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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 523 records · Page 29
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NASA NTRS2013

Lessons Learned During Instrument Testing for the Thermal Infrared Sensor (TIRS)

The Themal InfraRed Sensor (TIRS) instrument, set to launch on the Landsat Data Continuity Mission in 2013, features a passively cooled telescope and IR detectors which are actively cooled by a two stage cryocooler. In order to proceed to the instrument level test campaign, at least one full functional test was required, necessitating a thermal vacuum test to sufficiently cool the detectors and demonstrate performance. This was fairly unique in that this test occurred before the Pre Environmental Review, but yielded significant knowledge gains before the planned instrument level test. During the pre-PER test, numerous discrepancies were found between the model and the actual hardware, which were revealed by poor correlation between model predictions and test data. With the inclusion of pseudo-balance points, the test also provided an opportunity to perform a pre-correlation to test data prior to the instrument level test campaign. Various lessons were learned during this test related to modeling and design of both the flight hardware and the Ground Support Equipment and test setup. The lessons learned in the pre-PER test resulted in a better test setup for the nstrument level test and the completion of the final instrument model correlation in a shorter period of time. Upon completion of the correlation, the flight predictions were generated including the full suite of off-nominal cases, including some new cases defined by the spacecraft. For some of these ·new cases, some components now revealed limit exceedances, in particular for a portion of the hardware that could not be tested due to its size and chamber limitations.. Further lessons were learned during the completion of flight predictions. With a correlated detalled instrument model, significant efforts were made to generate a reduced model suitable for observatory level analyses. This proved a major effort both to generate an appropriate network as well as to convert to the final model to the required format and yielded additional lessons learned. In spite of all the challenges encountered by TIRS, the instrument was successfully delivered to the spacecraft and will soon be tested at observatory level in preparation for a successful mission launch.

Peabody, Hume L.↗
NASA NTRS2016

A Quantum-Assisted Algorithm for Sampling Applications in Machine Learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact in deep learning and other machine learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggests it will do so with an instance-dependent effective temperature, different from the physical temperature of the device. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this talk, we present a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a kind of restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature we find a performance close to that of CD-100 for the case studied here. We discuss generalizations of the algorithm to other more expressive generative models, beyond restricted Boltzmann machines.

Perdomo-Ortiz, Alejandro↗
NASA NTRS2019

Application of Machine Learning Techniques to Delay Tolerant Network Routing

This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics.

Dudukovich, Rachel M.↗
NASA NTRS2018

NASA's Learn-to-Fly Project Overview

Learn-to-Fly (L2F) is an advanced technology development effort aimed at assessing the feasibility of real-time, self-learning flight vehicles. Specifically, research has been conducted on merging real-time aerodynamic modeling, learning adaptive control, and other disciplines with the goal of using this “learn to fly” methodology to replace the current iterative vehicle development paradigm, substantially reducing the typical ground and flight testing requirements for air vehicle design. Recent activities included an aggressive flight test program with unique fully autonomous fight test vehicles to rapidly advance L2F technology. This paper presents an overview of the project and key components.

Heim, Eugene H. D.↗
NASA NTRS2019

Reusing Data and Metadata to Create New Metadata Through Machine-Learning & Other Programmatic Methods

Recent improvements in natural language processing (NLP) enable metadata to be created programmatically from reused original metadata or even the dataset itself. Transfer-learning applied to NLP has greatly improved performance and reduced training data requirements. In this talk, we’ll compare machine-generated metadata to human-generated metadata and discuss characteristics of metadata and data archives that affect suitability for machine-learning reuse of metadata. Where as human-generated metadata is often populated once, populated from the perspective of data supplier, populated by many individuals with different words for the same thing, and limited in length, machine-generated metadata can be updated any number of times, generated from the perspective of any user, constrained to a standardized set of terms that can be evolved over time, and be any length required. Machine-learning generated metadata offers benefits but also additional needs in terms of version control, process transparency, human-computer interaction, and IT requirements. As a successful example, we’ll discuss how a dataset of abstracts and associated human-tagged keywords from a standardized list of several thousand keywords were used to create a machine-learning model that predicted keyword metadata for open-source code projects on code.nasa.gov. We’ll also discuss a less successful example from data.nasa.gov to show how data archive architecture and characteristics of initial metadata can be strong controls on how easy it is to leverage programmatic methods to reuse metadata to create additional metadata.

