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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

AN INDUCTIVE MAPPING WITH CONVOLUTIONAL REPRESENTATIONS FOR HUMAN SETTLEMENT DETECTION: PRELIMINARY RESULTS

Undoubtedly, deep convolutional learning methods continue to improve performance in image-level classification for computer vision and remote sensing applications. However, the spatio-temporal nature of remote sensing imagery offers other interesting challenges in multiscale image understanding. Emerging opportunities include seeking fine-grained and neighborhood mapping with overhead imagery. Limitations due to lack of relevant scale ground-truth often mandates that these challenges are pursued as disjoint. We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate surprising potential on multiscale experiments that incorporate edge-level details up to semantic-level informationWe test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level informationWe test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.

Lunga, Dalton↗
DOE OSTI2025

Converting sWeights to probabilities with density ratios

The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real experimental data as the source of training data and demonstrate how one might subtract background contributions through the use of probabilistic weights which can be readily applied to training data. The sPlot formalism is a common tool used to isolate distributions from different sources. However, the negative sWeights produced by the sPlot technique can cause training problems and poor predictive power. This article demonstrates how density ratio estimation can be applied to convert sWeights to event probabilities, which we call drWeights. The drWeights can then be applied to produce the distributions of interest and are consistent with direct use of the sWeights. This article will also show how decision trees are particularly well suited to convert sWeights, with the benefit of fast prediction rates and adaptability to aspects of experimental data such as the data sample size and proportions of different event sources. We also show that a density ratio product approach in which the initial drWeights are reweighted by an additional converter gives substantially better results.

Glazier, Derek I. [Univ. of Glasgow, Scotland (Uni↗
DOE OSTI2013

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and function. • The results have potential applications to green chemistry. • Models are publicly available for virtual screening via a web portal.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The Chemical Management System (CMS): A Useful Tool for Inventory Management

Comprehensive inventory management is central to the efficient operation of any facility that uses and stores chemicals. There are numerous software packages available that perform this function satisfactorily. Most commercially available inventory management software is regulatory or procurement focused and usually comes with upfront or monthly fees. Finally, this report describes freely downloadable software developed at Sandia National Laboratories that delivers an effective inventory management system with an additional focus on chemical security.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

Abstract Applying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults.

58 GEOSCIENCES↗
DOE OSTI2025

Automatic speech recognition predicts contemporaneous earthquake fault displacement

Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.

58 GEOSCIENCES↗
DOE OSTI2014

Fatty acid biosynthesis revisited: Structure elucidation and metabolic engineering

Fatty acids are primary metabolites synthesized by complex, elegant, and essential biosynthetic machinery. Fatty acid synthases resemble an iterative assembly line, with an acyl carrier protein conveying the growing fatty acid to necessary enzymatic domains for modification. Each catalytic domain is a unique enzyme spanning a wide range of folds and structures. Although they harbor the same enzymatic activities, two different types of fatty acid synthase architectures are observed in nature. During recent years, strained petroleum supplies have driven interest in engineering organisms to either produce more fatty acids or specific high value products. Such efforts require a fundamental understanding of the enzymatic activities and regulation of fatty acid synthases. Despite more than one hundred years of research, we continue to learn new lessons about fatty acid synthases' many intricate structural and regulatory elements. Lastly, in this review, we summarize each enzymatic domain and discuss efforts to engineer fatty acid synthases, providing some clues to important challenges and opportunities in the field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact

As deep reinforcement learning (DRL) continues to gain interest in the smart building research community, there is a transition from simulation-based evaluations to deploying DRL control strategies in actual buildings. While the efficacy of a solution could depend on a particular implementation, there are common obstacles that developers have to overcome to deliver an effective controller. Additionally, a deployment in a physical building can invalidate some of the assumptions made during the controller development. Assumptions on the sensor placement or on the equipment behavior can quickly come undone. This paper presents some of the significant assumptions made during the development of DRL based controllers that could affect their operations in a physical building. Furthermore, a preliminary evaluation revealed that controllers developed with some of these assumptions can incur twice the expected costs when they are deployed in a building.

