Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method
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97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “long memory”
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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Memcomputing the Spectrum of Correlated Quantum Systems
The goals and objectives of the grant DE‐SC0020892 were to apply a new computing paradigm, MemComputing, to efficiently simulate properties of correlated quantum systems. The method has been applied to a wide set of problems ranging from quantum state tomography to finding the ground state of correlated systems. In all cases, substantial advantages compared to state-of-the-art approaches have been obtained. The project has also led to the suggestion of the transformer architecture (used nowadays in large-language models) as an efficient quantum state representation, and a better understanding of the role of memory in the generation of long-range order in neural networks. This grant has supported the work of a PhD student, inspired a new class on unconventional computing taught at the University of California, San Diego and has generated several peer-reviewed papers.
97 MATHEMATICS AND COMPUTING↗
Models and Processes to Extract Drug-like Molecules From Natural Language Text
Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.
60 APPLIED LIFE SCIENCES↗
Efficient derivative computation for unsteady fatigue-constrained nonlinear aero-structural wind turbine blade optimization
Gradient-based optimization offers significant efficiency advantages for wind turbine blade design, but its application has often been limited by the cost and accuracy of finite-difference derivative calculations, especially when fatigue constraints are considered. In this work, we systematically compare and evaluate four differentiation techniques, namely algorithmic differentiation, implicit differentiation, sparsity exploitation, and parallelization, to determine their effectiveness in computing accurate gradients through time-domain aero-structural simulations. By integrating these techniques with unsteady nonlinear aerodynamic and structural models, we develop software designed for accurate gradient computation. We show that combining these techniques addresses memory and runtime challenges associated with long simulations required by design load cases. Specifically, the most effective combination reduces derivative computation wall time by over an order of magnitude compared to finite differencing while maintaining superior accuracy. We demonstrate this approach in a proof-of-concept aero-structural optimization of a wind turbine blade that improves the cost of energy by 12.78 %. This comparative study establishes a viable approach for fatigue-aware blade design that balances computational efficiency with modeling accuracy.
17 WIND ENERGY↗
An improved holographic recording medium
Solid, linear chain hydrocarbons with molecular weight ranging from about 300 to 2000 can serve as long-lived recording medium in optical memory system. Suitable recording hydrocarbons include microcrystalline waxes and low molecular weight polymers or ethylene.
Gange, R. A.↗
SCF calculation of chemical shifts in polycyclic aromatic hydrocarbons
Explore the source record for details and available documents.
Long, E. R.↗
Sparse distributed memory overview
The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.
Raugh, Mike↗
A Cognitive System Model for Human/Automation Dynamics in Airspace Management
NASA has initiated a significant thrust of research and development focused on providing the flight crew and air traffic managers automation aids to increase capacity in en route and terminal area operations through the use of flexible, more fuel-efficient routing, while improving the level of safety in commercial carrier operations. In that system development, definition of cognitive requirements for integrated multi-operator dynamic aiding systems is fundamental. In order to support that cognitive function definition, we have extended the Man Machine Integrated Design and Analysis System (MIDAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems) operating aircraft, airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The MIDAS operator models have undergone significant development in order to understand the requirements for operator aiding and the impact of that aiding in the complex nondeterminate system of national airspace operations. The operator model's structure has been modified to include attention functions, action priority, and situation assessment. The cognitive function model has been expanded to include working memory operations including retrieval from long-term store, interference, visual-motor and verbal articulatory loop functions, and time-based losses. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. The model's internal representation has been be modified so that multiple, autonomous sets of equipment will function in a scenario as the single equipment sets do now. In order to support the analysis requirements with multiple items of equipment, it is necessary for equipment to access the state of other equipment objects at initialization time (a radar object may need to access the position and speed of aircraft in its area, for example), and as a function of perception and sensor system interaction. The model has been improved to include multiple world-states as a function of equipment am operator interaction. The model has been used -1o predict the impact of warning and alert zones in aircraft operation, and, more critic-ally, the interaction of flight-deck based warning mechanisms and air traffic controller action in response to ground-based conflict prediction and alerting systems. In this operation, two operating systems provide alerting to two autonomous, but linked sets of operators, whose view of the system and whose dynamics in response are radically different. System stability and operator action was predicted using the MIDAS model.
Corker, Kevin M.↗
Computational Model of Human and System Dynamics in Free Flight: Studies in Distributed Control Technologies
This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.
