects of plausible inference, strongly emphasize the synergetic relation between data and information. As such, they are ideally suited for the analysis of the massive data streams to be provided by both MTPE and EOS. To demonstrate this, we address both the satellite imagery and model enhancement issues for the problem of ozone profile retrieval through a method based on plausible scientific inferencing. Since in the retrieval problem, the atmospheric ozone profile that is consistent with a given set of measured radiances may not be unique, an optimum statistical method is used to estimate a 'best' profile solution from the radiances and from additional a priori information.

Bonavito, N. L.↗

NASA NTRS2018

Performing a Comprehensive Unmanned Aircraft System Full Integration Analysis for NASA ARMD

For many years, the concept of routinely flying unmanned aircraft systems (UAS) within the national airspace system (NAS) has been a long-term goal with numerous known and unknown technology and policy obstacles. Just within the last few years, the efforts and advancements from government, industry, and academia-sponsored research and development have greatly shortened the distance to the goal. The National Aeronautics and Space Administration (NASA) Aeronautics Research Mission Directorate (ARMD) has recognized that it is uniquely positioned to play a lead role in addressing the remaining UAS airspace integration (AI) challenges. To fully understand the magnitude and scope of these challenges, NASA ARMD initiated a study in 2015 to identify what would be needed to enable full integration of UAS for civil/commercial operations within the NAS by 2025. The desired outcome was a comprehensive analysis framework that ARMD could use to develop a research portfolio focused on retiring the remaining gaps and challenges standing in the way of full UAS integration. This document is a comprehensive assessment of UAS integration research to date.

UAS in the NAS↗

NASA NTRS1993

SMART: Situated Multimodal Advanced Real-time Trainer

We have begun work on an intelligent support tool (SMART) for operating and maintenance procedures. This work extends McDonnell Douglas's recent success in developing electronic job aids for onboard applications. SMART is based on advanced AI technology that derives the sequential, situational, operational, and temporal views of operating and maintenance procedures from a single internal representation of the procedures. SMART guides the astronauts through detailed operating and maintenance procedures. It also helps the astronauts decide when and how it is safe to deviate from the established procedures. SMART's user interface presents the sequential and procedural views of the procedures to the astronauts. The views are supplemented with vivid color graphics of internal system functions, digitized photographs of flight hardware, and full-motion video. SMART also allows the astronauts to keep their own personal notes attached to a procedure. SMART will be delivered on portable hand-held systems. SMART provides highly descriptive, realistic, 'hands-on' training which is available 'on-demand' wherever and whenever it it needed.

Desrosiers, Stephen↗

NASA NTRS1992

A recent Cleanroom success story: The Redwing project

Redwing is the largest completed Cleanroom software engineering project in IBM, both in terms of lines of code and project staffing. The product provides a decision-support facility that utilizes artificial intelligence (AI) technology for predicting and preventing complex operating problems in an MVS environment. The project used the Cleanroom process for development and realized a defect rate of 2.6 errors/KLOC, measured from first execution. This represents the total amount of errors that were found in testing and installation at three field test sites. Development productivity was 486 LOC/PM, which included all development labor expended in design specification through completion of incremental testing. In short, the Redwing team produced a complex systems software product with an extraordinarily low error rate, while maintaining high productivity. All of this was accomplished by a project team using Cleanroom for the first time. An 'introductory implementation' of Cleanroom was defined and used on Redwing. This paper describes the quality and productivity results, the Redwing project, and how Cleanroom was implemented.

Hausler, Philip A.↗

NASA NTRS1993

An intelligent planning and scheduling system for the HST servicing missions

A new, intelligent planning and scheduling system has been delivered to NASA-Goddard Space Flight Center (GSFC) to provide support for the up-coming Hubble Space Telescope (HST) Servicing Missions. This new system is the Servicing Mission Planning and Replanning Tool (SM/PART). SM/PART is written in C and runs on a UNlX-based workstation (IBM RS/6000) under Motif. SM/PART effectively automates the complex task of building or rebuilding integrated timelines and command plans which are required by HST Servicing Mission personnel at their consoles during the missions. SM/PART is able to quickly build or rebuild timelines based on information stored in a Knowledge Base (KB) by using an Artificial Intelligence (AI) tool called the Planning And Resource Reasoning (PARR) shell. After a timeline has been built in the batch mode, it can be displayed and edited in an interactive mode with help from the PARR shell. Finally a detailed command plan is generated. The capability to quickly build or rebuild timelines and command plans provides an additional safety factor for the HST, Shuttle and Crew.

