Search NASA⌕ Search

SEARCH · Search NASA

Results for “Network control”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Extended Galerkin Neural Network Approximation of Singular Variational Problems with Error Control

We present extended Galerkin neural networks, a variational framework for approximating general boundary value problems (BVPs) with error control. The main contributions of this work are (1) a rigorous theory guiding the construction of new weighted least squares variational formulations suitable for use in neural network approximation of general BVPs, and (2) an “extended” feedforward network architecture which incorporates and is even capable of learning singular solution structures, thus greatly improving approximability of singular solutions. Furthermore, numerical results are presented for several problems, including steady Stokes flow around reentrant corners and in convex corners with Moffatt eddies in order to demonstrate efficacy of the method.

a posteriori error estimate↗
DOE OSTI2024

Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO 3 ) n /(SrTiO 3 ) n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
NASA NTRS1977

Survey: Federal Aviation Administration National Communication Center

The National Communication (NATCOM) Center (commonly known as the FAA Weather Message Switching Center), is a Federal Aviation Administration (FAA) facility with responsibility for providing communication switching services to the National Weather Service (NWS), the FAA, commercial and private flight organizations under the auspices of the FAA, and DoD. Data handled by NATCOM include weather data, flight plans, and Notices to Airmen (NOTAMS) on both national and international networks. The communications and data management functions are handled through four computer-controlled communication networks designated as WMSC, AFTN, A-BDIS, and NASNET. The functions of these networks are discussed with emphasis on those networks that support the different elements of the NWS. The primary network of concern to NASA, the WMSC (Weather Message Switching Center) network, performs approximately 60 percent of its work for the NWS, 20 percent for the State Department, and 20 percent for the FAA. This document discusses the current and future systems capabilities and workload of NATCOM in terms of new roles.

Source record↗
NASA NTRS1981

NOCA upgrade

The network operations control area (NOCA) of the space flight operations facility (SFOF) is being upgraded in both hardware and software capabilities to provide the visibility and operability necessary for the operations control function to be performed within the present technological environment of the tracking network. The hardware implementation currently in progress is addressed. It provides a standard operating console with increased display capacity and improved ergonomics as a replacement for the existing, outmoded, circular consoles original to the SFOF.

Gould, G. D.↗
DOE OSTI2026

Towards Secure Autonomous Vehicles: An Integrated Edge and Multi-Modal Machine Learning Framework for Intrusion Detection

Autonomous vehicles (AVs) are vulnerable to cyberattacks targeting both internal communication networks and external perception sensors. While edge-based intrusion de- tection for Controller Area Network (CAN) buses offers real-time protection, it cannot detect cross-modal threats. Conversely, multi-modal fusion approaches improve coverage but often lack efficiency for in-vehicle deployment. This thesis integrates two complemen- tary solutions: (1) a lightweight, edge-deployable machine learning framework for CAN bus intrusion detection, and (2) a late-fusion system combining CAN FD and LiDAR data. Together, they form a hierarchical defense capable of handling single-modality and coordi- nated attacks. Simulations show that CAN-only models reach 93% accuracy on simulated DoS, spoofing, replay, and fuzzy attacks, while the fusion system achieves 0.87 AUC and 0.82 F1-score at 2 ms latency. This unified framework establishes a scalable, explainable, and field-ready strategy for AV cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optimization-based control using input convex neural networks

Input convex neural networks (ICNNs) are a family of deep learning models where the outputs are constructed to be convex functions of the inputs. By parameterizing system models using ICNNs, optimization-based control problems can be solved as convex optimization problems, leading to improved performance and robustness. This work proposes a novel framework where the control objective function and constraints are modelled using ICNNs. A case study of optimization-based control with output constraints is conducted on a process with input multiplicity and nonminimum phase behavior. The simulation results demonstrate improved economic yield compared with normal neural networks. Additionally, the input convexity formulation is compared with simple regularization techniques, and unique benefits such as improved data efficiency and robustness of the proposed formulation are shown. Finally, by explicitly incorporating prior knowledge about convexity, this framework provides a good balance between the universal approximation power of deep learning and computational feasibility required by control.

