Neurolab: learning how the nervous system adapts to microgravity
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short duration↗
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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.
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A strategic systems perspective of organizational learning theory: models for a case study at the Jet Propulsion Laboratory
Organizational learning is an umbrella term that covers a variety of topics including; learning curves, productivity, organizational memory, organizational forgetting, knowledge transfer, knowledge sharing and knowledge creation. This treatise will review some of these theories in concert with a model of how organizations learn.
organizational learning strategy innovation design↗
Machine Learning-Based Composite Power System Reliability Assessment
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Nguyen, Nga↗
A self-learning rule base for command following in dynamical systems
In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.
Tsai, Wei K.↗
An Integrated Planning Representation Using Macros, Abstractions, and Cases
Planning will be an essential part of future autonomous robots and integrated intelligent systems. This paper focuses on learning problem solving knowledge in planning systems. The system is based on a common representation for macros, abstractions, and cases. Therefore, it is able to exploit both classical and case based techniques. The general operators in a successful plan derivation would be assessed for their potential usefulness, and some stored. The feasibility of this approach was studied through the implementation of a learning system for abstraction. New macros are motivated by trying to improve the operatorset. One heuristic used to improve the operator set is generating operators with more general preconditions than existing ones. This heuristic leads naturally to abstraction hierarchies. This investigation showed promising results on the towers of Hanoi problem. The paper concludes by describing methods for learning other problem solving knowledge. This knowledge can be represented by allowing operators at different levels of abstraction in a refinement.
Baltes, Jacky↗
Inductive System Health Monitoring
The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.
Iverson, David L.↗
Does practice make perfect? Lessons learned from full-scale power system incident response exercise
While threats to the energy sector occur daily, few utilities get the opportunity to fully test out their detection and response mechanisms to advanced threats in the real world. With the high demand for reliability, few grid operators would allow execution of simulated cyber-attacks on their live systems. The DOE-funded Liberty Eclipse project offers a unique opportunity for small and large utilities and coops to practice their combined IT/OT responses to a live red team executing attacks against an isolated power system on an island in New York. Both cyber teams and power operations teams must work together to detect and respond to attacks, even restoring the power system against extreme impacts. Lessons learned from these exercises reveal key takeaways for understanding what a real attack against the electric sector will look like, gaps in execution of the best-laid plans when the pressure of a real event is bearing down, and how organizations can better prepare for advanced attacks by optimizing participation in exercises. This presentation will discuss successes and opportunities for improvement both in how utilities can prepare for and respond to events, as well as how full-scale IT/OT exercises can be coordinated.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Recommendations on Evidence and Process for Certification of Learning-enabled Components in Aerospace Systems
This report primarily identifies a collection of relevant and necessary evidence for assurance of machine learnt components (MLCs)—also known as learning-enabled components—integrated into aircraft systems, and gives preliminary suggestions on the elements of a certification process that invoke the identified evidence. The main focus is on feedforward neural networks that are static and trained offline through supervised learning. A brief background on the generic elements of the lifecycle of an MLC is given to contextualize the assurance considerations and, consequently, the evidence that is relevant and necessary to support certification. At the level of an MLC, those considerations relate to: (i) the consistency and correctness of MLC contributions to system functions in the context of a validated functional intent; and (ii) the absence of MLC contributions to aircraft-level failure conditions. At an ML model level, confidence in model and data properties contribute to assurance of the containing MLC, in particular: (a) generalizability and robustness of models, in the presence of inputs not previously seen during training, disturbances to inputs, and unexpected inputs; and (b) valid data, i.e., data that are at least representative, relevant, complete, and accurate. Evidence for the above span the elements of the ML lifecycle, and includes, at a minimum, lifecycle artifacts that pertain to: (1) properties of requirements capturing functional intent, safety constraints, and aspects of the intended use and operating environment; (2) model performance, model complexity and design, and algorithm choice; (3) achievement of required performance at the levels of a trained model during model development, a trained model after model development is complete, and a trained model that is transformed into an executable equivalent; (4) model implementation aspects necessary for transforming a trained model into the executable equivalent; (5) integration of the executable trained model into the containing MLC, and eventually the larger system; and, (6) lastly, the verification and validation (V&V) of each of the above. Such V&V lifecycle artifacts themselves include: aspects of coverage, e.g., of various levels of requirements by the input space of the model and the data; traceability (where applicable); application of formal methods for property specification, analysis, and checking. Examples of evidence generation methods and tools further ground the discussion on what constitutes evidence, and the contribution to assurance during certification. The identified assurance considerations and supporting evidence is not a comprehensive set. Additionally, neither what should be considered as sufficient evidence relative to the assigned criticality of an MLC, nor how criticality ought to be determined and adjusted, have been considered in this report. However, suggestions are made for potential activities of the ML lifecycle that are aimed at providing confidence that an MLC can be relied upon when integrated into its containing (aircraft) system. Those activities are proposed as candidate elements of a certification process for MLCs. The main purpose of this report to inform regulatory guidance and consensus standards that may be used to meet the safety intent of the applicable regulations.
