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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Wizard Staff: A Mobile Tool Supporting Human Exploration on The Lunar Surface

The Wizard Staff is a rechargeable, battery powered mobile tool with multiple uses for lunar surface exploration. It supports human activity as a surface survey tool, provides external illumination to enhance safety, delivers networking, communications, and navigation features to remote sites, and assists crew rescue with an emergency beacon and rigid splint structure. As a survey tool, it assists crewed geologic field site exploration with external lights, cameras, and LiDAR to illuminate, map, and record the sample collection process. As a relay station, it provides a surface intranet communications and navigation node for extended exploration far from the lander or habitation module. A separately developed Search-and-Rescue (SAR) handheld device can broadcast a signal to the Gandalf Staff containing crew position and biometric data. The staff then formats the message and sends it orbiting satellite(s) for emergency response via the LunaNET on a dedicated frequency similar to terrestrial applications using an Emergency Position-Indicating Radiobeacon (EPIRB). The staff is designed with an upper and lower segment. Components in the upper segment are the LiDAR, external lighting arrays, cameras, communications equipment, intelligence devices, and user interface for crew operations. The lower segment is a set of FeLiPO4 battery cells packed into the 2” diameter carbon fiber tube. When the battery pack is discharged, a spring connector (designed for the dusty lunar environment) easily disconnects the two segments for replacement with another fully charged battery pack. The lower segment can be used for Incapacitated Crew Rescue (ICR) as a rigid splint. As a stand-alone science platform, the Wizard Staff provides infrastructure for instruments and sensors to record the lunar environment over long periods of time. After a crew installs the components on the lunar surface, the platform delivers data to the lunar lander or base station for analysis. The stand-alone mode requires an auxiliary power source (e.g. solar array) and energy storage system (e.g. battery), so it could also be used as an electrical recharging station. A working prototype of Wizard Staff has been developed with collaboration between NASA (JSC, MSFC, and GSFC), Texas Space Technology Applications and Research (T STAR), and Texas A&M University (TAMU) using $300k of innovation funding in FY’21 and FY’22. The functional staff weights 36# on Earth (6# on the Moon), stands 2.1m tall and delivers external lighting with an array of 40 LED bulbs generating 1460 Lux at 2m range (near field), or 2 Lux at 50m (far field).

Gandalf Staff↗
DOE OSTI2016

Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automated and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.

active canvas↗
DOE OSTI2024

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities

Automated Mobility Platform (AMP) is a general term for intelligently managed passenger mobility systems that leverage automation and electrification to improve system performance and sustainability through the integration of real-time data streams with advanced decision-making processes tailored for use in large facilities. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for passengers with reduced mobility [PRMs]) and sustainability - both of which are important metrics often neglected during the engineering design process. Therefore, to evaluate systems and inform critical decisions more effectively, a holistic evaluation framework is proposed that includes equity, sustainability, and infrastructure perspectives - referred to as a multidimensional benefit assessment. More specifically, the following analysis focuses on: (1) mobility, with emphasis on travel time and accessibility within an airport, contrasting existing systems with the proposed AMP system; (2) equity analysis, specifically to the PRM community, but with an eye to the whole of society; (3) energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, comparing and contrasting modes as well as impacts on overall airport operations efficiency; and (4) fundamental changes in building design enabled by AMPs for larger and more flexible, functional, and energy-efficient structures.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗
DOE OSTI2021

Scoreboard

Emerging HPC machines have given rise to enhanced compute power that far outstrips the machine's ability to save large scale results for post-processing. To combat this, in situ data analysis techniques are slowly being adopted. With in situ data management favoring workflows composed of multiple simulations and analyses connected in transit on heterogeneous machines, scientists and engineers need a tool that enables them to create data extracts, visualizations, and interactively monitor and steer their simulations. Scoreboard Phase II is a next generation analysis software that supports composite in transit workflows on heterogeneous architectures and restores interactivity to in situ data analysis through simulation monitoring and computational steering. Scoreboard provides a simulation dashboard with graphs of metrics over time, controls for setting custom simulation steering parameters, controls for managing the set of data extracts being produced in the simulation, as well as the ability to explore data extracts, all from a web browser. Realizing the vision outlined in this project required research into making a system that integrates end to end from simulations all the way to the user. In situ tools generally suffer from complexity and excessive software dependencies. Scoreboard, by contrast, is easy to build and integrate into simulation codes and it provides first class FORTRAN support. The Scoreboard library is capable of in situ and in transit data analysis that can produce data extracts commonly needed for Computational Fluid Dynamics (CFD) analysis. Simulations can transparently stage data in transit to a Scoreboard Endpoint program, which can accept their data and produce the requested data extracts. This lets simulations return to their work while the Endpoint works on the analysis. Efficiently staging the data at scale was a topic of this research. Scoreboard provides the means to let the user manage data extracts and monitor/steer many simulations from a web browser. This area of the research focused on discovery of in transit network components to expose and control their steering parameters within an interactive browser-based user interface that includes: system topology, gathered metrics, notifications, dynamically-generated steering controls, and exploration of visualization data products.

