A system approach to the voice insertion of data.
Functional requirements for man-machine dialogue, noting voice insertion of data
VOICE DATA PROCESSING SYSTEM↗
SEARCH · Engineering Papers
Results for “Functional data”
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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Dynamic Docking Test System (DDTS) active table computer program NASA Advanced Docking System (NADS)
A computer program was developed to describe the three-dimensional motion of the Dynamic Docking Test System active table. The input consists of inertia and geometry data, actuator structural data, forcing function data, hydraulics data, servo electronics data, and integration control data. The output consists of table responses, actuator bending responses, and actuator responses.
Gates, R. M.↗
The Generic Data Capture Facility
The Generic Data Capture Facility, which can provide data capture support for a variety of different types of spacecraft while enabling operations costs to be carefully controlled, is discussed. The data capture functions, data protection, isolation of users from data acquisition problems, data reconstruction, and quality and accounting are addressed. The TDM and packet data formats utilized by the system are described, and the development of generic facilities is considered.
Connell, Edward B.↗
ACTS data center
Viewgraphs on ACTS Data Center status report are included. Topics covered include: ACTS Data Center Functions; data flow overview; PPD flow; RAW data flow; data compression; PPD distribution; RAW Data Archival; PPD Audit; and data analysis.
Syed, Ali↗
Data Reduction Functions for the Langley 14- by 22-Foot Subsonic Tunnel
The Langley 14- by 22-Foot Subsonic Tunnel's data reduction software utilizes six major functions to compute the acquired data. These functions calculate engineering units, tunnel parameters, flowmeters, jet exhaust measurements, balance loads/model attitudes, and model /wall pressures. The input (required) variables, the output (computed) variables, and the equations and/or subfunction(s) associated with each major function are discussed.
Boney, Andy D.↗
Description of the SSF PMAD DC testbed control system data acquisition function
The NASA LeRC in Cleveland, Ohio has completed the development and integration of a Power Management and Distribution (PMAD) DC Testbed. This testbed is a reduced scale representation of the end to end, sources to loads, Space Station Freedom Electrical Power System (SSF EPS). This unique facility is being used to demonstrate DC power generation and distribution, power management and control, and system operation techniques considered to be prime candidates for the Space Station Freedom. A key capability of the testbed is its ability to be configured to address system level issues in support of critical SSF program design milestones. Electrical power system control and operation issues like source control, source regulation, system fault protection, end-to-end system stability, health monitoring, resource allocation, and resource management are being evaluated in the testbed. The SSF EPS control functional allocation between on-board computers and ground based systems is evolving. Initially, ground based systems will perform the bulk of power system control and operation. The EPS control system is required to continuously monitor and determine the current state of the power system. The DC Testbed Control System consists of standard controllers arranged in a hierarchical and distributed architecture. These controllers provide all the monitoring and control functions for the DC Testbed Electrical Power System. Higher level controllers include the Power Management Controller, Load Management Controller, Operator Interface System, and a network of computer systems that perform some of the SSF Ground based Control Center Operation. The lower level controllers include Main Bus Switch Controllers and Photovoltaic Controllers. Power system status information is periodically provided to the higher level controllers to perform system control and operation. The data acquisition function of the control system is distributed among the various levels of the hierarchy. Data requirements are dictated by the control system algorithms being implemented at each level. A functional description of the various levels of the testbed control system architecture, the data acquisition function, and the status of its implementationis presented.
Baez, Anastacio N.↗
Elastic Bayesian Model Calibration
Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.
97 MATHEMATICS AND COMPUTING↗
Kepler Planet Detection Metrics: Window and One-Sigma Depth Functions for Data Release 25
This document describes the window and one-sigma depth functions relevant to the Transiting Planet Search (TPS) algorithm in the Kepler pipeline (Jenkins 2002; Jenkins et al. 2017). The window function specifies the fraction of unique orbital ephemeris epochs over which three transits are observable as a function of orbital period. In this context, the epoch and orbital period, together, comprise the ephemeris of an orbiting companion, and ephemerides with the same period are considered equivalent if their epochs differ by an integer multiple of the period. The one-sigma depth function specifies the depth of a signal (in ppm) for a given light curve that results in a one-sigma detection of a transit signature as a function of orbital period when averaged over all unique orbital ephemerides. These planet detection metrics quantify the ability of TPS to detect a transiting planet signature on a star-by-star basis. They are uniquely applicable to a specific Kepler data release, since they are dependent on the details of the light curves searched and the functionality of the TPS algorithm used to perform the search. This document describes the window and one-sigma depth functions relevant to Kepler Data Release 25 (DR25), where the data were processed (Thompson et al. 2016) and searched (Twicken et al. 2016) with the SOC 9.3 pipeline. In Section 4, we describe significant differences from those reported in Kepler Data Release 24 (Burke Seader 2016) and document our verification method.
Planet Detection Metrics↗
Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440
ABSTRACT There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida . Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.
09 BIOMASS FUELS↗
VEESA R package
SAND2024-04584O R package for applying the VEESA pipeline method is a technique used for explainable machine learning with functional data. The VEESA pipeline makes use of the elastic-shape analysis framework for functional data. It also implements functional principal component analysis and permutation feature importance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
Tucker, James↗
Universal Utility Data Exchange (UUDEX) Functional Design Requirements - Rev 1
This document provides a set of high-level functional design requirements for Universal Utility Data Exchange (UUDEX). These requirements include a set of use cases, data exchanges supported by UUDEX, both for grid operations and for cyber security data, functional requirements for data exchange, data models used by UUDEX, data exchange architectures, and security considerations. These functional design requirements are intended to be used in more detailed design documents.
