Search NASA⌕ Search

SEARCH · Search NASA

Results for “LEARNING SYSTEM”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Designing Specification Languages for Process Control Systems: Lessons Learned and Steps to the Future

Previously, we defined a blackbox formal system modeling language called RSML (Requirements State Machine Language). The language was developed over several years while specifying the system requirements for a collision avoidance system for commercial passenger aircraft. During the language development, we received continual feedback and evaluation by FAA employees and industry representatives, which helped us to produce a specification language that is easily learned and used by application experts. Since the completion of the PSML project, we have continued our research on specification languages. This research is part of a larger effort to investigate the more general problem of providing tools to assist in developing embedded systems. Our latest experimental toolset is called SpecTRM (Specification Tools and Requirements Methodology), and the formal specification language is SpecTRM-RL (SpecTRM Requirements Language). This paper describes what we have learned from our use of RSML and how those lessons were applied to the design of SpecTRM-RL. We discuss our goals for SpecTRM-RL and the design features that support each of these goals.

Leveson, Nancy G.↗
NASA NTRS1994

Finding accurate frontiers: A knowledge-intensive approach to relational learning

An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory.

Pazzani, Michael↗
NASA NTRS1994

Customizing the JPL Multimission Ground Data System: Lessons Learned

This paper will describe lessons learned in adapting JPL's Multimission Ground Data System (MGDS) to fly the Voyager, Galileo, and Mars Observer missions. We will explain how powerful, existing ground data systems can be adapted and packaged in a cost effective way for operations of small and large planetary missions. We will also describe how the MGDS was adapted to support operations within the Galileo Spacecraft Testbed. The Galileo testbed provided a unique opportunity to adapt MGDS to support command and control operations for a small autonomous operations team with a handful of engineers flying the Galileo Spacecraft flight system model.

ground↗
NASA NTRSDate not supplied

Problems in Machine Learning-Based Systems for Safety-Critical Avionics

To explore, discover, and understand the impact on safety of growing complexity introduced by modernization aimed at improving the efficiency of flight, the access to airspace, and/or the expansion of services provided by air vehicles. To develop and demonstrate innovative solutions that enable this modernization and the aviation transformation envisioned by ARMD through proactive mitigation of risks in accordance with target levels of safety.

Safety Critical Systems↗
NASA NTRS2018

Toward Justifiable Trust in Autonomous Systems Incorporating Human Knowledge in Autonomous Systems through Machine Learning

Trust in Autonomous Systems is largely about humans trusting the decisions made by autonomous systems. This trust can be increased through learning from domain experts. In particular, autonomous systems can learn offline from past mission operations before conducting any operations of its own. Additionally, autonomous systems can learn online by obtaining human feedback during operations. We will discuss several classes of machine learning methods and our application of them to autonomous systems. The first class of methods is anomaly detection, which uses operations data to identify examples of anomalous operations. The second class of methods is inverse reinforcement learning, also known as apprenticeship learning, that takes past operations data as input and yields a controller that is able to duplicate the operations described by the data. The third class is active learning, which identifies examples on which the model is most uncertain and requests domain expert feedback.

Oza, Nikunj C.↗
NASA NTRS1987

Fallible humans and vulnerable systems - Lessons learned from aviation

It is suggested that the problems being experienced in complex automatic systems are essentially due to the failure of information management and communication. The failure covers the entire spectrum: display devices and techniques, coding information so as to reduce human error, and information economy, i.e., resisting the temptation to bombard the operator with unlimited information simply because the system possesses the capability to do so. Since there has been great progress in hardware engineering, it is suggested that further attention is needed in the 'soft' side of systems. The approach should focus on (1) preventing human cognitive slips and (2) making the systems less vulnerable to such slips when they do occur. Most of the examples are taken from studies of cockpit automation.

