• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 2
  • Tagged with
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • 2
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

A Proactive Risk-Aware Robotic Sensor Network for Critical Infrastructure Protection

McCausland, Jamieson 17 December 2013 (has links)
In this thesis a Proactive Risk-Aware Robotic Sensor Network (RSN) is proposed for the application of Critical Infrastructure Protection (CIP). Each robotic member of the RSN is granted a perception of risk by means of a Risk Management Framework (RMF). A fuzzy-risk model is used to extract distress-based risk features and potential intrusion-based risk features for CIP. Detected high-risk events invoke a fuzzy-auction Multi-Robot Task Allocation (MRTA) algorithm to create a response group for each detected risk. Through Evolutionary Multi-Objective (EMO) optimization, a Pareto set of optimal robot configurations for a response group will be generated using the Non-Dominating Sorting Genetic Algorithm II (NSGA-II). The optimization objectives are to maximize sensor coverage of essential spatial regions and minimize the amount of energy exerted by the response group. A set of non-dominated solutions are produced from EMO optimization for a decision maker to select a single response. The RSN response group will re-organize based on the specifications of the selected response.
2

A Proactive Risk-Aware Robotic Sensor Network for Critical Infrastructure Protection

McCausland, Jamieson January 2014 (has links)
In this thesis a Proactive Risk-Aware Robotic Sensor Network (RSN) is proposed for the application of Critical Infrastructure Protection (CIP). Each robotic member of the RSN is granted a perception of risk by means of a Risk Management Framework (RMF). A fuzzy-risk model is used to extract distress-based risk features and potential intrusion-based risk features for CIP. Detected high-risk events invoke a fuzzy-auction Multi-Robot Task Allocation (MRTA) algorithm to create a response group for each detected risk. Through Evolutionary Multi-Objective (EMO) optimization, a Pareto set of optimal robot configurations for a response group will be generated using the Non-Dominating Sorting Genetic Algorithm II (NSGA-II). The optimization objectives are to maximize sensor coverage of essential spatial regions and minimize the amount of energy exerted by the response group. A set of non-dominated solutions are produced from EMO optimization for a decision maker to select a single response. The RSN response group will re-organize based on the specifications of the selected response.

Page generated in 0.0775 seconds