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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Paradigms for machine learning

Five paradigms are described for machine learning: connectionist (neural network) methods, genetic algorithms and classifier systems, empirical methods for inducing rules and decision trees, analytic learning methods, and case-based approaches. Some dimensions are considered along with these paradigms vary in their approach to learning, and the basic methods are reviewed that are used within each framework, together with open research issues. It is argued that the similarities among the paradigms are more important than their differences, and that future work should attempt to bridge the existing boundaries. Finally, some recent developments in the field of machine learning are discussed, and their impact on both research and applications is examined.

Schlimmer, Jeffrey C.↗
NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗
DOE OSTI2022

Alternative stable states of the forest mycobiome are maintained through positive feedbacks

Most trees on Earth form a symbiosis with either arbuscular mycorrhizal or ectomycorrhizal fungi. By forming common mycorrhizal networks, actively modifying the soil environment and other ecological mechanisms, these contrasting symbioses may generate positive feedbacks that favour their own mycorrhizal strategy (that is, the con-mycorrhizal strategy) at the expense of the alternative strategy. Positive con-mycorrhizal feedbacks set the stage for alternative stable states of forests and their fungi, where the presence of different forest mycorrhizal strategies is determined not only by external environmental conditions but also mycorrhiza-mediated feedbacks embedded within the forest ecosystem. Here, in this work, we test this hypothesis using thousands of US forest inventory sites to show that arbuscular and ectomycorrhizal tree recruitment and survival exhibit positive con-mycorrhizal density dependence. Data-driven simulations show that these positive feedbacks are sufficient in magnitude to generate and maintain alternative stable states of the forest mycobiome. Given the links between forest mycorrhizal strategy and carbon sequestration potential, the presence of mycorrhizal-mediated alternative stable states affects how we forecast forest composition, carbon sequestration and terrestrial climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Research-Driven Radiation Oncology: A Narrative on the Ongoing Legacy of Henry S. Kaplan

In this rapidly evolving time of precision medicine and scientifically based cancer care, how radiation oncology became a strong research-based scientific discipline in the United States after its separation from diagnostic radiology might be lost. The importance of generational mentorship, “family trees,” and interpersonal relationships can be difficult or impossible to trace absent personal narrative recollections of those involved. Henry S. Kaplan is a central figure and the focal point for 3 generations of research-based academic department chairs. This report establishes a first draft of a living record of the radiation oncology history of the Kaplan legacy to serve as an example of how knowledge networks grow and flourish and as an impetus for others to trace the legacy of other radiation oncology academic “trees.”.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2020

Fuel availability not fire weather controls boreal wildfire severity and carbon emissions

Carbon (C) emissions from wildfires are a key terrestrial–atmosphere interaction that influences global atmospheric composi-tion and climate. Positive feedbacks between climate warming and boreal wildfires are predicted based on top-down controls of fire weather and climate, but C emissions from boreal fires may also depend on bottom-up controls of fuel availability related to edaphic controls and overstory tree composition. Here we synthesized data from 417 field sites spanning six ecoregions in the northwestern North American boreal forest and assessed the network of interactions among potential bottom-up and top-down drivers of C emissions. Our results indicate that C emissions are more strongly driven by fuel availability than by fire weather, highlighting the importance of fine-scale drainage conditions, overstory tree species composition and fuel accumulation rates for predicting total C emissions. By implication, climate change-induced modification of fuels needs to be considered for accu-rately predicting future C emissions from boreal wildfires.

X J Walker↗
NASA NTRS1990

Information Security and Integrity Systems

Viewgraphs from the Information Security and Integrity Systems seminar held at the University of Houston-Clear Lake on May 15-16, 1990 are presented. A tutorial on computer security is presented. The goals of this tutorial are the following: to review security requirements imposed by government and by common sense; to examine risk analysis methods to help keep sight of forest while in trees; to discuss the current hot topic of viruses (which will stay hot); to examine network security, now and in the next year to 30 years; to give a brief overview of encryption; to review protection methods in operating systems; to review database security problems; to review the Trusted Computer System Evaluation Criteria (Orange Book); to comment on formal verification methods; to consider new approaches (like intrusion detection and biometrics); to review the old, low tech, and still good solutions; and to give pointers to the literature and to where to get help. Other topics covered include security in software applications and development; risk management; trust: formal methods and associated techniques; secure distributed operating system and verification; trusted Ada; a conceptual model for supporting a B3+ dynamic multilevel security and integrity in the Ada runtime environment; and information intelligence sciences.

Source record↗
NASA NTRS2011

Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR System

JPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design.

