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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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At least 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Genomics and Proteomics Based Security Protocols for Secure Network Architectures

A hardware design that integrates live and algorithmic inhabitants to produce patterns of gene expression in vivo and in silico Protocols and algorithms based upon the processes of regulation of gene expression to produce cryptographic representations of genes, RNA, proteins, and gene expression to perform authentication and confidentiality functions for computers and networks. A network concept of operations integrating all of the above into existing legacy networks.

Security Genomics↗
NASA NTRS2014

Nodes - Network & Operation Demonstration Satellite

Nodes is a technology demonstration mission that will launch from the International Space Station (ISS) in early 2015 and will demonstrate new network capabilities critical to the operation of swarms of multiple spacecraft. Nodes continues the legacy of the PhoneSat series of small satellites that first introduced and successfully implemented the use of Android Smartphone technology to perform many of the spacecraft functions previously accomplished through custom technology development efforts. The Nodes mission consists of two 1.5-unit (1.5U) nanosatellites each weighing approximately 2 kilograms (4 pounds) and measuring 10 centimeters by 10 centimeters by 15 centimeters. The Nodes spacecraft are derived from the hardware and software developed for the EDSN (Edison Demonstration of Smallsat Networks) mission (a swarm of eight spacecraft). Each Node utilizes the Android operating system with EDSN-specific software programmed to perform command and data handling tasks that allow the satellites to 1) relay ground commands through one satellite to the second satellite, 2) collect and relay science data from each satellite to the ground station, and 3) autonomously determine which of the two satellites is best suited to control the space network and relay data to the ground (“Captain”) and notify the ground system and second satellite (“Lieutenant”) of the result.

Network and Operations↗
NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
NASA NTRS2019

Application of Delay Tolerant Networking on the International Space Station

Networking between ISS payloads and the ground presents challenges given the delays in communication and frequent satellite handovers that occur in orbit. Delay Tolerant Networking (DTN) provides a method to store and forward information when the link is available without intervention by the user. While DTN is helpful for the ISS in Low Earth Orbit (LEO), it will become much more important for deep space missions where round trip delays make use of most standard network protocols impossible. The lessons learned from ISS and other programs will help improve standards and ultimately support future NASA deep space missions.

Delay Tolerant Networking↗
NASA NTRSDate not supplied

A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.

Deep Neural networks↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRS1993

Discrete Recurrent Neural Networks for Grammatical Inference

Recurrent neural networks have recently been shown to have the ability to learn regular and context-free grammars from examples. We show that while conventional analog recurrent networks try to form clusters in activation space to represent discrete states of the grammars during learning, and can be successful in doing so, the clusters so formed tend to become unstable as longer and longer test input strings are presented to the network.

Neural↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗
NASA NTRS2021

An Evaluation of Timely Communications Access Methods Using NASA Space Network

There is a growing interest among scientists to coordinate diverse and temporally responsive measurements from multiple space and ground-based observatories in order to obtain greater insights into transient scientific events than would otherwise be possible. Many transient scientific events of interest occur randomly and the scientific value of follow-up observations decays with time. As a result, transient science operations among distributed observatories require timely access to communications network services. This paper develops two complimentary methods to improve users’ network access timeliness using the NASA Space Network (SN). Descriptive models of each method are developed using Systems Modeling Language. Pathfinder experiments are designed and executed. Suitability of the methods is discussed in light of the experimental results. Innovative applications of the methods are presented. This research establishes that the as-built SN systems have adequate capabilities and capacity to support infusion of the methods to current and future users with emergent time-sensitive service needs. The minimum service access wait time was determined to be approximately 10 min and is achievable 84% of the time. An average access wait time of approximately 14 min is expected when SN resource blocking occurs. A roadmap to improve SN access timeliness is presented.

