Results for “knowledge systems” — Page 96

NASA NTRS1992

A reinforcement learning-based architecture for fuzzy logic control

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

Berenji, Hamid R.↗

NASA NTRSDate not supplied

Gateway Program Safety And Mission Assurance Integration - The Future Of Safe Deep Space Human Exploration

This next wave of lunar exploration will be fundamentally different than the past. The goal today is long term human presence in Earth orbit, exploration around the Moon, exploration anywhere on the Moon, and challenging exploration missions beyond the Moon. Sustainability is the key to enabling human expansion across the solar system and bringing back to Earth new knowledge and opportunities, and requires the early engagement of scientific, international, and U.S. commercial interests, innovations, and new approaches. The Gateway’s agile acquisition strategy will shape the entire system life cycle, from design and analysis through production, verification, launch, logistics and operations. This strategy will encourage new ways of doing business to accommodate new techniques, technologies and approaches; improving affordability and maximizing Gateway utility. The full range of acquisition authorities and contracting mechanisms available to NASA will be considered and appropriately tailored in response to the unique demands of each procurement activity. Gateway will provide scientific and research benefits, in addition to providing a sustainable staging location for crewed lunar surface landings and technology demonstrations that help NASA develop the knowledge and technology needed for Mars missions. These payloads are expected to provide operational and scientific benefit for the duration of their stay on Gateway, as the hardware will provide data during the transit to the Gateway and while in lunar orbit as changes in solar activity occur over time. Gateway is designed to support a variety of utilization activities during both crewed and uncrewed periods and is planned for continuous operations throughout its lifetime. These operations include demonstrating key exploration capabilities in a relevant deep space environment, conducting science beyond the influence of Earth, and providing opportunities for Gateway partners and customers for deep space exploration. Located in cislunar orbit, the Gateway capabilities can be further leveraged to enhance lunar and Mars exploration. With the Gateway and other elements of the Artemis Program, NASA is encouraging new ways of doing business to accommodate new techniques, technologies and approaches, consider affordability, and encourage innovation from U.S. industry.

Helen Vaccaro↗

NASA NTRSDate not supplied

Gateway Program Safety and Mission Assurance IAASS Conference Keynote Presentation

This next wave of lunar exploration will be fundamentally different than the past. The goal today is long term human presence in Earth orbit, exploration around the Moon, exploration anywhere on the Moon, and challenging exploration missions beyond the Moon. Sustainability is the key to enabling human expansion across the solar system and bringing back to Earth new knowledge and opportunities, and requires the early engagement of scientific, international, and U.S. commercial interests, innovations, and new approaches. The Gateway’s agile acquisition strategy will shape the entire system life cycle, from design and analysis through production, verification, launch, logistics and operations. This strategy will encourage new ways of doing business to accommodate new techniques, technologies and approaches; improving affordability and maximizing Gateway utility. The full range of acquisition authorities and contracting mechanisms available to NASA will be considered and appropriately tailored in response to the unique demands of each procurement activity. Gateway will provide scientific and research benefits, in addition to providing a sustainable staging location for crewed lunar surface landings and technology demonstrations that help NASA develop the knowledge and technology needed for Mars missions. These payloads are expected to provide operational and scientific benefit for the duration of their stay on Gateway, as the hardware will provide data during the transit to the Gateway and while in lunar orbit as changes in solar activity occur over time. Gateway is designed to support a variety of utilization activities during both crewed and uncrewed periods and is planned for continuous operations throughout its lifetime. These operations include demonstrating key exploration capabilities in a relevant deep space environment, conducting science beyond the influence of Earth, and providing opportunities for Gateway partners and customers for deep space exploration. Located in cislunar orbit, the Gateway capabilities can be further leveraged to enhance lunar and Mars exploration. With the Gateway and other elements of the Artemis Program, NASA is encouraging new ways of doing business to accommodate new techniques, technologies and approaches, consider affordability, and encourage innovation from U.S. industry.

Terri Castillo↗

NASA NTRS1972

Analyses of requirements for computer control and data processing experiment subsystems. Volume 2: ATM experiment S-056 image data processing system software development

The IDAPS (Image Data Processing System) is a user-oriented, computer-based, language and control system, which provides a framework or standard for implementing image data processing applications, simplifies set-up of image processing runs so that the system may be used without a working knowledge of computer programming or operation, streamlines operation of the image processing facility, and allows multiple applications to be run in sequence without operator interaction. The control system loads the operators, interprets the input, constructs the necessary parameters for each application, and cells the application. The overlay feature of the IBSYS loader (IBLDR) provides the means of running multiple operators which would otherwise overflow core storage.

Source record↗

NASA NTRS1989

An improved adaptive control for repetitive motion of robots

An adaptive control algorithm is proposed for a class of nonlinear systems, such as robotic manipulators, which is capable of improving its performance in repetitive motions. When the task is repeated, the error between the desired trajectory and that of the system is guaranteed to decrease. The design is based on the combination of a direct adaptive control and a learning process. This method does not require any knowledge of the dynamic parameters of the system.