Johnson, Jay↗

NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗

NASA NTRS2007

Real-Time Data Display

RT-Display is a MATLAB-based data acquisition environment designed to use a variety of commercial off-the-shelf (COTS) hardware to digitize analog signals to a standard data format usable by other post-acquisition data analysis tools. This software presents the acquired data in real time using a variety of signal-processing algorithms. The acquired data is stored in a standard Operator Interactive Signal Processing Software (OISPS) data-formatted file. RT-Display is primarily configured to use the Agilent VXI (or equivalent) data acquisition boards used in such systems as MIDDAS (Multi-channel Integrated Dynamic Data Acquisition System). The software is generalized and deployable in almost any testing environment, without limitations or proprietary configuration for a specific test program or project. With the Agilent hardware configured and in place, users can start the program and, in one step, immediately begin digitizing multiple channels of data. Once the acquisition is completed, data is converted into a common binary format that also can be translated to specific formats used by external analysis software, such as OISPS and PC-Signal (product of AI Signal Research Inc.). RT-Display at the time of this reporting was certified on Agilent hardware capable of acquisition up to 196,608 samples per second. Data signals are presented to the user on-screen simultaneously for 16 channels. Each channel can be viewed individually, with a maximum capability of 160 signal channels (depending on hardware configuration). Current signal presentations include: time data, fast Fourier transforms (FFT), and power spectral density plots (PSD). Additional processing algorithms can be easily incorporated into this environment.

Pedings, Marc↗

NASA NTRSDate not supplied

Celestial Mapping System and Digital Lunar Library Initiative

We are preparing to create an interactive, global 3D lunar environment with integrated dataset and AI/ML tools to provide unique value to mission planners, scientists and the entire lunar community. This lunar environment will be based on NASA Ames Celestial Mapping System (CMS) [1] and Digital Lunar Library (DLL) Initiative. CMS provides a 3D virtual Lunar Globe with extensive user friendly tool sets, that include high resolution terrain visualization, elevation profiles, measurement kits, slope analysis, path optimization, line of sight analysis, equipment planning and placement tools and many other functionalities [1]. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The application is developed to provide situational and domain awareness on the Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on the Lunar surface. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. In the future, these capabilities will be enhanced by incorporating AI/ML tools and a plug-in architecture to enable customization by the user groups. With the help of DLL we will be able to : 1) Amplify the value of lunar information with AI-powered data enhancements 2) Acquire and integrate lunar data with AI-assisted georectification and homogenization 3) Analyze lunar data with advanced 3D visualization, intelligent search-by-example 4) Apply lunar data insights to specific use cases with an open plug-in architecture. The CMS-DLL initiative will have several potential use cases for NASA and the lunar community in general, including subsurface lava tube visualization and analysis, soil analysis, in-situ lunar resource visualization and representation on 3D globe, and data analytics for utilization. REFERENCES: [1] https://celestial.arc.nasa.gov/

3D Globe↗

NASA NTRS2002

Preliminary Effect of Synthetic Vision Systems Displays to Reduce Low-Visibility Loss of Control and Controlled Flight Into Terrain Accidents

