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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Obtaining correct compile results by absorbing mismatches between data types representations

Methods and a system are provided. A method includes implementing a function, which a compiler for a first language does not have, using a compiler for a second language. The implementing step includes generating, by the compiler for the first language, a first abstract syntax tree. The implementing step further includes converting, by a converter, the first abstract syntax tree to a second abstract syntax tree of the compiler for the second language using a conversion table from data representation types in the first language to data representation types in the second language. When a compilation error occurs, the implementing step also includes generating a special node for error processing in the second abstract syntax tree and storing an error token in the special node. When unparsing, the implementing step additionally includes outputting the error token, in the form of source code written in the first language.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Metadata Standards for the NSE: Extended Field Standards

This standard presents a set of optional metadata fields for managed digital objects within the Nuclear Security Enterprise (NSE) and provides a deeper look at data representation in metadata by looking at the representation of 1) Records Management required metadata, and 2) common representations of technical/scientific data. Metadata standardization is a critical enabler for effectively sharing data, documents, and other digital objects between NSE sites, and for tracing the digital thread at the object level. Standardization is necessary for both schemas and vocabularies, meaning that both field standards and value standards must be specified. This document serves as a complementary field standard, recommending an optional set of fields that should be uniformly built for all managed digital objects within the NSE. This document specifically focuses on extending the shared discovery layer defined in the first white paper by introducing additional descriptive and data representation fields that improve cross-site search and interpretation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1985

An algorithm for computing the number of distinct spectral vectors in thematic mapper data

A computationally efficient method was developed to compute the number of distinct spectral vectors and their frequency of occurrence in Landsat-4 Thematic Mapper (TM) data. The algorithm first partitions the image into spectrally disjoint subsets and then computes the frequency distribution of distinct spectral vectors within each subset from a multidimensional histogram. The overall frequency distribution is tabulated by accumulating the results from each subset. The number of distinct spectral vectors could be used as a measure of potential storage compaction of alternate data representations for data compression, or as a measure of information content in the comparison of spectral band combinations and/or spatial resolutions for an image. Results from processing three 512 x 512 pixel Landsat-4 TM images and one Landsat-4 Multispectral Scanner (MSS) image are presented as examples. An algorithm for computing the frequency distribution of distinct spectral vectors in MSS data is given in the Appendix.

Wharton, S. W.↗
NASA NTRS1986

"Noiseless" Data-Compression Algorithm

Gamma-ray spectrometer data compressed to enable more frequent sampling. Proposed data-compression algorithm efficiently represents gamma-ray spectrometer spectra at any spectrum collection interval from 5 seconds to 5 minutes. Data representations "noiseless" (Data exactly constructed). Techniques useful in designing datacompression algorithms for other spectral instruments, which have varying data-rate requirements.

Rice, R. F.↗
DOE OSTI2019

Conditions Databases at FNAL

Conditions databases is an important class of database applications where the database is used to record the state of a set of quantities as a function of observation time. Conditions databases are used in Hight Energy Physics to record the state of the detector apparatus during data taking, and then to use the data during the event reconstruction and analysis phases. At FNAL, we have a set of 3 different conditions database products, Minerva Conditions DB, ConDB and UConDB, which cover the whole range of the use cases presented by the FNAL experiments. These products have common features such as conditions data representation model, data version control, ability to restore the database to a previous state, scalable web service data access interface. In the paper, we will present the common features of the products, common solutions used to build them and also the differences between the products and their target use cases.

Mandrichenko, Igor V.↗
DOE OSTI2021

Effects of raster terrain representation on GIS shortest path analysis

Spatial analysis extracts meaning and insights from spatially referenced data, where the results are highly dependent on the quality of the data used and the manipulations on the data when preparing it for analysis. Users should understand the impacts that data representations may have on their results in order to prevent distortions in their outcomes. We study the consequences of two common data preparations when locating a linear feature performing shortest path analysis on raster terrain data: 1) the connectivity of the network generated by connecting raster cells to their neighbors, and 2) the range of the attribute scale for assigning costs. Such analysis is commonly used to locate transmission lines, where the results could have major implications on project cost and its environmental impact. Experiments in solving biobjective shortest paths show that results are highly dependent on the parameters of the data representations, with exceedingly variable results based on the choices made in reclassifying attributes and generating networks from the raster. Based on these outcomes, we outline recommendations for ensuring geographic information system (GIS) data representations maintain analysis results that are accurate and unbiased.

