Fiscal Year: 2013
1248 Texas A&M University-Kingsville (20726)
Principal Investigator: Ozcelik, S.
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 700,000
Exceeds $250,000 (Is it flagged?): Yes
Start and End Dates: 8/14/12 <> 8/31/14
Restricted Research: YES
Academic Discipline: Mechanical and Industrial Engineering
Department, Center, School, or Institute: College of Engineering
Title of Contract, Award, or Gift: Security Engineering: Development of Curriculum and Research for Homeland Security
Name of Granting or Contracting Agency/Entity:
Department of Homeland Security
CFDA Link: DHS
CFDA Linked: Scholars and Fellows
To increase student’s knowledge and to improve hands-on skills and to prepare them for professional work environment as well as for graduate studies, teaching activities will be fostered with research activities for undergraduate students. Students will be supported to have summer research experience and internships. To achieve the objectives, the research activities will be conducted in four phases as follows. First, a modeling methodology will be developed to hybrid agent-based and discrete event simulations as well as other information and decision modules into one integrated platform. The research will focus on enhancing the efficiency and scalability of hybrid complex systems by designing a unique model structure for network-centric models. Second, rare-event simulation validation methods will be explored comprehensively and a unique theory based validation will be proposed and implemented to validate the system from different angles when actual system experiments are impossible. Third, an evolutionary simulation procedure will be developed to strengthen dynamic situation awareness. The proposed research will be capable of handling the unexpected situations when the simulation is used in real time so as to enhance situation awareness. Fourth, efficient simulation optimization algorithms will be developed to incorporate analytical models, offline experimental results and random factors to obtain near-optimal solutions quickly for the management of complex systems in real time.
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