Aviation safety↗
Learning Nonlinear Predictive Models for Lossless Image Compression
We describe a genetic programming system which learns nonlinear predictive models for lossless image compression.
genome-complier↗
Segmentation of Lightweight Ablator Micro-Tomography Using Deep Learning
Ablative thermal protection systems (TPS) are essential for high speed entry of planetary atmospheres, such as those of Earth and Mars. Upon entry the kinetic energy of the spacecraft is converted into thermal energy, leading to high heat fluxes at the wall of the craft. Because of this extreme heating, a robust ablative TPS material must be selected. A common material selection today is phenolic-impregnated carbon ablator (PICA), which is a low-density carbon material known for producing dust that is not suitable to a cleanroom environment. To mitigate dust created by a PICA heatshield, a silicone-based spray called NuSil is applied to the surface of the TPS, creating PICA-NuSil (PICA-N). PICA-N has been observed to have a different material response from regular PICA during high enthalpy flow testing, producing surface temperatures up to 200K less than those seen for PICA [1]. To better understand this phenomenon, it is critical that robust methods of PICA-N material characterization are developed. The purpose of this project is to investigate Object Research Systems’ (ORS) Dragonfly deep learning tools as a means of accurately segmenting and characterizing PICA-N. Systematic testing of this software has shown that Dragonfly deep learning tools have strong potential for accurate segmentation/ characterization of PICA-N and other TPS materials.
Micro-tomography↗
Lessons Learned in Space Life Support System Testing
The earlier problems can be found and corrected, the easier and cheaper it is to fix them. Doing less testing saves cost and time but doing too little testing increases the risk of operational failures causing large costs and delays. Integrated test is necessary to determine if the subsystems work together and the overall architecture performs as intended. This report reviews the testing lessons learned from the NASA Systems Engineering Handbook, a National Research Council report, and five reviews of International Space Station (ISS) lessons learned. The five reviews all mention two important points. First, that testing should be performed on the final integrated system, one as close as possible to the intended flight system. Second, “test as you fly,” while operating as planned in an environment as close as possible to the expected flight environment. Other lessons are the need for extensive preflight ground testing, the need to establish and defend an adequate budget, the problems using protoflight hardware on ISS, and the benefit of having ISS as a zero gravity test bed. The major ISS life support systems, carbon dioxide, water recycling, and oxygen recovery, were protoflight systems with little testing before launch to ISS. The failure rates these systems have been much greater than predicted and this has caused dissatisfaction with the protoflight approach. The more costly traditional approach is building qualification and test units in addition to flight units. The test units are used to test, analyze, and fix failure modes. Other work shows that there is an optimum cost-effective intuitive appeal of a human ecosystem in space.
