Results for “information visualization” — Page 83

NASA NTRS2004

Spaceflight Operations Services Grid (SOSG) Prototype Implementation and Feasibility Study

Science Operations Services Grid is focusing on building a prototype grid-based environment that incorporates existing and new spaceflight services to enable current and future NASA programs with cost savings and new and evolvable methods to conduct science in a distributed environment. The Science Operations Services Grid (SOSG) will provide a distributed environment for widely disparate organizations to conduct their systems and processes in a more efficient and cost effective manner. These organizations include those that: 1) engage in space-based science and operations, 2) develop space-based systems and processes, and 3) conduct scientific research, bringing together disparate scientific disciplines like geology and oceanography to create new information. In addition educational outreach will be significantly enhanced by providing to schools the same tools used by NASA with the ability of the schools to actively participate on many levels in the science generated by NASA from space and on the ground. The services range from voice, video and telemetry processing and display to data mining, high level processing and visualization tools all accessible from a single portal. In this environment, users would not require high end systems or processes at their home locations to use these services. Also, the user would need to know minimal details about the applications in order to utilize the services. In addition, security at all levels is an underlying goal of the project. The Science Operations Services Grid will focus on four tools that are currently used by the ISS Payload community along with nine more that are new to the community. Under the prototype four Grid virtual organizations PO) will be developed to represent four types of users. They are a Payload (experimenters) VO, a Flight Controllers VO, an Engineering and Science Collaborators VO and an Education and Public Outreach VO. The User-based services will be implemented to replicate the operational voice, video, telemetry and commanding systems. Once the User-based services are in place, they will be analyzed to establish feasibility for Grid enabling. If feasible then each User-based service will be Grid enabled. The remaining non-Grid services if not already Web enabled will be so enabled. In the end, four portals will be developed one for each VO. Each portal will contain the appropriate User-based services required for that VO to operate.

Bradford, Robert N.↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗

NASA NTRSDate not supplied

Clinical Decision Support - Concepts of Operation

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support↗

NASA NTRSDate not supplied

Clinical Decision Support - Overview and Update

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support system↗

NASA NTRS2021

PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral com- pounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.

Davidoff, Scott↗

NASA NTRS1981

Detection of regional air pollution episodes utilizing satellite data in the visual range

A comparative analysis of satellite-observed haze patterns and ground-based aerosol measurements is carried out for July 20-23, 1978. During this period, a significant regional air pollution episode existed across the northeastern United States, accompanied by widespread haze, reduced surface visibility, and elevated sulfate levels measured by the Sulfate Regional Experiment (SURE) network. The results show that the satellite-observed haze patterns correlate closely with the area of reported low surface visibility (less than 4 mi) and high sulfate levels. Quantitative information on total aerosol loading derived from the satellite-digitized data, using an atmospheric radiative transfer model, agrees well with the results obtained from the ground-based measurements.

Bowley, C. J.↗

NASA NTRS1986

An experimental investigation of vortex breakdown on a delta wing

An experimental investigation of vortex breakdown on delta wings at high angles is presented. Thin delta wings having sweep angles of 70, 75, 80 and 85 degrees are being studied. Smoke flow visualization and the laser light sheet technique are being used to obtain cross-sectional views of the leading edge vortices as they break down. At low tunnel speeds (as low as 3 m/s) details of the flow, which are usually imperceptible or blurred at higher speeds, can be clearly seen. A combination of lateral and longitudinal cross-sectional views provides information on the three dimensional nature of the vortex structure before, during and after breakdown. Whereas details of the flow are identified in still photographs, the dynamic characteristics of the breakdown process were recorded using high speed movies. Velocity measurements were obtained using a laser Doppler anemometer with the 70 degree delta wing at 30 degrees angle of attack. The measurements show that when breakdown occurs the core flow transforms from a jet-like flow to a wake-like flow.

Payne, F. M.↗

NASA NTRS1997

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Decker, Arthur J.↗

NASA NTRS1997

A Geometry Based Infra-structure for Computational Analysis and Design

The computational steps traditionally taken for most engineering analysis (CFD, structural analysis, and etc.) are: Surface Generation - usually by employing a CAD system; Grid Generation - preparing the volume for the simulation; Flow Solver - producing the results at the specified operational point; and Post-processing Visualization - interactively attempting to understand the results For structural analysis, integrated systems can be obtained from a number of commercial vendors. For CFD, these steps have worked well in the past for simple steady-state simulations at the expense of much user interaction. The data was transmitted between phases via files. Specifically the problems with this procedure are: (1) File based. Information flows from one step to the next via data files with formats specified for that procedure. (2) 'Good' Geometry. A bottleneck in getting results from a solver is the construction of proper geometry to be fed to the grid generator. With 'good' geometry a grid can be constructed in tens of minutes (even with a complex configuration) using unstructured techniques. (3) One-Way communication. All information travels on from one phase to the next. Until this process can be automated, more complex problems such as multi-disciplinary analysis or using the above procedure for design becomes prohibitive.