42 ENGINEERING↗
DOE OSTI2026

Constellation: The autonomous control and data acquisition system for dynamic experimental setups

The operation of instruments and detectors in laboratory or beamline environments presents a complex challenge, requiring stable operation of multiple concurrent devices, often controlled by separate hardware and software solutions. These environments frequently undergo modifications, such as the inclusion of different auxiliary devices depending on the experiment or facility, adding further complexity. The successful management of such dynamic configurations demands a flexible and robust system capable of controlling data acquisition, monitoring experimental setups, enabling seamless reconfiguration, and integrating new devices with limited effort. This paper presents Constellation, a flexible and network-distributed control and data acquisition software framework tailored to laboratory and beamline environments, that addresses the limitations of existing solutions. The framework is designed with a focus on extensibility, providing a streamlined interface for instrument integration. It supports efficient system setup via network discovery mechanisms, promotes stability through autonomous operational features, and provides comprehensive documentation and supporting tools for operators and application developers such as controllers and logging interfaces. At the core of the architectural design is the autonomy of the individual components, called satellites, which can make independent decisions about their operation and communicate these decisions to other components. This paper introduces the design principles and framework architecture of Constellation, presents the available graphical user interfaces, shares insights from initial successful deployments, and provides an outlook on future developments and applications.

Autonomy↗
DOE OSTI2021

From CAN to ROS: A Monitoring and Data Recording Bridge

The Controller Area Network (CAN) bus protocol is used in modern vehicles for sharing messages between several control units within a vehicle. CAN bus messages are encoded with unknown scheme and decoding these messages provide unlimited access to valuable information that is used in many autonomous vehicles applications. This paper proposes a ROS based package (CAN-to-ROS) for monitoring, recording, and real-time and offline decoding of CAN bus messages. The package is developed in the ROS framework to add modularity and ease of integration with other software, and it is written in C++ to guarantee speed of the execution during run-time. For realtime decoding of CAN bus data, CAN-to-ROS package used in conjunction with other library called Libpanda that provide access to CAN bus message from a vehicle. The package was evaluated and tested on a Raspberry Pi with real CAN bus data from a Toyota RAV4. The results confirm the capabilities of CAN-to-ROS package and resulted in using the package in other research projects.

Elmadani, Safwan↗
DOE OSTI2018

LCLS-II Cryomodule and Cryogenic Distribution Control

LCLS-II is a superconducting upgrade to the existing Linear Coherent Light Source at the SLAC National Accelerator Laboratory. Construction is underway with a planned continuous wave beam rate of up to 1 MHz. Two cryogenic plants provide helium to a distribution system, and 37 cryomodules with superconducting cavities will operate with Liquid helium at 2.2K. The cryomodules and distribution system is controlled with networked PLC's and EPICS as an integrated system that work in concert for controlling valves, pressure, flow, and temperature. Interlocks and critical process information is communicated with the Low Level Radio Frequency, vacuum, and magnet systems. Engaging the controls community proved vital in advancing the controls architecture from a conventional design to a centralized, reliable, and cost-effective distributed platform.

43 PARTICLE ACCELERATORS↗
NASA NTRS2007

An Investigation of Synchrony in Transport Networks

The cumulative degree distributions of transport networks, such as air transportation networks and respiratory neuronal networks, follow power laws. The significance of power laws with respect to other network performance measures, such as throughput and synchronization, remains an open question. Evolving methods for the analysis and design of air transportation networks must address network performance in the face of increasing demands and the need to contain and control local network disturbances, such as congestion. Toward this end, we investigate functional relationships that govern the performance of transport networks; for example, the links between the first nontrivial eigenvalue of a network's Laplacian matrix - a quantitative measure of network synchronizability - and other global network parameters. In particular, among networks with a fixed degree distribution and fixed network assortativity (a measure of a network's preference to attach nodes based on a similarity or difference), those with the small eigenvalue are shown to be poor synchronizers, to have much longer shortest paths and to have greater clustering in comparison to those with large. A simulation of a respiratory network adds data to our investigation. This study is a beginning step in developing metrics and design variables for the analysis and active design of air transport networks.

Kincaid, Rex K.↗
DOE OSTI2023

Synthesis, Hole Doping, and Electrical Properties of a Semiconducting Azatriangulene-Based Covalent Organic Framework

Two-dimensional covalent organic frameworks (2D COFs) containing heterotriangulenes have been theoretically identified as semiconductors with tunable, Dirac-cone-like band structures, which are expected to afford high charge-carrier mobilities ideal for next-generation flexible electronics. However, few bulk syntheses of these materials have been reported, and existing synthetic methods provide limited control of network purity and morphology. Here, we report transimination reactions between benzophenone-imine-protected azatriangulenes (OTPA) and benzodithiophene dialdehydes (BDT), which afforded a new semiconducting COF network, OTPA-BDT. The COFs were prepared as both polycrystalline powders and thin films with controlled crystallite orientation. Here, the azatriangulene nodes are readily oxidized to stable radical cations upon exposure to an appropriate p type dopant, tris(4-bromophenyl)ammoniumyl hexachloroantimonate, after which the network’s crystallinity and orientation are maintained. Oriented, hole-doped OTPA-BDT COF films exhibit electrical conductivities of up to 1.2 × 10 –1 S cm –1 , which are among the highest reported for imine-linked 2D COFs to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Anomaly detection↗
NASA NTRS1965

