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A learning paradigm for motion control of mobile manipulators

100%
Abdessemed F., Monacelli E., Benmahammed K.
International Journal of Applied Mathematics and Computer Science
|
2006
|
tom 16
|
nr 4
475-484
EN
Motion control of a mobile manipulator is discussed. The objective is to allow the end-effector to track a given trajectory in a fixed world frame. The motion of the platform and that of the manipulator are coordinated by a neural network which is a kind of graph designed from the kinematic model of the system. A learning paradigm is used to produce the required reference variables for each of the mobile platform and the robot manipulator for an overall coordinate behavior. Simulation results are presented to show the effectiveness of the proposed scheme.
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A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent

88%
Napoli C., Pappalardo G., Tramontana E.
International Journal of Applied Mathematics and Computer Science
|
2016
|
tom 26
|
nr 1
147-160
EN
BitTorrent splits the files that are shared on a P2P network into fragments and then spreads these by giving the highest priority to the rarest fragment. We propose a mathematical model that takes into account several factors such as the peer distance, communication delays, and file fragment availability in a future period also by using a neural network module designed to model the behaviour of the peers. The ensemble comprising the proposed mathematical model and a neural network provides a solution for choosing the file fragments that have to be spread first, in order to ensure their continuous availability, taking into account that some peers will disconnect.
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