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Showing 1 to 11 of 11 for “"Graph-based modeling"”.
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An Approach For Scalable First-Order Rule Learning On Twitter Data
… is a scalable divide-and-conquer approach with graph-based modeling of social media data, to scale up first-order rule learning through Markov Logic Networks on a commodity cluster on large scale Twitter data. SRLearn takes advantage of distributed systems to partition large-scale data into …
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Grasp - A Graph-Based SLA Breach Prediction Framework at the Service Level in Neural Inference
… time-spectrum framework, called GRASP (Graph-based SLA Breach Prediction). It leverages time-series data, sequence processing, and graph-based modeling to proactively detect performance anomalies and predict SLA breaches in microservice-based systems within upcoming time windows. In our …
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Enhancing Road Safety through Machine Learning for Prediction of Unsafe Driving Behaviors
… to identify and mitigate conflicts, employing graph-based modeling of traffic scenarios to evaluate contributing parameters. Additionally, we leverage graph neural networks to detect and track potential crash situations from intersection videos, offering practical recommendations to enhance …
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MULTI-ROBOT COORDINATION AND COOPERATION VIA GRAPH-BASED COMPUTATION
… apply to large multi-robot teams. We introduce a graph-based modeling framework for the multi-robot resource allocation problem that offers unprecedented richness in representing inter-region relationships and reward models. We first address the multi-agent coverage control problem, introducing …
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Modeling, control, and design of hybrid electrical and thermal energy storage systems
… loops for thermal energy storage. Multi-domain graph-based modeling techniques are used to facilitate modeling, control, and design optimization of the energy storage system. Graph-based models capture multi-domain dynamics in a unified framework. A heuristic control strategy is used, which …
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Modular modeling and control of a hybrid unmanned aerial vehicle’s powertrain
… for a hybrid unmanned aerial vehicle. The graph-based modeling framework for multi-domain systems is leveraged to capture the interactions between relevant energy domains. Additionally, the modularity and scalability of this modeling approach is used to develop a dynamic model for a hybrid …
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Hierarchical control for multi-domain coordination of vehicle energy systems with switched dynamics
… multi-domain storage and exchange of energy, a graph-based dynamic modeling approach is proposed and experimentally validated. This modeling approach is then leveraged for model-based control, in which the complex task of energy management is decomposed into a hierarchical network of model …
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Hierarchical power management in vehicle systems
… of the system for predicting future behavior based on current and future control decisions and known disturbances. To capture the energy storage and power flow throughout the vehicle, a graph-based modeling framework is proposed, where vertices represent capacitive elements that store energy …
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A framework for the control of electro-thermal aircraft power systems
… using high-fidelity component models. Graph-based modeling techniques are used to generate suitable models for MPC controllers at each layer of the hierarchical control structure. The proposed hierarchical control framework is tested on the high-fidelity Simulink model and compared to a …
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Analytical sensitivity analysis methodology for the co-design of thermal management systems
… it has been specifically designed to work with a graph-based modelling framework. This project was carried out with the intention of improving thermal management systems for electrified vehicles. A fluid loop with additional heat loads was used as the example framework to showcase the sensitivity …
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Integrated design optimization methods for optimal sensor placement and cooling system architecture design for electro-thermal systems
DSpace SAF Submission Ingestion Package generated from Vireo submission #13273 on 2019-02-07 at 14:23:18