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At least 379 records · Page 21

Multiscale Neural Networks for Approximating Green’s Functions

Neural networks (NNs) have been widely used to solve partial differential equations (PDEs) in the applications of physics, biology, and engineering. One effective approach for solving PDEs with a fixed differential operator is learning Green’s functions. However, Green’s functions are notoriously difficult to learn due to their poor regularity, which typically requires larger NNs and longer training times. In this work, we address these challenges by leveraging multiscale NNs to learn Green’s functions. Through theoretical analysis using multiscale Barron space methods and experimental validation, we show that the multiscale approach significantly reduces the necessary NN size and accelerates training.

97 MATHEMATICS AND COMPUTING↗

Conflation of Geospatial POI Data and Ground-level Imagery via Link Prediction on Joint Semantic Graph

With the proliferation of smartphone cameras and social networks, we have rich, multi-modal data about points of interest (POIs) - like cultural landmarks, institutions, businesses, etc. - within a given areas of interest (AOI) (e.g., a county, city or a neighborhood) available to us. Data conflation across multiple modalities of data sources is one of the key challenges in maintaining a geographical information system (GIS) which accumulate data about POIs. Given POI data from nine different sources, and ground-level geo-tagged and scene-captioned images from two different image hosting platforms, in this work we explore the application of graph neural networks (GNNs) to perform data conflation, while leveraging a natural graph structure evident in geospatial data. The preliminary results demonstrate the capacity of a GNN operation to learn distributions of entity (POIs and images) features, coupled with topological structure of entity's local neighborhood in a semantic nearest neighbor graph, in order to predict links between a pair of entities.

Gurav, Rutuja↗

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗

Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials

The Abisko project aims to develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation. The SNN architecture explores novel neuromorphic devices that are based on resistive-switching materials, such as memristors and electrochemical RAM. Equally important, Abisko uses a deep codesign approach to pursue this goal by engaging experts from across the entire range of disciplines: materials, devices and circuits, architectures and integration, software, and algorithms. Here, the key objectives of our Abisko project are threefold. First, we are designing an energy-optimized high-performance neuromorphic accelerator based on SNNs. This architecture is being designed as a chiplet that can be deployed in contemporary computer architectures and we are investigating novel neuromorphic materials to improve its design. Second, we are concurrently developing a productive software stack for the neuromorphic accelerator that will also be portable to other architectures, such as field-programmable gate arrays and GPUs. Third, we are creating a new deep codesign methodology and framework for developing clear interfaces, requirements, and metrics between each level of abstraction to enable the system design to be explored and implemented interchangeably with execution, measurement, a model, or simulation. As a motivating application for this codesign effort, we target the use of SNNs for an analog event detector for a high-energy physics sensor.

97 MATHEMATICS AND COMPUTING↗

Conceptual Design of an Improved Sodium Plugging Loop

Based upon lessons learned in operating the initial sodium portion of the Sodium Plugging Phenomena Loop that itself became plugged and could not be unplugged, a set of improved design features have been identified for a new sodium loop that is less prone to inadvertent plugging. The new design features include a new sodium vessel that combines the functionalities of an expansion tank and a cold trap, increasing the loop tubing diameter/size, improving the thermal designs of the pump and flow meter, improved heater zoning, and an improved temperature distribution strategy along the loop. A new plugging loop conceptual design implementing these features has been developed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Cyber Resilience and Social Equity: Twin Pillars of a Sustainable Energy Future

This paper examines the intersection of security and accessibility within energy systems amidst the rise of grid modernization and digitization, especially considering the regulatory changes and the imperatives of inclusive energy strategies. It addresses the dual need for secure, resilient infrastructure and a commitment to mitigate energy poverty while maintaining equitable access to energy. Amid escalating cybersecurity and physical threats, the paper advocates for sustainable energy delivery systems that ensure robust defenses without compromising the goals of reducing energy poverty and ensuring energy security. This paper identifies the pressing need for Cyber-Informed Engineering (CIE) and Secure-by-Design (SbD) principles, highlighting how these strategies can protect critical infrastructure and democratize access to secure energy, particularly for disadvantaged communities. The analysis underscores the challenges presented by the expansion of attack surfaces, interoperability requirements, and grid-edge analytics, offering innovative solutions that leverage advanced technologies and data-driven insights. Furthermore, this paper addresses the workforce development gap, emphasizing the necessity for public-private partnerships and vendor engagement in creating a skilled cybersecurity workforce. This paper has a dual focus on both the technological aspect of cybersecurity and the social dimension of equity within the context of sustainable energy development. It suggests a comprehensive examination of how these two critical elements interact and support the overarching goal of a sustainable energy future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The efficacy of using human myoelectric signals to control the limbs of robots in space

