ENHANCED DIFFERENTIATION OF HUMAN PREADIPOCYTES

Not Available The objective of Phase I is to develop and demonstrate a neural networks system to predict, map, and interpolate rotorcraft ground noise exposure data. It is well-known that the relationships between ground noise exposures. Helicopter flight paths and operation conditions are extremely complicated and involved with many parameters, such as type of helicopters, power setting, approach path, and meteorological conditions. The formulation of such a function relationship with neural networks using experimental data seems the only viable approach. A neural network system, which is based on some existing data under theoretical guidance, will be designed and implemented, and be used to demonstrate the feasibility of the selected neural network algorithms. With the prediction system developed in Phase I, a prototype system will be designed and constructed in Phase II for a helicopter cockpit to display acoustic noise characteristics, noise level variability of different flight operation conditions, including various meteorological environments. The definition of Phase II will be formulate in Phase I in order to accomplish the implementation of prototype system under various flight test-conditions on a real-time base.BENEFITS: The rotorcraft ground noise exposure can be predicted by the newly developed system on a real-time base. In military application, the prototype system in the helicopter cockpit can guide a pilot to maneuver the vehicle to avoid potential threats i