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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 343 records · Page 19

All sources NASA NTRS DOE OSTI

NASA NTRS1991

Modular, Multilayer Perceptron

Combination of proposed modular, multilayer perceptron and algorithm for its operation recognizes new objects after relatively brief retraining sessions. (Perceptron is multilayer, feedforward artificial neural network fully connected and trained via back-propagation learning algorithm.) Knowledge pertaining to each object to be recognized resides in subnetwork of full network, therefore not necessary to retrain full network to recognize each new object.

Cheng, Li-Jen↗

NASA NTRS1992

Fault tolerance of artificial neural networks with applications in critical systems

This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

Protzel, Peter W.↗

NASA NTRS1993

Vector Quantization With Emergent Codebook Structure

Proposed scheme under development for transmission of vector-quantized digital video images, vector quantizer codebook updated to adapt quantizer to changing signal statistics. Intended to be realized with electronic neural network. Codebook, which consists of patterns constituting video images, will undergo training during operation and scheme will develop codebooks ordered during training. System enables coding more compact, more immune to noise, and supports variable rate compression.

Ahalt, Stanley C.↗

NASA NTRS1998

Neural Network Control of a Magnetically Suspended Rotor System

Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

Choi, Benjamin B.↗

NASA NTRS2010

Human Perception of Ambiguous Inertial Motion Cues

Human daily activities on Earth involve motions that elicit both tilt and translation components of the head (i.e. gazing and locomotion). With otolith cues alone, tilt and translation can be ambiguous since both motions can potentially displace the otolithic membrane by the same magnitude and direction. Transitions between gravity environments (i.e. Earth, microgravity and lunar) have demonstrated to alter the functions of the vestibular system and exacerbate the ambiguity between tilt and translational motion cues. Symptoms of motion sickness and spatial disorientation can impair human performances during critical mission phases. Specifically, Space Shuttle landing records show that particular cases of tilt-translation illusions have impaired the performance of seasoned commanders. This sensorimotor condition is one of many operational risks that may have dire implications on future human space exploration missions. The neural strategy with which the human central nervous system distinguishes ambiguous inertial motion cues remains the subject of intense research. A prevailing theory in the neuroscience field proposes that the human brain is able to formulate a neural internal model of ambiguous motion cues such that tilt and translation components can be perceptually decomposed in order to elicit the appropriate bodily response. The present work uses this theory, known as the GIF resolution hypothesis, as the framework for experimental hypothesis. Specifically, two novel motion paradigms are employed to validate the neural capacity of ambiguous inertial motion decomposition in ground-based human subjects. The experimental setup involves the Tilt-Translation Sled at Neuroscience Laboratory of NASA JSC. This two degree-of-freedom motion system is able to tilt subjects in the pitch plane and translate the subject along the fore-aft axis. Perception data will be gathered through subject verbal reports. Preliminary analysis of perceptual data does not indicate that the GIF resolution hypothesis is completely valid for non-rotational periodic motions. Additionally, human perception of translation is impaired without visual or spatial reference. The performance of ground-base subjects in estimating tilt after brief training is comparable with that of crewmembers without training.

Zhang, Guan-Lu↗

NASA NTRS2019

Novel Classifier of Neural Impairment

The proposed research aligns with Ames Core Competency within the area of Intelligent/Adaptive Systems under Human Systems Integration. This will serve as a robust, efficient, and user-friendly human performance assessment tool that could provide immediate neural status of crewmembers in low-orbit and deep space missions. The human operator is a critical element of crew safety and should be evaluated consistently to mitigate potential off-nominal situations propagated from human error. Flagged deficits in oculomotor performance could act as an alert signal for crewmembers to opt out of cognitively demanding tasks or to prevent irreversible compromise in crewmember health.

impairment↗

NASA NTRS2022

Spatial Grid-Based Object Localization from A Single Passive Sensor: A Deep Learning-Integrated Approach

