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At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

NASA NTRS1995

Rapid Airplane Parametric Input Design (RAPID)

RAPID is a methodology and software system to define a class of airplane configurations and directly evaluate surface grids, volume grids, and grid sensitivity on and about the configurations. A distinguishing characteristic which separates RAPID from other airplane surface modellers is that the output grids and grid sensitivity are directly applicable in CFD analysis. A small set of design parameters and grid control parameters govern the process which is incorporated into interactive software for 'real time' visual analysis and into batch software for the application of optimization technology. The computed surface grids and volume grids are suitable for a wide range of Computational Fluid Dynamics (CFD) simulation. The general airplane configuration has wing, fuselage, horizontal tail, and vertical tail components. The double-delta wing and tail components are manifested by solving a fourth order partial differential equation (PDE) subject to Dirichlet and Neumann boundary conditions. The design parameters are incorporated into the boundary conditions and therefore govern the shapes of the surfaces. The PDE solution yields a smooth transition between boundaries. Surface grids suitable for CFD calculation are created by establishing an H-type topology about the configuration and incorporating grid spacing functions in the PDE equation for the lifting components and the fuselage definition equations. User specified grid parameters govern the location and degree of grid concentration. A two-block volume grid about a configuration is calculated using the Control Point Form (CPF) technique. The interactive software, which runs on Silicon Graphics IRIS workstations, allows design parameters to be continuously varied and the resulting surface grid to be observed in real time. The batch software computes both the surface and volume grids and also computes the sensitivity of the output grid with respect to the input design parameters by applying the precompiler tool ADIFOR to the grid generation program. The output of ADIFOR is a new source code containing the old code plus expressions for derivatives of specified dependent variables (grid coordinates) with respect to specified independent variables (design parameters). The RAPID methodology and software provide a means of rapidly defining numerical prototypes, grids, and grid sensitivity of a class of airplane configurations. This technology and software is highly useful for CFD research for preliminary design and optimization processes.

Smith, Robert E.↗

DOE OSTI2020

ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data

The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at https://earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (https://maap-project.org) and ESA (https://esa-maap.org/) landing pages.

cloud computing↗

DOE OSTI2023

Alternatives to Contour Visualizations for Power Systems Data

Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system - Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗

DOE OSTI2023

Alternatives to Contour Visualizations for Power Systems Data

Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system-Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.

MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A Visual Designer of Layer‐wise Relevance Propagation Models

Abstract Layer‐wise Relevance Propagation (LRP) is an emerging and widely‐used method for interpreting the prediction results of convolutional neural networks (CNN). LRP developers often select and employ different relevance backpropagation rules and parameters, to compute relevance scores on input images. However, there exists no obvious solution to define a “best” LRP model. A satisfied model is highly reliant on pertinent images and designers' goals. We develop a visual model designer, named as VisLRPDesigner, to overcome the challenges in the design and use of LRP models. Various LRP rules are unified into an integrated framework with an intuitive workflow of parameter setup. VisLRPDesigner thus allows users to interactively configure and compare LRP models. It also facilitates relevance‐based visual analysis with two important functions: relevance‐based pixel flipping and neuron ablation. Several use cases illustrate the benefits of VisLRPDesigner. The usability and limitation of the visual designer is evaluated by LRP users.

Huang, Xinyi↗

NASA NTRS2014

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

computational performance↗

DOE OSTI2021

A Visual Designer of Layer-wise Relevance Propagation Models

Layer-wise Relevance Propagation (LRP) is an emerging and widely-used method for interpreting the prediction results of convolutional neural networks (CNN). LRP developers often select and employ different relevance backpropagation rules and parameters, to compute relevance scores on input images. However, there exists no obvious solution to define a "best" LRP model. A satisfied model is highly reliant on pertinent images and designers' goals. We develop a visual model designer, named as VisLRPDesigner, to overcome the challenges in the design and use of LRP models. Various LRP rules are unified into an integrated framework with an intuitive workflow of parameter setup. VisLRPDesigner thus allows users to interactively configure and compare LRP models. It also facilitates relevance-based visual analysis with two important functions: relevance-based pixel flipping and neuron ablation. Several use cases illustrate the benefits of VisLRPDesigner. The usability and limitation of the visual designer is evaluated by LRP users.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1968

Single unit analysis of backward visual masking and metacontrast in the cat lateral geniculate nucleus.