Prakash, Anand Krishnan↗
DOE OSTI2024

A prospective on machine learning challenges, progress, and potential in polymer science

Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. Graphical Abstract

Struble, Daniel C. (ORCID:0009000093410612)↗
DOE OSTI2019

Scientific and Historic Impacts from the Manhattan Project [Slides]

The Manhattan Project (MP) was the international project that led to production of the first nuclear weapons. It opened up a new chapter in human history that we now call the atomic age. The MP is an example of living history where multiple generations continue to learn and evaluate the full magnitude of its history, science, technology, and deterrence and security implications throughout time. The idea to have a student symposium about the MP grew out of conversations with colleagues and students. The prevailing opinion was indeed that there is a growing interest in history, science, politics, and security implications of the MP. We thought it would be useful to keep the direct dialog and discussion going to allow younger generations the chance to formulate their own opinion. The scope of the symposium is large, covers multiple topics, and is impossible to cover in detail in one day. We are trying to embrace a very wide spectrum of questions. The world around us is evolving and it is important to present, as completely as possible, an arc of facts about the MP and what followed. Hence, we have presentations covering the MP and broad issues of the nuclear age from the initial stages of the MP in 1942 to modern day. I believe it is important to have a forum that enables better appreciation and understanding of the MP among younger generations. Our hope is that this symposium will strengthen the ongoing dialog and create an open student forum to hear and learn about the MP.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Witness the Trinity test through Lab artifacts

July 16 marks the 77th anniversary of the Trinity test, conducted in a desert in New Mexico. The test subject, an atomic bomb called The Gadget, was successfully detonated from a 100-foot steel tower. This event marks the commencement of the Atomic Age, a new era where fission capabilities could be employed for national security purposes. Shortly after the Trinity test, two Los Alamos-created atomic weapons were released above Japan, helping to end the world’s bloodiest conflict just weeks later. “Trinity was one of the greatest scientific experiments ever,” said NSRC Senior Historian Alan Carr said. “Los Alamos scientists changed the world forever on that day. Not only was it the dawn of the Atomic Age, but also the beginning of the Lab’s eight decades of cutting-edge science and its national security charge.” To preserve this event, and to continue to learn more about this critical moment in history, the National Security Research Center (NSRC) curates a collection of photographs, films, notes, unclassified artifacts and numerous other materials related to the science of the test. Notably, the collection includes a novel material that formed at the site, trinitite, and artifacts from one of the intriguing scientists present at the test, Enrico Fermi.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Comparing Occupant Light Exposure in Factory and Office Spaces

There is an increasing desire to provide healthy workplace environments that better serve people while minimizing the impact on the planet. Building recommendations help designers to incorporate the latest research and best practices, but translating research studies conducted in different settings to broader recommendations is challenging. This is particularly true for lighting, with researchers continuing to learn more about circadian, neuroendocrine, and neurobehavioral responses to light. The recommendations are regularly adjusting to incorporate the latest research as well as feedback from practitioners implementing these recommendations. There remains a need to better document the actual light exposure that people experience over the course of a day and across seasons, so that researchers can better understand the influence of light on people and recommendations can be refined. Thankfully, advances in sensor technology are making this more possible than ever. PNNL collaborated with ETC Inc. to understand actual occupant light exposure in factory and office spaces at ETC’s Headquarters in Middleton, WI. The primary building at ETC headquarters opened in 2004 and houses approximately 800 employees that contribute to central business functions, customer support, research and development, as well as the company’s primary 60,000 ft2 manufacturing facility. Vertical light exposure measurements including electric light and daylight were recorded in factory, research and development, and office work areas at occupant eye height. This document will focus on lighting measurements collected in six factory and office locations across work location, season, and time of day. Ultimately, detailed lighting measurements can be coupled with employee surveys to support development of satisfactory work environments.

