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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Risk-Informed Decision Making: Application to Technology Development Alternative Selection

NASA NPR 8000.4A, Agency Risk Management Procedural Requirements, defines risk management in terms of two complementary processes: Risk-informed Decision Making (RIDM) and Continuous Risk Management (CRM). The RIDM process is used to inform decision making by emphasizing proper use of risk analysis to make decisions that impact all mission execution domains (e.g., safety, technical, cost, and schedule) for program/projects and mission support organizations. The RIDM process supports the selection of an alternative prior to program commitment. The CRM process is used to manage risk associated with the implementation of the selected alternative. The two processes work together to foster proactive risk management at NASA. The Office of Safety and Mission Assurance at NASA Headquarters has developed a technical handbook to provide guidance for implementing the RIDM process in the context of NASA risk management and systems engineering. This paper summarizes the key concepts and procedures of the RIDM process as presented in the handbook, and also illustrates how the RIDM process can be applied to the selection of technology investments as NASA's new technology development programs are initiated.

Dezfuli, Homayoon↗
NASA NTRS2010

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

Riggs, George A.↗
NASA NTRS2011

Use of Probabilistic Risk Assessment in Shuttle Decision Making Process

This slide presentation reviews the use of Probabilistic Risk Assessment (PRA) to assist in the decision making for the shuttle design and operation. Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and disciplined approach to identifying and analyzing risk in complex systems and/or processes that seeks answers to three basic questions: (i.e., what can go wrong? what is the likelihood of these occurring? and what are the consequences that could result if these occur?) The purpose of the Shuttle PRA (SPRA) is to provide a useful risk management tool for the Space Shuttle Program (SSP) to identify strengths and possible weaknesses in the Shuttle design and operation. SPRA was initially developed to support upgrade decisions, but has evolved into a tool that supports Flight Readiness Reviews (FRR) and near real-time flight decisions. Examples of the use of PRA for the shuttle are reviewed.

Boyer, Roger L.↗
NASA NTRS2011

Pilot/Controller Coordinated Decision Making in the Next Generation Air Transportation System

Introduction: NextGen technologies promise to provide considerable benefits in terms of enhancing operations and improving safety. However, there needs to be a thorough human factors evaluation of the way these systems will change the way in which pilot and controllers share information. The likely impact of these new technologies on pilot/controller coordinated decision making is considered in this paper using the "operational, informational and evaluative disconnect" framework. Method: Five participant focus groups were held. Participants were four experts in human factors, between x and x research students and a technical expert. The participant focus group evaluated five key NextGen technologies to identify issues that made different disconnects more or less likely. Results: Issues that were identified were: Decision Making will not necessarily improve because pilots and controllers possess the same information; Having a common information source does not mean pilots and controllers are looking at the same information; High levels of automation may lead to disconnects between the technology and pilots/controllers; Common information sources may become the definitive source for information; Overconfidence in the automation may lead to situations where appropriate breakdowns are not initiated. Discussion: The issues that were identified lead to recommendations that need to be considered in the development of NextGen technologies. The current state of development of these technologies provides a good opportunity to utilize recommendations at an early stage so that NextGen technologies do not lead to difficulties in resolving breakdowns in coordinated decision making.

Bearman, Chris↗
NASA NTRS2012

Evaluation of Algorithms for a Miles-in-Trail Decision Support Tool

Four machine learning algorithms were prototyped and evaluated for use in a proposed decision support tool that would assist air traffic managers as they set Miles-in-Trail restrictions. The tool would display probabilities that each possible Miles-in-Trail value should be used in a given situation. The algorithms were evaluated with an expected Miles-in-Trail cost that assumes traffic managers set restrictions based on the tool-suggested probabilities. Basic Support Vector Machine, random forest, and decision tree algorithms were evaluated, as was a softmax regression algorithm that was modified to explicitly reduce the expected Miles-in-Trail cost. The algorithms were evaluated with data from the summer of 2011 for air traffic flows bound to the Newark Liberty International Airport (EWR) over the ARD, PENNS, and SHAFF fixes. The algorithms were provided with 18 input features that describe the weather at EWR, the runway configuration at EWR, the scheduled traffic demand at EWR and the fixes, and other traffic management initiatives in place at EWR. Features describing other traffic management initiatives at EWR and the weather at EWR achieved relatively high information gain scores, indicating that they are the most useful for estimating Miles-in-Trail. In spite of a high variance or over-fitting problem, the decision tree algorithm achieved the lowest expected Miles-in-Trail costs when the algorithms were evaluated using 10-fold cross validation with the summer 2011 data for these air traffic flows.