Wiener, Earl L.↗
NASA NTRS1993

Learning fuzzy information in a hybrid connectionist, symbolic model

An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

Romaniuk, Steve G.↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
NASA NTRS1996

Adaptive Fuzzy Systems in Computational Intelligence

In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

Berenji, Hamid R.↗
NASA NTRS1995

Inductive Approaches to Improving Diagnosis and Design for Diagnosability

The first research area under this grant addresses the problem of classifying time series according to their morphological features in the time domain. A supervised learning system called CALCHAS, which induces a classification procedure for signatures from preclassified examples, was developed. For each of several signature classes, the system infers a model that captures the class's morphological features using Bayesian model induction and the minimum message length approach to assign priors. After induction, a time series (signature) is classified in one of the classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. A second area of research assumes two sources of information about a system: a model or domain theory that encodes aspects of the system under study and data from actual system operations over time. A model, when it exists, represents strong prior expectations about how a system will perform. Our work with a diagnostic model of the RCS (Reaction Control System) of the Space Shuttle motivated the development of SIG, a system which combines information from a model (or domain theory) and data. As it tracks RCS behavior, the model computes quantitative and qualitative values. Induction is then performed over the data represented by both the 'raw' features and the model-computed high-level features. Finally, work on clustering for operating mode discovery motivated some important extensions to the clustering strategy we had used. One modification appends an iterative optimization technique onto the clustering system; this optimization strategy appears to be novel in the clustering literature. A second modification improves the noise tolerance of the clustering system. In particular, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus making post-clustering analysis easier.

Fisher, Douglas H.↗
NASA NTRS2010

NASA's Earth Science Data Systems - Lessons Learned and Future Directions

In order to meet the increasing demand for Earth Science data, NASA has significantly improved the Earth Science Data Systems over the last two decades. This improvement is reviewed in this slide presentation. Many Earth Science disciplines have been able to access the data that is held in the Earth Observing System (EOS) Data and Information System (EOSDIS) at the Distributed Active Archive Centers (DAACs) that forms the core of the data system.

Ramapriyan, Hampapuram K.↗
NASA NTRS2014

ATLAS LTCS Vertically Challenged System Lessons Learned

Re-planning of LTCS TVAC testing and supporting RTA (Receiver Telescope Assembly) Test Plan and Procedure document preparation. The Laser Thermal Control System (LTCS) is designed to maintain the lasers onboard Advanced Topographic Laser Altimeter System (ATLAS) at their operational temperatures. In order to verify the functionality of the LTCS, a thermal balance test of the thermal hardware was performed. During the first cold start of the LTCS, the Loop Heat Pipe (LHP) was unable to control the laser mass simulators temperature. The control heaters were fully on and the loop temperature remained well below the desired setpoint. Thermal analysis of the loop did not show these results. This unpredicted behavior of the LTCS was brought up to a panel of LHP experts. Based on the testing and a review of all the data, there were multiple diagnostic performed in order to narrow down the cause. The prevailing theory is that gravity is causing oscillating flow within the loop, which artificially increased the control power needs. This resulted in a replan of the LTCS test flow and the addition of a GSE heater to allow vertical operation.

Loop Heat Pipe↗
NASA NTRS1994

X-29 flight control system: Lessons learned

Two X-29A aircraft were flown at the NASA Dryden Flight Research Center over a period of eight years. The airplanes' unique features are the forward-swept wing, variable incidence close-coupled canard and highly relaxed longitudinal static stability (up to 35-percent negative static margin at subsonic conditions). This paper describes the primary flight control system and significant modifications made to this system, flight test techniques used during envelope expansion, and results for the low- and high-angle-of-attack programs. Through out the paper, lessons learned will be discussed to illustrate the problems associated with the implementation of complex flight control systems.