Whitlock, BradJoseph [Intelligent Light] (00000001↗
NASA NTRS2001

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗
NASA NTRSDate not supplied

Creating Formal Characterizations ofRoutine Contingency Management inCommercial Aviation

Traditional approaches to safety management focus on collection of data describing unwanted states (i.e., accidents and incidents) and analysis of undesired behaviors (i.e., faults and errors) that precede those states. Thus, in the traditional view of safety, safety is both defined and measured by its absence, namely the lack of safety. In extremely high confidence systems like commercial air transport, however, opportunities to measure the absence of safety are relatively rare. Ironically, a critical barrier to measuring safety and the impact of mitigation strategies in commercial aviation is the lack of opportunities for measurement.

Intelligent Contingency Management,↗
DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2003

Design and Characterization of Carbon Nanotube Nanocomposites

Revolutionary design concepts in future aerospace vehicles will depend on extraordinary material properties to permit significant reduction of mass and size of components, while imparting intelligence. Due to their combination of remarkable electrical and mechanical properties, carbon nanotubes (CNT) are expected to enable this paradigm shift in design concepts. However, significant challenges still exist in translating these CNT properties into the macrostructures required for future generations of aerospace vehicles. While an accepted route for making the leap from nanostructures to useful macrostructures has not been fully charted, this paper will give an overview of the approach taken by some researchers at NASA Langley Research Center to sort out issues involved in the development of CNT nanocomposites for multifunctional structures. Specifically, the dispersion of carbon nanotubes in polymer matrices, characterization of nanocomposites, the role of quantum computation in providing guidance for processing and the use of computational analysis in data interpretation will be covered. Significant improvements in mechanical and electrical properties of CNT nanocomposites with very low loadings of CNTs are described and lend credence to the potential for using CNTs in achieving technological leaps in composite development.

Siochi, Emilie J.↗
NASA NTRS2005

Intelligent Systems: Terrestrial Observation and Prediction Using Remote Sensing Data

NASA has made science and technology investments to better utilize its large space-borne remote sensing data holdings of the Earth. With the launch of Terra, NASA created a data-rich environment where the challenge is to fully utilize the data collected from EOS however, despite unprecedented amounts of observed data, there is a need for increasing the frequency, resolution, and diversity of observations. Current terrestrial models that use remote sensing data were constructed in a relatively data and compute limited era and do not take full advantage of on-line learning methods and assimilation techniques that can exploit these data. NASA has invested in visualization, data mining and knowledge discovery methods which have facilitated data exploitation, but these methods are insufficient for improving Earth science models that have extensive background knowledge nor do these methods refine understanding of complex processes. Investing in interdisciplinary teams that include computational scientists can lead to new models and systems for online operation and analysis of data that can autonomously improve in prediction skill over time.

Coughlan, Joseph C.↗
DOE OSTI2016

Performance analysis of Wald-statistic based network detection methods for radiation sources

There have been increasingly large deployments of radiation detection networks that require computationally fast algorithms to produce prompt results over ad-hoc sub-networks of mobile devices, such as smart-phones. These algorithms are in sharp contrast to complex network algorithms that necessitate all measurements to be sent to powerful central servers. In this work, at individual sensors, we employ Wald-statistic based detection algorithms which are computationally very fast, and are implemented as one of three Z-tests and four chi-square tests. At fusion center, we apply the K-out-of-N fusion to combine the sensors hard decisions. We characterize the performance of detection methods by deriving analytical expressions for the distributions of underlying test statistics, and by analyzing the fusion performances in terms of K, N, and the false-alarm rates of individual detectors. We experimentally validate our methods using measurements from indoor and outdoor characterization tests of the Intelligence Radiation Sensors Systems (IRSS) program. In particular, utilizing the outdoor measurements, we construct two important real-life scenarios, boundary surveillance and portal monitoring, and present the results of our algorithms.

Sen, Satyabrata↗
NASA NTRSDate not supplied

Distributed Lunar Data Platform with Advanced Machine Learning Capabilities in Support of Lunar Science and Exploration

The United States 2020 Space Policy directive declares that NASA, in cooperation with private industry, will “extend human economic activity into deep space by establishing a permanent human presence on the Moon”. This goal will require advanced data management, as well as analysis, modeling and representation of lunar information in order to prepare for Artemis human missions, lunar science investigations and exploration. To meet this requirement, we conceptualize and present an implementation strategy for a distributed platform for lunar data retrieval, inferencing and analysis, which will be based on federated learning and the NASA Celestial Mapping System (CMS). In addition to demonstrating the imperative of enabling lunar-borne data to remain in-situ but still accessible, this presentation will also include examples of how third parties could contribute both datasets and new functionality into this platform using an AI-based data import pipeline and a plug-in architecture respectively.