Notes on System Theory, Volume VII

System theory - matrices, feedback control system, network synthesis, set theory, stability, shift registers, coding, theorem proving, polynomial roots, channels, and signal flow graphs

FEEDBACK CONTROL SYSTEM↗
DOE OSTI2016

First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science and Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Comparison of Vehicle-Broadcasted Fuel Consumption Rates against Precise Fuel Measurements for Medium- and Heavy-Duty Vehicles and Engines

Vehicles continuously report real-time fuel consumption estimates over their data bus, known as the controller area network (CAN). However, the accuracy of these fueling estimates is uncertain to researchers who collect these data from any given vehicle. To assess the accuracy of these estimates, CAN-reported fuel consumption data are compared against fuel measurements from precise instrumentation. The data analyzed consisted of eight medium/heavy-duty vehicles and two medium-duty engines. Varying discrepancies between CAN fueling rates and the more accurate measurements emerged but without a vehicular trend-for some vehicles the CAN under-reported fuel consumption and for others the CAN over-reported fuel consumption. Furthermore, a qualitative real-time analysis revealed that the operating conditions under which these fueling discrepancies arose varied among vehicles. A drive cycle analysis revealed that while CAN fueling estimate accuracy differs for individual vehicles, that CAN estimates capture the relative fuel consumption differences between drive cycles within 4% for all vehicles and even more accurately for some vehicles. Furthermore, in situations where only CAN-reported data are available, CAN fueling estimates can provide relative fuel consumption trends but not accurate or precise fuel consumption rates.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Final Technical Report: Commercial Advanced Lighting Control (ALC) Demonstration and Deployment

This three-year demonstration and deployment project sought to address market barriers to accelerating the adoption of Advanced Lighting Controls (ALCs), an underutilized technology with low market penetration. ALCs are defined as networked, addressable lighting control systems that utilize software or intelligent controllers to combine multiple energy-saving lighting control strategies in a single space (e.g., smart-time scheduling, daylight harvesting, task tuning, occupancy control, personal control, variable load-shedding, and plug-load control). The networked intelligent aspect of these systems allows applicable lighting control strategies to be combined in a single space, layered over one another, maximizing overall energy-savings. The project included five real building demonstrations of ALCs across the Northeast US region. The demonstrations provided valuable data and experience to support deployment tasks that are necessary to overcome market barriers. These deployment tasks included development of training resources for building designers, installers, and trades, as well as development of new energy efficiency rebates for the technology from Efficiency Forward’s utility partners. Educating designers, installers, and trades on ALCs is a critical task for reducing the cost of the technology that is currently inflated due to perceived complexity and unfamiliarity with how to design and install the systems. Further, utility and non-utility energy efficiency programs continue to relegate the technology to custom or ill-suited prescriptive program designs that do not effectively deploy the technology at scale. This project developed new, scalable rebate approaches for the technology. Efficiency Forward utilized their DesignLights Consortium® (DLC) brand and network of 81 DLC member utilities to develop and deploy the results of the project. The outputs of the project have included five published case studies, a six-hour ALC technology training curriculum that has already been deployed in five US states, and new rebates offered for the technology that have been deployed by a dozen utilities across the US. Widespread adoption of ALC technology in commercial buildings would provide tremendous benefits. The current market penetration of ALC systems is estimated at <0.1% in commercial buildings. If ALC systems were installed in all commercial buildings, approximately 1,051 TBtu of energy could be saved. This would translate into customer cost savings of approximately $10.7 billion annually.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Voltage positioning using co-optimization of controllable grid assets in radial networks

With increasing penetration of solar PV, some distribution feeders are experiencing highly variable net-load flows and even reverse flows. To optimize distribution systems under such conditions, the scheduling of mechanical devices, such as OLTCs and capacitor banks, needs to take into account forecasted solar PV and actual grid conditions. However, these legacy switching assets are operated on a daily or hourly timescale, due to the wear and tear associated with mechanical switching, which makes them unsuitable for real-time control. Therefore, there is a natural timescale- separation between these slower mechanical assets and the responsive nature of inverter-based resources. In this paper, we present a network admissible convex formulation for holistically scheduling controllable grid assets to position voltage optimally against solar PV. An optimal hourly schedule is presented that utilizes mechanical resources to position the predicted voltages close to nominal values, while minimizing the use of inverter-based resources (i.e., DERs), making them available for control at a faster time-scale (after the uncertainty reveals itself). A convex, inner approximation of the OPF problem is adapted to a mixed-integer linear program that minimizes voltage deviations from nominal (i.e., maximizes voltage margins). Here, the resulting OPF solution respects all the network constraints and is, hence, robust against modeling simplifications. Simulation based analysis on IEEE distribution feeders validates the approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