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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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At least 667 records · Page 37

Low-temperature emergent neuromorphic networks with correlated oxide devices

Significance Designing neuromorphic hardware for cryoelectronics is an important area of research as the field of computing paradigms beyond complementary metal-oxide-semiconductor (CMOS) progresses. Superconductivity and metal−insulator transitions are two of the most celebrated emergent, collective properties found in quantum materials such as strongly correlated oxides. Here, we present simulations of artificial neural networks that can be designed by combining superconducting devices (e.g. Josephson junctions) with Mott metal−insulator transition−based tunable resistor devices. Our simulations show that 1) neurons and synapses can be seamlessly created, 2) their functions can be tuned via learning, and 3) controlling disorder by incorporating light ions enables exponential multiplicity of states. The results open up directions for incorporating emergent behavior seen in condensed matter into hardware design for artificial intelligence.

97 MATHEMATICS AND COMPUTING↗

Implementing two-qubit gates at the quantum speed limit

The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine-learning-inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high fidelity. Thus the method is applicable to a variety of platforms, including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions. We expect our method to offer significant speedups for non-native two-qubit gates that are typically achieved with a long sequence of single-qubit and native two-qubit gates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Evaluating Efficiency and Security of Connected and Autonomous Vehicle Applications

Evaluating efficiency and security of Connected and Autonomous Vehicles (CAVs) requires an environment that can support applications and measurements under real-world conditions. This work introduces our implementation and evaluation of a Connected and Autonomous Vehicle Research Environment (CAVRE). We implement and evaluate an existing CAV application called Cooperative Adaptive Cruise Control (CACC) using physical Vehicle-to-Vehicle (V2V) communications between a virtual agent and a real autonomous vehicle operating on a steerable dynamometer. CAVRE allows the follower to autonomously control longitudinal behavior on the dynamometer in order to maintain a steady following time gap from the leader. The effects of a wireless jamming attack on CACC and fuel efficiency is also evaluated. By executing attacks in a controlled environment, we learn how compromised communications can degrade CAV applications. We show that jamming V2V communications can impact CACC’s string stability and decrease fuel efficiency by more than 50%.

Taylor, Curtis↗

A Review of Cyber-Physical Security for Photovoltaic Systems

In this paper, the challenges and a future vision of the cyber-physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyber-attacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyber-attacks in software and cyber networks is described. Simulation and experimental results that show the impact of cyber-attacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cyber-secure power electronics systems. These opportunities include multi-scale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

14 SOLAR ENERGY↗

Findings from Design and Operation of Connected Neighborhoods

Distributed energy resources and load management are an important emerging part of smart grids. Concurrent management of homeowner preferences and utility objectives requires an intelligent negotiation strategy supported by physical infrastructure that learns, optimizes, and controls the system. While there is a wide body of research related to modelling and simulation of distributed resources, relatively little is known about their practical feasibility. One of the first attempts to address this knowledge gap was through a 62-house connected neighborhood in Alabama. This study makes one more step in advancing this knowledge through a 46-townhome demonstration neighborhood located in Atlanta, GA. It reports hardware design, system architecture, and the results from the summer experimental work. The findings are discussed in the context of earlier experience, and analysis is expanded to areas which were not the focus of earlier research.

Tsybina, Eve↗

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the developed hardware can greatly depend on the size of the network that is being evaluated on the chip. Furthermore, the accuracy of a trained SNN that is evaluated on chip can change due to voltage and current variations in the hardware that perturb the learned weights of the network. While efforts are made on the hardware side to minimize those perturbations, a software based strategy to make the deployed networks more resilient can help further alleviate that issue. In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults. We leverage an evolutionary algorithm to train the SNNs and propose a multiobjective fitness function to optimize the size and resiliency of the SNN. We demonstrate that this strategy leads to well-performing, small-sized networks that are more resilient to hardware faults.

Dimovska, Mihaela↗

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. Here, this work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

97 MATHEMATICS AND COMPUTING↗

foresee™ (SWR-17-15)

foresee™ is a home energy management system (HEMS) that provides a user centric energy automation solution for residential building occupants. Built upon advanced control and machine learning algorithms, foresee™ intelligently manages the home appliances and distributed energy resources (DERs) such as photovoltaics and battery storage in a home. Unlike existing HEMS in the market, foresee™ provides a tailored home automation solution for individual occupants by learning and adapting to their preferences on cost, comfort, convenience and carbon. foresee™ improves not only the energy efficiency of the home but also its capability to provide grid services such as demand response. Highly reliable demand response services are likely to be incentivized by utility companies, making foresee™ economically viable for most homes.