Space Communications and Navigation↗
NASA NTRSDate not supplied

Case Study: Analysis of Autonomous Center line Tracking Neural Networks

Deep neural networks have gained widespread usage in a number of applications. However, limitations such as lack of explainability and robustness inhibit building trust in their behavior, which is crucial in safety critical applications such as autonomous driving. Therefore, techniques which aid in understanding and providing guarantees for neural network behavior are the need of the hour. In this paper, we present a case study applying a recently proposed technique, Prophecy, to analyze the behavior of a neural network model, provided by our industry partner and used for autonomous guiding of airplanes on taxi runways. This regression model takes as input an image of the runway and produces two outputs, cross-track error and heading error, which represent the position of the plane relative to the center line. We use the Prophecy tool to extract neuron activation patterns for the correctness and safety properties of the model. We show the use of these patterns to identify features of the input that explain correct and incorrect behavior. We also use the patterns to provide guarantees of consistent behavior. We explore a novel idea of using sequences of images (instead of single images) to obtain good explanations and identify regions of consistent behavior.

Deep Neural Networks↗
NASA NTRSDate not supplied

Pilot Workload Rating Predictions Using Image Data and Recurrent Neural Networks

In this work, we augmented existing methods for estimating pilot workload ratings with deep neural networks trained using data from simulated flight tests in the Vertical Motion Simulator (VMS). We used an existing method, Spare Capacity Operations Estimator (SCOPE), along with a recurrent neural network and conducted comparison studies between the two methods individually, and when used together. We found that using both methods together can improve the result over using either approach alone. In our first test case, we achieved an improved linear correlation coefficient of 0.409 over that of SCOPE alone at 0.352 on the training dataset. Through cross validation, we also found that the results may be dependent on the split of training vs. validation data, and that further investigation should be conducted to understand what additional inputs to the neural network model should be made.

Image Data↗
NASA NTRSDate not supplied

Entry, Descent, and Landing Analysis of Low Mass Martian Probe for Surface Climate Network

Aeolus is a mission to provide the first direct measurements of Martian atmospheric wind speeds and correlate them with thermal and compositional data to bring together a complete systematic description for the global energy balance and climate cycles of Mars. Objective A of the Mars Exploration Program Analysis Group (MEPAG) Goal II cites a Science Investigation Area to develop a network of surface landers to provide global, diurnal and synoptic coverage of the near-surface environment to characterize present Mars climate [1]. Aeolus will characterize the present Martian global climate system via an orbiter element and a novel surface network element. A series of low mass, low power probes will be distributed across the Martian surface and measure pressure, sky opacity, temperature, wind speed, water vapor and other trace gases. Low mass and power electrical systems designed to operate in extreme cold and daily thermal cycling for one Martian year could facilitate lower cost, Class-D science probes. Aeolus is targeting a total per-probe mass of <5 kg. Recent advances in nanofabrication of chip-scale sensors that are highly robust to temperature and shock, combined with a novel packaging concept (the deployable sensor probe [2]) originally developed by the Aerospace Corp., for the first time enables a Mars surface climate sensor network. These probes are folded flat during transit to Mars and automatically deploy into the final reentry configuration once released from the orbiter. The capability to fold for stowage significantly increases the probe quantity carried by the orbiter.

EDL↗
NASA NTRSDate not supplied

Open-source Wireless Sensor Network (Wi-Se Net) for Flexible Deployment

Wireless sensors, especially if battery powered, have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Recent advances in wireless technology and microprocessor boards have produced a variety of inexpensive off-the shelf chips which can communicate wirelessly with simple protocols. This paper describes the design and implementation of a highly customizable wireless sensor network (called WiSe Net) using inexpensive open-source hardware components as wireless nodes. These wireless sensor nodes can transmit data at a rate <250 Hz, can be battery powered, and have a small footprint (2x5 cm). In addition, a preliminary over-the-air programming system was developed to allow for simple wireless configuration when active. The network performance was demonstrated by taking distributed and electrically isolated temperature measurements on a high-voltage lab apparatus. Although this test case is in a laboratory setting, this network architecture could be easily repurposed for various other forms of monitoring