Bloem, Michael↗
NASA NTRS2012

Using NASA Environmental Data to Enhance Public Health Decision Making

The Universities Space Research Association at the NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this collaboration are to develop high-quality spatial data sets of environmental variables, and deliver the data sets and associated analyses to local, state and federal end-user groups. These data can be linked spatially and temporally to public health data, such as mortality and disease morbidity, for further analysis and decision making. Three daily environmental data sets have been developed for the conterminous U.S. on different spatial resolutions for the time period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be made available to public health professionals, researchers and the general public through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer reviewed publications. To date, two of the data sets have been released to the public in CDC WONDER, Daily Air Temperature and Heat Index for years 1979-2010, and Daily Fine Particulate Matter (PM2.5) air quality measures for years 2003-2008. These data in CDC WONDER can be aggregated to the county-level, state-level, or regional-level as per users need and downloaded in tabular, graphical, and map formats. The summary statistical output are available to web and app developers via the WONDER Application Programming Interface (API). The linkage of these data with the CDC WONDER system provides a significant addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER online system. It also substantially expands public access to NASA environmental data, making their use by a wide range of decision makers feasible.

Al-Hamdan, Mohammad↗
NASA NTRS2013

Test and Evaluation Metrics of Crew Decision-Making And Aircraft Attitude and Energy State Awareness

NASA has established a technical challenge, under the Aviation Safety Program, Vehicle Systems Safety Technologies project, to improve crew decision-making and response in complex situations. The specific objective of this challenge is to develop data and technologies which may increase a pilot's (crew's) ability to avoid, detect, and recover from adverse events that could otherwise result in accidents/incidents. Within this technical challenge, a cooperative industry-government research program has been established to develop innovative flight deck-based counter-measures that can improve the crew's ability to avoid, detect, mitigate, and recover from unsafe loss-of-aircraft state awareness - specifically, the loss of attitude awareness (i.e., Spatial Disorientation, SD) or the loss-of-energy state awareness (LESA). A critical component of this research is to develop specific and quantifiable metrics which identify decision-making and the decision-making influences during simulation and flight testing. This paper reviews existing metrics and methods for SD testing and criteria for establishing visual dominance. The development of Crew State Monitoring technologies - eye tracking and other psychophysiological - are also discussed as well as emerging new metrics for identifying channelized attention and excessive pilot workload, both of which have been shown to contribute to SD/LESA accidents or incidents.

Bailey, Randall E.↗
NASA NTRS2014

Providing Global Change Information for Decision-Making: Capturing and Presenting Provenance

Global change information demands access to data sources and well-documented provenance to provide evidence needed to build confidence in scientific conclusions and, in specific applications, to ensure the information's suitability for use in decision-making. A new generation of Web technology, the Semantic Web, provides tools for that purpose. The topic of global change covers changes in the global environment (including alterations in climate, land productivity, oceans or other water resources, atmospheric composition and or chemistry, and ecological systems) that may alter the capacity of the Earth to sustain life and support human systems. Data and findings associated with global change research are of great public, government, and academic concern and are used in policy and decision-making, which makes the provenance of global change information especially important. In addition, since different types of decisions benefit from different types of information, understanding how to capture and present the provenance of global change information is becoming more of an imperative in adaptive planning.

provenance tracking↗
NASA NTRS2014

A Formally-Verified Decision Procedure for Univariate Polynomial Computation Based on Sturm's Theorem

Sturm's Theorem is a well-known result in real algebraic geometry that provides a function that computes the number of roots of a univariate polynomial in a semiopen interval. This paper presents a formalization of this theorem in the PVS theorem prover, as well as a decision procedure that checks whether a polynomial is always positive, nonnegative, nonzero, negative, or nonpositive on any input interval. The soundness and completeness of the decision procedure is proven in PVS. The procedure and its correctness properties enable the implementation of a PVS strategy for automatically proving existential and universal univariate polynomial inequalities. Since the decision procedure is formally verified in PVS, the soundness of the strategy depends solely on the internal logic of PVS rather than on an external oracle. The procedure itself uses a combination of Sturm's Theorem, an interval bisection procedure, and the fact that a polynomial with exactly one root in a bounded interval is always nonnegative on that interval if and only if it is nonnegative at both endpoints.