Haimes, Robert↗

NASA NTRS1998

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Decker, Arthur J.↗

NASA NTRS2002

Fundamental Studies on Two-Phase Gas-Liquid Flows Through Packed Beds in Microgravity

In the typical operation of a packed-bed reactor, gas and liquid flow simultaneously through a fixed bed of solid particles. Depending on the application, the particles can be of various shapes and sizes and provide for intimate contact and high rates of transport between the phases needed to sustain chemical or biological reactions. The packing may also serve as either a catalyst or as a support for growing biological material. NASA has flown two of these packed-bed systems in a microgravity environment with limited or no success. The goal of this research is to develop models (with scale-up capability) needed for the design of the physicochemical equipment to carry out these unit operations in microgravity. New insight will also lead to improvements in normal gravity operations. Our initial experiment was flown using an existing KC-135 two-phase flow rig with a modified test section. The test section is a clear polycarbonate rectangular column with a depth of 2.54 cm, a width of 5.08 cm, and 60 cm long. The column was randomly packed with spherical glass beads by slowly dropping the beads into the bed. Even though care was taken in handling the column after it was filled with packing, the alternating high and low gravity cycles with each parabola created a slightly tighter packed bed than is typically reported for this type. By the usual method of comparing the weight difference of a completely dry column versus a column filled with water, the void fraction was found to be .345 for both sizes of beads used. Five flush mounted differential pressure transducers are spaced at even intervals with the first location 4 cm from the inlet port and the subsequent pressure transducers spaced at 13 cm intervals along the column. Differential pressure data was acquired at 1000 Hz to adequately observe pulse formation and characteristics. Visual images of the flow were recorded using a high-speed SVHS system at 500 frames per second. Over 250 different test conditions were evaluated along with a companion set of tests in normal gravity. The flow rates, fluid properties and packing properties were selected to provide a range of several orders-of-magnitude for the important dimensionless parameters. Additional information is included in the original extended abstract.

Balakotaiah, Vemuri↗

NASA NTRS2010

Functional Mobility Performance and Balance Confidence in Older Adults after Sensorimotor Adaptation Training

Research indicates a main contributor of injury in older adults is from falling. The decline in sensory systems limits information needed to successfully maneuver through the environment. The objective of this study was to determine if prolonged exposure to the realignment of perceptual-motor systems increases adaptability of balance, and if balance confidence improves after training. A total of 16 older adults between ages 65-85 were randomized to a control group (walking on a treadmill while viewing a static visual scene) and an experimental group (walking on a treadmill while viewing a rotating visual scene). Prior to visual exposure, participants completed six trials of walking through a soft foamed obstacle course. Participants came in twice a week for 4 weeks to complete training of walking on a treadmill and viewing the visual scene for 20 minutes each session. Participants completed the obstacle course after training and four weeks later. Average time, penalty, and Activity Balance Confidence Scale scores were computed for both groups across testing times. The older adults who trained, significantly improved their time through the obstacle course F (2, 28) = 9.41, p < 0.05, as well as reduced their penalty scores F (2, 28) = 21.03, p < 0.05, compared to those who did not train. There was no difference in balance confidence scores between groups across testing times F (2, 28) = 0.503, p > 0.05. Although the training group improved mobility through the obstacle course, there were no differences between the groups in balance confidence.

Buccello-Stout, Regina R.↗

NASA NTRS2002

Neurovestibular Effects of Long-Duration Spaceflight: A Summary of Mir-Phase 1 Experiences

Space motion sickness and associated neurovestibular dysfunction though not completely understood - have been relatively well clinically and operationally characterized on short-duration (1-2 week) Space Shuttle missions (Oman, et al, 1984, 1986; Thornton, et al, 1987; Reschke, et al, 1994). Between March 1995 and June 1998, seven NASA astronauts flew on the Russian Mir space station, as "Phase 1" of the joint effort to build the International Space Station, and provided NASA with invaluable experience on the operational and biomedical problems associated with flights of up to six months in duration. The goal of this paper is to provide a summary of the available information on neurovestibular dysfunction, space motion sickness, and readaptation to Earth's gravity on the NASA Mir flights, based on a set of medical questionnaire data, transcripts, and interviews which are available from the NASA-Mir Phase I program. Records were incomplete and anecdotal. All references to specific crewmembers have been removed, to respect their individual privacy. Material was excerpted from multiple sources of information relating to neurologic function, sensory illusions and motion sickness of NASA-Mir Phase I Program crewmembers. Data were compiled by epoch (in-flight vs landing/postflight) and grouped by neurovestibular topic. The information was recorded either contemporaneously during or within days after landing, or retrospectively weeks to months later. Space motion sickness symptoms are more intense and longer in duration. Sense of spatial orientation takes at least a month to become "natural and instinctive" in space station structures, but mental survey knowledge is apparently not completely developed even after 3 months in some cases. Visual reorientation illusions (VRI) are more easily induced after long exposure to weightlessness. Head movements can cause illusory spinning sensations for up to 7 days postflight. Postural and balance control does not fully recover for at least a month postflight.

