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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Event Definition for the Automated Detection of Nuclear Proliferation Activities

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Discovery Analytics Center (DAC) at Virginia Polytechnic Institute and State University (VT) began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on largescale open data sources to identify new, developing, or undeclared nuclear programs. One of the most challenging aspects of applying machine learning techniques to such a problem is the high likelihood of extremely sparse data from disparate sources. To overcome this challenge, the current work will use a strategic combination of supervised, semi-supervised, and unsupervised learning techniques to ingest and fuse data streams to make a forecast of nuclear activities in a targeted geospatial location. Identifying potential data sources and training supervised learning algorithms is dependent upon the development of a robust foundation of targeted event domains that fundamentally define the nuclear activities of interest. This report documents the definition of a hierarchical structure for both nuclear activity and event domains that will be used to guide the research team in development or use of existing semantic dictionaries that are instrumental to searching, parsing, and categorizing events for the forecasting system’s use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Software development: A paradigm for the future

A new paradigm for software development that treats software development as an experimental activity is presented. It provides built-in mechanisms for learning how to develop software better and reusing previous experience in the forms of knowledge, processes, and products. It uses models and measures to aid in the tasks of characterization, evaluation and motivation. An organization scheme is proposed for separating the project-specific focus from the organization's learning and reuse focuses of software development. The implications of this approach for corporations, research and education are discussed and some research activities currently underway at the University of Maryland that support this approach are presented.

Basili, Victor R.↗
NASA NTRS1993

Discrete Recurrent Neural Networks for Grammatical Inference

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

Neural↗
NASA NTRS2006

PBMA Pause and Learn Video Nuggets Transcript

This document is a transcript for a video about a practice practiced at Goddard Space Flight Center called Pause and Learn (PaL). The PaL process is intended to, first of all, help the team learn. So, the team that was involved in the activity, the group that actually did the work, that handled the review, or ran the tests, or developed the piece of equipment, they sit down and actually say, "What did we learn from this exercise?" The idea is to create a learning environment at various key milestones in the execution of a process, rather than wait until the end of the given process, be it a launch or a mission.

Rogers, Ed↗
DOE OSTI2018

Predicting tumor cell line response to drug pairs with deep learning

The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity. We present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%–20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

De novo design of programmable inducible promoters

Abstract Ligand-responsive allosteric transcription factors (aTF) play a vital role in genetic circuits and high-throughput screening because they transduce biochemical signals into gene expression changes. Programmable control of gene expression from aTF-regulated promoter is important because different downstream effector genes function optimally at different expression levels. However, tuning gene expression of native promoters is difficult due to complex layers of homeostatic regulation encoded within them. We engineered synthetic promoters de novo by embedding operator sites with varying affinities and radically reshaped binding preferences within a minimal, constitutive Escherichia coli promoter. Multiplexed cell-based screening of promoters for three TetR-like aTFs generated with this approach gave rich diversity of gene expression levels, dynamic ranges and ligand sensitivities and were 50- to 100-fold more active over their respective native promoters. Machine learning on our dataset revealed that relative position of the core motif and bases flanking the core motif play an important role in modulating induction response. Our generalized approach yields customizable and programmable aTF-regulated promoters for engineering cellular pathways and enables the discovery of new small molecule biosensors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Philympics 2021: Prophage Predictions Perplex Programs

Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f 1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.

Roach, Michael J.↗
DOE OSTI2024

Robustness of topological persistence in knowledge distillation for wearable sensor data

Topological data analysis (TDA) has shown great success in various applications involving wearable sensor data. However, there are difficulties in leveraging topological features in machine learning and wearable sensors because of the large time consumption and computational resources required to extract the features. To address this problem, knowledge distillation (KD) is utilized to generate a small model and accommodate topological features with persistence image (PI) representations from the raw time series data. Deploying topological knowledge in KD enables the student to achieve better performance compared to the one trained solely on raw time series data. However, it is not yet known if there are coherent characteristics for topological features in PI, which can aid in improving the performance during KD. In this paper, we investigate the suitability and challenges of utilizing topological features in KD for wearable sensor data, thereby contributing to the advancement of the field. Our study explores the impact of transferred topological features by comparing the Teacher-to-Student framework with Multiple Teachers-to-Student where teachers utilize both time series data and persistence images obtained by TDA as inputs. Additionally, we conduct a rigorous examination of topological knowledge effects by testing under various corruptions, knowledge types, and learning strategies in the context of human activity recognition tasks. Our analysis of topological features in KD presents the optimal strategy for incorporating these features. This study includes datasets of varying scales, window lengths, and activity classes, providing a comprehensive evaluation. Our results demonstrate that leveraging topological features in KD to enhance performance across databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Anomalous And Normal High Performance Computing Datacenter Activities

This code provides annotation and an interface to 10+ hours of video activities in a high performance datacenter with 20+ different types of anomalous activities. The purpose is to enable machine learning for video surveillance systems in high performance computing centers. This is the first code of this type addressing the space of high performance computing datacenters.