Gosses, Justin↗
NASA NTRS2019

Machine Learning-Based Atmospheric Phenomena Detection Platform

As the number of Earth pointing satellites has increased over the last several decades, the data volume retrieved from instruments onboard these satellites has also increased. It is expected that this trend will continue as more data intensive missions and small satellite constellations are launched. Currently, feature detection - namely atmospheric phenomena - in these datasets is performed manually and is thus not scalable with the growing data archives. Recent advancements in computational efficiency allow for the Earth science community to leverage machine learning to identify interesting atmospheric phenomena. Given the wide range of distinctive features in various atmospheric phenomena, a specialized machine learning model is required for accurate detection of these phenomena independently. The Phenomena Portal, developed at NASA IMPACT, is designed to provide visualization for the output from these machine learning models. In addition, detected events for each atmospheric phenomena are stored in a database that can be used to more easily use/subset larger spatiotemporal datasets. The user interface also incorporates additional features to enhance the user experience including spatiotemporal analysis, multiple base layer images, and a slider to filter events with lower probabilities of positive detection. Each detection supports user feedback on whether the detection is true or false that can then be stored and used to improve the machine learning model performance.

Gurung, Iksha↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRS2019

Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors

New generation geostationary satellites make reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating Earth monitoring products from these observations requires retrieval of the basic parameter, surface reflectance (SR), by atmospheric correction (AC). Algorithms for atmospheric correction, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), are adapted for each sensor and are too computationally complex to be run in real time, relying instead on look-up tables with precomputed values. Machine learning methods, including convolutional neural networks, have demonstrated performance in learning complex, nonlinear mappings and extracting insight from high-dimensional remote sensing data. In this work, we present a deep learning emulator of MAIAC to retrieve both SR and cloud products. Using this adaptation of deep learning-based emulation to remote sensing, we demonstrate stable SR retrieval over a variety of land covers and viewing conditions and accurate cloud detection. Further, a comparison of computation time suggests emulation as a compelling alternative for expensive physical simulation, especially for applications benefited by near-real time data, such as agricultural management and disaster response.

Duffy, Kate↗
NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗
NASA NTRS2020

Hazard Contribution Modes of Machine Learning Components

Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)—are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool.

Smith, Colin↗
NASA NTRS2018

Practical Aspects of Real-Time Modeling for the Learn-To-Fly Concept

Practical aspects of autonomous onboard real-time modeling for the NASA Learn-to-Fly concept are examined using flight data from two subscale test aircraft. A practical real-time global nonlinear aerodynamic modeling method is developed and explained, along with the multiple-input excitation needed for effective real-time modeling. Critical issues for integrating real-time global nonlinear aerodynamic modeling and local linear modeling with the real-time learning adaptive control and guidance elements of the Learn-to-Fly algorithm are identified and discussed. Real-time modeling results from NASA Learn-to-Fly flight demonstrations are presented and evaluated using model fit metrics and prediction tests.

Morelli, Eugene A.↗
NASA NTRSDate not supplied

Autonomous Spacecraft Attitude Control Using Deep Reinforcement Learning

While machine learning and spacecraft autonomy continue to gain research interest, significant work remains to be done in efficiently applying modern machine learning techniques to problems in space ight. This study presents a framework for deriving a discrete neural spacecraft attitude controller using reinforcement learning, a paradigm of machine learning, without the need for high-performance computing. The developed attitude controller is an approximately time-optimal solution to a highly constrained control problem, able to achieve well above industry-standard pointing accuracies. Control examples are also presented of the agent performing large-angle spacecraft slews in the developed simulation environment and future extensions of this work are discussed.

ATAP↗
NASA NTRSDate not supplied

ImageLabler: Labeling and Managing Image Data for Machine Learning in the Earth Sciences

While machine learning techniques for image classification have been around for a long time, storing and managing the vast number of images required as training data is still a problem for scientists. This is especially true for the field of Earth science, where only recently have experts begun using machine learning techniques for image-based phenomena classification. Image Labeler, a fast and scalable cloud-based tagging platform for Earth science images, seeks to improve upon existing methods of managing images and associated metadata, such as maintaining categorized folders of images on a local machine, a process that can be cumbersome and difficult to scale. The platform facilitates rapid development of image-based Earth science phenomena training datasets by allowing scientists to upload their existing imagery as well as extract new samples from open satellite imagery services made available through NASA’s Global Imagery Browse Service (GIBS). Image Labeler also supports GeoTIFF data, with capabilities such as displaying GeoTIFFs on an interactive map, drawing shapefiles over them, and tagging them with additional metadata. This allows scientists to perform spatiotemporal subsetting with geographic information and develop training data more quickly. Built using modern web technologies, Image Labeler includes additional capabilities such as team collaboration for large-scale image tagging projects. Users can download their data in a machine-learning-ready format, allowing scientists to spend time on experimentation rather than on the collection of training data. In this presentation, we demonstrate how Image Labeler seeks to become a one-stop image data management solution for machine learning applications in Earth science.

