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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integration of a DER Management System in Riverside. Final report

The tasks in this project covered various aspects, including algorithm development, algorithm integration into a commercial Active Network Management (ANM) platform, hardware-in-the-loop (HIL) testing in an industry-standard testing platform, pilot demonstration in Riverside, California, and also cost and benefit analysis. The DERMS platform in this project can host different algorithms developed on different platforms (e.g., MATLAB and Python) and it can interact with different hardware devices (e.g., different PV inverters, battery inverters, and different sensors). The DER control solution are based on an advanced model-free, layered, and clustered DER control paradigm. At the core of the DER control algorithms was the concept of Extremum Seeking (ES), which is a model-free probing-based control technique. The ES-based control algorithms were tested on major real-world inverters; both individually and in a cluster. It was shown that even legacy equipment (or when paired with a few additional advanced equipment) can support such advanced control. The monitoring algorithms utilize a heterogeneous set of legacy and advanced sensor measurements, such as behind-the-meter DER sensors, distribution-level Phase Measurement Units, distribution-substation Supervisory Control and Data Acquisition (SCADA), and line current sensors, with their limited availability; in order to infer practical network conditions. Sensor data are utilized to achieve resource forecasting, phase identification, and distribution system state estimation. The technology that was developed and demonstrated in this project could be transformational to utilities, including the smaller municipal utilities such as in Riverside, which may not have the resources to deploy advanced distribution system and DERMS solutions in order to support high penetration of solar power integration. This project created a real-world prototype to provide utilities with an assessment of smart grid monitoring and control technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Sensor fusion techniques for predicting thunderstorm evolution using lightning and radar networks

A pattern recognition scheme that is used to generate initial seeds or 'first guess' fields for clustering the discrete lightning discharges into storm cells is presented. The clustering process is critically dependent on the prior accuracy of the lightning location estimates. The generation of subsequent storm life-cycle time series (for extrapolation forecasting) also relies on the cluster analysis procedure assigning the correct number of lightning discharges (objects) to the proper storms (groups). The advantages and limitations of different clustering strategies for storm identification and tracking are examined. Storm identification with lightning data alone is compared to storm identification with radar alone, and some synergies for sensor fusion are explored.

Goodman, Steven J.↗
NASA NTRS1990

Learning and diagnosing faults using neural networks

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗
DOE OSTI2020

Heuristic based analytics for gas leak source identification

Heuristic-based techniques for gas leak source identification are provided. In one aspect, a method for identifying a location of a gas leak source includes: obtaining gas sensor data and wind data synchronously from a gas leak detection system having a network of interconnected motes comprising gas sensors and wind sensors, with the gas sensors arranged around possible gas leak sources in a given area of interest; identifying the location of the gas leak source using the gas sensor data and wind data; and determining a magnitude of gas leak from the gas leak source using the location of the gas leak source and a distance d between the location of the gas leak source and a select one of the gas sensors from which the gas sensor data was obtained. A gas leak detection system is also provided.

03 NATURAL GAS↗
NASA NTRS2010

Virtual Mission Operations of Remote Sensors With Rapid Access To and From Space

This paper describes network-centric operations, where a virtual mission operations center autonomously receives sensor triggers, and schedules space and ground assets using Internet-based technologies and service-oriented architectures. For proof-of-concept purposes, sensor triggers are received from the United States Geological Survey (USGS) to determine targets for space-based sensors. The Surrey Satellite Technology Limited (SSTL) Disaster Monitoring Constellation satellite, the United Kingdom Disaster Monitoring Constellation (UK-DMC), is used as the space-based sensor. The UK-DMC s availability is determined via machine-to-machine communications using SSTL s mission planning system. Access to/from the UK-DMC for tasking and sensor data is via SSTL s and Universal Space Network s (USN) ground assets. The availability and scheduling of USN s assets can also be performed autonomously via machine-to-machine communications. All communication, both on the ground and between ground and space, uses open Internet standards.

Ivancic, William D.↗
DOE OSTI2018

Regression for Radioactive Source Detection

Detection of radioactive sources is an important capability that led to the deployment of networks for detecting and localizing low-level, hazardous radiation sources. It is generally expected that such networks outperform the individual detectors by intelligently fusing information from several dispersed sensors. In this paper, we develop a network detection method that uses the slope of a linear regression fit as a test statistic for detecting a point radioactive source within a field of detectors. In our regression model, we compute a least-squared linear fit between the average radiation counts at the detectors and the inverse-squared distances of known detector locations to an estimated source location. We show that the slope of this regression fit is an estimate of the source intensity and can be used as a threshold for source detection purposes. We compare performance of our proposed detection method with that of a fusion-based Sequential Probability Ratio Test (SPRT) method. For performance analyses, two datasets from the Domestic Nuclear Detection Ofces Intelligence Radiation Sensors Systems (IRSS) outdoor tests are used. Each of these tests consists of several runs of a single radioactive source moving in and out of a detector network. We present receiver operating characteristic (ROC) curves and optimal threshold values for the performance each detection method, and determine that our detection method using linear regression fit has slightly better overall performance than SPRT method.