Richards, Jason T.↗

NASA NTRS2004

Prioritizing Scientific Data for Transmission

A software system has been developed for prioritizing newly acquired geological data onboard a planetary rover. The system has been designed to enable efficient use of limited communication resources by transmitting the data likely to have the most scientific value. This software operates onboard a rover by analyzing collected data, identifying potential scientific targets, and then using that information to prioritize data for transmission to Earth. Currently, the system is focused on the analysis of acquired images, although the general techniques are applicable to a wide range of data modalities. Image prioritization is performed using two main steps. In the first step, the software detects features of interest from each image. In its current application, the system is focused on visual properties of rocks. Thus, rocks are located in each image and rock properties, such as shape, texture, and albedo, are extracted from the identified rocks. In the second step, the features extracted from a group of images are used to prioritize the images using three different methods: (1) identification of key target signature (finding specific rock features the scientist has identified as important), (2) novelty detection (finding rocks we haven t seen before), and (3) representative rock sampling (finding the most average sample of each rock type). These methods use techniques such as K-means unsupervised clustering and a discrimination-based kernel classifier to rank images based on their interest level.

Castano, Rebecca↗

NASA NTRSDate not supplied

Preserving NASA Historic and Current Mission Data and Adding Value to These for Future Researchers

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been actively involved in many aspects of ensuring the long-term preservation of NASA earth science data and knowledge. This involves both the recovery and preservation of early NASA meteorological and other earth observation data, as well as preserving the more recent Earth Observation System (EOS) mission data sets which continue or have reached their end of lifetime. The GES DISC adds value to these preserved data by adding metadata and making the data available online to future researchers. The early NASA meteorological and earth observation data sets from the 1960s and 70s were originally archived on magnetic tapes, and visualizations of these data were preserved on 70-mm film. As these media have aged, their contents have been at risk of permanent loss. NASA has given the task of preserving these early data sets to the GES DISC and making these data sets easily available to the public. The data from these early missions are potentially useful to climate researchers as these are some of the only global measurements made at their time. These old data on magnetic tapes and film strips do not contain easily readable metadata, and so to add value the GES DISC has added digital metadata to them so that the data are searchable and findable. The GES DISC is also involved in preserving the data and knowledge from the EOS era missions. The GES DISC follows the guidelines developed for the preservation of data as specified in the NASA EOS Data and Information System (EOSDIS) Earth Science Data Preservation Content Specification (423-SPEC-001) document. To date, the GES DISC has consulted with the data science teams from the following missions: UARS, Earth Probe TOMS, Aura HIRDLS, and SORCE, in order to properly preserve their data and accompanying documentation. The GES DISC is also currently working with the EOS science teams from TRMM, AIRS, MLS, OMI and additional missions to ensure that the relevant documents and data sets are properly archived for future researchers. A standardized procedure for mission data preservation following 423-SPEC-001 makes preservation among the many NASA EOSDIS data centers uniform, so that these could be transitioned easily to a common EOSDIS preservation repository. This presentation will give an overview of the preservation and recovery of the old NASA historical data sets archived at the GES DISC, as well as the data and documentation preservation efforts of the EOS era missions.

James Johnson↗

NASA NTRS2000

An Advanced User Interface Approach for Complex Parameter Study Process Specification in the Information Power Grid

The creation of parameter study suites has recently become a more challenging problem as the parameter studies have now become multi-tiered and the computational environment has become a supercomputer grid. The parameter spaces are vast, the individual problem sizes are getting larger, and researchers are now seeking to combine several successive stages of parameterization and computation. Simultaneously, grid-based computing offers great resource opportunity but at the expense of great difficulty of use. We present an approach to this problem which stresses intuitive visual design tools for parameter study creation and complex process specification, and also offers programming-free access to grid-based supercomputer resources and process automation.

Yarrow, Maurice↗

NASA NTRS2009

Data-Link and Surface Map Traffic Intent Displays for NextGen 4DT and Equivalent Visual Surface Operations

By 2025, U.S. air traffic is predicted to increase 3-fold and may strain the current air traffic management system, which may not be able to accommodate this growth. In response to this challenge, a consortium of industry, academia and government agencies have proposed a revolutionary new concept for U.S. aviation operations, termed the Next Generation Air Transportation System or "NextGen". Many key capabilities are being identified to enable NextGen, including the concept of "net-centric" operations whereby each aircraft and air services provider shares information to allow real-time adaptability to ever-changing factors such as weather, traffic, flight trajectories, and security. Data-link is likely to be the primary source of communication in NextGen. Because NextGen represents a radically different approach to air traffic management and requires a dramatic shift in the tasks, roles, and responsibilities for the flight deck, there are numerous research issues and challenges that must be overcome to ensure a safe, sustainable air transportation system. Flight deck display and crew-vehicle interaction concepts are being developed that proactively investigate and overcome potential technology and safety barriers that might otherwise constrain the full realization of NextGen.

Shelton, Kevin J.↗