Anderson, Matthew↗
DOE OSTI2014

Space Geodesy and Geochemistry Applied to the Monitoring, Verification of Carbon Capture and Storage

This award was a training grant awarded by the U.S. Department of Energy (DOE). The purpose of this award was solely to provide training for two PhD graduate students for three years in the general area of carbon capture and storage (CCS). The training consisted of course work and conducting research in the area of CCS. Attendance at conferences was also encouraged as an activity and positive experience for students to learn the process of sharing research findings with the scientific community, and the peer review process. At the time of this report, both students have approximately two years remaining of their studies, so have not fully completed their scientific research projects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Lessons Learned from the Development of an Example Precision Information Environment for International Safeguards

The International Atomic Energy Agency (IAEA) is interested in increasing capabilities of IAEA safeguards inspectors to access information that would improve their situational awareness on the job. A mobile information platform could potentially provide access to information, analytics, and technical and logistical support to inspectors in the field, as well as providing regular updates to analysts at IAEA Headquarters in Vienna or at satellite offices. To demonstrate the potential capability of such a system, Pacific Northwest National Laboratory (PNNL) implemented a number of example capabilities within a PNNL-developed precision information environment (PIE), and using a tablet as a mobile information platform. PNNL’s safeguards proof-of-concept PIE intends to; demonstrate novel applications of mobile information platforms to international safeguards use cases; demonstrate proof-of-principle capability implementation; and provide “vision” for capabilities that could be implemented. This report documents the lessons learned from this two-year development activity for the Precision Information Environment for International Safeguards (PIE-IS), describing the developed capabilities, technical challenges, and considerations for future development, so that developers working to develop a similar system for the IAEA or other safeguards agencies might benefit from our work.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Study of MHD Stability, Active Mode Control, and Disruption Avoidance in NSTX-U

Learning how to achieve sustained operation at high plasma pressure is an important goal of the National Spherical Torus Upgrade (NSTX-U) program and is critically important to any future fusion power plant. This research addresses high priority stability and control issues outlined by the NSTX-U Program. Reduced models of the stabilization physics learned continue to be created and tested against actual plasma operation in NSTX, NSTX-U, and other tokamak devices. This research is forming physics-based models for disruption forecasting that aims to unify results across different fusion machines for more confident extrapolation of the techniques studied to future devices including ITER. The Disruption Event Characterization and Forecasting (DECAF) code was expanded to analyze data from the TCV tokamak, including real-time MHD analysis signals. The presence of a mode and likelihood of its m/n numbers can be tracked, as well as the mode frequency, although a limitation at low frequency does not yet allow tracking modes to the locking point in TCV. Initial analyses of NSTX-U operation were conducted using the DECAF code. The timing of sawtooth oscillations was investigated taking advantage of the more extensive L-mode operation of the device in its first run campaign. An initial investigation of thermal collapses in NSTX-U was also conducted with specific comparison to the Greenwald density limit. A local island power balance model to explain the density limit in tokamaks has been tested with NSTX data and is being evaluated as a potential disruption forecaster in the DECAF code, presently showing similarities to the global Greenwald density evolution and limit. A module was written for the DECAF code that generalizes magnetic spectrogram analysis of MHD modes. This new portable code, written in Python, discriminates the toroidal mode number of instabilities and can track each mode as a DECAF event, monitoring the evolution of the mode through bifurcation and locking points. This is a critical new capability to allow disruption event characterization for general tokamak databases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a feature map concatenated with a Bidirectional Encoder Representation from Transformers (BERT) model. We discuss deployment of this new concept and the research insights that resulted in the selection of Convolution Neural Networks for its feature mapping aspects. We also present our results showing ClaimsBERT to outperform all other evaluated approaches. This new claims classifier represents an essential processing stage within our vetting framework aiming to improve the cybersecurity of industrial control systems (ICS). Furthermore, in order to maximize the accuracy of our new ClaimsBERT classifier, we propose an approach for optimal architecture selection and determination of optimized hyperparameters, in particular the best learning rate, number of convolutions, filter sizes, activation function, the number of dense layers, as well as the number of neurons and the drop-out rate for each layer. Fine-tuning these hyperparameters within our model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original model to a 97% accuracy obtained with ClaimsBERT.

Ameri, Kimia (ORCID:0000000328791871)↗
DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1985

A software technology evaluation program

A set of quantitative approaches is presented for evaluating software development methods and tools. The basic idea is to generate a set of goals which are refined into quantifiable questions which specify metrics to be collected on the software development and maintenance process and product. These metrics can be used to characterize, evaluate, predict, and motivate. They can be used in an active as well as passive way by learning form analyzing the data and improving the methods and tools based upon what is learned from that analysis. Several examples were given representing each of the different approaches to evaluation. The cost of the approaches varied inversely with the level of confidence in the interpretation of the results.

Novaes-Card, David N.↗
NASA NTRS1991

LDEF mechanical systems

Following the Long Duration Exposure Facility (LDEF), the Systems Special Investigation Group (SIG) was involved in a considerable amount of testing of mechanical hardware flown on the LDEF. The primary objectives were to determine the effects of the long term exposure on: (1) mechanisms employed both on the LDEF or as part of individual experiments; (2) structural components; and (3) fasteners. Results of testing the following LDEF hardware are presented: LDEF structure, fasteners, trunnions, end support beam, environment exposure control cannisters, motors, and lubricants. A limited discussion of PI test results is included. The lessons learned are discussed along with the future activities of the System SIG.

Spear, Steve↗