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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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NiMC: Characterizing and Eliminating Network-Induced Memory Contention.
Abstract not provided.
Level-Spread: A New Job Allocation Policy for Dragonfly Networks.
Abstract not provided.
Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network
Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan.
The Confluence of Networks, Games, and Learning a Game-Theoretic Framework for Multiagent Decision Making Over Networks
Not provided.
Local Voltage Control in Distribution Networks: A Game-Theoretic Perspective
Inverter-based voltage regulation is gaining importance to alleviate emerging reliability and power-quality concerns related to distribution systems with high penetration of photovoltaic (PV) systems. This paper seeks contribution in the domain of reactive power compensation by establishing stability of local Volt/VAr controllers. In lieu of the approximate linear surrogate used in the existing work, the paper establishes existence and uniqueness of an equilibrium point using nonlinear AC power flow model. Key to this end is to consider a nonlinear dynamical system with non-incremental local Volt/VAr control, cast the Volt/VAr dynamics as a game, and leverage the fixed-point theorem as well as pertinent contraction mapping argument. Numerical examples are provided to complement the analytical results.
A Multi-Site Networked Hardware-in-Loop Platform for Evaluation of Interoperability and Distributed Intelligence at Grid-Edge
Electric power systems have experienced large increases in the number of intelligent, connected and controllable devices being deployed, leading to a high degree of distributed intelligence at the grid-edge. These devices, both utility-owned and consumer-owned, include but are not limited to: renewable generation sources, energy storage, remote switches, voltage regulators, and smart controllable loads such as electric vehicles. These new devices provide significant potential for increased operational flexibility that can be leveraged to achieve system reconfiguration, resiliency improvements, power quality improvements, and distribution system automation. However, there are two significant challenges that must be addressed before these assets can be leveraged for operations: interoperability and system level validation prior to deployment. Because of the complexity of distributed control systems, and their interactions with legacy centralized controls, a purely simulations-based approach for pre-deployment validation is not sufficient. It requires hardware-in-loop testing to emulate the operational hardware devices and evaluate their performance. Additionally, securely integrating multiple test facilities at utility operators and vendors might enable rapid scale-up of evaluation platforms, and remove the need for multiple expensive standalone installations. Presented in this paper, is the development of a multi-site evaluation platform that employs Advanced Distribution Management Systems (ADMS), distributed control devices, real-time hardware-in-loop assets, secure communication links, and protocol adapters. This platform uses standards-based approaches and open-source tools, and hence can serve as a template for other researchers and institutions to implement their multi-site evaluation frameworks for pre-deployment testing.
Intrusion Detection & Response using an Unsupervised Artificial Neural Network on a Single Board Computer for Building Control Resilience.
Abstract not provided.
WEC Array Electro-Mechanical Drivetrain Networked Microgrid Control Design and Energy Storage System Analysis.
Abstract not provided.
Efficient Detection of Communities in Biological Bipartite Networks
Not provided.
Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics
Not provided.
Evolving Decision Trees to Detect Anomalies in Recurrent ICS Networks.
Abstract not provided.
Use Of Convolutional Neural Network Image Classification And High-Speed Ion Probe Data Towards Real-Time Detonation Characterization In A Water-Cooled Rotating Detonation Engine
Conference paper for the ASME 2022 Turbomachinery Technical Conference & ExpositionJune 13-17, 2022, Rotterdam, The Netherlands<br>
Semi-supervised learning and inference in domain-wall magnetic tunnel junction (DW-MTJ) neural networks.
Abstract not provided.
Paired Neural Networks for Hyperspectral Target Detection.
Abstract not provided.
One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery
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