Search NASA⌕ Search

SEARCH · Search NASA

Results for “learning efficiency”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Search for near-Earth asteroids with the spacewatch camera

The Spacewatch Program began as an engineering development of the discovery of near-earth asteroids and comets, with the firs observations starting in 1983, using an RCA charge coupled device (CCD) that has 320 x 512 pixels. Extensive software was written to scan the sky and learn how to do astrometry for precise orbits of newly discovered objects. The merit of the concept was proven, with many observations reported and comets recovered. The precision of astrometric observations for comets and asteroids was improved. For efficient scanning and discovery of new objects, a Tektronix 2048 x 2048 CCD was obtained and is now permanently on the Spacewatch Telescope. Other aspects of the Spacewatch Program are briefly discussed.

Gehrels, T.↗
NASA NTRS1999

One for All: Maintaining a Single Schedule Database for Large Development Projects

Efficiently maintaining and controlling a single schedule database in an Integrated Product Team environment is a significant challenge. It's accomplished effectively with the right combination of tools, skills, strategy, creativity, and teamwork. We'll share our lessons learned maintaining a 20,000 plus task network on a 36 month project.

Hilscher, R.↗
NASA NTRS1999

Unlocking the Keys to Vortex/Flame Interactions in Turbulent Gas-Jet Diffusion Flames--Dynamic Behavior Explored on the Space Shuttle

Most combustion processes in industrial applications (e.g., furnaces and engines) and in nature (e.g., forest fires) are turbulent. A better understanding of turbulent combustion could lead to improved combustor design, with enhanced efficiency and reduced emissions. Despite its importance, turbulent combustion is poorly understood because of its complexity. The rapidly changing and random behavior of such flames currently prevents detailed analysis, whether experimentally or computationally. However, it is possible to learn about the fundamental behavior of turbulent flames by exploring the controlled interaction of steady laminar flames and artificially induced flow vortices. These interactions are an inherent part of turbulent flames, and understanding them is essential to the characterization of turbulent combustion. Well-controlled and defined experiments of vortex interaction with laminar flames are not possible in normal gravity because of the interference of buoyancy- (i.e., gravity) induced vortices. Therefore, a joint microgravity study was established by researchers from the Science and Technology Development Corp. and the NASA Lewis Research Center. The experimental study culminated in the conduct of the Turbulent Gas-Jet Diffusion Flames (TGDF) Experiment on the STS-87 space shuttle mission in November 1997. The fully automated hardware, shown in photo, was designed and built at Lewis. During the mission, the experiment was housed in a Get Away Special (GAS) canister in the cargo bay.

Stocker, Dennis P.↗
NASA NTRS2005

Experiments on Supervised Learning Algorithms for Text Categorization

Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

Namburu, Setu Madhavi↗
NASA NTRSDate not supplied

NEUROSPF: A Tool For the Symbolic Analysis of Neural Networks

This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and translates them into a Java representation that is amenable for analysis using the Symbolic PathFinder symbolic execution tool. Notably, NEUROSPF encodes specialized peer classes for parsing the model’s parameters, thereby enabling efficient analysis. With NEUROSPF the user has the flexibility to specify either the inputs or the network internal parameters as symbolic, promoting the application of program analysis and testing approaches from software engineering to the field of machine learning. For instance, NEUROSPF can be used for coverage-based testing and test generation, finding adversarial examples and also constraint-based repair of neural networks, thus improving the reliability of neural networks and of the applications that use them.

neural networks↗
NASA NTRS2018

A Systems Architecting Methodology Using Bloom’s Taxonomy to Promote Creative Engineering Synthesis

