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Full-Text Articles in Engineering
Satisfaction-Aware Data Offloading In Surveillance Systems, Marcos Paul Torres
Satisfaction-Aware Data Offloading In Surveillance Systems, Marcos Paul Torres
Electrical and Computer Engineering ETDs
In this thesis, exploiting Fully Autonomous Aerial Systems' (FAAS) and Mobile Edge Computing (MEC) servers' computing capabilities to introduce a novel data offloading framework to support the energy and time-efficient video processing in surveillance systems based on satisfaction games. A surveillance system is introduced consisting of Areas of Interest (AoIs), where a MEC server is associated with each AoI, and a FAAS is flying above the AoIs to support the IP cameras' computing demands. Each IP camera adopts a utility function capturing its Quality of Service (QoS) considering the experienced time and energy overhead to offload and process remotely or …
Artificial Intelligence Empowered Uavs Data Offloading In Mobile Edge Computing, Nicholas Alexander Kemp
Artificial Intelligence Empowered Uavs Data Offloading In Mobile Edge Computing, Nicholas Alexander Kemp
Electrical and Computer Engineering ETDs
The advances introduced by Unmanned Aerial Vehicles (UAVs) are manifold and have paved the path for the full integration of UAVs, as intelligent objects, into the Internet of Things (IoT). This paper brings artificial intelligence into the UAVs data offloading process in a multi-server Mobile Edge Computing (MEC) environment, by adopting principles and concepts from game theory and reinforcement learning. Initially, the autonomous MEC server selection for partial data offloading is performed by the UAVs, based on the theory of the stochastic learning automata. A non-cooperative game among the UAVs is then formulated to determine the UAVs' data to be …
Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Master's Theses
Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …
Reinforcement Learning And Game Theory For Smart Grid Security, Shuva Paul
Reinforcement Learning And Game Theory For Smart Grid Security, Shuva Paul
Electronic Theses and Dissertations
This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and …
Design And Investigation Of Genetic Algorithmic And Reinforcement Learning Approaches To Wire Crossing Reductions For Pnml Devices, Alexander Keith Gunter
Design And Investigation Of Genetic Algorithmic And Reinforcement Learning Approaches To Wire Crossing Reductions For Pnml Devices, Alexander Keith Gunter
Electronic Theses and Dissertations
Perpendicular nanomagnet logic (pNML) is an emerging post-CMOS technology which encodes binary data in the polarization of single-domain nanomagnets and performs operations via fringing field interactions. Currently, there is no complete top-down workflow for pNML. Researchers must instead simultaneously handle place-and-route, timing, and logic minimization by hand. These tasks include multiple NP-Hard subproblems, and the lack of automated tools for solving them for pNML precludes the design of large-scale pNML circuits.