Unmanned underwater vehicles (UUVs) are mostly used for safe underwater explorations and researches. UUVs are subject to different parameters that changes over time. Such parameters are not considered in kinematic modelling of vehicles. As such, a dynamic modelling of underwater vehicles is necessary. This study proposes a dynamic model that is utilizing Artificial Neural Network (ANN), for a 5-thruster underwater vehicle design. The training data for the ANN model is gathered by empirical methods. The dynamic model is represented by UUV variables: thrusters input voltages and resulting velocity vector. The results of the neural network showed accuracy and reliability due to the low Mean Square Error (MSE) and satisfactory regression plots.
Since its inception at the close of the past millennium, e-business has rapidly and continuously changed the conduct of business. However, the incoming generation of the Philippine workforce, particularly those who do not have sufficient exposure to computer technology and business, may not be able to cope, due to lack of academic preparation. Information technology practitioners and academicians can turn this threat into an opportunity by adopting a system of breakthrough practices in e-business education based on benchmarking studies in various universities in Asia and North America. The system proved to be highly effective, based on its pilot run.
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