Life support↗
Lessons Learned in Space Life Support System Testing
The earlier problems can be found and corrected, the easier and cheaper it is to fix them. Doing less testing saves cost and time but doing too little testing increases the risk of operational failures causing large costs and delays. Integrated test is necessary to determine if the subsystems work together and the overall architecture performs as intended. This report reviews the testing lessons learned from the NASA Systems Engineering Handbook, a National Research Council report, and five reviews of International Space Station (ISS) lessons learned. The five reviews all mention two important points. First, that testing should be performed on the final integrated system, one as close as possible to the intended flight system. Second, “test as you fly,” while operating as planned in an environment as close as possible to the expected flight environment. Other lessons are the need for extensive preflight ground testing, the need to establish and defend an adequate budget, the problems using protoflight hardware on ISS, and the benefit of having ISS as a zero gravity test bed. The major ISS life support systems, carbon dioxide removal, water recycling, and oxygen recovery, were protoflight systems with little testing before launch to ISS. The failure rates of these systems have been much greater than predicted and this has caused dissatisfaction with the protoflight approach. The more costly traditional approach builds qualification and test units in addition to flight units. The test units are used to find, analyze, and fix failure modes. Other work shows that there is an optimum cost-effective amount of testing when redundant systems must have a specified reliability and confidence.
Harry W. Jones↗
Resilience Assessment for Distribution Systems during Hurricanes: A Learning-Based Framework
This paper presents a proactive strategy for hurricane-resilient distribution systems. It proposes a Bayesian Neural Network-based outage prediction model considering various parameters, including electrical components, and weather and environmental factors. Addressing challenges in imbalanced outage datasets, a Bias-Variance Tradeoff method is proposed. A resilience assessment model quantifies resilience indices, providing insights into system weaknesses. The approach identifies weak points and serves as a planning benchmark. Numerical results on the modified IEEE 123-node test system demonstrate effectiveness in realistic hurricane scenarios.
Vahedi, Soroush↗
Looking at NGST Through the Eyes of DCATT
The first phase of the Developmental Characterized Active Telescope Testbed (DCATT) hardware system was integrated at Goddard Space Flight Center in early March of 1999 and has been operational for almost a year. Experiments are currently being conducted on the testbed to quantify and refine various methods and techniques in the baseline wavefront sensing and control process. This paper addresses the actual optical design of the Phase 0 hardware configuration and discusses the open loop system level performance of the system. Lessons learned for NGST during the buildup and characterization of the system are presented.
Davila, Pam↗
INDUCTIVE SYSTEM HEALTH MONITORING WITH STATISTICAL METRICS
Model-based reasoning is a powerful method for performing system monitoring and diagnosis. Building models for model-based reasoning is often a difficult and time consuming process. The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS processes nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. In particular, a clustering algorithm forms groups of nominal values for sets of related parameters. This establishes constraints on those parameter values that should hold during nominal operation. During monitoring, IMS provides a statistically weighted measure of the deviation of current system behavior from the established normal baseline. If the deviation increases beyond the expected level, an anomaly is suspected, prompting further investigation by an operator or automated system. IMS has shown potential to be an effective, low cost technique to produce system monitoring capability for a variety of applications. We describe the training and system health monitoring techniques of IMS. We also present the application of IMS to a data set from the Space Shuttle Columbia STS-107 flight. IMS was able to detect an anomaly in the launch telemetry shortly after a foam impact damaged Columbia's thermal protection system.
Iverson, David L.↗
There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge
With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.
Kedar, Smadar T.↗
Assuring Intelligent Systems: Contingency Management for UAS
Unmanned aircraft systems (UAS) collaborate with humans to operate in diverse, safety-critical applications. However, assurance technologies need to be integrated into the design process in order to guarantee safe behavior, thereby enabling UAS operations in the National Airspace System (NAS). In this paper, formal methods are integrated with learning-enabled systems representations. The generation and representation of knowledge are captured via monadic second-order logic rules in the cognitive architecture Soar. These rules are translated into timed automata, and a proof of correctness for the translation is provided so that safety and liveness properties can be checked in the formal verification environment Uppaal. This approach is agnostic to the learning mechanism used to generate the learned rules (e.g., chunking, etc.). An example of a fault-tolerant, learning-enabled UAS deciding which of four contingency procedures to execute under a lost link scenario while overflying an urban area is used to illustrate the approach.
Intelligent Systems↗