Ongoing efforts at NASA’s Langley Research Center have produced a single passive sensor system for detecting ground objects and pinpointing their real-world location to a desired level of precision. The Langley center serves as a test range for unmanned aerial systems (UAS) and real-time knowledge about the location of people on campus is needed to inform least-risk UAS flight operations. The proposed system provides this knowledge through a camera combined with a convolutional neural network and an algorithm that projects an imaginary grid of square cells from the ground plane onto the perspective view of the camera. The position of detected objects on the camera’s projected grid determines their location in the real-world. The imaginary grid is easily mapped to a universal coordinate system, such as longitude and latitude, to provide both relative and absolute positional information of the detected objects. This simple system is shown to be accurate and effective, with decisive advantages over alternative multi-sensor and active sensor approaches. Extensions to the system are described to allow adaptation to a variety of other use cases.

object localization↗

NASA NTRS2016

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

passive microwave↗

DOE OSTI2024

Training quantum neural networks using the quantum information bottleneck method

Abstract We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well founded quantity to optimize when training autoencoders for problems where the inputs and outputs are fully quantum. We provide a rigorous algorithm for computing the value of the quantum information bottleneck quantity within error ε that requires O ( log 2 ⁡ ( 1 / ϵ ) + 1 / δ 2 ) queries to a purification of the input density operator if its spectrum is supported on { 0 } ⋃ [ δ , 1 − δ ] for δ > 0 and the kernels of the relevant density matrices are disjoint. We further provide algorithms for estimating the derivatives of the QIB function, showing that quantum neural networks can be trained efficiently using the QIB quantity given that the number of gradient steps required is polynomial.

Çatlı, Ahmet Burak (ORCID:0000000152294141)↗

NASA NTRS2006

Using Neural Networks to Explore Air Traffic Controller Workload

When a new system, concept, or tool is proposed in the aviation domain, one concern is the impact that this will have on operator workload. As an experience, workload is difficult to measure in a way that will allow comparison of proposed systems with those already in existence. Chatterji and Sridhar (2001) suggested a method by which airspace parameters can be translated into workload ratings, using a neural network. This approach was employed, and modified to accept input from a non-real time airspace simulation model. The following sections describe the preparations and testing work that will enable comparison of a future airspace concept with a current day baseline in terms of workload levels.

Martin, Lynne↗

DOE OSTI2026

Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network

Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 μ$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $χ^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|Δq| \in [0.1, 0.5] e$ at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.

Gaytan-Villarreal, Daniel [Carnegie Mellon U.]↗

NASA NTRS1998

Hidden Markov Models and Neural Networks for Fault Detection

Continuous online monitoring of complex dynamic systems is common in applications as diverse as industrial plant operations, telecommunications network, and biomedical health monitoring. For monitoring purposes, the exact nature of the system under observation is typically not relevant provided there exist some measurements or symptoms which provide diagnostics information regarding the underlying system state.

Markov Fault Detection↗

DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗

NASA NTRS1993

Man-machine cooperation in advanced teleoperation

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints.

Fiorini, Paolo↗

NASA NTRS2001

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗

NASA NTRS2004

Shortcomings with Tree-Structured Edge Encodings for Neural Networks

In evolutionary algorithms a common method for encoding neural networks is to use a tree structured assembly procedure for constructing them. Since node operators have difficulties in specifying edge weights and these operators are execution-order dependent, an alternative is to use edge operators. Here we identify three problems with edge operators: in the initialization phase most randomly created genotypes produce an incorrect number of inputs and outputs; variation operators can easily change the number of input/output (I/O) units; and units have a connectivity bias based on their order of creation. Instead of creating I/O nodes as part of the construction process we propose using parameterized operators to connect to preexisting I/O units. Results from experiments show that these parameterized operators greatly improve the probability of creating and maintaining networks with the correct number of I/O units, remove the connectivity bias with I/O units and produce better controllers for a goal-scoring task.

Hornby, Gregory S.↗

DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗

DOE OSTI2025

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. However, the effectiveness of KAEs is hindered by limited and noisy training datasets, leading to poor generalizability. To address this, we introduce the Temporally-Consistent Koopman Autoencoder (tcKAE), designed to generate accurate long-term predictions even with limited and noisy training data. This is achieved through a consistency regularization term that enforces prediction coherence across different time steps, thus enhancing the robustness and generalizability of tcKAE over existing models. We provide analytical justification for this approach based on Koopman spectral theory and empirically demonstrate tcKAE’s superior performance over state-of-the-art KAE models across a variety of test cases, including simple pendulum oscillations, kinetic plasma, and fluid flow data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