Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Intelligent Decision Support Systems”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

Automated Diagnosis and Control of Complex Systems

Livingstone2 is a reusable, artificial intelligence (AI) software system designed to assist spacecraft, life support systems, chemical plants, or other complex systems by operating with minimal human supervision, even in the face of hardware failures or unexpected events. The software diagnoses the current state of the spacecraft or other system, and recommends commands or repair actions that will allow the system to continue operation. Livingstone2 is an enhancement of the Livingstone diagnosis system that was flight-tested onboard the Deep Space One spacecraft in 1999. This version tracks multiple diagnostic hypotheses, rather than just a single hypothesis as in the previous version. It is also able to revise diagnostic decisions made in the past when additional observations become available. In such cases, Livingstone might arrive at an incorrect hypothesis. Re-architecting and re-implementing the system in C++ has increased performance. Usability has been improved by creating a set of development tools that is closely integrated with the Livingstone2 engine. In addition to the core diagnosis engine, Livingstone2 includes a compiler that translates diagnostic models written in a Java-like language into Livingstone2's language, and a broad set of graphical tools for model development.

Kurien, James↗

Intelligent machines in the twenty-first century: foundations of inference and inquiry

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Review↗

Supporting Responsible Machine Learning in Heliophysics

Over the last decade, Heliophysics researchers have increasingly adopted a variety of machine learning methods such as artificial neural networks, decision trees, and clustering algorithms into their workflow. Adoption of these advanced data science methods had quickly outpaced institutional response, but many professional organizations such as the European Commission, the National Aeronautics and Space Administration (NASA), and the American Geophysical Union have now issued (or will soon issue) standards for artificial intelligence and machine learning that will impact scientific research. These standards add further (necessary) burdens on the individual researcher who must now prepare the public release of data and code in addition to traditional paper writing. Support for these is not reflected in the current state of institutional support, community practices, or governance systems. We examine here some of these principles and how our institutions and community can promote their successful adoption within the Heliophysics discipline.

Machine learning↗

Decision Support Tools Evaluation Report for FAS/PECAD, Version 2.0

Global agricultral intelligence is a key element of decision support eithin the U.S. Department of Agriculture (USDA). Estimeates of production and yield issued by the USDA for both foreign and domestic agriculture are primary sources of information for policy and management decision making. The USDA monitors the major global agricultural commodities through the Production Estimates and Crop Assessment Division (PECAD) of its Foreign Agricultural Service (FAS). Specifically, PECAD iintelligence focuses on global agricultural production and on conditions that affect food security. In conjunction with the USDA, NASA is evaluating the potential for products from NASA's Earth Science Enterprise (ESE) missions to add value to PECAD's decision support tools. NASA is usig a systems engineering approach to evaluate the potential enhancement of PECAD's decision support system (DSS)-first by understanding the components of the system and its input requirements, then by recommending NASA products that may be integrated as system inputs to improve the accuracy, quality, or efficiency of the DSS output. This report documents the evaluation phase of the systems engineering process and includes an examination of the system architecture, operations, and input requirements, as well as an initial assessment of specific ESE measurement systems and products that should be considered for their potential to enhance the PECAD DSS.

Ross, Kenton↗

Market Survey 2020: Commercial Clinical Decision Support Systems and Wellness Tools

