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Looking forward to an autonomous Unmanned Combat Aerial Vehicle (UCAV) for future applications, it becomes apparent that on-board intelligent controllers will be necessary for these advanced systems. LETHA (Learning Enhanced Tactical Handling Algorithm) was created to develop intelligent managers for these advanced unmanned craft through the novel means of a genetic cascading fuzzy system. In this approach, a genetic algorithm creates rule bases and optimizes membership functions for multiple fuzzy logic systems, whose inputs and outputs feed into one another alongside crisp data. A simulation space referred to as HADES (Hoplological Autonomous Defend and Engage Simulation) was created in which LETHA can train the UCAVs intelligent controllers.

Acoustically absorptive liners are used in the inlet and exhaust ducts of commercial aircraft engines. A double layer acoustic liner consists of a porous face sheet, a porous septum, an impervious back sheet, and a honeycomb structure. The Acousti-Cap™ technology, developed by Hexcel Corporation, is a significant innovation in the manufacture of double layer acoustic liners. It consists of a non-metallic permeable cap material embedded into each cell of the honeycomb core to create an acoustic septum. This paper presents details of the extensive acoustic testing and analysis carried out to support the development of acoustic liners with embedded mesh-caps.

The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.

Efforts have been made to utilize Al constructs to identify flaws in the Space Shuttle Main Engine (SSME) faceplate regions. In order to expand the applicability of these algorithms to a larger problem domain, the automatic visual inspection system(AVIS) has been modified to enable a user with little or no image processing background to define a system capable of identifying flaws on a given set of imagery. This system requires the user to simply identify flawed regions and the selection of processing and feature descriptors is performed automatically. This paper explicates the motivations, definitions, and performance issues associated with the AVIS paradigm.

Building and operating dependable systems is fundamental to many critical applications, such as designing integrated hardware and software systems for vehicles or satellites. Dependable systems techniques, methods, and tools are developed and used by researchers and practitioners working in widely varying disciplines. In order to provide a unifying framework for the successful dissemination and sharing of dependability results, the development of a dependable systems knowledge base is underway.1 Two database support subsystems are under development: one that manages the storage and retrieval of document information, as well as communicating between the user interface layer and the physical database layer, and another that manages the lexicon of dependability terminology for the user interface layer. The system will provide access to information in a sophisticated, intelligent manner that enables a human user to function more effectively in learning and decision-making capacities.