EDUCATION ON OPIATE ADDICTION AND METHADONE TREATMENT

Not Available This project will build upon R&D work currently underway at the Georgia Institute of Technology in the development of Condition Based Maintenance systems for critical shipboard machines and processes to select and assess prognostic and human-system interface concepts and to suggest means to integrate them with existing shipboard CBM systems such as ICAS. Prognostic algorithms will be chosen to represent two basic technology trends: model-based state estimation/filtering methods and intelligent data-driven techniques relying upon soft computing (neural networks, fuzzy logic and genetic algorithms). A key element of the Phase I effort is the transitioning of the prognostic/HSI concepts into the ICAS platform. This task will be facilitated by an open systems interface based on a novel Open Control Platform developed by Georgia Tech that supports such capabilities as play-and-play evolution, interoperability, dynamic reconfiguration and real-time quality of service. The framework for an assessment methodology will be developed by defining measures of effectiveness and performance for the prognostic and HSI modules. Software design and interface control documents will be used to demonstrate the selected concepts. Three shipboard systems - an industrial chiller, a gas turbine and a helicopter tail rotor failure - will constitute the testbeds for eventual implementation and proof-of-concept purposes.