An investigation of multi-dimensional evolutionary algorithms for virtual reality scenario development

<p>Virtual reality (VR) has emerged as a powerful visualization tool for design, simulation, and analysis in modem complex industrial systems. The primary motivation for this thesis is to develop a framework for the effective use of VR in design-simulation-analysis cycles, particularly in situations involving large, complex, multi-dimensional data-sets. This thesis develops a framework that is intended to support not only the integration of such data for visual, interactive, and immersive displays, but also provides a method for performing risk analysis. Previously "static" VR environments are enhanced with time-evolutionary capabilities. Four candidate algorithms are evaluated for this purpose – deterministic modeling, auto-regressive moving average modeling, genetic algorithm modeling, and hidden Markov modeling. Benefits, drawbacks, and trade-offs are evaluated with reference to their suitability for development in a VR environment. The methods developed in this research work are demonstrated by applying them to multi-sensor data obtained during the in-line, nondestructive evaluation of gas transmission pipelines.</p>

Name thesis:degree_name

M.S. in Engineering

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engineering

Year dc:date.available

2004

Author dc:creator

• Papson, Scott

Contributors dc:contributor

• Mandayam, Shreekanth

• Computer algorithms; Data structures (Computer science); Virtual reality
• Electrical and Computer Engineering

Repository record dc:identifier

https://rdw.rowan.edu/etd/1212

OAI identifier oai:identifier

oai:rdw.rowan.edu:etd-2212