Medrano, F. Antonio (ORCID:000000025913632X)↗
NASA NTRS2008

A Data Type for Efficient Representation of Other Data Types

A self-organizing, monomorphic data type denoted a sequence has been conceived to address certain concerns that arise in programming parallel computers. A sequence in the present sense can be regarded abstractly as a vector, set, bag, queue, or other construct. Heretofore, in programming a parallel computer, it has been necessary for the programmer to state explicitly, at the outset, what parts of the program and the underlying data structures must be represented in parallel form. Not only is this requirement not optimal from the perspective of implementation; it entails an additional requirement that the programmer have intimate understanding of the underlying parallel structure. The present sequence data type overcomes both the implementation and parallel structure obstacles. In so doing, the sequence data type provides unified means by which the programmer can represent a data structure for natural and automatic decomposition to a parallel computing architecture. Sequences exhibit the behavioral and structural characteristics of vectors, but the underlying representations are automatically synthesized from combinations of programmers advice and execution use metrics. Sequences can vary bidirectionally between sparseness and density, making them excellent choices for many kinds of algorithms. The novelty and benefit of this behavior lies in the fact that it can relieve programmers of the details of implementations. The creation of a sequence enables decoupling of a conceptual representation from an implementation. The underlying representation of a sequence is a hybrid of representations composed of vectors, linked lists, connected blocks, and hash tables. The internal structure of a sequence can automatically change from time to time on the basis of how it is being used. Those portions of a sequence where elements have not been added or removed can be as efficient as vectors. As elements are inserted and removed in a given portion, then different methods are utilized to provide both an access and memory strategy that is optimized for that portion and the use to which it is put.

James, Mark↗
NASA NTRS1988

Representation and matching of knowledge to design digital systems

A knowledge-based expert system is described that provides an approach to solve a problem requiring an expert with considerable domain expertise and facts about available digital hardware building blocks. To design digital hardware systems from their high level VHDL (Very High Speed Integrated Circuit Hardware Description Language) representation to their finished form, a special data representation is required. This data representation as well as the functioning of the overall system is described.

Jones, J. U.↗
NASA NTRS2008

Non-Linear System Identification for Aeroelastic Systems with Application to Experimental Data

Representation and identification of a non-linear aeroelastic pitch-plunge system as a model of the NARMAX class is considered. A non-linear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (i) the outputs of the NARMAX model match closely those generated using continuous-time methods and (ii) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Kukreja, Sunil L.↗
NASA NTRS2008

Nonlinear System Identification for Aeroelastic Systems with Application to Experimental Data

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (1) the outputs of the NARMAX model closely match those generated using continuous-time methods, and (2) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Kukreja, Sunil L.↗
NASA NTRS2012

Determination of Ice Cloud Models Using MODIS and MISR Data

Representation of ice clouds in radiative transfer simulations is subject to uncertainties associated with the shapes and sizes of ice crystals within cirrus clouds. In this study, we examined several ice cloud models consisting of smooth, roughened, homogeneous and inhomogeneous hexagonal ice crystals with various aspect ratios. The sensitivity of the bulk scattering properties and solar reflectances of cirrus clouds to specific ice cloud models is investigated using the improved geometric optics method (IGOM) and the discrete ordinates radiative transfer (DISORT) model. The ice crystal habit fractions in the ice cloud model may significantly affect the simulations of cloud reflectances. A new algorithm was developed to help determine an appropriate ice cloud model for application to the satellite-based retrieval of ice cloud properties. The ice cloud particle size retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data, collocated with Multi-angle Imaging Spectroradiometer (MISR) observations, is used to infer the optical thicknesses of ice clouds for nine MISR viewing angles. The relative differences between view-dependent cloud optical thickness and the averaged value over the nine MISR viewing angles can vary from −0.5 to 0.5 and are used to evaluate the ice cloud models. In the case for 2 July 2009, the ice cloud model with mixed ice crystal habits is the best fit to the observations (the root mean square (RMS) error of cloud optical thickness reaches 0.365). This ice cloud model also produces consistent cloud property retrievals for the nine MISR viewing configurations within the measurement uncertainties.