Ashish Acharya↗
NASA NTRSDate not supplied

NASA Earth: Synthetic Spectranomics - Deep Learning of Surface 3-D Geometry, Chemistry, and Hyperspectra to Inform Next-generation Land Models

Machine and deep learning (ML/DL) have transformed our approach to Earth observation and system modeling (EOSM), unifying both in view of ML/DL models as a form of data assimilation (DA). Trained on diverse Earth observation records, detailed physical models, or hybrids of both in physics-informed machine learning, ML/DL may improve upon existing Earth system model (ESM) formulations while creating entirely new classes of models. One important application of deep learning is observation synthesis, allowing ESM developers to prepare for the increased spatial, temporal, and spectral/polar resolution of proposed future observing systems. This may involve the retrospective application of learning algorithms to existing observational records, with or without physical radiative transfer models, to co-inform mission planning and ESM development while providing a degree of data continuity for new missions.

Adam Erickson↗
NASA NTRSDate not supplied

How Do Different Knowledge Frameworks Help Us Learn From Aviation Line Observations?

Human performance includes actions that increase safety, as well as actions that can reduce safety. Ensuring safety in complex dynamic operations like commercial aviation depends on the ability to institute appropriate responses based on what is learned from flightcrew performance and the contexts in which it occurs. To do this systemically at the organization level requires collecting data on flightcrew performance, developing effective approaches to analyzing those data, and understanding how to translate what has been learned into policies, procedures, and practice. Systematic observation of front-line operators is a vital source of human performance data. Much has been learned from such observations, including methodological principles. Most observations have been based on a framework focused on managing safety challenges and the ensuing unsafe events. A complementary perspective focuses on flexibility and actions that promote continued safe and effective operation. We consider lessons learned about observational methods from an established framework focused on undesired actions and how these might be extended for a framework focused on desired actions.

safety↗
NASA NTRSDate not supplied

DELTA: An Open-Source Framework to Simplify Machine Learning with Satellite Imagery

DELTA (Deep Earth Learning, Tools, and Analysis) is an open-source framework developed at NASA to simplify running and training machine learning (ML) models on satellite imagery. Users new to machine learning can run existing ML models on satellite imagery with minimal setup and configuration. For experienced ML users, DELTA helps simplify data engineering, preprocessing steps, and reduces the need for boilerplate code that needs written to make satellite imagery datasets palatable for machine learning. This lets data scientists focus on model development while DELTA handles the imagery manipulation. This presentation will demonstrate DELTA’s functionality and share some examples from an active project using it for flood mapping using imagery from multiple satellite sources

Michael von Pohle↗
NASA NTRSDate not supplied

Exploring Requirements for Software that Learns: A Research Preview

Context & motivation: The development of software that learns has revolutionized how many systems perform. For the most part, these systems are neither safety- nor mission-critical. However, as technology and aspirations advance, there is an increased desire and need for Machine Learning (ML) software in safety- and mission-critical systems, e.g., driverless cars or autonomous space robotics. Problem: In these domains, reliability is crucial and systems have to undergo much scrutiny in terms of both the developed artefacts and the adopted development process. Central to the development of such systems is the elicitation and definition of software requirements that are used to guide the design and verification process. The addition of software components that learn, and the associated capability for unforeseen behavior, makes defining detailed software requirements especially difficult. Principal ideas/results: In this paper, we identify unique characteristics of software requirements that are specific to ML components. To this end, we collect and examine requirements from both academic and industrial sources. Contribution: To the best of our knowledge, this is the first work that presents real-life, industrial patterns of requirements for ML components. Furthermore, this paper identifies key characteristics and provides a foundation for developing a taxonomy of requirements for software that learns.

Probabilistic requirements↗
NASA NTRSDate not supplied

Why Learning From All Operations Is Imperative

Learning from All Operations – Panel Discussion Introduction: Tzvetomir Blajev, Flight Safety Foundation Why Learning from All Operations is Imperative: Jon Holbrook, NASA Implemented Approaches to Learning from All Operations American Airlines Japan Airlines Southwest Airlines Delta Air Lines Why Learning from All Operations is Imperative (Summary): Immanuel Barshi, NASA

Jon Holbrook↗