Single unit analysis of backward visual masking and metacontrast in lateral geniculate nucleus in cats

MASKING↗

DOE OSTI2021

Observations with the Differential Speckle Survey Instrument. X. Preliminary Orbits of K-dwarf Binaries and Other Stars

This paper details speckle observations of binary stars taken at the Lowell Discovery Telescope, the WIYN telescope, and the Gemini telescopes between 2016 January and 2019 September. The observations taken at Gemini and Lowell were done with the Differential Speckle Survey Instrument (DSSI), and those done at WIYN were taken with the successor instrument to DSSI at that site, the NN-EXPLORE Exoplanet Star and Speckle Imager (NESSI). In total, we present 378 observations of 178 systems, and we show that the uncertainty in the measurement precision for the combined data set is ∼2 mas in separation, ∼1°–2° in position angle depending on the separation, and ∼0.1 mag in magnitude difference. Together with data already in the literature, these new results permit 25 visual orbits and one spectroscopic-visual orbit to be calculated for the first time. In the case of the spectroscopic-visual analysis, which is done on the ternary star HD 173093, we calculate masses with a precision of better than 1% for all three stars in that system. Twenty-one of the visual orbits calculated have a K dwarf as the primary star; we add these to the known orbits of K-dwarf primary stars and discuss the basic orbital properties of these stars at this stage. Although incomplete, the data that exist so far indicate that binaries with K-dwarf primaries tend not to have low-eccentricity orbits at separations of one to a few tens of astronomical units, that is, on solar system scales.

47 OTHER INSTRUMENTATION↗

NASA NTRS1993

Computer-aided light sheet flow visualization

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) data sets, was chosen to interactively display the reconstructed light sheet images, along with the numerical surface geometry for the model or aircraft under study. A description is provided of the photogrammetric reconstruction technique, and the image processing and computer graphics techniques and equipment. Results of the computer aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images and CFD solutions in the same graphics environment is also demonstrated.

Stacy, Kathryn↗

NASA NTRS1994

Computer-aided light sheet flow visualization using photogrammetry

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and a visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) results, was chosen to interactively display the reconstructed light sheet images with the numerical surface geometry for the model or aircraft under study. The photogrammetric reconstruction technique and the image processing and computer graphics techniques and equipment are described. Results of the computer-aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images with CFD solutions in the same graphics environment is also demonstrated.

Stacy, Kathryn↗

NASA NTRS1993

Computer-Aided Light Sheet Flow Visualization

Stacy, Kathryn↗

NASA NTRS1982

The computer treatment of remotely sensed data: An introduction to techniques which have geologic applications

Several aspects of computer-assisted analysis techniques for image enhancement and thematic classification by which LANDSAT MSS imagery may be treated quantitatively are explained. On geological applications, computer processing of digital data allows, possibly, the fullest use of LANDSAT data, by displaying enhanced and corrected data for visual analysis and by evaluating and assigning each spectral pixel information to a given class.

Parada, N. D. J.↗

NASA NTRS1993

On evaluating the accuracy of SAR sea-ice classification using multifrequency polarimetric AIRSAR data

We investigate how multifrequency and polarimetric synthetic aperture radar (SAR) imagery enhances present capability to discriminate different ice conditions in single-frequency, single-polarization satellite SAR data. Frequencies considered are C- (lambda = 5.6cm), L- (lambda = 24cm) and P- (lambda = 68cm) band. Radar backscatter characteristics of six radiometrically and polarimetrically distinct ice types are selected from a cluster analysis of the multifrequency polarimetric SAR data and used to classify SAR images. Validation of these ice conditions is based on information provided by aerial photos, weather and ice surface measurements acquired at an ice camp, together with airborne passive microwave imagery, and visual analysis of the SAR data. The six identified sea-ice types are: (1) multiyear sea-ice; (2) compressed first year ice; (3) first year rubble and ridges; (4) first year rough ice; (5) first year smooth ice; and (6) thin ice. Open water is absent in all analyzed data. Classification of the SAR imagery into those six ice types is performed using a Bayesian Maximum A Posteriori classifier. Two complete scenes acquired at different dates in different locations are classified. The scenes were chosen such that they are representative of typical ice conditions in the Beaufort Sea in March 1988 and because ancillary information is available for validating the segmentation of various ice surface conditions.