Source record↗

Strain Distributions and Structural Changes in Motor Driven Gels (Final Report)

The goal of this project was to study the effects of DNA-based, force-generating motor proteins on the structure and dynamics of a DNA hydrogel. Motor proteins are nanoscale transducers, converting chemical energy embedded in the solution into local mechanical work on hydrogel strands, and thus potentially driving structural changes and/or non-equilibrium dynamics within the gel material. To explore this, we used self-assembly to create condensed DNA phases, and activated the phases with proteins. The specific aim was to study the deformations (strain fields) generated within a DNA gel by motor forces. We succeeded in this aim, developing methods to experimentally create gel/motor systems, and measure strain with high spatio-temporal resolution. A key finding was that simple continuum strain-field models fail to describe the data. A second major outcome was the development of novel models of hydrogel elasticity incorporating solvent effects that can be used to model dynamic motor-driven strains. A third major outcome was to learn how to control the phase and structure of condensed DNA particles, including both liquid-crystalline behavior, and the formation of DNA liquids.

59 BASIC BIOLOGICAL SCIENCES↗

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.↗

Distributed memory approaches for robotic neural controllers

The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

Jorgensen, Charles C.↗

An intelligent robotic aid system for human services

The long term goal of our research at the Intelligent Robotic Laboratory at Vanderbilt University is to develop advanced intelligent robotic aid systems for human services. As a first step toward our goal, the current thrusts of our R&D are centered on the development of an intelligent robotic aid called the ISAC (Intelligent Soft Arm Control). In this paper, we describe the overall system architecture and current activities in intelligent control, adaptive/interactive control and task learning.

Kawamura, K.↗

Expert systems in the process industries

This paper gives an overview of industrial applications of real-time knowledge based expert systems (KBES's) in the process industries. After a brief overview of the features of a KBES useful in process applications, the general roles of KBES's are covered. A particular focus is diagnostic applications, one of the major applications areas. Many applications are seen as an expansion of supervisory control. The lessons learned from numerous online applications are summarized.

Stanley, G. M.↗

Effects of Inboard Horizontal Field of View Display Limitations on Pilot Path Control During Total In-Flight Simulator (TIFS) Flight Test

A flight test was conducted aboard Calspan's Total In-Flight Simulator (TIFS) aircraft by researchers within the External Visibility System (XVS) element of the High-Speed Research program. The purpose was to investigate the effects of inboard horizontal field of view (FOV) display limitations on pilot path control and to learn about the TIFS capabilities and limitations for possible use in future XVS flight tests. The TIFS cockpit windows were masked to represent the front XVS display area and the High-Speed Civil Transport side windows, as viewed by the pilot. Masking limited the forward FOV to 40 deg. horizontal and 50 deg. vertical for the basic flight condition, With an increase of 10 deg. horizontal in the inboard direction for the increased FOV flight condition. Two right-hand approach tasks (base-downwind-final) with a left crosswind on final were performed by three pilots using visual flight rules at Niagara Falls Airport. Each of the two tasks had three replicates for both horizontal FOV conditions, resulting in twelve approaches per test subject. Limited objective data showed that an increase of inboard FOV had no effect (deficiences in objective data measurement capabilities were noted). However, subjective results showed that a 50 deg. FOV was preferred over the 40 deg. FOV.

Kramer, Lynda J.↗

Locomotor Dysfunction after Spaceflight: Characterization and Countermeasure Development

Astronauts returning from space flight show disturbances in locomotor control manifested by changes in various sub-systems including head-trunk coordination, dynamic visual acuity, lower limb muscle activation patterning and kinematics (Glasauer, et al., 1995; Bloomberg, et al., 1997; McDonald, et al., 1996; 1997; Layne, et al., 1997; 1998, 2001, 2004; Newman, et al., 1997; Bloomberg and Mulavara, 2003). These post flight changes in locomotor performance, due to neural adaptation to the microgravity conditions of space flight, affect the ability of crewmembers especially after a long duration mission to egress their vehicle and perform extravehicular activities soon after landing on Earth or following a landing on the surface of the Moon or Mars. At present, no operational training intervention is available pre- or in- flight to mitigate post flight locomotor disturbances. Our laboratory is currently developing a gait adaptability training program that is designed to facilitate recovery of locomotor function following a return to a gravitational environment. The training program exploits the ability of the sensorimotor system to generalize from exposure to multiple adaptive challenges during training so that the gait control system essentially "learns to learn" and therefore can reorganize more rapidly when faced with a novel adaptive challenge. Ultimately, the functional goal of an adaptive generalization countermeasure is not necessarily to immediately return movement patterns back to "normal". Rather the training regimen should facilitate the reorganization of available sensorimotor sub-systems to achieve safe and effective locomotion as soon as possible after space flight. We have previously confirmed that subjects participating in adaptive generalization training programs, using a variety of visuomotor distortions and different motor tasks from throwing to negotiating an obstacle course as the dependent measure, can learn to enhance their ability to adapt to a novel sensorimotor environment (Roller et al., 2001; Cohen et al. 2005). Importantly, this increased adaptability is retained even one month after completion of the training period. Our laboratory is currently developing adaptive generalization training procedures and the associated flight hardware to implement such a training program, using variations of visual flow, subject loading, and treadmill speed; during regular in-flight treadmill operations.

