Varied Delays in Spiking Neural Networks Support Learning with Conjunctive Temporal Features.
Abstract not provided.
Wang, Felix↗
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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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A model validation approach for echo state networks applied to spatio-temporal climate data
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McClernon, Kellie Lynn↗
An Enabling Technology for New Planning and Scheduling Paradigms
The Night Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called ?ask models," from the scientists and technologists for the tasks that are to be scheduled. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next, a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, the models are modified to be compatible with the scheduling engine. Then the models are submitted to the scheduling engine for automatic scheduling or, when requirements are expressed in notes, the timeline is built manually. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components: (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphical methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm that can exploit timing flexibilities and constraint and relationship options. (3) An innovative architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the remote users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.
Jaap, John↗
Enabling a New Planning and Scheduling Paradigm
The Flight Projects Directorate at NASA's Marshall Space Flight Center is developing a new planning and scheduling environment and a new scheduling algorithm to enable a paradigm shift in planning and scheduling concepts. Over the past 33 years Marshall has developed and evolved a paradigm for generating payload timelines for Skylab, Spacelab, various other Shuttle payloads, and the International Space Station. The current paradigm starts by collecting the requirements, called "tasks models," from the scientists and technologists for the tasks that they want to be done. Because of shortcomings in the current modeling schema, some requirements are entered as notes. Next a cadre with knowledge of vehicle and hardware modifies these models to encompass and be compatible with the hardware model; again, notes are added when the modeling schema does not provide a better way to represent the requirements. Finally, another cadre further modifies the models to be compatible with the scheduling engine. This last cadre also submits the models to the scheduling engine or builds the timeline manually to accommodate requirements that are expressed in notes. A future paradigm would provide a scheduling engine that accepts separate science models and hardware models. The modeling schema would have the capability to represent all the requirements without resorting to notes. Furthermore, the scheduling engine would not require that the models be modified to account for the capabilities (limitations) of the scheduling engine. The enabling technology under development at Marshall has three major components. (1) A new modeling schema allows expressing all the requirements of the tasks without resorting to notes or awkward contrivances. The chosen modeling schema is both maximally expressive and easy to use. It utilizes graphics methods to show hierarchies of task constraints and networks of temporal relationships. (2) A new scheduling algorithm automatically schedules the models without the intervention of a scheduling expert. The algorithm is tuned for the constraint hierarchies and the complex temporal relationships provided by the modeling schema. It has an extensive search algorithm which can exploit timing flexibilities and constraint and relationship options. (3) A web-based architecture allows multiple remote users to simultaneously model science and technology requirements and other users to model vehicle and hardware characteristics. The architecture allows the users to submit scheduling requests directly to the scheduling engine and immediately see the results. These three components are integrated so that science and technology experts with no knowledge of the vehicle or hardware subsystems and no knowledge of the internal workings of the scheduling engine have the ability to build and submit scheduling requests and see the results. The immediate feedback will hone the users' modeling skills and ultimately enable them to produce the desired timeline. This paper summarizes the three components of the enabling technology and describes how this technology would make a new paradigm possible.
Jaap, John↗
Engineering Automated Operations for NASA's Deep Space Network
The NASA Deep Space Network is unique among space communication systems.
operations↗
Representing Operations Procedures Using Temporal Dependency Algorithms
The research presented in this paper is investigating new ways of specifying operations procedures that incorporate the insight of operations, engineering, and science personnel to improve mission operations. The paper describes the rationale for using Temporal Dependency Networks to represent the procedures, a description of how the data is acquired, and the knowledge engineering effort required to represent operations procedures.
automation knowledge engineering operations↗
NASA Tech Briefs, April 2002
The contents include: 1) Application Briefs; 2) Sneak Preview of Sensors Expo; 3) The Complexity of the Diagnosis Problem; 4) Design Concepts for the ISS TransHab Module; 5) Characteristics of Supercritical Transitional Mixing Layers; 6) Electrometer for Triboelectric Evaluation of Materials; 7) Infrared CO2 Sensor With Built-In Calibration Chambers; 8) Solid-State Potentiometric CO Sensor; 9) Planetary Rover Absolute Heading Detection Using a Sun Sensor; 10) Concept for Utilizing Full Areas of STJ Photodetector Arrays; 11) Development of Cognitive Sensors; 12) Enabling Higher-Voltage Operation of SOl CMOS Transistors; 13) Estimating Antenna-Pointing Errors From Beam Squints; 14) Advanced-Fatigue-Crack-Growth and Fracture- Mechanics Program; 15) Software for Sequencing Spacecraft Actions; 16) Program Distributes and Tracks Organizational Memoranda; 16) Flat Membrane Device for Dehumidification of Air; 17) Inverted Hindle Mount Reduces Sag of a Large, Precise Mirror; 18) Heart-Pump-Outlet/Cannula Coupling; 19) Externally Triggered Microcapsules Release Drugs In Situ; 20) Combinatorial Drug Design Augmented by Information Theory; 21) Multiple-Path-Length Optical Absorbance Cell; 22) Model of a Fluidized Bed Containing a Mixture of Particles; 23) Refractive Secondary Concentrators for Solar Thermal Systems; 24) Cold Flow Calorimeter; 25) Methodology for Tracking Hazards and Predicting Failures; 26) Estimating Heterodyne-Interferometer Polarization Leakage; 27) An Efficient Algorithm for Propagation of Temporal- Constraint Networks; 28) Software for Continuous Replanning During Execution; 29) Surface-Launched Explorers for Reconnaissance/Scouting; 30) Firmware for a Small Motion-Control Processor; 31) Gear Bearings and Gear-Bearing Transmissions; and 32) Linear Dynamometer With Variable Stroke and Frequency.