Corker, Kevin M.↗
Auto Code Generation for Simulink-Based Attitude Determination Control System
This paper details the work done to auto generate C code from a Simulink-Based Attitude Determination Control System (ADCS) to be used in target platforms. NASA Marshall Engineers have developed an ADCS Simulink simulation to be used as a component for the flight software of a satellite. This generated code can be used for carrying out Hardware in the loop testing of components for a satellite in a convenient manner with easily tunable parameters. Due to the nature of the embedded hardware components such as microcontrollers, this simulation code cannot be used directly, as it is, on the target platform and must first be converted into C code; this process is known as auto code generation. In order to generate C code from this simulation; it must be modified to follow specific standards set in place by the auto code generation process. Some of these modifications include changing certain simulation models into their atomic representations which can bring new complications into the simulation. The execution order of these models can change based on these modifications. Great care must be taken in order to maintain a working simulation that can also be used for auto code generation. After modifying the ADCS simulation for the auto code generation process, it is shown that the difference between the output data of the former and that of the latter is between acceptable bounds. Thus, it can be said that the process is a success since all the output requirements are met. Based on these results, it can be argued that this generated C code can be effectively used by any desired platform as long as it follows the specific memory requirements established in the Simulink Model.
MolinaFraticelli, Jose Carlos↗
Toward Accurate Thermal Modeling of Phase Change Material Based Photonic Devices
Reconfigurable or programmable photonic devices are rapidly growing and have become an integral part of many optical systems. The ability to selectively modulate electromagnetic waves through electrical stimuli is crucial in the advancement of a variety of applications from data communication and computing devices to environmental science and space explorations. Chalcogenide-based phase change materials (PCMs) are one of the most promising material candidates for reconfigurable photonics due to their large optical contrast between their different solid-state structural phases. Although significant efforts have been devoted to accurate simulation of PCM-based devices, in this paper, we highlight three important aspects which have often evaded prior models yet having significant impacts on the thermal and phase transition behavior of these devices: the enthalpy of fusion, the heat capacity change upon glass transition, as well as the thermal conductivity of liquid-phase PCMs. We further investigated the important topic of switching energy scaling in PCM devices, which also helps explain why the three above-mentioned effects have long been overlooked in electronic PCM memories but only become important in photonics. Our findings offer insight to facilitate accurate modeling of PCM-based photonic devices and can inform the development of more efficient reconfigurable optics.
Phase change materials↗
Reconfigurable quantum phononic circuits via piezo-acoustomechanical interactions
Abstract We show that piezoelectric strain actuation of acoustomechanical interactions can produce large phase velocity changes in an existing quantum phononic platform: aluminum nitride on suspended silicon. Using finite element analysis, we demonstrate a piezo-acoustomechanical phase shifter waveguide capable of producing ± π phase shifts for GHz frequency phonons in 10s of μm with 10s of volts applied. Then, using the phase shifter as a building block, we demonstrate several phononic integrated circuit elements useful for quantum information processing. In particular, we show how to construct programmable multi-mode interferometers for linear phononic processing and a dynamically reconfigurable phononic memory that can switch between an ultra-long-lifetime state and a state strongly coupled to its bus waveguide. From the master equation for the full open quantum system of the reconfigurable phononic memory, we show that it is possible to perform read and write operations with over 90% quantum state transfer fidelity for an exponentially decaying pulse.
Taylor, Jeffrey C. (ORCID:0000000215982436)↗
Speeding up and reducing memory usage for scientific machine learning via mixed precision
Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets require significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. Here, we also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.