97 MATHEMATICS AND COMPUTING↗
Science Workflows using Kamodo
Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.
software↗
Science Workflows using Kamodo
Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.
python↗
Some design constraints required for the assembly of software components: The incorporation of atomic abstract types into generically structured abstract types
It is nearly axiomatic, that to take the greatest advantage of the useful features available in a development system, and to avoid the negative interactions of those features, requires the exercise of a design methodology which constrains their use. A major design support feature of the Ada language is abstraction: for data, functions processes, resources, and system elements in general. Atomic abstract types can be created in packages defining those private types and all of the overloaded operators, functions, and hidden data required for their use in an application. Generically structured abstract types can be created in generic packages defining those structured private types, as buildups from the user-defined data types which are input as parameters. A study is made of the design constraints required for software incorporating either atomic or generically structured abstract types, if the integration of software components based on them is to be subsequently performed. The impact of these techniques on the reusability of software and the creation of project-specific software support environments is also discussed.
Johnson, Charles S.↗
An upper bound on the radius of a highly electrically conducting lunar core
Parker's (1980) nonlinear inverse theory for the electromagnetic sounding problem is converted to a form suitable for analysis of lunar day-side transfer function data by: (1) transforming the solution in plane geometry to that in spherical geometry; and (2) transforming the theoretical lunar transfer function in the dipole limit to an apparent resistivity function. The theory is applied to the revised lunar transfer function data set of Hood et al. (1982), which extends in frequency from 10 to the -5th to 10 to the -3rd Hz. On the assumption that an iron-rich lunar core, whether molten or solid, can be represented by a perfect conductor at the minimum sampled frequency, an upper bound of 435 km on the maximum radius of such a core is calculated. This bound is somewhat larger than values of 360-375 km previously estimated from the same data set via forward model calculations because the prior work did not consider all possible mantle conductivity functions.
Hobbs, B. A.↗
Single point random modal test technology application to failure detection
Frequency response function data, normally acquired as a data base for use in development of empirically based mode shapes, has additional utility. Comparison and analysis of frequency response function data sets obtained prior to and after environmental tests of an Orbiter body flap have enabled identification of structural damage that was not detected by conventional visual, X-ray, and ultrasonic inspections. The analyses and conclusions reported demonstrate that specific damaged areas within a relatively complex structure identified on a timely basis.
West, W. M., Jr.↗
Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections
Abstract In many fields of science and engineering, we frequently encounter experiments or simulations datasets that describe the behavior of complex systems and uncovering human interpretable patterns between their inputs and outputs via exploratory data analysis is essential for building intuition and facilitating discovery. Often, we resort to 2D embeddings for examining these high-dimensional relationships (e.g. dimensionality reduction). However, most existing embedding methods treat the dimensions as coordinates for samples in a high-dimensional space, which fail to capture the potential functional relationships, and the few methods that do take function into consideration either only focus on linear patterns or produce non-linear embeddings that are hard to interpret. To address these challenges, we proposed function preserving projections (FPP), which construct 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns between the domain and the range of a high-dimensional function. The intuition here is that humans are good at understanding potentially non-linear patterns in 2D but unable to interpret non-linear mapping from high-dimensional space to 2D. Therefore, we should restrict the projection to linear but not the pattern we are seeking. Using FPP on real-world datasets, one can obtain fundamentally new insights about high-dimensional relationships in extremely large datasets that could not be processed with existing dimension reduction methods.
97 MATHEMATICS AND COMPUTING↗
Conservation of endo -glucanase 16 (EG16) activity across highly divergent plant lineages
Plant cell walls are highly dynamic structures that are composed predominately of polysaccharides. As such, endogenous carbohydrate active enzymes (CAZymes) are central to the synthesis and subsequent modification of plant cells during morphogenesis. The endo-glucanase 16 (EG16) members constitute a distinct group of plant CAZymes, angiosperm orthologs of which were recently shown to have dual β-glucan/xyloglucan hydrolase activity. Molecular phylogeny indicates that EG16 members comprise a sister clade with a deep evolutionary relationship to the widely studied apoplastic xyloglucan endo-transglycosylases/hydrolases (XTH). A cross-genome survey indicated that EG16 members occur as a single ortholog across species and are widespread in early diverging plants, including the non-vascular bryophytes, for which functional data were previously lacking. Remarkably, enzymological characterization of an EG16 ortholog from the model moss Physcomitrella patens (PpEG16) revealed that EG16 activity and sequence/structure are highly conserved across 500 million years of plant evolution, vis-à-vis orthologs from grapevine and poplar. Ex vivo biomechanical assays demonstrated that the application of EG16 gene products caused abrupt breakage of etiolated hypocotyls rather than slow extension, thereby indicating a mode-of-action distinct from endogenous expansins and microbial endo-glucanases. As a result, the biochemical data presented here will inform future genomic, genetic, and physiological studies of EG16 enzymes.
59 BASIC BIOLOGICAL SCIENCES↗