Architecting complex systems requires high-level cognitive processing and extensive knowledge of the system elements, functions, relationships, and constraints. This paper describes a systems architecting methodology implemented through cognitive psychological creative processes using Bloom’s taxonomy as a framework to generate the expert knowledge required to effectively and systematically synthesize new systems. Systems architecting activities were carried out to identify, develop, and capture factual and conceptual knowledge relevant to the system subject matter and functional elements, as well as to facilitate active processing of the knowledge through remembering, understanding, applying, analyzing, evaluating, and creating. Dual channel and limited capacity principles of learning were incorporated into the format of the systems engineering tools developed to assist with information processing and retention. Meta-cognitive strategies associated with memory and creative idea generation were implemented into the methodology to effectively and efficiently develop system alternatives using the full collection of knowledge. Synthesis of an orbiting sample capture and orientation system architecture to enable spacecraft-based on orbit capture of a Mars sample container for a potential Mars Sample Return campaign was used as a case study.

Adajian, Rama↗
NASA NTRS1989

Learning in stochastic neural networks for constraint satisfaction problems

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

Johnston, Mark D.↗
NASA NTRS2009

Inflatable Habitat Health Monitoring: Implementation, Lessons Learned, and Application to Lunar or Martian Habitat Health Monitoring

NASA's exploration mission is to send humans to the Moon and Mars, in which the purpose is to learn how to live and work safely in those harsh environments. A critical aspect of living in an extreme environment is habitation, and within that habitation element there are key systems which monitor the habitation environment to provide a safe and comfortable living and working space for humans. Expandable habitats are one of the options currently being considered due to their potential mass and volume efficiencies. This paper discusses a joint project between the National Science Foundation (NSF), ILC Dover, and NASA in which an expandable habitat was deployed in the extreme environment of Antarctica to better understand the performance and operations over a one-year period. This project was conducted through the Innovative Partnership Program (IPP) where the NSF provided the location at McMurdo Station in Antarctica and support at the location, ILC Dover provided the inflatable habitat, and NASA provided the instrumentation and data system for monitoring the habitat. The outcome of this project provided lessons learned in the implementation of an inflatable habitat and the systems that support that habitat. These lessons learned will be used to improve current habitation capabilities and systems to meet the objectives of exploration missions to the moon and Mars.

Rojdev, Kristina↗
NASA NTRS2014

Liquefied Bleed for Stability and Efficiency of High Speed Inlets

A mission analysis code was developed to perform a trade study on the effectiveness of liquefying bleed for the inlet of the first stage of a TSTO vehicle. By liquefying bleed, the vehicle weight (TOGW) could be reduced by 7 to 23%. Numerous simplifying assumptions were made and lessons were learned. Increased accuracy in future analyses can be achieved by: Including a higher fidelity model to capture the effect of rescaling (variable vehicle TOGW). Refining specific thrust and impulse models ( T m a and Isp) to preserve fuel-to-air ratio. Implementing LH2 for T m a and Isp. Correlating baseline design to other mission analyses and correcting vehicle design elements. Implementing angle-of-attack effects on inlet characteristics. Refining aerodynamic performance (to improve L/D ratio at higher Mach numbers). Examining the benefit with partial cooling or densification of the bleed air stream. Incorporating higher fidelity weight estimates for the liquefied bleed system (heat exchange and liquid storage versus bleed duct weights) could be added when more fully developed. Adding trim drag or 6-degree-of-freedom trajectory analysis for higher fidelity. Investigating vehicle optimization for each of the bleed configurations.

Saunders, J. David↗
NASA NTRS2015

Who's Got the Bridge? - Towards Safe, Robust Autonomous Operations at NASA Langley's Autonomy Incubator

NASA aeronautics research has made decades of contributions to aviation. Both aircraft and air traffic management (ATM) systems in use today contain NASA-developed and NASA sponsored technologies that improve safety and efficiency. Recent innovations in robotics and autonomy for automobiles and unmanned systems point to a future with increased personal mobility and access to transportation, including aviation. Automation and autonomous operations will transform the way we move people and goods. Achieving this mobility will require safe, robust, reliable operations for both the vehicle and the airspace and challenges to this inevitable future are being addressed now in government labs, universities, and industry. These challenges are the focus of NASA Langley Research Center's Autonomy Incubator whose R&D portfolio includes mission planning, trajectory and path planning, object detection and avoidance, object classification, sensor fusion, controls, machine learning, computer vision, human-machine teaming, geo-containment, open architecture design and development, as well as the test and evaluation environment that will be critical to prove system reliability and support certification. Safe autonomous operations will be enabled via onboard sensing and perception systems in both data-rich and data-deprived environments. Applied autonomy will enable safety, efficiency and unprecedented mobility as people and goods take to the skies tomorrow just as we do on the road today.