MODIS↗
NASA NTRS1990

Experience with abstract notation one

The development of computer science has produced a vast number of machine architectures, programming languages, and compiler technologies. The cross product of these three characteristics defines the spectrum of previous and present data representation methodologies. With regard to computer networks, the uniqueness of these methodologies presents an obstacle when disparate host environments are to be interconnected. Interoperability within a heterogeneous network relies upon the establishment of data representation commonality. The International Standards Organization (ISO) is currently developing the abstract syntax notation one standard (ASN.1) and the basic encoding rules standard (BER) that collectively address this problem. When used within the presentation layer of the open systems interconnection reference model, these two standards provide the data representation commonality required to facilitate interoperability. The details of a compiler that was built to automate the use of ASN.1 and BER are described. From this experience, insights into both standards are given and potential problems relating to this development effort are discussed.

Harvey, James D.↗
DOE OSTI2025

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗
NASA NTRS1997

Performance Data Gathering and Representation from Fixed-Size Statistical Data

The two commonly-used performance data types in the super-computing community, statistics and event traces, are discussed and compared. Statistical data are much more compact but lack the probative power event traces offer. Event traces, on the other hand, are unbounded and can easily fill up the entire file system during program execution. In this paper, we propose an innovative methodology for performance data gathering and representation that offers a middle ground. Two basic ideas are employed: the use of averages to replace recording data for each instance and 'formulae' to represent sequences associated with communication and control flow. The user can trade off tracing overhead, trace data size with data quality incrementally. In other words, the user will be able to limit the amount of trace data collected and, at the same time, carry out some of the analysis event traces offer using space-time views. With the help of a few simple examples, we illustrate the use of these techniques in performance tuning and compare the quality of the traces we collected with event traces. We found that the trace files thus obtained are, indeed, small, bounded and predictable before program execution, and that the quality of the space-time views generated from these statistical data are excellent. Furthermore, experimental results showed that the formulae proposed were able to capture all the sequences associated with 11 of the 15 applications tested. The performance of the formulae can be incrementally improved by allocating more memory at runtime to learn longer sequences.

Yan, Jerry C.↗
DOE OSTI2021

Facilitating Staging-based Unstructured Mesh Processing to Support Hybrid In-Situ Workflows

In-situ and in-transit processing alleviate the gap between the computing and I/O capabilities by scheduling data analytics close to the data source. Hybrid in-situ processing splits data analytics into two stages: the data processing that runs in-situ aims to extract regions of interest, which are then transferred to staging services for further in-transit analytics. To facilitate this type of hybrid in-situ processing, the data staging service needs to support complex intermediate data representations generated/consumed by the in-situ tasks. Unstructured (or irregular) mesh is one such derived data representation that is typically used and bridges simulation data and analytics. However, how staging services efficiently support unstructured mesh transfer and processing remains to be explored. This paper investigates design options for transferring and processing unstructured mesh data using staging services. Using polygonal mesh data as an example, we show that hybrid in-situ workflows with staging-based unstructured mesh processing can effectively support hybrid in-situ workflows, and can significantly decrease data movement overheads.

data-driven↗
DOE OSTI2022

Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network

Abstract Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify putative cell types. However, due to noise impacts, high dimensionality and pervasive dropout events, clustering analysis of scRNA-seq data remains a computational challenge. Here, we propose a new deep structural clustering method for scRNA-seq data, named scDSC, which integrate the structural information into deep clustering of single cells. The proposed scDSC consists of a Zero-Inflated Negative Binomial (ZINB) model-based autoencoder, a graph neural network (GNN) module and a mutual-supervised module. To learn the data representation from the sparse and zero-inflated scRNA-seq data, we add a ZINB model to the basic autoencoder. The GNN module is introduced to capture the structural information among cells. By joining the ZINB-based autoencoder with the GNN module, the model transfers the data representation learned by autoencoder to the corresponding GNN layer. Furthermore, we adopt a mutual supervised strategy to unify these two different deep neural architectures and to guide the clustering task. Extensive experimental results on six real scRNA-seq datasets demonstrate that scDSC outperforms state-of-the-art methods in terms of clustering accuracy and scalability. Our method scDSC is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/scDSC.

Gan, Yanglan↗