This project was designed to investigate the usefulness of the myoelectric signal as a control in robotics applications. More specifically, the neural patterns associated with human arm and hand actions were studied to determine the efficacy of using these myoelectric signals to control the manipulator arm of a robot. The advantage of this approach to robotic control was the use of well-defined and well-practiced neural patterns already available to the system, as opposed to requiring the human operator to learn new tasks and establish new neural patterns in learning to control a joystick or mechanical coupling device.

Clark, Jane E.↗

Adjoint-operators and non-adiabatic learning algorithms in neural networks

Adjoint sensitivity equations are presented, which can be solved simultaneously (i.e., forward in time) with the dynamics of a nonlinear neural network. These equations provide the foundations for a new methodology which enables the implementation of temporal learning algorithms in a highly efficient manner.

Toomarian, N.↗

Planning and scheduling the Hubble Space Telescope: Practical application of advanced techniques

NASA's Hubble Space Telescope (HST) is a major astronomical facility that was launched in April, 1990. In late 1993, the first of several planned servicing missions refurbished the telescope, including corrections for a manufacturing flaw in the primary mirror. Orbiting above the distorting effects of the Earth's atmosphere, the HST provides an unrivaled combination of sensitivity, spectral coverage and angular resolution. The HST is arguably the most complex scientific observatory ever constructed and effective use of this valuable resource required novel approaches to astronomical observation and the development of advanced software systems including techniques to represent scheduling preferences and constraints, a constraint satisfaction problem (CSP) based scheduler and a rule based planning system. This paper presents a discussion of these systems and the lessons learned from operational experience.

Miller, Glenn E.↗

Two Nights on a Flying Observatory: A KAO Journal

We are the first participants in the "Science in the Stratosphere" program, sponsored by NASA and the University of Texas at Austin. The purpose of the program is to bring the techniques and excitement of modem astronomy into the classroom. Our job is to become familiar with the normal KAO operations, and learn from the staff and scientists during an actual observing flight. We are going to fly on missions to observe galactic and extragalactic star-forming regions in the far infrared region of the spectrum. All of our expectations spring from this simple working description. Little do we know, however, what really lies ahead for us!

Stryker, Pam↗

Heavy particle irradiation, neurochemistry and behavior: thresholds, dose-response curves and recovery of function

Exposure to heavy particles can affect the functioning of the central nervous system (CNS), particularly the dopaminergic system. In turn, the radiation-induced disruption of dopaminergic function affects a variety of behaviors that are dependent upon the integrity of this system, including motor behavior (upper body strength), amphetamine (dopamine)-mediated taste aversion learning, and operant conditioning (fixed-ratio bar pressing). Although the relationships between heavy particle irradiation and the effects of exposure depend, to some extent, upon the specific behavioral or neurochemical endpoint under consideration, a review of the available research leads to the hypothesis that the endpoints mediated by the CNS have certain characteristics in common. These include: (1) a threshold, below which there is no apparent effect; (2) the lack of a dose-response relationship, or an extremely steep dose-response curve, depending on the particular endpoint; and (3) the absence of recovery of function, such that the heavy particle-induced behavioral and neural changes are present when tested up to one year following exposure. The current report reviews the data relevant to the degree to which these characteristics are common to neurochemical and behavioral endpoints that are mediated by the effects of exposure to heavy particles on CNS activity. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

Review↗

STS-114 Flight Day 13 and 14 Highlights

On Flight Day 13, the crew of Space Shuttle Discovery on the STS-114 Return to Flight mission (Commander Eileen Collins, Pilot James Kelly, Mission Specialists Soichi Noguchi, Stephen Robinson, Andrew Thomas, Wendy Lawrence, and Charles Camarda) hear a weather report from Mission Control on conditions at the shuttle's possible landing sites. The video includes a view of a storm at sea. Noguchi appears in front of a banner for the Japanese Space Agency JAXA, displaying a baseball signed by Japanese MLB players, demonstrating origami, displaying other crafts, and playing the keyboard. The primary event on the video is an interview of the whole crew, in which they discuss the importance of their mission, lessons learned, shuttle operations, shuttle safety and repair, extravehicular activities (EVAs), astronaut training, and shuttle landing. Mission Control dedicates the song "A Piece of Sky" to the Shuttle crew, while the Earth is visible below the orbiter. The video ends with a view of the Earth limb lit against a dark background.