42 ENGINEERING↗
DOE OSTI2024

Towards Field Emission Free Cavity Processing and String Assembly at Fermilab

Cavities and cryomodules assembled at Fermilab have demonstrated unprecedented field emission (FE) free gradients. However, consistent FE-free performance is not guaranteed. Many lessons were learned, and continued vigilance is a must. In addition, several improvements have been identified to further push the state-of-the-art low particulate cavity processing and assembly at Fermilab. Those included the optimization of nitrogen flow, robotic-assisted assembly, and low-particulate fasteners. We share our latest results and vision for the future clean assemblies of cavities and cryomodule strings.

43 PARTICLE ACCELERATORS↗
NASA NTRS1990

Neural networks for self-learning control systems

It is shown how a neural network can learn of its own accord to control a nonlinear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The 'truck backer-upper,' a neural network controller that steers a trailer truck while the truck is backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored should be applicable to a wide variety of nonlinear control problems.

Nguyen, Derrick H.↗
NASA NTRS1991

Applications of fuzzy logic to control and decision making

Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.

Lea, Robert N.↗
NASA NTRS1993

Neuromorphic Learning From Noisy Data

Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.

Merrill, Walter C.↗
NASA NTRS2008

The User Community and a Multi-Mission Data Project: Services, Experiences and Directions of the Space Physics Data Facility

From a user's perspective, the multi-mission data and orbit services of NASA's Space Physics Data Facility (SPDF) project offer a unique range of important data and services highly complementary to other services presently available or now evolving in the international heliophysics data environment. The VSP (Virtual Space Physics Observatory) service is an active portal to a wide range of distributed data sources. CDAWeb (Coordinate Data Analysis Web) enables plots, listings and file downloads for current data cross the boundaries of missions and instrument types (and now including data from THEMIS and STEREO). SSCWeb, Helioweb and our 3D Animated Orbit Viewer (TIPSOD) provide position data and query logic for most missions currently important to heliophysics science. OMNIWeb with its new extension to 1- and 5-minute resolution provides interplanetary parameters at the Earth's bow shock as a unique value-added data product. SPDF also maintains NASA's CDF (common Data Format) standard and a range of associated tools including translation services. These capabilities are all now available through webservices-based APIs as well as through our direct user interfaces. In this paper, we will demonstrate the latest data and capabilities now supported in these multi-mission services, review the lessons we continue to learn in what science users need and value in this class of services, and discuss out current thinking to the future role and appropriate focus of the SPDF effort in the evolving and increasingly distributed heliophysics data environment.

Fung, Shing F.↗
NASA NTRS2008

Services, Perspective and Directions of the Space Physics Data Facility

The multi-mission data and orbit services of NASA's Space Physics Data Facility (SPDF) project offer unique capabilities supporting science of the Heliophysics Great Observatory and that are highly complementary to other services now evolving in the international heliophysics data environment. The VSPO (Virtual Space Physics Observatory) service is an active portal to a wide rage of distributed data sources. CDAWeb (Coordinated Data Analysis Web) offers plots, listings and file downloads for current data from many missions across the boundaries of missions and instrument types. CDAWeb now includes extensive new data from STEREO and THEMIS, plus new ROCSAT IPEI data, the latest data from all four TIMED instruments and high-resolution data from all DE-2 experiments. SSCWeb, Helioweb and out 3D Animated Orbit Viewer (TIPSOD) provide position data and identification of spacecraft and ground conjunctions. OMNI Web, with its new extension to 1- and 5-minute resolution, provides interplanetary parameters at the Earth's bow shock. SPDF maintains NASA's CDF (Common Data Format) standard and a range of associated tools including format translation services. These capabilities are all now available through web services based APIs, one element in SPDF's ongoing work to enable heliophysics community development of Virtual discipline Observatories (e.g. VITMO). We will demonstrate out latest data and capabilities, review the lessons we continue to learn in what science users need and value in this class of services, and discuss out current thinking to the future role and appropriate focus of the SPDF effort in the evolving and increasingly distributed heliophysics data environment.

McGuire, Robert E.↗