Wireless Sensor Network↗
NASA NTRSDate not supplied

Pathfinder Networks for Measuring Operator Mental Model Structure with a Simple Autopilot System

Pathfinder networks are a method to represent mental models from empirically generated pairwise relatedness ratings. This study examined the effects of training exposure on mental model structures based on relatedness ratings collected using the Target Rating method. Forty-eight participants read instruction slides with or without explicit information on the functionality of an autopilot system (Advanced Mental Model or Basic Mental Model groups, respectively). Participants provided relatedness ratings and completed a comprehension test. The Advanced Mental Model group had more common links with the expected model, higher within-group network similarity scores, and higher mental model assessment questionnaire scores than the Basic Mental Model group. Both groups had coherence scores above the minimum threshold for internal consistency. Pathfinder network analysis was sensitive to changes produced by a simple exposure training intervention. In practice, a simple training program may effectively influence operator mental models in novel technological environments such as Advanced Air Mobility.

Mental Models↗
NASA NTRSDate not supplied

NASA's Deep Space Network (DSN) Lunar Exploration Upgrades (DLEU)

In the near future, the National Aeronautics and Space Administration (NASA) will return to the moon beginning the next era of human exploration. NASA’s Space Communications and Navigation (SCaN) program will play a vital role in establishing communications and navigation support to realize the ambitious goals of the Artemis program. SCaN’s overall lunar communications support plan will be covered in a separate 2023 SpaceOps paper: “NASA’s Communications and Navigation Architecture Plans to Support the Return to the Moon and a Sustainable Lunar Presence”. The plan as it currently stands, includes a three-fold approach of lunar relay services, a dedicated set of new ground stations and support through the Deep Space Network (DSN). This paper will have a more granular focus on the DSN and NASA’s plans to upgrade and expand the network to be better suited for human spaceflight on and around the lunar surface. NASA’s Deep Space Network (DSN) will be a critical communications component for the upcoming lunar activities. There will be multiple spacecraft, using different bands, and some of those spacecraft will be transmitting and receiving using multiple bands, requiring DSN support of S-band (2 GHz), X-band (7 GHz up, 8 GHz down), and K-band (22.5 GHz up, 26 GHz down). Since there may be more than one spacecraft in the beamwidth of the DSN antennas, the DSN support will require an extension of the DSN’s capability to support multiple spacecraft using one antenna, expanding it to provide two simultaneous uplinks in the different bands at each antenna. Achieving this requires using new techniques for manufacturing the frequency selective surfaces, called dichroics, which steer the different frequency beams from and to the appropriate transmitting and receiving equipment, along with the addition of a new K-band uplink system. Additionally, due to the relative closeness of the moon from Earth (as opposed to the planetary missions the DSN supports daily), significantly higher data rates on both uplink and downlink are required, specifically up to 20 Mbps on the uplink and 150 Mbps on the downlink, both using Low Density Parity Check (LDPC) error correcting codes. And, again due to the relative closeness of the moon, there is a need for low latency data delivery of the high rate downlink telemetry which requires a change in the current DSN paradigm of delivering higher rate data with higher latency.

deep space network↗
NASA NTRSDate not supplied

Open-source Wireless Sensor Network (Wi-Se Net) for Flexible Deployment

Wireless sensors, especially if battery powered, have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Recent advances in wireless technology and microprocessor boards have produced a variety of inexpensive off-the shelf chips which can communicate wirelessly with simple protocols. This paper describes the design and implementation of a highly customizable wireless sensor network (called Wi-Se Net) using inexpensive open-source hardware components as wireless nodes. These wireless sensor nodes can transmit data at a rate <250 Hz, can be battery powered, and have a small footprint (2x5 cm). In addition, a preliminary over-the-air programming system was developed to allow for simple wireless configuration when active. The network performance was demonstrated by taking distributed and electrically isolated temperature measurements on a high-voltage lab apparatus. Although this test case is in a laboratory setting, this network architecture could be easily repurposed for various other forms of monitoring.

Wireless Sensor Network↗