For long-duration, deep space exploration missions, current methods for managing and supporting crew health and medical conditions will be unsuitable. Communication and data transmission lags will necessitate the use of a sophisticated clinical decision support system (CDSS) that will tailor diagnosis and treatment guidance that is context-sensitive for anticipated astronaut health, wellness, and medical conditions. A variety of clinical decision support (CDS) and wellness tools (WT) are currently available in the commercial market and a broad-brush survey of this market can provide an initial impression of the current state of the art which, in turn, can inform the roadmap of NASA deep space CDSS development and associated requirements. Such a survey was undertaken during the first six months of 2020 using directed convenience sampling to obtain information provided by vendors on their websites; both commercially available CDS and WT (such as those used to track and monitor nutrition, exercise, and sleep) were included. Areas assessed were item type (e.g., software/application, device); primary purpose of the item (e.g., diagnostic support, nutrition tracking); additional purposes (if any); reported features, capabilities, and functionality; setting of use (e.g., inpatient, outpatient); intended user (e.g., clinician, patient); location and sources of data/information used or produced by the item; integration with patient electronic health record (EHR); compliance with interoperability ontologies and standards (e.g., Health Level 7 [HL7], Systematized Nomenclature of Medicine – Clinical Terminology [SNOMED-CT]); and whether the item is knowledge-based (derived from research findings) or non-knowledge-based (derived through artificial intelligence, machine learning, advanced probability and statistics), among others. Ninety-seven (97) vendor websites describing 196 CDS and 73 WT (269 total) were reviewed and coded. The primary purpose of the majority of CDS reviewed is diagnosis or diagnosis/treatment/drug decision support—targeted for clinician use— and the primary purpose of the majority of WT reviewed is the monitoring of different health metrics, most often through the use of a biosensor device (e.g., blood pressure)—targeted for patient use. Very few CDS or WT appear to comply with major international interoperability standards or can be integrated with a patient’s EHR data. None consider contextual factors, such as conditions of the physical environment (e.g., CO2 levels). The majority of CDS and WT reviewed are non-knowledge, cloud- or web-based applications or software. Forty-three (43) major findings were identified and the implications those findings have for NASA will be discussed. Example major findings include: CDS-WT capabilities range from diagnosis to treatment applications, CDS-WT may be wearable or non-wearable and are technologically advanced and only a few CDS-WT tools referenced compliance to ensure interoperability, among other findings. Recommendations will also be offered that will help to address ExMC Gap, Medical-701: Enhance medical capabilities within an exploration medical system.

market survey↗

Overview of the Smart Network Element Architecture and Recent Innovations

In industrial environments, system operators rely on the availability and accuracy of sensors to monitor processes and detect failures of components and/or processes. The sensors must be networked in such a way that their data is reported to a central human interface, where operators are tasked with making real-time decisions based on the state of the sensors and the components that are being monitored. Incorporating health management functions at this central location aids the operator by automating the decision-making process to suggest, and sometimes perform, the action required by current operating conditions. Integrated Systems Health Management (ISHM) aims to incorporate data from many sources, including real-time and historical data and user input, and extract information and knowledge from that data to diagnose failures and predict future failures of the system. By distributing health management processing to lower levels of the architecture, there is less bandwidth required for ISHM, enhanced data fusion, make systems and processes more robust, and improved resolution for the detection and isolation of failures in a system, subsystem, component, or process. The Smart Network Element (SNE) has been developed at NASA Kennedy Space Center to perform intelligent functions at sensors and actuators' level in support of ISHM.

Perotti, Jose M.↗

Space station data system analysis/architecture study. Task 2: Options development DR-5. Volume 1: Technology options

The second task in the Space Station Data System (SSDS) Analysis/Architecture Study is the development of an information base that will support the conduct of trade studies and provide sufficient data to make key design/programmatic decisions. This volume identifies the preferred options in the technology category and characterizes these options with respect to performance attributes, constraints, cost, and risk. The technology category includes advanced materials, processes, and techniques that can be used to enhance the implementation of SSDS design structures. The specific areas discussed are mass storage, including space and round on-line storage and off-line storage; man/machine interface; data processing hardware, including flight computers and advanced/fault tolerant computer architectures; and software, including data compression algorithms, on-board high level languages, and software tools. Also discussed are artificial intelligence applications and hard-wire communications.

