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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Decision Support Systems for Launch and Range Operations Using Jess

The virtual test bed for launch and range operations developed at NASA Ames Research Center consists of various independent expert systems advising on weather effects, toxic gas dispersions and human health risk assessment during space-flight operations. An individual dedicated server supports each expert system and the master system gather information from the dedicated servers to support the launch decision-making process. Since the test bed is based on the web system, reducing network traffic and optimizing the knowledge base is critical to its success of real-time or near real-time operations. Jess, a fast rule engine and powerful scripting environment developed at Sandia National Laboratory has been adopted to build the expert systems providing robustness and scalability. Jess also supports XML representation of knowledge base with forward and backward chaining inference mechanism. Facts added - to working memory during run-time operations facilitates analyses of multiple scenarios. Knowledge base can be distributed with one inference engine performing the inference process. This paper discusses details of the knowledge base and inference engine using Jess for a launch and range virtual test bed.

Thirumalainambi, Rajkumar↗
NASA NTRS2013

Decision Making Training in the Mission Operations Directorate

At JSC, we train our new flight controllers on a set of team skills that we call Space Flight Resource Management (SFRM). SFRM is akin to Crew Resource Management for the airlines and trains flight controllers to work as an effective team to reduce errors and improve safety. We have developed this training over the years with the assistance of Ames Research Center, Wyle Labs and University of Central Florida. One of the skills we teach is decision making/ problem solving (DM/PS). We teach DM/PS first in several classroom sessions, reinforce it in several part task training environments, and finally practice it in full-mission, full-team simulations. What I am proposing to talk about is this training flow: its content and how we teach it.

O'Keefe, William S.↗
NASA NTRS2007

Measurement Decision Risk - The Importance of Definitions

The idea behind this presentation is how the difference in definitions can change the application. 1. Look at history, concepts, and definitions. 2. Link the test uncertainty ratio (TUR) to measurement decision risk.

Mimbs, Scott↗
NASA NTRS2013

Identification of Crew-Systems Interactions and Decision Related Trends

NASA Vehicle System Safety Technology (VSST) project management uses systems analysis to identify key issues and maintain a portfolio of research leading to potential solutions to its three identified technical challenges. Statistical data and published safety priority lists from academic, industry and other government agencies were reviewed and analyzed by NASA Aviation Safety Program (AvSP) systems analysis personnel to identify issues and future research needs related to one of VSST's technical challenges, Crew Decision Making (CDM). The data examined in the study were obtained from the National Transportation Safety Board (NTSB) Aviation Accident and Incident Data System, Federal Aviation Administration (FAA) Accident/Incident Data System and the NASA Aviation Safety Reporting System (ASRS). In addition, this report contains the results of a review of safety priority lists, information databases and other documented references pertaining to aviation crew systems issues and future research needs. The specific sources examined were: Commercial Aviation Safety Team (CAST) Safety Enhancements Reserved for Future Implementation (SERFIs), Flight Deck Automation Issues (FDAI) and NTSB Most Wanted List and Open Recommendations. Various automation issues taxonomies and priority lists pertaining to human factors, automation and flight design were combined to create a list of automation issues related to CDM.

Jones, Sharon Monica↗
NASA NTRS2013

Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions.

Weather↗
NASA NTRS2014

Project Assessment Framework through Design (PAFTD) - A Project Assessment Framework in Support of Strategic Decision Making

Research and development organizations that push the innovation edge of technology frequently encounter challenges when attempting to identify an investment strategy and to accurately forecast the cost and schedule performance of selected projects. Fast moving and complex environments require managers to quickly analyze and diagnose the value of returns on investment versus allocated resources. Our Project Assessment Framework through Design (PAFTD) tool facilitates decision making for NASA senior leadership to enable more strategic and consistent technology development investment analysis, beginning at implementation and continuing through the project life cycle. The framework takes an integrated approach by leveraging design principles of useability, feasibility, and viability and aligns them with methods employed by NASA's Independent Program Assessment Office for project performance assessment. The need exists to periodically revisit the justification and prioritization of technology development investments as changes occur over project life cycles. The framework informs management rapidly and comprehensively about diagnosed internal and external root causes of project performance.

Depenbrock, Brett T.↗
NASA NTRS2013

Improving Decision-Making Activities for Meningitis and Malaria

Public health professionals are increasingly concerned about the potential impact that climate variability and change can have on infectious disease. The International Research Institute for Climate and Society (IRI) is developing new products to increase the public health community's capacity to understand, use and demand the appropriate climate data and climate information to mitigate the public health impacts of climate on infectious disease, in particular meningitis and malaria. In this paper, we present the new and improved products that have been developed for: (i) estimating dust aerosol for forecasting risks of meningitis and (ii) for monitoring temperature and rainfall and integrating them into a vectorial capacity model for forecasting risks of malaria epidemics. We also present how the products have been integrated into a knowledge system (IRI Data Library Map Room, SERVIR) to support the use of climate and environmental information in climate-sensitive health decision-making.

parasitic diseases↗
NASA NTRS2013

Improved Targeting Through Collaborative Decision-Making and Brain Computer Interfaces

This paper reports a first step toward a brain-computer interface (BCI) for collaborative targeting. Specifically, we explore, from a broad perspective, how the collaboration of a group of people can increase the performance on a simple target identification task. To this end, we requested a group of people to identify the location and color of a sequence of targets appearing on the screen and measured the time and accuracy of the response. The individual results are compared to a collective identification result determined by simple majority voting, with random choice in case of drawn. The results are promising, as the identification becomes significantly more reliable even with this simple voting and a small number of people (either odd or even number) involved in the decision. In addition, the paper briefly analyzes the role of brain-computer interfaces in collaborative targeting, extending the targeting task by using a BCI instead of a mechanical response.