An experimental investigation was conducted to study the effectiveness of Synthetic Vision Systems (SVS) flight displays as a means of eliminating Low Visibility Loss of Control (LVLOC) and Controlled Flight Into Terrain (CFIT) accidents by low time general aviation (GA) pilots. A series of basic maneuvers were performed by 18 subject pilots during transition from Visual Meteorological Conditions (VMC) to Instrument Meteorological Conditions (IMC), with continued flight into IMC, employing a fixed-based flight simulator. A total of three display concepts were employed for this evaluation. One display concept, referred to as the Attitude Indicator (AI) replicated instrumentation common in today's General Aviation (GA) aircraft. The second display concept, referred to as the Electronic Attitude Indicator (EAI), featured an enlarged attitude indicator that was more representative of a glass display that also included advanced flight symbology, such as a velocity vector. The third concept, referred to as the SVS display, was identical to the EAI except that computer-generated terrain imagery replaced the conventional blue-sky/brown-ground of the EAI. Pilot performance parameters, pilot control inputs and physiological data were recorded for post-test analysis. Situation awareness (SA) and qualitative pilot comments were obtained through questionnaires and free-form interviews administered immediately after the experimental session. Initial pilot performance data were obtained by instructor pilot observations. Physiological data (skin temperature, heart rate, and muscle flexure) were also recorded. Preliminary results indicate that far less errors were committed when using the EAI and SVS displays than when using conventional instruments. The specific data example examined in this report illustrates the benefit from SVS displays to avoid massive loss of SA conditions. All pilots acknowledged the enhanced situation awareness provided by the SVS display concept. Levels of pilot stress appear to be correlated with skin temperature measurements.

Glaab, Louis J.↗

NASA NTRS1991

AI techniques for a space application scheduling problem

Scheduling is a very complex optimization problem which can be categorized as an NP-complete problem. NP-complete problems are quite diverse, as are the algorithms used in searching for an optimal solution. In most cases, the best solutions that can be derived for these combinatorial explosive problems are near-optimal solutions. Due to the complexity of the scheduling problem, artificial intelligence (AI) can aid in solving these types of problems. Some of the factors are examined which make space application scheduling problems difficult and presents a fairly new AI-based technique called tabu search as applied to a real scheduling application. the specific problem is concerned with scheduling application. The specific problem is concerned with scheduling solar and stellar observations for the SOLar-STellar Irradiance Comparison Experiment (SOLSTICE) instrument in a constrained environment which produces minimum impact on the other instruments and maximizes target observation times. The SOLSTICE instrument will gly on-board the Upper Atmosphere Research Satellite (UARS) in 1991, and a similar instrument will fly on the earth observing system (Eos).

Thalman, N.↗

NASA NTRS1986

Artificial intelligence and the space station software support environment

In a software system the size of the Space Station Software Support Environment (SSE), no one software development or implementation methodology is presently powerful enough to provide safe, reliable, maintainable, cost effective real time or near real time software. In an environment that must survive one of the most harsh and long life times, software must be produced that will perform as predicted, from the first time it is executed to the last. Many of the software challenges that will be faced will require strategies borrowed from Artificial Intelligence (AI). AI is the only development area mentioned as an example of a legitimate reason for a waiver from the overall requirement to use the Ada programming language for software development. The limits are defined of the applicability of the Ada language Ada Programming Support Environment (of which the SSE is a special case), and software engineering to AI solutions by describing a scenario that involves many facets of AI methodologies.

Marlowe, Gilbert↗

NASA NTRS1992

Identification of time-varying structural dynamic systems - An artificial intelligence approach

An application of the artificial intelligence-derived methodologies of heuristic search and object-oriented programming to the problem of identifying the form of the model and the associated parameters of a time-varying structural dynamic system is presented in this paper. Possible model variations due to changes in boundary conditions or configurations of a structure are organized into a taxonomy of models, and a variant of best-first search is used to identify the model whose simulated response best matches that of the current physical structure. Simulated model responses are verified experimentally. An output-error approach is used in a discontinuous model space, and an equation-error approach is used in the parameter space. The advantages of the AI methods used, compared with conventional programming techniques for implementing knowledge structuring and inheritance, are discussed. Convergence conditions and example problems have been discussed. In the example problem, both the time-varying model and its new parameters have been identified when changes occur.