Source record↗

Context-Aware Intelligent Assistant Approach to Improving Pilot's Situational Awareness

Faulty decision making due to inaccurate or incomplete awareness of the situation tends to be the prevailing cause of fatal general aviation accidents. Of these accidents, loss of weather situational awareness accounts for the largest number of fatalities. We describe a method for improving weather situational awareness through the support of a contextaware,domain and task knowledgeable, personalized and adaptive assistant. The assistant automatically monitors weather reports for the pilot's route of flight and warns her of detected anomalies. When and how warnings are issued is determined by phase of flight, the pilot s definition of acceptable weather conditions, and the pilot's preferences for automatic notification. In addition to automatic warnings, the pilot is able to verbally query for weather and airport information. By noting the requests she makes during the approach phase of flight, our system learns to provide the information without explicit requests on subsequent flights with similar conditions. We show that our weather assistant decreases the effort required to maintain situational awareness by more than 5.5 times when compared to the conventional method of in-flight weather briefings.

Spirkovska, Lilly↗

An Open Computing Infrastructure that Facilitates Integrated Product and Process Development from a Decision-Based Perspective

Computer applications for design have evolved rapidly over the past several decades, and significant payoffs are being achieved by organizations through reductions in design cycle times. These applications are overwhelmed by the requirements imposed during complex, open engineering systems design. Organizations are faced with a number of different methodologies, numerous legacy disciplinary tools, and a very large amount of data. Yet they are also faced with few interdisciplinary tools for design collaboration or methods for achieving the revolutionary product designs required to maintain a competitive advantage in the future. These organizations are looking for a software infrastructure that integrates current corporate design practices with newer simulation and solution techniques. Such an infrastructure must be robust to changes in both corporate needs and enabling technologies. In addition, this infrastructure must be user-friendly, modular and scalable. This need is the motivation for the research described in this dissertation. The research is focused on the development of an open computing infrastructure that facilitates product and process design. In addition, this research explicitly deals with human interactions during design through a model that focuses on the role of a designer as that of decision-maker. The research perspective here is taken from that of design as a discipline with a focus on Decision-Based Design, Theory of Languages, Information Science, and Integration Technology. Given this background, a Model of IPPD is developed and implemented along the lines of a traditional experimental procedure: with the steps of establishing context, formalizing a theory, building an apparatus, conducting an experiment, reviewing results, and providing recommendations. Based on this Model, Design Processes and Specification can be explored in a structured and implementable architecture. An architecture for exploring design called DREAMS (Developing Robust Engineering Analysis Models and Specifications) has been developed which supports the activities of both meta-design and actual design execution. This is accomplished through a systematic process which is comprised of the stages of Formulation, Translation, and Evaluation. During this process, elements from a Design Specification are integrated into Design Processes. In addition, a software infrastructure was developed and is called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment). This represents a virtual apparatus in the Design Experiment conducted in this research. IMAGE is an innovative architecture because it explicitly supports design-related activities. This is accomplished through a GUI driven and Agent-based implementation of DREAMS. A HSCT design has been adopted from the Framework for Interdisciplinary Design Optimization (FIDO) and is implemented in IMAGE. This problem shows how Design Processes and Specification interact in a design system. In addition, the problem utilizes two different solution models concurrently: optimal and satisfying. The satisfying model allows for more design flexibility and allows a designer to maintain design freedom. As a result of following this experimental procedure, this infrastructure is an open system that it is robust to changes in both corporate needs and computer technologies. The development of this infrastructure leads to a number of significant intellectual contributions: 1) A new approach to implementing IPPD with the aid of a computer; 2) A formal Design Experiment; 3) A combined Process and Specification architecture that is language-based; 4) An infrastructure for exploring design; 5) An integration strategy for implementing computer resources; and 6) A seamless modeling language. The need for these contributions is emphasized by the demand by industry and government agencies for the development of these technologies.