Clarke, Robert↗
NASA NTRS2011

NASA Ares I Launch Vehicle Roll and Reaction Control Systems Lessons Learned

On April 15, 2010 President Barak Obama made the official announcement that the Constellation Program, which included the Ares I launch vehicle, would be canceled. NASA s Ares I launch vehicle was being designed to launch the Orion Crew Exploration Vehicle, returning humans to the moon, Mars, and beyond. It consisted of a First Stage (FS) five segment solid rocket booster and a liquid J-2X Upper Stage (US) engine. Roll control for the FS was planned to be handled by a dedicated Roll Control System (RoCS), located on the connecting interstage. Induced yaw or pitch moments experienced during FS ascent would have been handled by vectoring of the booster nozzle. After FS booster separation, the US Reaction Control System (ReCS) would have provided the US Element with three degrees of freedom control as needed. The lessons learned documented in this paper will be focused on the technical designs and producibility of both systems along with the partnership between NASA and Boeing, who was on contract to build the Ares I US Element, which included the FS RoCS and US ReCS. In regards to partnership, focus will be placed on integration along with technical work accomplished by Boeing with special emphasis on each task order. In summary, this paper attempts to capture key lessons learned that should be helpful in the development of future launch vehicle RCS designs.

Butt, Adam↗
NASA NTRSDate not supplied

Lessons Learned in Systems Engineering Availability and Recommendations for Mission Technical Leaders

In spaceflight missions at Goddard Space Flight Center (GSFC), the Mission System Engineer (MSE) is the technical leader of the overall engineering team and also is the Independent Engineering Technical Authority. The responsibility of this role includes the definition of the mission design architecture, concept of operations, and mission requirements, management of risk throughout the development, and verification and validation of the final system performance and function amongst other duties. This responsibility inherently requires time management, enabling focus on a balanced development with appropriate risk. Time is the most valuable resource of the MSE. The system engineer’s availability to interact with the development team (often product or component design leads and technicians, often in different worksites) to discover and mitigate mission risks during development is key to mission success. This paper presents examples from Lunar Reconnaissance Orbiter, Landsat 9, and Neutron star Interior Composition ExploreR (NICER) which represent in-house and out-of-house hardware builds. These examples demonstrate how interactions between the Mission Systems Engineers and other project and partner engineers result in discovery of critical risks, leading to early mitigation with significant cost and performance savings. These three missions would have suffered test failures or on-orbit failures had their MSEs not set aside time to visit engineers and technicians that were working on key pieces of space flight hardware. Availability is more than just time; it is openness to listen to concerns and questions. It begins by building a level of trust in the team that it is safe to ask questions or share concerns without the fear of blame or additional workload. It also requires enabling informal conversations (over lunch, coffee, in the clean room, or at the team members desk, etc.) where key information can be exchanged, and team members may even provide an easy-to-implement mitigation idea for another subsystem. Availability is a highly valuable commodity and completely non-obvious to protect and optimize. The natural tendency of engineers is to keep themselves busy with solving problems that they know about. This paper is encouraging MSEs to resist this tendency to try and solve all the complex problems themselves and actively devote daily time to learning and solving problems that are found with informal communications with other team members.

Lessons Learned↗
NASA NTRS1992

Some problems with the design of self-learning management systems

In this paper some problems in the design of management systems for complex objects are discussed. Considering the absence of adequate models and the fact that human expertise in the management of non-stationary objects becomes obsolete quickly, the use of self-learning together with a two-step optimization of on-line control rules is suggested. To prepare for the object analysis, a set of definitions has been proposed. Traditional and fuzzy sets approaches are used in the analysis. To decrease the reaction time of the control system, we propose the development of control rules without feedback.

Flikop, Ziny↗
NASA NTRS1994

A hypertext system that learns from user feedback

Retrieving specific information from large amounts of documentation is not an easy task. It could be facilitated if information relevant in the current problem solving context could be automatically supplied to the user. As a first step towards this goal, we have developed an intelligent hypertext system called CID (Computer Integrated Documentation). Besides providing an hypertext interface for browsing large documents, the CID system automatically acquires and reuses the context in which previous searches were appropriate. This mechanism utilizes on-line user information requirements and relevance feedback either to reinforce current indexing in case of success or to generate new knowledge in case of failure. Thus, the user continually augments and refines the intelligence of the retrieval system. This allows the CID system to provide helpful responses, based on previous usage of the documentation, and to improve its performance over time. We successfully tested the CID system with users of the Space Station Freedom requirements documents. We are currently extending CID to other application domains (Space Shuttle operations documents, airplane maintenance manuals, and on-line training). We are also exploring the potential commercialization of this technique.

Mathe, Nathalie↗