Source record↗
The Behavior, Constraint, and Scenario (BeCoS) Tool: A Web-Based Software Application for Modeling Behaviors and Scenarios
The Behavior, Constraint, and Scenario (BeCoS) tool has been developed to allow engineers to specify system and component behaviors. The tool is a web application that is developed in JavaScript and uses the React framework for the user interface and Redux for maintaining application state. The foundation of the tool is its underlying ontology, which expands upon a previously-defined behavior ontology with a scenario ontology. The behavior ontology includes elements like behaving elements, state variables, parameters, and constraints, while the scenario ontology includes core constructs like activities, temporal constraints, and timepoints. BeCoS allows users to easily create behaving elements and to specify their state variables, parameters, state machines, and constraints. BeCoS also allows users to develop temporal constraint networks that specify constraints on component states over time. BeCoS is a prototype tool that has been deployed and tested by systems engineers on the Europa Clipper project, which generated several use cases and helped steer its current developmental effort. By enabling systems engineers to specify behavior in a semantically-rigorous manner, BeCoS is an enabling technology for analyses that previously could not be performed, and when exporting its model to other tools, allows for consistent behavior models to be used.
Ingham, Michel D.↗
The Behavior, Constraint, and Scenario (BeCoS) Tool: A Web-Based Software Application for Modeling Behaviors and Scenarios
The Behavior, Constraint, and Scenario (BeCoS) tool has been developed to allow engineers to specify system and component behaviors. The tool is a web application that is developed in JavaScript and uses the React framework for the user interface and Redux for maintaining application state. The foundation of the tool is its underlying ontology, which expands upon a previously-defined behavior ontology with a scenario ontology. The behavior ontology includes elements like behaving elements, state variables, parameters, and constraints, while the scenario ontology includes core constructs like activities, temporal constraints, and timepoints. BeCoS allows users to easily create behaving elements and to specify their state variables, parameters, state machines, and constraints. BeCoS also allows users to develop temporal constraint networks that specify constraints on component states over time. BeCoS is a prototype tool that has been deployed and tested by systems engineers on the Europa Clipper project, which generated several use cases and helped steer its current developmental effort. By enabling systems engineers to specify behavior in a semantically-rigorous manner, BeCoS is an enabling technology for analyses that previously could not be performed, and when exporting its model to other tools, allows for consistent behavior models to be used.
Inghams, Michel D.↗
SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting
Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.
36 MATERIALS SCIENCE↗
Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning
Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.
Gong, Qian↗
Detecting Large Explosions With Machine Learning Models Trained on Synthetic Infrasound Data
Explosions produce low-frequency acoustic (infrasound) waves capable of propagating globally, but the spatio-temporal variability of the atmosphere makes detecting events difficult. Machine learning (ML) is well-suited to identify the subtle and nonlinear patterns in explosion infrasound signals, but a previous lack of ground-truth data inhibited training of generalized models. We introduce a physics-based method that propagates infrasound sources through realistic atmospheres to create 28,000 synthetic events, which are used to train ML classifiers. A simple artificial neural network and modern temporal convolutional network discriminate synthetic events from background noise with >90% accuracy and, more importantly, successfully identify the majority of real-world explosion signals recorded during the Humming Road Runner experiment. ML models trained entirely on physics-based synthetics advance explosion detection capabilities and make ML more viable to related fields lacking training data.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
On the effect of temporal sampling on the observation of mean rainfall
Rainfall characteristics using data from dense recording raingage networks is reviewed. Data from such networks have quantified temporal and spatial rainfall distributions, and have supplied specialized information about local and orographic effects. The natural variability, temporally and spatially, for annual, seasonal, monthly, and individual events is treated. Especially important are the spatial variations of precipitation as a function of synoptic type, precipitation type, amount, and duration. Results from dense raingage networks in Illinois, and some data from other climatic regions is also treated.
Laughlin, C. R.↗
Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks
Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.
electric load forecasting↗
StressNet - Deep learning to predict stress with fracture propagation in brittle materials
Abstract Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.
36 MATERIALS SCIENCE↗
Characterizing thermal-induced temporal drift in deployed quantum fiber networks
We study temperature-induced fiber-delay drifts affecting entanglement fidelity in deployed quantum systems. Using field data and simulations, we model timing uncertainties and evaluate their impact on coincidence detection, revealing critical challenges for robust quantum networks.
Alshowkan, Muneer [ORNL] (ORCID:0000000264293450)↗
Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps
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Faraz Enayati Ahangar↗
A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis
With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.
97 MATHEMATICS AND COMPUTING↗