Pourboghrat, F.↗

NASA NTRS1994

Engineering large-scale agent-based systems with consensus

The paper presents the consensus method for the development of large-scale agent-based systems. Systems can be developed as networks of knowledge based agents (KBA) which engage in a collaborative problem solving effort. The method provides a comprehensive and integrated approach to the development of this type of system. This includes a systematic analysis of user requirements as well as a structured approach to generating a system design which exhibits the desired functionality. There is a direct correspondence between system requirements and design components. The benefits of this approach are that requirements are traceable into design components and code thus facilitating verification. The use of the consensus method with two major test applications showed it to be successful and also provided valuable insight into problems typically associated with the development of large systems.

Bokma, A.↗

NASA NTRS1996

The Impact of Information Technology on the Design, Development, and Implementation of a Lunar Exploration Mission

From the beginning to the present expeditions to the Moon have involved a large investment of human labor. This has been true for all aspects of the process, from the initial design of the mission, whether scientific or technological, through the development of the instruments and the spacecraft, to the flight and operational phases. In addition to the time constraints that this situation imposes, there is also a significant cost associated with the large labor costs. As a result lunar expeditions have been limited to a few robotic missions and the manned Apollo program missions of the 1970s. With the rapid rise of the new information technologies, new paradigms are emerging that promise to greatly reduce both the time and cost of such missions. With the rapidly increasing capabilities of computer hardware and software systems, as well as networks and communication systems, a new balance of work is being developed between the human and the machine system. This new balance holds the promise of greatly increased exploration capability, along with dramatically reduced design, development, and operating costs. These new information technologies, utilizing knowledge-based software and very highspeed computer systems, will provide new design and development tools, scheduling mechanisms, and vehicle and system health monitoring capabilities that have hitherto been unavailable to the mission and spacecraft designer and the system operator. This paper will utilize typical lunar missions, both robotic and crewed, as a basis to describe and illustrate how these new information system technologies could be applied to all aspects such missions. In particular, new system design tradeoff tools will be described along with technologies that will allow a very much greater degree of autonomy of exploration vehicles than has heretofore been possible. In addition, new information technologies that will significantly reduce the human operational requirements will be discussed.

Gross, Anthony R.↗

NASA NTRS1987

Diagnosis: Reasoning from first principles and experiential knowledge

Completeness, efficiency and autonomy are requirements for suture diagnostic reasoning systems. Methods for automating diagnostic reasoning systems include diagnosis from first principles (i.e., reasoning from a thorough description of structure and behavior) and diagnosis from experiential knowledge (i.e., reasoning from a set of examples obtained from experts). However, implementation of either as a single reasoning method fails to meet these requirements. The approach of combining reasoning from first principles and reasoning from experiential knowledge does address the requirements discussed above and can possibly ease some of the difficulties associated with knowledge acquisition by allowing developers to systematically enumerate a portion of the knowledge necessary to build the diagnosis program. The ability to enumerate knowledge systematically facilitates defining the program's scope, completeness, and competence and assists in bounding, controlling, and guiding the knowledge acquisition process.

Williams, Linda J. F.↗

NASA NTRS2019

Effects of radiation on immune system and latent virus reactivation

The purpose of this NCRP commentary is to provide the current state of knowledge on the effects of ionizing radiation on the immune system and on latent herpes virus reactivation to the scientific community and government agencies. Its purpose is to better understand radiation-induced latent virus reactivation, which is possibly an underestimated consequence of ionizing radiation exposure. This activity should involve the radiation research community (academia, industry and regulatory agencies) and government agencies (NASA, DOD, CDC).

Plante, Ianik↗

NASA NTRS2021

Mass Inferencing Model Creation And Deployment To Lunar Excavation Robot, RASSOR

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. This research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. Radio wave propagation time to the Moon and back is ~2.56 seconds. Though teleoperation is possible with this delay, autonomous capability that enables RASSOR to plan and execute excavation missions intelligently and efficiently is preferred. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation (e.g. knowledge of whether drums are full informs the task of highest priority, whether it be continuing to dig, or returning to a processing plant to offload regolith). A configurable data reduction and analysis pipeline was created to allow for straightforward incorporation of new data, such as that from lunar excavation, to improve model performance in new environments. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All four models take in system states and output a mass prediction for each set of the robot’s bucket drums. Initial results from deployment to RASSOR and testing in a simulated lunar environment show that the models have <10% mean error during robot operation. Future work includes refinement of a model that estimates regolith mass in real-time during excavation as well as further testing of the developed models on the hardware.

ROS↗

NASA NTRS1994

A Spacelab Expert System for Remote Engineering and Science

NASA's space science program is based on strictly pre-planned activities. This approach does not always result in the best science. We describe an existing computer system that enables space science to be conducted in a more reactive manner through advanced automation techniques that have recently been used in SLS-2 October 1993 space shuttle flight. Advanced computing techniques, usually developed in the field of Artificial Intelligence, allow large portions of the scientific investigator's knowledge to be "packaged" in a portable computer to present advice to the astronaut operator. We strongly believe that this technology has wide applicability to other forms of remote science/engineering. In this brief article, we present the technology of remote science/engineering assistance as implemented for the SLS-2 space shuttle flight. We begin with a logical overview of the system (paying particular attention to the implementation details relevant to the use of the embedded knowledge for system reasoning), then describe its use and success in space, and conclude with ideas about possible earth uses of the technology in the life and medical sciences.