Cordone, G.↗
NASA NTRS2012

Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms

Wireless sensors connected in a local network offer revolutionary exploration capabilities, but the current solutions do not work in extreme environments of low temperatures (200K) and low to moderate radiation levels (<50 krad). These sensors (temperature, radiation, infrared, etc.) would need to operate outside the spacecraft/ lander and be totally independent of power from the spacecraft/lander. Flash memory field-programmable gate arrays (FPGAs) are being used as the main signal processing and protocol generation platform in a new receiver. Flash-based FPGAs have been shown to have at least 100 reduced standby power and 10 reduction operating power when compared to normal SRAM-based FPGA technology.

Sheldon, Douglas J.↗
NASA NTRS1961

Study of a Satellite Attitude Control System Using Integrating Gyros as Torque Sources

This report considers the use of single-degree-of-freedom integrating gyros as torque sources for precise control of satellite attitude. Some general design criteria are derived and applied to the specific example of the Orbiting Astronomical Observatory. The results of the analytical design are compared with the results of an analog computer study and also with experimental results from a low-friction platform. The steady-state and transient behavior of the system, as determined by the analysis, by the analog study, and by the experimental platform agreed quite well. The results of this study show that systems using integrating gyros for precise satellite attitude control can be designed to have a reasonably rapid and well-damped transient response, as well as very small steady-state errors. Furthermore, it is shown that the gyros act as rate sensors, as well as torque sources, so that no rate stabilization networks are required, and when no error sensor is available, the vehicle is still rate stabilized. Hence, it is shown that a major advantage of a gyro control system is that when the target is occulted, an alternate reference is not required.

White, John S.↗
NASA NTRS1991

Sensor-knowledge-command fusion paradigm for man/machine systems

Sensing-knowledge-command (SKC) fusion is presented as a fundamental paradigm of implementing cooperative control for an advanced man-machine system. SKC fusion operates on the 'SKC fusion network,' which represents the connection between sensor data to commands through knowledge. Sensing, knowledge, and command of a human and a machine are tapped into the network to provide inputs, or stimuli, to the network. Such stimuli automatically invoke an SKC fusion process and generate a fused output for cooperative control. Once invoked by stimuli, the SKC fusion process forces the network to converge to a new equilibrium state through the network dynamics composed of data fusion, feature transformation, and constraint propagation. The SKC fusion process thus integrates redundant information, maintains network consistency, identifies faulty data and concepts, and specifies those concepts to be strengthened through sensor planning.

Lee, Sukhan↗
NASA NTRS2007

Switched Band-Pass Filters for Adaptive Transceivers

Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.

Wang, Ray↗
NASA NTRS1999

ELIPS: Toward a Sensor Fusion Processor on a Chip

The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI.

sensor↗
DOE OSTI2025

Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 $\frac{𝑀𝐻𝑧}{°C}$. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2019

Thread/OpenThread: A Compromise in Low-Power Wireless Multihop Network Architecture for the Internet of Things

Extending an Internet subnet by connecting resource-constrained nodes (e.g., embedded sensors and actuators) over multiple wireless hops is necessary to support the future Internet of Things (IoT). RPL, the IPv6 routing standard for low-power and lossy networks, tried to achieve this goal but has not seen wide adoption in practice. As an alternative, Thread is a recently standardized low-power network protocol for IoT, driven by the Thread group, an industry consortium led by Google/Nest. Here, we provide a comparative analysis of the technical aspects of RPL and Thread based on their specifications, explaining why using Thread, as opposed to RPL, may make sense for the future Internet. Specifically, the fundamental differences between RPL and Thread are their respective scopes and multihop network architectures, which result in Thread's unique design and advantages over RPL. Lastly, we evaluate Thread in an indoor multihop wireless testbed using OpenThread, an official open source implementation of Thread. This work serves as the first analysis of the Thread protocol in academia.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Intelligent Electrochemical Sensors for Precise Identification of Volatile Organic Compounds Enabled by Neural Network Analysis

The volatile organic compounds (VOCs) in a wide spectrum of categories were identified as biomarkers in aquatic environments, playing an important role in marine and freshwater ecology and global atmospheric chemistry. VOCs released from biofuel have also attracted increasing attention. Although the importance has been recognized, the portable detection and analysis methods of VOC in aquatic systems have not yet been well developed and understood. Here, in this work, we innovatively proposed an intelligent electrochemical sensing approach to classify and quantify VOCs in solution. Utilizing the cyclic voltammetry (CV) method with an ionic liquid (IL)-based electrolyte, we analyzed 50 μL samples of various VOC analytes, including acetic acid (AC), acetone, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, formaldehyde, formic acid, methanol, methyl formate (MF), toluene, and a formaldehyde-methanol mixture, along with deionized water (DI water). The generated voltammograms were subsequently analyzed using our uniquely designed and optimized 1-D convolutional neural network (1D-CNN). This deep-learning algorithm achieved a 99.09% accuracy in VOC classification validated through fivefold cross-validation and demonstrated an impressive 94.4% test accuracy for methanol detection within a 10 μL error range. For quantification, the system accurately categorized methanol volumes ranging from 0 to 50 μL in 10 μL increments, achieving a 98.18% accuracy. A notable linear correlation (R2 = 95.56%) was found between max current density at the oxidation peak and methanol volume, with the limit of detection (LOD) at 9.3 μL. Such a sensing method exhibits potential for portability, high accuracy, and generalization in the classification and quantification, ultimately reshaping the realm of VOC analysis in solution.

42 ENGINEERING↗
NASA NTRS2005

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

Ellerbrock, Philip J.↗