The goal of this project was to try and accurately model the performance of the F-104C Starfighter which achieved a new world altitude record on December 14 th , 1959. In completing the project, the goal was to gain a better understanding of the performance modeling of today and flight test methods from the past while creating a drag polar and engine deck which could be used for future applicat...

Sea Floor ElectroMagnetic Stations (SFEMSs) are now operating at two deep seafloor sites called the 'WPB' and the 'NWP' in the West Philippine Basin and the Northwest Pacific Basin, respectively. One of the main objectives of the SFEMSs is to detect the geomagnetic secular variations on the deep seafloor where long-term geomagnetic observations have not so far been achieved. SFEMSs can measure ...

AbstrAct The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that ...

an artificial neural network (ann) was used to analyse the capillary rise in porous media. wetting experiments were performed with fifteen liquids and fifteen different powders. the liquids covered a wide range  of  surface  tension ( 15.45-71.99  mj/m2 )  and  viscosity (0.25-21 mpa.s). the powders also provided an acceptable range of particle size (0.012-45 μm) and surface free energy (25.54-...

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Abstract In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. This learning process in this method is similar to conventional gradient descent learning process, except that the input patterns and parameters are stored in mem...