Adaboost↗
NASA NTRS1989

A design philosophy for multi-layer neural networks with applications to robot control

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

Vadiee, Nader↗
DOE OSTI2022

Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS 2 through Machine‐Learning Models

Abstract 2D transition metal dichalcogenides (TMDCs) with intense and tunable photoluminescence (PL) have opened up new opportunities for optoelectronic and photonic applications such as light‐emitting diodes, photodetectors, and single‐photon emitters. Among the standard characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non‐destructive technique capable of probing material's crystallinity and perturbations such as doping and strain. However, a comprehensive understanding of the correlation between photoluminescence and Raman spectra in monolayer MoS 2 remains elusive due to its highly nonlinear nature. Here, the connections between PL signatures and Raman modes are systematically explored, providing comprehensive insights into the physical mechanisms correlating PL and Raman features. This study's analysis further disentangles the strain and doping contributions from the Raman spectra through machine‐learning models. First, a dense convolutional network (DenseNet) to predict PL maps by spatial Raman maps is deployed. Moreover, a gradient boosted trees model (XGBoost) with Shapley additive explanation (SHAP) to bridge the impact of individual Raman features in PL features is applied. Last, a support vector machine (SVM) to project PL features on Raman frequencies is adopted. This work may serve as a methodology for applying machine learning to characterizations of 2D materials.

Lu, Ang‐Yu↗
NASA NTRS1985

Language for Allocation and Network Scheduling

Programing Language for Allocation and Network Scheduling, Plans, handles many scheduling requirements of Space Shuttle program. Also generalized, high-level tree manipulation language.

Willoughby, J. K.↗
NASA NTRSDate not supplied

Comparison Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

This paper addresses the need to predict the flight energy consumption of aerial vehicles in the presence of wind using machine learning techniques. The presented work is critical to achieving sustainable and efficient operations for Advanced Air Mobility (AAM) and to evaluating the readiness of the ground-supporting energy infrastructure, e.g., electric grid and AAM portals. The flight energy consumption is described using the "energy per meter" (EPM) metric. We present a comparison study of influential machine learning techniques in predicting EPM using real-world flight test data. We presented new results of using the Decision Tree, Random Forest, and linear regression techniques, along with our previous results using the Recurrent Neural Network and Feed Forward Neural Network techniques. The comparison results show that the Linear Regression method outperforms other methods on the basis of the Mean Squared Error and error variance.

Machine Learning↗
NASA NTRS2019

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

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

Zheng, Laura↗
DOE OSTI2023

Ancient Rapid Radiation Explains Most Conflicts Among Gene Trees and Well-Supported Phylogenomic Trees of Nostocalean Cyanobacteria

Prokaryotic genomes are often considered to be mosaics of genes that do not necessarily share the same evolutionary history due to widespread horizontal gene transfers (HGTs). Consequently, representing evolutionary relationships of prokaryotes as bifurcating trees has long been controversial. However, studies reporting conflicts among gene trees derived from phylogenomic data sets have shown that these conflicts can be the result of artifacts or evolutionary processes other than HGT, such as incomplete lineage sorting, low phylogenetic signal, and systematic errors due to substitution model misspecification. Here, we present the results of an extensive exploration of phylogenetic conflicts in the cyanobacterial order Nostocales, for which previous studies have inferred strongly supported conflicting relationships when using different concatenated phylogenomic data sets. We found that most of these conflicts are concentrated in deep clusters of short internodes of the Nostocales phylogeny, where the great majority of individual genes have low resolving power. We then inferred phylogenetic networks to detect HGT events while also accounting for incomplete lineage sorting. Our results indicate that most conflicts among gene trees are likely due to incomplete lineage sorting linked to an ancient rapid radiation, rather than to HGTs. Moreover, the short internodes of this radiation fit the expectations of the anomaly zone, i.e., a region of the tree parameter space where a species tree is discordant with its most likely gene tree. In this work, we demonstrated that concatenation of different sets of loci can recover up to 17 distinct and well-supported relationships within the putative anomaly zone of Nostocales, corresponding to the observed conflicts among well-supported trees based on concatenated data sets from previous studies. Our findings highlight the important role of rapid radiations as a potential cause of strongly conflicting phylogenetic relationships when using phylogenomic data sets of bacteria. We propose that polytomies may be the most appropriate phylogenetic representation of these rapid radiations that are part of anomaly zones, especially when all possible genomic markers have been considered to infer these phylogenies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nev- ertheless, the majority of these studies examine how well deep neural networks can model functions with uniform regularities. In this paper, we explore a different angle: how deep neural networks can adapt to varying degrees of smoothness in functions and nonuni- form data distributions across different locations and scales. More precisely, we focus on a broad class of functions defined by nonlinear tree-based approximation methods. This class encompasses a range of function types, such as functions with uniform regularities and discontinuous functions. We develop nonparametric approximation and estimation theories for this class using deep ReLU networks. Our results show that deep neural networks are adaptive to the nonuniform smoothness of functions and nonuniform data distributions at different locations and scales. We apply our results to several function classes, and derive the corresponding approximation and generalization errors. The validity of our results is demonstrated through numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Embedding global barrier and collective in a torus network