Groleau, Nick↗

DOE OSTI2024

Stability-Constrained Learning for Frequency Regulation in Power Grids With Variable Inertia

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across 100 inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

data-driven control↗

NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗

NASA NTRS2001

ScienceOrganizer System and Interface Summary

ScienceOrganizer is a specialized knowledge management tool designed to enhance the information storage, organization, and access capabilities of distributed NASA science teams. Users access ScienceOrganizer through an intuitive Web-based interface that enables them to upload, download, and organize project information - including data, documents, images, and scientific records associated with laboratory and field experiments. Information in ScienceOrganizer is "threaded", or interlinked, to enable users to locate, track, and organize interrelated pieces of scientific data. Linkages capture important semantic relationships among information resources in the repository, and these assist users in navigating through the information related to their projects.

Keller, Richard M.↗

NASA NTRS2012

Human Research Program: 2012 Fiscal Year Annual Report

Crew health and performance are critical to successful human exploration beyond low Earth orbit. Risks to health and performance include physiologic effects from radiation, hypogravity, and planetary environments, as well as unique challenges in medical treatment, human factors, and support of behavioral health. The scientists and engineers of the Human Research Program (HRP) investigate and reduce the greatest risks to human health and performance, and provide essential countermeasures and technologies for human space exploration. In its seventh year of operation, the HRP continued to refine its management architecture of evidence, risks, gaps, tasks, and deliverables. Experiments continued on the International Space Station (ISS), on the ground in analog environments that have features similar to those of spaceflight, and in laboratory environments. Data from these experiments furthered the understanding of how the space environment affects the human system. These research results contributed to scientific knowledge and technology developments that address the human health and performance risks. As shown in this report, HRP has made significant progress toward developing medical care and countermeasure systems for space exploration missions which will ultimately reduce risks to crew health and performance.

Effenhauser, Laura↗

NASA NTRS1993

Knowledge-based machine indexing from natural language text: Knowledge base design, development, and maintenance

One strategy for machine-aided indexing (MAI) is to provide a concept-level analysis of the textual elements of documents or document abstracts. In such systems, natural-language phrases are analyzed in order to identify and classify concepts related to a particular subject domain. The overall performance of these MAI systems is largely dependent on the quality and comprehensiveness of their knowledge bases. These knowledge bases function to (1) define the relations between a controlled indexing vocabulary and natural language expressions; (2) provide a simple mechanism for disambiguation and the determination of relevancy; and (3) allow the extension of concept-hierarchical structure to all elements of the knowledge file. After a brief description of the NASA Machine-Aided Indexing system, concerns related to the development and maintenance of MAI knowledge bases are discussed. Particular emphasis is given to statistically-based text analysis tools designed to aid the knowledge base developer. One such tool, the Knowledge Base Building (KBB) program, presents the domain expert with a well-filtered list of synonyms and conceptually-related phrases for each thesaurus concept. Another tool, the Knowledge Base Maintenance (KBM) program, functions to identify areas of the knowledge base affected by changes in the conceptual domain (for example, the addition of a new thesaurus term). An alternate use of the KBM as an aid in thesaurus construction is also discussed.

Genuardi, Michael T.↗

NASA NTRSDate not supplied

Plans for NASA Contributions to Position, Navigation, and Timing at the Moon

Dating back to the Apollo missions, navigation in cislunar space has traditionally been performed with radiometric tracking and orbit estimation in Earth-based systems. The selected landing zones for Apollo enabled direct to Earth line of sight. Up to the present day, lunar orbiting science missions continue to rely on Earth sensors and processing. The upcoming Artemis and science missions seek different objectives, including landing and traversing in lunar locations occulted from view by Earth. Sustaining a burgeoning and diverse lunar mission set requires in-situ navigation capabilities that do not rely exclusively on Earth-based tracking and provide accurate and timely position, navigation, and timing (PNT) knowledge defined in a lunar reference system. These missions prefer a seamless transition from Earth-centric to lunar-centric PNT systems to reduce complexity and size, weight, and power (SWaP) on their missions. NASA’s heritage in successful high-altitude Global Navigation Satellite System (GNSS) reception is informing an expanded Space Service Volume to the moon and the development of lower SWaP GNSS receivers for the lunar missions. To overcome the geometric restrictions from GNSS –from both limited Dilution of Precision at the moon and lunar occultation – NASA is pursuing PNT services from the Lunar Communications Relay and Navigation System (LCRNS) as part of the interoperable LunaNet architecture to provide lunar-centric navigation services. For situations that require additional sensors, e.g. hazard detection, NASA’s optical navigation algorithms and close-range topographic sensors combine with observables from LunaNet in autonomous navigation software that brings robustness to the PNT solution. This presentation will provide an overview of NASA’s LCRNS PNT services, high-altitude GNSS and close-range sensors, processing algorithms and performance analysis, as well as efforts underway to define the underpinning Lunar Reference System.

Cheryl J. Gramling↗