Glass, B. J.↗

NASA NTRS2023

The Magnesium Isotope Composition of Samples Returned From Asteroid Ryugu

The nucleosynthetic isotope composition of planetary materials provides a record of the heterogeneous distribution of stardust within the early solar system. In 2020 December, the Japan Aerospace Exploration Agency Hayabusa2 spacecraft returned to Earth the first samples of a primitive asteroid, namely, the Cb-type asteroid Ryugu. This provides a unique opportunity to explore the kinship between primitive asteroids and carbonaceous chondrites. We report high-precision μ 26 Mg* and μ 25 Mg values of Ryugu samples together with those of CI, CM, CV, and ungrouped carbonaceous chondrites. The stable Mg isotope composition of Ryugu aliquots defines μ 25 Mg values ranging from –160 ± 20 ppm to –272 ± 30 ppm, which extends to lighter compositions relative to Ivuna-type (CI) and other carbonaceous chondrite groups. We interpret the μ 25 Mg variability as reflecting heterogeneous sampling of a carbonate phase hosting isotopically light Mg (μ 25 Mg ∼ –1400 ppm) formed by low temperature equilibrium processes. After correcting for this effect, Ryugu samples return homogeneous μ 26 Mg* values corresponding to a weighted mean of 7.1 ± 0.8 ppm. Thus, Ryugu defines a μ 26 Mg* excess relative to the CI and CR chondrite reservoirs corresponding to 3.8 ± 1.1 and 11.9 ± 0.8 ppm, respectively. These variations cannot be accounted for by in situ decay of 26 Al given their respective 27 Al/ 24 Mg ratios. Instead, it requires that Ryugu and the CI and CR parent bodies formed from material with a different initial 26 Al/ 27 Al ratio or that they are sourced from material with distinct Mg isotope compositions. Thus, our new Mg isotope data challenge the notion that Ryugu and CI chondrites share a common nucleosynthetic heritage.

Solar system evolution↗

NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗

NASA NTRS1991

Collected notes from the Benchmarks and Metrics Workshop

In recent years there has been a proliferation of proposals in the artificial intelligence (AI) literature for integrated agent architectures. Each architecture offers an approach to the general problem of constructing an integrated agent. Unfortunately, the ways in which one architecture might be considered better than another are not always clear. There has been a growing realization that many of the positive and negative aspects of an architecture become apparent only when experimental evaluation is performed and that to progress as a discipline, we must develop rigorous experimental methods. In addition to the intrinsic intellectual interest of experimentation, rigorous performance evaluation of systems is also a crucial practical concern to our research sponsors. DARPA, NASA, and AFOSR (among others) are actively searching for better ways of experimentally evaluating alternative approaches to building intelligent agents. One tool for experimental evaluation involves testing systems on benchmark tasks in order to assess their relative performance. As part of a joint DARPA and NASA funded project, NASA-Ames and Teleos Research are carrying out a research effort to establish a set of benchmark tasks and evaluation metrics by which the performance of agent architectures may be determined. As part of this project, we held a workshop on Benchmarks and Metrics at the NASA Ames Research Center on June 25, 1990. The objective of the workshop was to foster early discussion on this important topic. We did not achieve a consensus, nor did we expect to. Collected here is some of the information that was exchanged at the workshop. Given here is an outline of the workshop, a list of the participants, notes taken on the white-board during open discussions, position papers/notes from some participants, and copies of slides used in the presentations.

Drummond, Mark E.↗

NASA NTRS1992

A new activity index for comets

An activity index, AI, is derived from observational data to measure the increase of activity in magnitudes for comets when brightest near perihelion as compared to their inactive reflective brightness at great solar distances. Because the observational data are still instrumentally limited in the latter case and because many comets carry particulate clouds about them at great solar distances, the application of the activity index is still limited. A tentative application is made for the comets observed by Max Beyer over a period of nearly 40 years, providing a uniform magnitude system for the near-perihelion observations. In all, 32 determinations are made for long-period (L-P) comets and 15 for short-period (S-P). Although the correlations are scarcely definitive, the data suggest that the faintest comets are just as active as the brightest and that the S-P comets are almost as active as those with periods (P) exceeding 10(exp 4) years or those with orbital inclinations of i less than 120 deg. Comets in the range 10(exp 2) less than P less than 10(exp 4) yr. or with i greater than 120 deg appear to be somewhat more active than the others. There is no evidence to suggest aging among the L-P comets or to suggest other than a common nature for comets generally.

Whipple, Fred L.↗