Hale, Mark A.↗

Sustaining Space Systems for Strategic and Theater Operations: A Study Perspective

Our Desert Storm experience in the tactical utility of DoD space vehicles demonstrated that DoD's investment in space technology can provide a significant military advantage during times of crisis and war. The satellites what gave us such marvelous intelligence in locating, tracking, and enabling the successful attack of key targets resulted in a spectacular military success. However, without an on-orbit servicing capability, the fuel consumed to maneuver these satellites into position over the battlefield shortened their useful life by as much as two years. During the 1970's and 1980's, the Air Force aggressively pursued an or-orbit support capability to support and maintain it's space-based assets. However, in the early 1990's, budgetary and political priorities canceled the programs that would have made this a reality. Realizing that a future decision may be made to re-investigate on-orbit support, the United States Space Command (USSPACECOM) sponsored a study to document efforts undertaken by the Air Force during the 1970's and 1980's in developing strategies and actions to achieve certain tenets of on-orbit support. The study represents an attempt to gather, review, summarize, and archive the most important research performed during this period. It will serve as a historical perspective upon which to base future research and development activities. This paper presents an overview of that study.

McCoy, Walbert↗

Smart built-in test

The work which built-in test (BIT) is asked to perform in today's electronic systems increases with every insertion of new technology or introduction of tighter performance criteria. Yet the basic purpose remains unchanged -- to determine with high confidence the operational capability of that equipment. Achievement of this level of BIT performance requires the management and assimilation of a large amount of data, both realtime and historical. Smart BIT has taken advantage of advanced techniques from the field of artificial intelligence (AI) in order to meet these demands. The Smart BIT approach enhances traditional functional BIT by utilizing AI techniques to incorporate environmental stress data, temporal BIT information and maintenance data, and realtime BIT reports into an integrated test methodology for increased BIT effectiveness and confidence levels. Future research in this area will incorporate onboard fault-logging of BIT output, stress data and Smart BIT decision criteria in support of a singular, integrated and complete test and maintenance capability. The state of this research is described along with a discussion of directions for future development.

Richards, Dale W.↗

Control of intelligent robots in space

In view of space activities like International Space Station, Man-Tended-Free-Flyer (MTFF) and free flying platforms, the development of intelligent robotic systems is gaining increasing importance. The range of applications that have to be performed by robotic systems in space includes e.g., the execution of experiments in space laboratories, the service and maintenance of satellites and flying platforms, the support of automatic production processes or the assembly of large network structures. Some of these tasks will require the development of bi-armed or of multiple robotic systems including functional redundancy. For the development of robotic systems which are able to perform this variety of tasks a hierarchically structured modular concept of automation is required. This concept is characterized by high flexibility as well as by automatic specialization to the particular sequence of tasks that have to be performed. On the other hand it has to be designed such that the human operator can influence or guide the system on different levels of control supervision, and decision. This leads to requirements for the hardware and software concept which permit a range of application of the robotic systems from telemanipulation to autonomous operation. The realization of this goal requires strong efforts in the development of new methods, software and hardware concepts, and the integration into an automation concept.

Freund, E.↗

Software System for the Mars 2020 Mission Sampling and Caching Testbeds

The development of the Sampling and Caching Subsystem (SCS) of the Mars 2020 Rover Mission is highly dependent on testing of prototype hardware and software operating in explicit conditions as part of integrated testbeds. To achieve relevant integration of hardware and software while maintaining rapid algorithm development capabilities and high testing throughput, the Controls and Autonomy for Sample Acquisition and Handling (CASAH) software system was developed. CASAH is an implementation of the Intelligent Robotics System Architecture (IRSA),which mimics JPL Flight Software (FSW) in that it is divided into hierarchical modules that run separate processes that communicate via message passing, each module is assigned an owner that is a single developer, and the operator initiates requests via a text-based interface that interprets sequences of commands.IRSA enables a modular breakdown of CASAH that follows that of 2020 Flight Software,so developers can take an algorithm from a module in CASAH and re-code it into the same module in FSW. As deployment of CASAH has grown to ten testbeds - each with different hardware and objectives - bottom-up design decisions have been intentionally made to keep the system lightweight and maintainable by a very small team. To date, CASAH has been used to run 1393 different tests. This work describes CASAH, the testbeds and functionality it supports, the tools used to manage the development and sharing of code, and the features of the software. Lessons learned over the past three years of development and deployment are provided.