Allen, B. Danette↗
NASA NTRS2001

Launch Vehicle Design Process: Characterization, Technical Integration, and Lessons Learned

Engineering design is a challenging activity for any product. Since launch vehicles are highly complex and interconnected and have extreme energy densities, their design represents a challenge of the highest order. The purpose of this document is to delineate and clarify the design process associated with the launch vehicle for space flight transportation. The goal is to define and characterize a baseline for the space transportation design process. This baseline can be used as a basis for improving effectiveness and efficiency of the design process. The baseline characterization is achieved via compartmentalization and technical integration of subsystems, design functions, and discipline functions. First, a global design process overview is provided in order to show responsibility, interactions, and connectivity of overall aspects of the design process. Then design essentials are delineated in order to emphasize necessary features of the design process that are sometimes overlooked. Finally the design process characterization is presented. This is accomplished by considering project technical framework, technical integration, process description (technical integration model, subsystem tree, design/discipline planes, decision gates, and tasks), and the design sequence. Also included in the document are a snapshot relating to process improvements, illustrations of the process, a survey of recommendations from experienced practitioners in aerospace, lessons learned, references, and a bibliography.

Blair, J. C.↗
NASA NTRS2022

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Bayesian Deep Learning↗
NASA NTRS2009

Lunar Regolith Excavation Student Competition Design

The Surface Systems team is working to learn about lunar regolith and how we can use it as a source of air, water, and fuel for spacecrafts. However, excavation of this valuable regolith is difficult because the robot has to conform to many specifications (mass limit, efficiency level, etc.). NASA has therefore decided to include college students and companies in the search to create the best robot by making it into a competition.

Nething, Julia↗
NASA NTRS2016

An Overview of Power, Energy Storage, and Conversion Efforts for 2014 SBIR Phases I and II

Technological innovation is the overall focus of NASA's Small Business Innovation Research (SBIR) program. The program invests in the development of innovative concepts and technologies to help NASA's mission directorates address critical research and development needs for agency projects. NASA's Small Business Innovation Research (SBIR) program focuses on technological innovation by investing in development of innovative concepts and technologies to help NASA mission directorates address critical research needs for Agency programs. This report highlights 15 of the innovative SBIR 2014 Phase I and II projects that focus on one of NASA Glenn Research Center's six core competencies-Power, Energy Storage and Conversion. The technologies cover a wide spectrum of applications such as high-radiation-tolerant ceramic voltage isolators, development of hermetic sealing glasses for solid oxide fuel cells, rechargeable lithium metal cells, high-efficiency direct methane solid oxide fuel cell systems, Li metal protection for high-energy space batteries, isolated bidirectional direct current converters for distributed battery energy applications, and high-efficiency rad-hard ultrathin Si photovoltaic cell technology for space. Each article describes an innovation and technical objective and highlights NASA commercial and industrial applications. This report provides an opportunity for NASA engineers, researchers, and program managers to learn how NASA SBIR technologies could help their programs and projects, and lead to collaborations and partnerships between the small SBIR companies and NASA that would benefit both.