Artificial Intelligence↗
DOE OSTI2022

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Abstract Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

47 OTHER INSTRUMENTATION↗
NASA NTRS1987

Discovery and problem solving: Triangulation as a weak heuristic

Recently the artificial intelligence community has turned its attention to the process of discovery and found that the history of science is a fertile source for what Darden has called compiled hindsight. Such hindsight generates weak heuristics for discovery that do not guarantee that discoveries will be made but do have proven worth in leading to discoveries. Triangulation is one such heuristic that is grounded in historical hindsight. This heuristic is explored within the general framework of the BACON, GLAUBER, STAHL, DALTON, and SUTTON programs. In triangulation different bases of information are compared in an effort to identify gaps between the bases. Thus, assuming that the bases of information are relevantly related, the gaps that are identified should be good locations for discovery and robust analysis.

Rochowiak, Daniel↗
NASA NTRS2023

Large-Scale Groundwater Monitoring in Brazil Assisted With Satellite-Based Artificial Intelligence Techniques

Here, we develop and test an artificial intelligence (AI)-based approach to monitor major Brazilian aquifers. The approach combines Gravity Recovery and Climate Experiment (GRACE) data and ground-based hydrogeological measurements from Brazil’s Integrated Groundwater Monitoring Network at hundreds of wells distributed in twelve aquifers across the country. We tested model ensembles based on three AI approaches: Extreme Gradient Boost, Light Gradient Boosting Model and CatBoost, followed by a Linear Regression (LR) step. The approach is further boosted with wavelet and seasonal decomposition processes applied to GRACE data. To determine the AI-based model’s sensitivity to data availability, we propose four experiments combining hydrogeological measurements from different aquifers. Groundwater storage estimates from the Global Land Data Assimilation System (GLDAS) are used as benchmark. A sensitivity analysis shows that the LR-based model ensemble is the best suited and to reproduce groundwater storage change in all studied Brazilian aquifers. Results show that the proposed approach outperforms GLDAS in all experiments, with an RMSE value of 2.68cm for the experiment that covers all monitored wells in Brazil. GLDAS resulted in RMSE=6.76cm. Using our AI model outputs, we quantified the groundwater storage change of two major aquifers, Urucuia and Bauru-Caiuá, over the past two decades: -31km 3 and -6km 3 , respectively. Water loss is driven by a prolonged drought across most of the country and intensification of groundwater pumping for irrigation. This study demonstrates that combining satellite data and AI can be a cost-effective alternative to monitor poorly equipped aquifers at the continental scale, with possible global replicability.

GRACE↗
NASA NTRS1988

PI-in-a-box: Intelligent onboard assistance for spaceborne experiments in vestibular physiology

In construction is a knowledge-based system that will aid astronauts in the performance of vestibular experiments in two ways: it will provide real-time monitoring and control of signals and it will optimize the quality of the data obtained, by helping the mission specialists and payload specialists make decisions that are normally the province of a principal investigator, hence the name PI-in-a-box. An important and desirable side-effect of this tool will be to make the astronauts more productive and better integrated members of the scientific team. The vestibular experiments are planned by Prof. Larry Young of MIT, whose team has already performed similar experiments in Spacelab missions SL-1 and D-1, and has experiments planned for SLS-1 and SLS-2. The knowledge-based system development work, performed in collaboration with MIT, Stanford University, and the NASA-Ames Research Center, addresses six major related functions: (1) signal quality monitoring; (2) fault diagnosis; (3) signal analysis; (4) interesting-case detection; (5) experiment replanning; and (6) integration of all of these functions within a real-time data acquisition environment. Initial prototyping work has been done in functions (1) through (4).

Colombano, Silvano↗
DOE OSTI2016

Creating an Energy Intelligent Campus: Data Integration Challenges and Solutions at a Large Research Campus

Rich, well-organized building performance and energy consumption data enable a host of analytic capabilities for building owners and operators, from basic energy benchmarking to detailed fault detection and system optimization. Unfortunately, data integration for building control systems is challenging and costly in any setting. Large portfolios of buildings--campuses, cities, and corporate portfolios--experience these integration challenges most acutely. These large portfolios often have a wide array of control systems, including multiple vendors and nonstandard communication protocols. They typically have complex information technology (IT) networks and cybersecurity requirements and may integrate distributed energy resources into their infrastructure. Although the challenges are significant, the integration of control system data has the potential to provide proportionally greater value for these organizations through portfolio-scale analytics, comprehensive demand management, and asset performance visibility. As a large research campus, the National Renewable Energy Laboratory (NREL) experiences significant data integration challenges. To meet them, NREL has developed an architecture for effective data collection, integration, and analysis, providing a comprehensive view of data integration based on functional layers. The architecture is being evaluated on the NREL campus through deployment of three pilot implementations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