Vieira, Peter↗

Imagine Moving Off the Planet

Moving off the planet will be a defining moment of this century as landing on the Moon was in the last. For that to happen for humans to go where humans cannot go-- simulation is the sole solution. NASA supports simulation for life-cycle activities: design, analysis, test, checkout, operations, review and training. We contemplate time spans of a century and more, teams dispersed to different planets and the need for systems that endure or adapt as missions, teams and technology change. Without imagination such goals are impossible. But with imagination we can go outside our present perception of reality to think about and take action on what has been, is and, especially, what might be. Consciously maturing an imagined, possibly workable, idea through framing it to optimization to design, and building the product provides us with a new approach to innovation and simulation fidelity. We address options, analyze, test and make improvements in how we think and work. Each step includes increasingly exact information about costs, schedule, who will be needed, where, when and how. NASA i integrating such thinking into its Exploration Product Realization Hierarchy for simulation and analysis, test and verification, and stimulus response goals. Technically NASA follows a timeline of studies, analysis, definition, design, development and operations with concurrent documentation. We have matched this Product Realization Hierarchy with a continuum from image to realization that incorporates commitment, current and needed research and communication to ensure superior and creative problem solving as well as advances in simulation. One result is a new approach to collaborative systems. Another is a distributed observer network prototyped using game engine technology bringing advanced 3-D simulation of a simulation to the desktop enabling people to develop shared consensus of its meaning. Much of the value of simulation comes from developing in people their ability to make good decisions and reflexes supporting impressive achievement. Synthesizing imagination systematically into our work - and thus our success - is a challenge. NASA engineers have inventive minds, and the task is determining how best to enable them to devise the simulation and other innovations that will make a story so clear and so intellectually sound that people can carry out the mission for 50-100 years. This demands skills and knowledge traditionally under-respected and under-represented in technology organizations. But we are beginning to see that the process encourages efficiency and enables us to attain more effective results. We have to elicit imaginative, intelligent and effective ways to make better use than ever of the minds we have and will have available. We have to accept the challenge to accomplish tasks among dispersed interdisciplinary teams who must overcome changing priorities and technology, time and distance in order to maximize interactivity and innovation as never before. Attention to the process of innovation is a practical means to increase the efficiency of our intelligence. We have an obligation to reexamine and improve the process by which we approach and exercise innovation as we accept the charge to move off the planet.

Elfrey, Priscilla R.↗

Definition and Development of Habitation Readiness Levels (HRLs) for Planetary Surface Habitats

One could argue that NASA has never developed a true habitat for a planetary surface, with only the Lunar Module from the 1960's-era Apollo Program providing for a sparse 2 person, 3 day capability. An integral part of NASA's current National Vision for Space Exploration is missions back to the moon and eventually to Mars. One of the largest leaps i11 lunar surface exploration beyond the Apollo lunar missions will be the conduct of these extended duration human missions. These missions could range from 30 to 90 days in length initially and may eventually range up to 500 days in length. To enable these extended duration human missions, probably the single-most important lunar surface element is the Surface Habitat. The requirements that must be met by the Surface Habitat will go far beyond the safety, performance and operational requirements of the Lunar Module, and NASA needs to develop a basis for making intelligent, technically correct habitat design decisions. This paper will discuss the possibilities of the definition and development of a Habitation Readiness Level (HRL) scale that might be mapped to current Technology Readiness Levels (TRLs) for technology development. HRLs could help measure how well a particular technology thrust is advanced by a proposed planetary habitat concept. The readiness level would have to be measured differently than TRLs, and may include such milestones as habitat design performance under simulated mission operations and constraints (including relevant field testing), functional allocation demonstrations, crew interface evaluation and post-occupancy evaluation. With many concepts for planetary habitats proposed over the past 20 years, there are many strategic technical challenges facing designers of planetary habitats that will support NASA's exploration of the moon and Mars. The systematic assessment of a variety of planetary habitat options will be an important approach and will influence the associated requirements for human design, volumetrics, functionality, systems hardware and operations.