Narkawicz, Anthony J.↗
NASA NTRS2016

Using Multimodal Input for Autonomous Decision Making for Unmanned Systems

Autonomous decision making in the presence of uncertainly is a deeply studied problem space particularly in the area of autonomous systems operations for land, air, sea, and space vehicles. Various techniques ranging from single algorithm solutions to complex ensemble classifier systems have been utilized in a research context in solving mission critical flight decisions. Realized systems on actual autonomous hardware, however, is a difficult systems integration problem, constituting a majority of applied robotics development timelines. The ability to reliably and repeatedly classify objects during a vehicles mission execution is vital for the vehicle to mitigate both static and dynamic environmental concerns such that the mission may be completed successfully and have the vehicle operate and return safely. In this paper, the Autonomy Incubator proposes and discusses an ensemble learning and recognition system planned for our autonomous framework, AEON, in selected domains, which fuse decision criteria, using prior experience on both the individual classifier layer and the ensemble layer to mitigate environmental uncertainty during operation.

Neilan, James H.↗
NASA NTRS2016

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Agricultural↗
NASA NTRS2017

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

taxi time prediction↗
NASA NTRSDate not supplied

Visualizations to Aid Decision-Making in the ACCP Value Framework

NASA’s priorities for Earth Science are informed by the 2017-2027 Decadal Survey for Earth Science and Applications from Space of the National Academies of Sciences, Engineering and Medicine. In that document, five Designated Observables are identified as priorities for research, two of which are Aerosols, and Clouds, Convection, and Precipitation. NASA is addressing these two Designated Observables by a combined study (the Aerosols, Clouds, Convection, and Precipitation, or ACCP, study). In this context of the ACCP study’s multiple objectives and multiple stakeholders, heuristic-based approaches are insufficient to assess candidate observing system concepts due to the complexity of the decision problem. For the ACCP study, this complexity in assessment is such that absent a structured approach that permits understanding of the steps leading to a decision, the decision risks being made on an incomplete basis. While prose and numeric data both have their place in effective communication, visualizations play an important part not only in presenting information, but also in structuring conversations around that information. Each of the visualizations developed for the ACCP Value Framework serve at least one of several functions: it structures communication about a method or process, it facilitates the elicitation and aggregation of data, or it summarizes complex information to enable analysis. Thus, these visualizations can present information, structure conversations, or do both.

Christopher A Jones↗
NASA NTRSDate not supplied

How Environment and Operational Considerations Guide Requirements Development for Clinical Decision Support Beyond Low Earth Orbit

The Aerospace Medical community is acutely aware of the need for automated cognitive systems to support astronauts in responding to and making critical decisions about assessing – and treating - medical conditions and assuring wellness during exploration spaceflight missions. Such systems will be critically important to mission success as we venture farther from terrestrial settings (particularly low-earth orbit platforms) and encounter increasingly Earth-independent medical decision-making scenarios and requirements. This need would likely be met through the development of a Clinical Decision Support System (CDSS) that assimilates information from various sources (e.g., vehicle environmental control system, blood pressure cuff, and heart rate monitor) integrated with medical and wellness data to provide detailed guidance tailored to individual crew member needs. This presentation will address some of the fundamental considerations and their implications that must be fully understood by CDSS requirement developers early in the system lifecycle.