Brain computer interfaces (BCI)↗
NASA NTRS2016

Decision Manifold Approximation for Physics-Based Simulations

With the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.

Wong, Jay Ming↗
NASA NTRS2018

Learning Hard Lessons from Columbia Decisions

This is a short presentation on lessons learned from the decision-making process around the Columbia Accident. It draws upon material in the CAIB report and uses two NASA public video clips.

Rogers, Ed↗
NASA NTRS2018

Airplane Capabilities: Translating Non-Normal Information for Operational Decision-Making

We consider how a jet transport airplane interface supports the flight crew in managing airplane system failures (or non-normals) for continued safe flight and landing. The existing state of the art starts with a list of airplane system component failures and asks the flight crew to determine, with the help of non-normal procedures, the operational consequences of those failures. As airplane systems become more complex and interconnected, the flight crew's ability to determine operational consequences will become inadequate. We describe an approach that attempts to translate airplane system failures directly into airplane "capabilities," which is a set of basic airplane functions, such as the ability to stop after landing. This paper describes the overall framework for supporting flight crews in operational decision making and the initial efforts to develop a language and display concepts.

managing airplane system non-normals↗
NASA NTRS2019

Connecting GPM Data to Decision Makers: Applications and Outreach

In this presentation we provided an overview of current GPM applications activities, highlighting multiple case studies that describe how GPM satellite data and products have been used to support decision- and policy-making. Understanding how and why stakeholders are using GPM data and products as well as the current challenges, barriers and opportunities for using GPM products within their applications enables us to learn and address the needs of end users as well as improve data usability.

Kirschbaum, Dalia↗
NASA NTRS2020

Application of Risk Informed Decision Making to Highly Reliable Three Dimensionally Woven Thermal Protection System for Mars Sample Return

The NASA Risk Informed Decision Making process is used to assess a trade space of three dimensionally woven thermal protection systems for application to the Mars Sample Return Earth Entry Vehicle. Candidate architectures are assessed based on mission assurance, technical development, cost, and schedule risk. Assessment methodology differed between the architectures, utilizing a four-point quantitative scale for mission assurance and technical development and highly tailored PERT techniques for cost and schedule. Risk results are presented, in addition to a review of RIDM effectiveness for this application.

Needels, J.↗
NASA NTRS2020

Application of Risk Informed Decision Making to a Highly Reliable Three-Dimensionally Woven Thermal Protection System for Mars Sample Return

The NASA Risk Informed Decision Making process is used to assess a trade space of three dimensionally woven thermal protection systems for application to the Mars Sample Return Earth Entry Vehicle. Candidate architectures are assessed based on mission assurance, technical development, cost, and schedule risk. Assessment methodology differed between the architectures, utilizing a four-point quantitative scale for mission assurance and technical development and highly tailored PERT techniques for cost and schedule. Risk results are presented, in addition to a review of RIDM effectiveness for this application.

Needels, J.↗
NASA NTRS2020

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.

Colombano, Silvano↗
NASA NTRS2019

In-Time Non-Participant Casualty Risk Assessment to Support Onboard Decision Making for Autonomous Unmanned Aircraft

Numerous operational paradigms, technologies, and missions are emerging as newcomers to the National Airspace System (NAS) develop small Unmanned Aircraft Systems (sUAS), personal air vehicles and other Urban Air Mobility (UAM) concepts. As the list of applications expands, maintaining the safety of the current airspace system remains one of the core concerns preventing widespread commercial implementation of these concepts. Further, the risks associated with unmanned aircraft operations themselves have to be recognized and mitigated in a timely manner. Safety-critical risks include, but are not limited to, flight outside of approved airspace, unsafe proximity to people or property, critical system failures, loss-of control, and cyber-security related risks. Instead of reacting to accidents, a set of predictive and data-driven risk monitoring, assessment, and mitigation capabilities are envisioned to help capture and eliminate hazards as these systems become operational. NASA’s System-wide Safety project is performing R&D on such a safety assurance concept. As part of this concept, this paper describes an architecture that continuously monitors a diverse set of onboard and ground-based sources to estimate and predict non-participant casualty risk during flight. Timely identification of the changing nature of this risk can inform decision making processes to mitigate current and impending situations.

Ancel, Ersin↗
NASA NTRSDate not supplied

The Challenger disaster was caused by an Apollo decision

NASA’s view of risk changed between early Apollo and the Space Shuttle. Risk was a known serious problem at the beginning of Apollo and the risk estimates were disturbingly high. To avoid public concern, risk analysis was discontinued. Risk analysis was avoided in Shuttle, leading to an unnecessarily risky design. The immediate cause of the Challenger tragedy was the mistaken decision to launch in cold weather. The fundamental cause was the high risk of the Shuttle design. Before Challenger, management thought and testified that the probability of an accident was 1 in 100,000. After Challenger, Probabilistic Risk Analysis (PRA) found a roughly 1 in 100 chance of a Shuttle failure. The recent Orion design uses the safer Apollo approach, with a hardened capsule, launch abort escape, and the crew placed above the rocket tanks and engines. During Apollo it was estimated that, “assuming all elements from propulsion to rendezvous and life support were done as well or better than ever before, that 30 astronauts would be lost before 3 were returned safely to the Earth.” The chance of astronaut survival was only 10%. After the Apollo 1 tragedy, the awareness of risk led to an intense focus on achieving safety. “The only possible explanation for the astonishing success – no losses in space and on time – was that every participant at every level in every area far exceeded the norm of human capabilities.” During Apollo, a NASA PRA found that the chance of success was “less than 5 percent.” The NASA Administrator felt that “the numbers could do irreparable harm,” and discontinued numerical risk assessment.

Harry W Jones↗