Embodiments of the invention provide a method, system and computer program product for embedding a global barrier and global interrupt network in a parallel computer system organized as a torus network. The computer system includes a multitude of nodes. In one embodiment, the method comprises taking inputs from a set of receivers of the nodes, dividing the inputs from the receivers into a plurality of classes, combining the inputs of each of the classes to obtain a result, and sending said result to a set of senders of the nodes. Embodiments of the invention provide a method, system and computer program product for embedding a collective network in a parallel computer system organized as a torus network. In one embodiment, the method comprises adding to a torus network a central collective logic to route messages among at least a group of nodes in a tree structure.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Mission Configuration Tree for the James Webb Space Telescope (JWST)

The GSFC Mission Configuration web-based application is a means of documenting the structure of the many elements used in building each mission that NASA launches, as well as archiving the documents associated with each of these elements and keeping track of suppliers. This includes all available drawings, engineering orders, parts lists, block diagrams, and photos of each system, subsystem, and component. While the information for several of NASA's missions is being input into Mission Configuration, the ten weeks of this project focused on collecting information for the instrument systems of the James Webb Space Telescope. Documents were collected from a database called the Next Generation Integrated Network (NGIN) so that they can eventually be uploaded into the application. From these documents, including drawings and parts lists, a configuration tree was created in Microsoft Visio which showed the relationships between each system, subsystem, and component. When both these documents and the configuration tree are input into Mission Configuration, they will be place into a drill down tree structure, from which drawings and documents can be accessed directly. Mission Configuration also includes search functions that make all parts searchable by keywords such as vendor, part number, taxonomy, and alternate nomenclature. This application is an important resource for providing access to documents related to the components used on each mission. It has created a central repository for information which can be used to benefit Safety & Mission Assurance, but may also be useful across codes. The process of collecting the information and documents to put into Mission Configuration is also allowing for a concise breakdown of information from diverse formats, creating standardization which makes information more readily accessible, while retaining project-specific information that makes it more easily searchable.

Cain, Mary C.↗
DOE OSTI2020

The functional roles of species in metacommunities, as revealed by metanetwork analyses of bird–plant frugivory networks

Abstract Understanding how biodiversity and interaction networks change across environmental gradients is a major challenge in ecology. We integrated metacommunity and metanetwork perspectives to test species’ functional roles in bird–plant frugivory interactions in a fragmented forest landscape in Southwest China, with consequences for seed dispersal. Availability of fruit resources both on and under trees created vertical feeding stratification for frugivorous birds. Bird–plant interactions involving birds feeding only on‐the‐tree or both on and under‐the‐tree (shared) had a higher centrality and contributed more to metanetwork organisation than interactions involving birds feeding only under‐the‐tree. Moreover, bird–plant interactions associated with large‐seeded plants disproportionately contributed to metanetwork organisation and centrality. Consequently, on‐the‐tree and shared birds contributed more to metanetwork organisation whereas under‐the‐tree birds were more involved in local processes. We would expect that species’ roles in the metanetwork will translate into different conservation values for maintaining functioning of seed‐dispersal networks.

Li, Hai‐Dong↗
NASA NTRS2001

Road Extraction from AVIRIS Using Spectral Mixture and Q-Tree Filter Techniques

Accurate road location and condition information are of primary importance in road infrastructure management. Additionally, spatially accurate and up-to-date road networks are essential in ambulance and rescue dispatch in emergency situations. However, accurate road infrastructure databases do not exist for vast areas, particularly in areas with rapid expansion. Currently, the US Department of Transportation (USDOT) extends great effort in field Global Positioning System (GPS) mapping and condition assessment to meet these informational needs. This methodology, though effective, is both time-consuming and costly, because every road within a DOT's jurisdiction must be field-visited to obtain accurate information. Therefore, the USDOT is interested in identifying new technologies that could help meet road infrastructure informational needs more effectively. Remote sensing provides one means by which large areas may be mapped with a high standard of accuracy and is a technology with great potential in infrastructure mapping. The goal of our research is to develop accurate road extraction techniques using high spatial resolution, fine spectral resolution imagery. Additionally, our research will explore the use of hyperspectral data in assessing road quality. Finally, this research aims to define the spatial and spectral requirements for remote sensing data to be used successfully for road feature extraction and road quality mapping. Our findings will facilitate the USDOT in assessing remote sensing as a new resource in infrastructure studies.

Gardner, Margaret E.↗