B Burian↗
NASA NTRSDate not supplied

The NASA Earth Science Applied Sciences Disasters Program: Making EO Data and Expertise Available through the Disasters Mapping Portal to Inform Decision-Makers throughout the Disaster Cycle

The NASA Earth Science Applied Sciences Disasters Program promotes the use of Earth Observations (EO) to inform disaster risk reduction and resilience throughout the disaster cycle, from local to global scales, by harnessing NASA science and technology capabilities, through engagement with end users to demonstrate the value and impact of EO to support decision-making, and by supporting end users in their use of EO in decision-making while developing relationships to grow as a trusted source of relevant science and useful results. Through the NASA Earth Science Applied Sciences Disasters Program Mapping Portal, an Esri-backed hub of geospatially enabled disaster products, event-based and near-real-time products are hosted to provide end users with analysis-ready data and data services, keeping in mind their expressed EO needs. In this presentation, case studies of past events will be highlighted, showcasing how EO data and services were provided by the Program and utilized by end users during the disaster cycle. Additionally, this presentation will highlight some of the Program’s ongoing collaboration activities to assist end users in understanding and preparing for utilization of EO data from the Mapping Portal to inform their decision-making throughout the disaster cycle. Ongoing efforts to advance the science provided by the Program on the Mapping Portal through new developments and capabilities will be highlighted, and common challenges and data needs end users often encounter when using EO data and our Program’s innovative solutions to these challenges will be addressed. Furthermore, this presentation will touch on the challenges faced and the solutions created by the Mapping Portal team in managing and hosting a plethora of EO data for end users across various disciplines.

Ronan Lucey↗
NASA NTRS2022

Using the Decision Tree (DT) to Help Scientists Navigate the Access to Space (ATS) Options

Abstract – The Decision Tree (DT) is a tool that uses a tree-like model of decisions and their possible consequences, including outcomes, costs, schedule, risks, and performance. The paper applies the decision tree tool to the Access To Space (ATS) options to help scientists and small sat teams select the best approach that meets their ATS requirements. The ATS DT has three primary branches: rideshare, hosted payloads, and dedicated launch vehicles. Each of these branches has multiple sub-branches of ATS options. This paper describes the ATS options and provides industry contacts for each ATS option.

Rideshare↗
NASA NTRSDate not supplied

Lunar Instrument Data Integration Into the Virtual Reality Mission Simulation System for Decision Communication and Situational Awareness

In situ resource utilization (ISRU) technologies are a key advancement required to make human habitation on the Moon and Mars viable. The upcoming Volatiles Investigating Polar Exploration Rover (VIPER) mission will provide crucial correlations between volatiles and lunar geology to understand the water content available for ISRU on the moon. The mission will require the coordination of multi-disciplinary teams across the country making real time decisions based on rover instrument data. The virtual reality Mission Simulation System (vMSS) is a virtual reality platform designed at MIT by the Resource Exploration and Science of our Cosmic Environment (RESOURCE) team to provide teams with a collaboration interface for planetary missions like VIPER. Herein we determine the integration pathway for analog based datasets that are examples of VIPER's two main instruments, the near-infrared volatile spectrometer subsystem (NIRVSS) and the neutron spectrometer subsystem (NSS), into vMSS to provide the most valuable visualization tools. Focusing on improving situational awareness, decision making, reducing task load and incorporating comments from scientists working previous analogs and on the current VIPER mission, we recommend critical elements to implement into vMSS and the best approaches for data visualization. We present a review of relevant analogs and state of the art mission software. We have developed a design concept and path to flight of analysed instrument data integrated with data maps that allow for virtual manipulation and annotation between non-co-located team members. We focus on pre-mission mapping of a priori data for improved situational awareness, layering of analysed instrument data, correlative mapping and interactive capabilities for in-mission decision making, as well as archiving and annotation tools for post-mission analysis. Finally, we lay out the roadmap for the future development of immersive sample site visualization capabilities and the use of integrated instrument data in vMSS with automated temporal and geospatial planning.

Cody Alison Paige↗
NASA NTRSDate not supplied

Design, Formalization, and Verification of Decision Making for Intelligent Systems

The development of autonomous systems requires a rigorous process that can guarantee a system’s reliability in critical applications. At its core, an autonomous system bases its behavior on a well-defined decision making system. In this paper, we present a methodological basis for the design, formalization and formal verification of Decision Making systems for autonomous agents. The approach is generally applicable to operational objectives that can be functionally decomposed and subsequently represented as Hierarchical Finite State Machines. As a case study, we present the application of this method to implement a Decision Making model in Simulink. Furthermore, we present how we use NASA’s FRET tool to write requirements in structured natural language and generate formal specifications that can be automatically digested by NASA’s CoCoSim tool. Finally, we present how, by leveraging CoCoSim, we perform formal verification against the Simulink model and present analysis results.

Model-based development↗