Source record↗

Aircraft Control Using Engine Thrust: A History of Learning TOC Real-Time

A history of learning the operation of Throttles Only Control (TOC) to control an aircraft in real time using engine thrust is shown. The topics include: 1) Past TOC Accidents/Incidents; 2) 1972: DC-10 American Airlines; 3) May 1974: USAF B-52H; 4) April 1975: USAF C-5A; 5) April 1975: USAF C-5A; 6) 1981: USAF B-52G; 7) August 1985: JAL 123 B-747; 8) JAL 123 Survivor Story; 9) JAL 123 Investigation Findings; 10) July 1989: UAL 232 DC-10; 11) UAL 232 DC-10; 12) Eastwind 517 B-737; 13) November 2003: DHL A-300; 14) Historically, TOC has saved lives; 15) Automated Throttles-Only Control; 16) PCA Project; 17) Propulsion-Controlled Aircraft; 18) MD-11 PCA System and Flight Test Envelope; 19) MD-11 Simulation, PCA ILS-Soupled Landing Dispersion; 20) Throttles-Only Pitch and Roll Control Power; 21) PCA in Commercial Fleet; 22) Fall 2005: PCAR Project; 23) PCAR Background - TOC; and 24) PCAR Background - TOC.

Cole, Jennifer H.↗

Lessons Learned for Planning and Estimating Operations Support Requirements

Operations (phase E) costs are typically small compared to the spacecraft development and test costs. This, combined with the long lead time for realizing operations costs, can lead projects to focus on hardware development schedules and costs, de-emphasizing estimation of operations support requirements during proposal, early design, and replan cost exercises. The Discovery and New Frontiers (D&NF) programs comprise small, cost-capped missions supporting scientific exploration of the solar system. Even moderate yearly underestimates of the operations costs can present significant LCC impacts for deep space missions with long operational durations, and any LCC growth can directly impact the programs ability to fund new missions. The D&NF Program Office at Marshall Space Flight Center recently studied cost overruns for 7 D&NF missions related to phase C/D development of operational capabilities and phase E mission operations. The goal was to identify the underlying causes for the overruns and develop practical mitigations to assist the D&NF projects in identifying potential operations risks and controlling the associated impacts to operations development and execution costs. The study found that the drivers behind these overruns include overly optimistic assumptions regarding the savings resulting from the use of heritage technology, late development of operations requirements, inadequate planning for sustaining engineering and the special requirements of long duration missions (e.g., knowledge retention and hardware/software refresh), and delayed completion of ground system development work. This presentation summarizes the study and the results, providing a set of lessons NASA can use to improve early estimation and validation of operations costs.

Newhouse, Marilyn↗

Cable Tester Box

Cables are very important electrical devices that carry power and signals across multiple instruments. Any fault in a cable can easily result in a catastrophic outcome. Therefore, verifying that all cables are built to spec is a very important part of Electrical Integration Procedures. Currently, there are two methods used in lab for verifying cable connectivity. (1) Using a Break-Out Box and an ohmmeter this method is time-consuming but effective for custom cables and (2) Commercial Automated Cable Tester Boxes this method is fast, but to test custom cables often requires pre-programmed configuration files, and cables used on spacecraft are often uniquely designed for specific purposes. The idea is to develop a semi-automatic continuity tester that reduces human effort in cable testing, speeds up the electrical integration process, and ensures system safety. The JPL-Cable Tester Box is developed to check every single possible electrical connection in a cable in parallel. This system indicates connectivity through LED (light emitting diode) circuits. Users can choose to test any pin/shell (test node) with a single push of a button, and any other nodes that are shorted to the test node, even if they are in the same connector, will light up with the test node. The JPL-Cable Tester Boxes offers the following advantages: 1. Easy to use: The architecture is simple enough that it only takes 5 minutes for anyone to learn how operate the Cable Tester Box. No pre-programming and calibration are required, since this box only checks continuity. 2. Fast: The cable tester box checks all the possible electrical connections in parallel at a push of a button. If a cable normally takes half an hour to test, using the Cable Tester Box will improve the speed to as little as 60 seconds to complete. 3. Versatile: Multiple cable tester boxes can be used together. As long as all the boxes share the same electrical potential, any number of connectors can be tested together.

Lee, Jason H.↗