Drinkwater, Mark R.↗

NASA NTRS2000

Supporting Scientific Analysis within Collaborative Problem Solving Environments

Collaborative problem solving environments for scientists should contain the analysis tools the scientists require in addition to the remote collaboration tools used for general communication. Unfortunately, most scientific analysis tools have been designed for a "stand-alone mode" and cannot be easily modified to work well in a collaborative environment. This paper addresses the questions, "What features are desired in a scientific analysis tool contained within a collaborative environment?", "What are the tool design criteria needed to provide these features?", and "What support is required from the architecture to support these design criteria?." First, the features of scientific analysis tools that are important for effective analysis in collaborative environments are listed. Next, several design criteria for developing analysis tools that will provide these features are presented. Then requirements for the architecture to support these design criteria are listed. Sonic proposed architectures for collaborative problem solving environments are reviewed and their capabilities to support the specified design criteria are discussed. A deficiency in the most popular architecture for remote application sharing, the ITU T. 120 architecture, prevents it from supporting highly interactive, dynamic, high resolution graphics. To illustrate that the specified design criteria can provide a highly effective analysis tool within a collaborative problem solving environment, a scientific analysis tool that contains the specified design criteria has been integrated into a collaborative environment and tested for effectiveness. The tests were conducted in collaborations between remote sites in the US and between remote sites on different continents. The tests showed that the tool (a tool for the visual analysis of computer simulations of physics) was highly effective for both synchronous and asynchronous collaborative analyses. The important features provided by the tool (and made possible by the specified design criteria) are: 1. The tool provides highly interactive, dynamic, high resolution, 3D graphics. 2. All remote scientists can view the same dynamic, high resolution, 3D scenes of the analysis as the analysis is being conducted. 3. The responsiveness of the tool is nearly identical to the responsiveness of the tool in a stand-alone mode. 4. The scientists can transfer control of the analysis between themselves. 5. Any analysis session or segment of an analysis session, whether done individually or collaboratively, can be recorded and posted on the Web for other scientists or students to download and play in either a collaborative or individual mode. 6. The scientist or student who downloaded the session can, individually or collaboratively, modify or extend the session with his/her own "what if" analysis of the data and post his/her version of the analysis back onto the Web. 7. The peak network bandwidth used in the collaborative sessions is only 1K bit/second even though the scientists at all sites are viewing high resolution (1280 x 1024 pixels), dynamic, 3D scenes of the analysis. The links between the specified design criteria and these performance features are presented.

Watson, Velvin R.↗

NASA NTRSDate not supplied

The Application of Artificial Intelligence Deep Learning to Visually Identify Micrometeoroid and Orbital Debris Impacts

Recent advances in Artificial Intelligence (AI) are changing the World. Novel approaches to training AI systems have led to dramatic reductions in the amount of time required. Training an AI system could take years and teams of people using traditional methods, but with the advancements of Deep Learning (DL) models this training can now be accomplished by an individual in a matter of minutes. The development of “fast AI” libraries has delivered AI to essentially everyone. Democratization of AI power has inspired many to revisit past problems that will benefit from DL approaches. For example, the application of AI has improved detection of breast cancer by 20% compared to traditional detection methods. Computer vision and machine learning are being used to identify soil deficiencies and provide planting recommendations to farmers. Success stories like these and many others have provided inspiration to see if AI can help improve one of our needed capabilities – that of visually identifying micrometeoroid and orbital debris (MMOD) impact damage to spacecraft from images of the spacecraft exterior. The need to visually locate and characterize spacecraft MMOD impact damage has been present since the early days of space travel. This is often done by either having a crew member take photographs of the spacecraft through a window using a hand-held camera or ground personnel directing externally-mounted cameras. The photographs are then transmitted back to Earth for visual analysis. This method of MMOD damage inspection works well and has been used on various spacecraft including the Space Shuttle and the International Space Station (ISS). One of the issues with the current method that we believe AI could improve is the speed and possibly the accuracy in identifying MMOD impacts. Note that detecting MMOD impacts in images can be very difficult. The visual appearance of an MMOD impact can change dramatically with lighting conditions, size of impact, depth of penetration, material types, surface waviness, fabric coverings, camera & lens, distance to surface, spacecraft orientation, analyst experience, and many other factors. Currently, this takes a team of highly-experienced specialists in both the fields of Image Analysis and MMOD impacts. This paper documents our initial research in training an AI DL model using the fast-AI library to identify actual and simulated MMOD impacts and perforations into exposed flat surfaces. While we recognize that this initial goal seems modest, it must be noted that what we have done would have taken teams of individuals and years of training just ten years ago. Our long-term goal is to add complexity and use-cases to the DL model being trained to expand the capabilities of this model so that it can be used to identify MMOD impacts on all types of spacecraft surfaces.

Cameron M Collins↗

NASA NTRS1994

A Knowledge-Based Representation Scheme for Environmental Science Models

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

Keller, Richard M.↗