Connolly, Janis H.↗

A Collaborative Decision Environment for UAV Operations

NASA is developing Intelligent Mission Management (IMM) technology for science missions employing long endurance unmanned aerial vehicles (UAV's). The IMM groundbased component is the Collaborative Decision Environment (CDE), a ground system that provides the Mission/Science team with situational awareness, collaboration, and decisionmaking tools. The CDE is used for pre-flight planning, mission monitoring, and visualization of acquired data. It integrates external data products used for planning and executing a mission, such as weather, satellite data products, and topographic maps by leveraging established and emerging Open Geospatial Consortium (OGC) standards to acquire external data products via the Internet, and an industry standard geographic information system (GIs) toolkit for visualization As a Science/Mission team may be geographically dispersed, the CDE is capable of providing access to remote users across wide area networks using Web Services technology. A prototype CDE is being developed for an instrument checkout flight on a manned aircraft in the fall of 2005, in preparation for a full deployment in support of the US Forest Service and NASA Ames Western States Fire Mission in 2006.

D'Ortenzio, Matthew V.↗

Real Time Safety Monitoring: Concept for Supporting Safe Flight Operations

A number of organizations are working on processes, procedures, regulations, and technologies to maintain or improve the safety of the National Airspace System (NAS). In this paper, we describe a Real Time Safety Monitoring (RTSM) system that benefits from these efforts to define a set of safety metrics that are automatically monitored in real-time. In addition to providing information about current potentially adverse conditions to a variety of users, from those who need a broad overview of a day's flight operations to those who need to decide on a control tactic to employ in the next five minutes, the RTSM system predicts conditions within a specified prediction horizon. Its intelligent interface alerts the user, presenting the information as appropriate considering the current context and circumstances. We illustrate the system concept with five conceptual use cases, describing which safety metrics may be of the most interest to five user groups and suggesting a multi-modal display format. We posit that having access to information about adverse conditions in time to make efficient preemptive decisions without sacrificing safety will improve the already high level of safety and aid in the expansion planned for the NAS under the Next Generation Air Transportation System (NextGen).

safety↗

A Framework for Assessment of Autonomy Challenges in Air Traffic Management

Traditionally, air traffic management services have been provided by air traffic controllers and managers stationed in ground facilities, employed or contracted by the public sector, and supported by automation. These centralized, human-centric air traffic management services do not scale to accommodate increasing demands from conventional and new entrant operations for access to the national airspace system. One transformation that provides much needed scalability is increasing the level of autonomy of air traffic management by enabling edge agents of the system, including vehicles, operators, and third-party service suppliers, to collectively self-manage independently from the centralized service providers and enabling the automation to also take on more independent traffic management responsibility from the human agents. This paper identifies challenges to increasing the level of autonomy of air traffic management services. It describes a framework to enable a systematic identification of these challenges. The framework consists of a functional breakdown of air traffic management services and several dimensions characterizing different autonomy scales. The autonomy dimensions include the automation level between human and machine agents, the locus of control between centralized and distributed edge agents, cognitive activities for autonomous situation awareness and decision making, intelligence levels ranging from skill-based to expertise-based autonomous behavior, and uncertainty levels of the dynamics and environment in which autonomous agents operate. Several challenges are identified and categorized using the different dimensions of the autonomy framework.

automation, autonomy framework, collective autonom↗