Mulavara, A. P.↗

Spheres: from Ground Development to ISS Operations

SPHERES (Synchronized Position Hold Engage and Reorient Experimental Satellites) is an internal International Space Station (ISS) Facility that supports multiple investigations for the development of multi-spacecraft and robotic control algorithms. The SPHERES National Lab Facility aboard ISS is managed and operated by NASA Ames Research Center (ARC) at Moffett Field California. The SPHERES Facility on ISS consists of three self-contained eight-inch diameter free-floating satellites which perform the various flight algorithms and serve as a platform to support the integration of experimental hardware. SPHERES has served to mature the adaptability of control algorithms of future formation flight missions in microgravity (6 DOF (Degrees of Freedom) / long duration microgravity), demonstrate key close-proximity formation flight and rendezvous and docking maneuvers, understand fault diagnosis and recovery, improve the field of human telerobotic operation and control, and lessons learned on ISS have significant impact on ground robotics, mapping, localization, and sensing in three-dimensions - among several other areas of study.

SPHERES↗

Run Time Assurance for Electric Vertical Takeoff and Landing Aircraft

NASA is conducting research to demonstrate and evaluate the application of Run Time Assurance (RTA) as a means to assure safety in Electric Vertical Takeoff and Landing (eVTOL) aircraft with highly automated or autonomous flight capability supervised by a single onboard pilot. The work described in this report demonstrates an application of RTA and examines the implications for design and analysis of aircraft functions and systems; aircraft safety hazards; safety assurance; development assurance; and pilot tasks and performance. This research effort also seeks to assess the efficacy of the combined application of traditional Functional Hazard Analysis (FHA) and the more modern System Theoretic Process Analysis (STPA) techniques to perform hazard analyses on aircraft with complex automated and autonomous systems and an onboard pilot. During the research effort we developed architectural designs of two alternate eVTOL aircraft, generally following the process characterized in the SAE standards ARP4754 and ARP4761. The design has focused on the control architectures of these aircraft, which are identical except that one incorporates RTA techniques to reduce the criticality of some key software components. Artifacts of this process include a taxonomy of aircraft-level functions, aircraft-level architecture diagrams, aircraft-level functional hazard assessments (AFHA), function allocations onto aircraft systems and subsystems, functional block diagrams for a select set of control-related functions, and system-level functional hazard assessments (SFHA) for those functions. This project has highlighted the notion that DAL D is something of a sweet spot for low-confidence controllers in an RTA-based design. Among the many activities described in DO-178C, the activities related to requirement verifiability, algorithmic accuracy, and test coverage can be the most challenging for the kinds of advanced control techniques that may be desirable in novel UAM designs, such as adaptive control, machine-learning, artificial intelligence, numerical search, and Monte Carlo based algorithms. Moreover, the standard requires that development teams demonstrate that errors leading to unacceptable failure conditions have been removed from the software. The RTA architecture, which cordons off the low-confidence function, makes it much easier to show this for these kinds of algorithms. With regard to the use of STPA and FHA as complementary hazard analysis techniques, our research effort led us to the conclusion that STPA should be used to derive requirements for hardware and software systems and/or components. Also, STPA is a natural complement to other processes in ARP4754A involving design studies and iteration.

Run-time assurance↗

Applying NASA’s Human Systems Integration Methodology in Implementing Voice-Control of Future Spacecraft Systems

Through the NASA Artemis program, a new era of space exploration will serve to lead humanity towards sustained lunar exploration in preparation for the next giant leap-human exploration of Mars. These crewed spacecraft and habitats will require more automation and autonomy to support these complex missions. Crew size will be small and therefore a more efficient command and control input method is desired. Speech recognition along with visual or auditory feedback is an alternative, providing an extra pair of hands and eyes for the crew. Yet, speech recognition demands a highly integrated development approach to ensure a successful system implementation. To ensure the voice control application is developed correctly will require a Human Systems Integration (HSI) approach. This paper provides an insight into the development of a speech/voice control application for a spacecraft system that encompasses automation and autonomy through an HSI approach. Results of the voice control experiment of the Space Shuttle camera system are provided as lessons learned about using voice control on a spacecraft. Limitations and challenges of the technology are addressed as well as how HSI along with Human Readiness Level can help successfully develop voice control command and control systems.

Systems Engineering↗