flywheels↗
NASA NTRS2018

Learning Model Structural Uncertainty with Gaussian Processes

The advent of commercially available quantum computers has marked the beginning of quantum computing as a reality. Both quantum gate and annealing computers have been released by major computer hardware companies. In this work, the D-Wave 2XTM quantum annealing computer housed at the NASA Advanced Systems computational facility is investigated to accelerate Machine Learning (ML) for image registration. NASA collects large amounts of images over the globe remotely using space-based monitoring. Images of a fixed areas of the land surface are taken over time. Due to the orbit of the sensors, the viewing angles deviate slightly, and it is necessary to align or register the images precisely to create image time series over the land surface. Unaligned images can lead to substantial analysis errors. These time-series are then used in modeling Earth Systems models such as hydrological, weather, and carbon monitoring models. In this work, we consider the Moderate Resolution Image Spectrometer (MODIS) data collected by the NASA's terra satellite. Artificial Neural Networks (ANNs) is a natural fit for ML modelling of images. Several successes have been reported using machine learning related to image processing. We investigate the use of ML to register MODIS images. ANNs are investigated in combination with a Restricted Boltzmann Machines (RBM) as an auto-encoder. We will present results showing the accuracy and efficiency of this approach.The D-Wave 2XTM quantum annealer samples the ground-state wave-function of a spin-Ising systems with quadratic interactions between qubits and a Chimera connectivity. The system sits in a ~15 mK thermal bath. One can think of the system as being placed in the ground state initially and subject to thermal excitations governed by Boltzmann statistics. If this is assumed true, one can use the statistics from the D-Wave 2XTM to train RBMs. Generating statistics for training Boltzmann machines is an NP-hard problem and constitutes the largest compute cost. We investigate the use of the D-Wave 2XTM to accelerate the training of the RBMs in our ANNs and report on the results.

Kouatchou, Jules↗
NASA NTRSDate not supplied

A Convolutional Neural Network for Multiscale Modeling of Composite Materials

An artificial convolutional neural network was created to efficiently mimic a micromechanics model, the High Fidelity Generalized Method of Cells, for use in multi-scale structural finite element analysis. The network was found to quickly and accurately replicate the stiffness predicted by the micromechanics model using a 2D image of an idealized representative volume element of a fiber/matrix microstructure. The long-term goal of this work is to efficiently apply multi-scale methods for predicting the damage progression of a composite structure.

Composites↗
NASA NTRSDate not supplied

Open-Source Data Engineering at NASA: CCMC's Approach to Managing Petabyte-Scale Heliophysics Data

The Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center (GSFC) leads heliophysics research by providing open access to numerous models and their outputs. Our resources are available on-demand and continuously updated with real-time data, covering sun-earth interactions across multiple domains. These domains include coronal, heliosphere, inner and global magnetosphere, ionosphere, thermosphere, and lower atmosphere interactions. Operating in a hybrid environment, CCMC utilizes both self-owned hardware and Amazon Web Services (AWS) cloud infrastructure. Managing petabytes of data across multiple locations necessitates robust data engineering solutions. To address this challenge, CCMC has adopted industry-standard and open-source tools. We use Apache Airflow as our primary data engineering platform, Python for scripting and data processing, and GitLab for version control and CI/CD. Additionally, we employ Kubernetes for containerized services, Grafana and Prometheus for metrics and monitoring, and Terraform and Puppet for reproducible infrastructure as code. This presentation will discuss lessons learned from our data engineering experiences, platforms evaluated but found unsuitable for our scientific data requirements, and specific techniques developed to enhance data transfer speed and reliability. By using these technologies effectively, CCMC continues to advance heliophysics research through efficient data management and open-access modeling.

space weather↗
NASA NTRSDate not supplied

Transitioning TEMPO Data for Air Quality Management Applications at the NASA SPoRT Center

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. The NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on stakeholder from air agencies. One component of our work is focused on evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations to characterize the uncertainties and develop best practices for processing and analyzing TEMPO data. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. A SPoRT viewer for TEMPO has been launched for providing visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air agency stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data at our stakeholder agencies. One major goal of our TEMPO initiatives at SPoRT is to better enable the inclusion of TEMPO data in air quality management applications such as exceptional event demonstrations through our close engagement with stakeholders. This talk will provide an update on our SPoRT activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

Air Quality↗