97 MATHEMATICS AND COMPUTING↗
A Study of Shape Memory Polymer Based Slat-Cove Filler
Aircraft noise reduction is an application of current intense interest for which smart materials show significant potential. Specifically, the aeroacoustic noise produced by the unsteady aerodynamic flow about the leading-edge high-lift device, such as leading-edge slat, of typical transport-aircraft wings is of particular interest. Concepts with the most promise to mitigate this noise source, most notably the slat-cove filler concept, have focused on highly reconfigurable structures that change shape between different phases of the flight envelope. These shape changes often involve large deformation, which has stimulated the consideration of shape memory materials. In recent years, shape memory materials (SMMs) have drawn greater interest for applications such as smart fabrics, intelligent medical devices, self-deployable space structures, morphing structures and packaging. Compared to other shape memory materials, like shape memory alloys (SMAs) or shape memory ceramics (SMCs), shape memory polymers (SMPs) have desirable advantages such as high elastic deformation to enable large shape change, broad tailorability of mechanical properties, potential biocompatibility and biodegradability, ductility, light weight and ease of processing. However, SMPs still have some critical disadvantages such as insufficient mechanical and thermal characteristics for structural applications, low recovery stress, and long response time. The new LaRC shape memory thermosetting polymer composite (LaRC-SMPC) discussed herein was synthesized with nontoxic monomers and conductive/magnetic fillers to yield enhanced thermal/mechanical characteristics and faster response times. LaRC-SMPCs with a variety of fiber reinforcements [Kevlar®, carbon fiber (standard and thin-ply), and carbon-nanotube (CNT) sheet] were fabricated to tailor the physical properties and test for suitability as a slat-cove filler (SCF). The performance of SCF prototypes fabricated with the developed LaRC-SMPCs was evaluated using a bench-top test apparatus. The SCFs made of Kevlar® fiber fabric or carbon fiber fabric infused shape memory polymer composite (SMPC) exhibited kinking during simulated deployment and stowage, which can be problematic during operation. The SCF made of CNT sheet/SMP composite did not exhibit kinking, but the deployment was sluggish compared to carbon fiber fabric/SMP composite. This report documents the evolution of designing SMPCs as slat-cove fillers for aircraft noise reduction. In the course of the investigation, several 2 approaches were investigated to address shortcomings in material characteristics based on performance requirements of operational slat-cove fillers.
Kang, Jin Ho↗
Memory management in traceback Viterbi decoders
The new Viterbi decoder for long constraint length codes, under development for the Deep Space Network, stores path information according to an algorithm called traceback. The details of a particular implementation of this algorithm, based on three memory buffers, are described. The penalties in increased storage requirement and longer decoding delay are offset by the reduced amount of data that needs to be exchanged between processors, in a parallel architecture decoder.
Collins, O.↗
Interlayer Exciton Polarons in Mesoscopic V 2 O 5 for Broadband Optoelectronic Synapses
Persistent photoconductivity and optoelectronic synaptic behavior are demonstrated in solution-processed mesoscopic α-phase vanadium pentoxide (V 2 O 5 ) thin films. First-principles simulations coupled with the two-site Holstein polaron hopping model show that vacancies at the terminal oxygen position lead to long recombination times because photoexcited electrons and holes reside on different layers separated by the van der Waals gap, forming a weakly coupled interlayer exciton polaron. Mid-gap polaronic states also significantly broaden the photoresponse of the films to span across visible and infrared wavelengths. By controlling the amplitude/intensity, duration, and/or number of optical pulses, tunable optoelectronic memory functions, such as short-term and long-term plasticity, are experimentally established in V 2 O 5 -based optoelectronic synapses. Furthermore, device fabrication was extended to mechanically flexible ultrathin glass substrates. Flexible optoelectronic synapses maintained high performance after 150 bending cycles.
Phan, Thanh Luan [National Renewable Energy Labora↗
Distributed Berkeley Efficient Long-Read to Long-Read Aligner and Overlapper (DiBELLA) v1.0.0
We present a parallel algorithm and scalable implementation for genome analysis, specifically the problem of finding overlaps and alignments for data from "third generation" long read sequencers. While long sequences of DNA offer enormous advantages for biological analysis and insight, current long read sequencing instruments have high error rates and therefore require different approaches to analysis than their short read counterparts. Our work focuses on an efficient distributed-memory parallelization of an accurate single-node algorithm for overlapping and aligning long reads. We achieve scalability of this irregular algorithm by addressing the competing issues of increasing parallelism, minimizing communication, constraining the memory footprint, and ensuring good load balance. The resulting application, DiBELLA, is the first distributed memory overlapper and aligner specifically designed for long reads and parallel scalability.
Ellis, Marquita↗
High-fidelity remote entanglement of trapped atoms mediated by time-bin photons
Abstract Photonic interconnects between quantum processing nodes are likely the only way to achieve large-scale quantum computers and networks. The bottleneck in such an architecture is the interface between well-isolated quantum memories and flying photons. We establish high-fidelity entanglement between remotely separated trapped atomic qubit memories, mediated by photonic qubits stored in the timing of their pulses. Such time-bin encoding removes sensitivity to polarization errors, enables long-distance quantum communication, and is extensible to quantum memories with more than two states. Using a measurement-based error detection process and suppressing a fundamental source of error due to atomic recoil, we achieve an entanglement fidelity of 97% and show that fundamental limits due to atomic recoil still allow fidelities in excess of 99.9%.
Science & Technology - Other Topics↗