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This paper is a contribution to the problems of road insecurity in Africa. Due to non-respect of road sign and to the lack of signing, roads have become places of all dangers. It becomes imperative to establish an interaction between the authorities and the offending drivers. To reach this goal, we modelled an interactive road-vehicle-signage system, who locally informs the driver on the requirements of traffic signs. This model having interest only in the event of driving by bad weather or deterioration of panels, we are amending by inserting functions aimed to warn and punish the driver in the event of maintenance of an offense. Indeed, when the driver is about to commit a fault, firstly the system issues a warming (visual, audible or mechanical). Then, a message (SMS) is sent to the authorities. We include the concept of floating process engaged by devices other than the signage. We show that, with a few considerations, from the functional point of view, they are identical to the process engaged by the signage. Furthermore, in terms of performance, the model renewed warnings that occurred just before the end panel of prohibitions. It stores messages of offenses occurred without the network, then notifies them when a network is detected. We propose algorithms for incremental design and analysis of the model, whose processes are activated and / or are extinguished, according to the type of sign or tag encountered. We show by simulation and by linear algebra that, the model retains its properties of absence of blocking and boundedness during the evolution of the system, hence its validation.

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The objective of this work is to improve the combustion management in W18V50DF dual fuel engine by determining for a desired power, the optimal values of the parameters of pressures and subsequently to map them in real time based on a power set point. The interest was mainly focused in pressure parameters, other being considered as constant. Two methods have been used, namely mathematical modeling and learning by neural networks. The results show that, in the beginning mathematical modeling result helps to monitor the ongoing process and with longer learning period the result with neural network become better and significant due to the adaptation to the reality. Furthermore, the neural network method improves significantly in the long term the rationalization of fuel consumption in such a system in order to significantly reduce the carbon dioxide emission rate. Finally, work has proved that for an immediate result mathematical model can be used but without robustness on the control process, this is obtained by a neural network. But this approach requires a good data base and long learning time.

This work investigates the effect of low frequency vibratory processing for cleaning and washing various machine components parts from rusts and old paints deposits. The experimental investigation was carried out with special prepared samples that were weighted and exposed to paints and rust contaminants. These samples were treated in universal horizontal vibration machine UVHM 4 × 10 with different combination of instrumental processing medium, process fluid, machine amplitude and frequency of oscillations. They were periodically reweighted after processing and compared to etalon with control of quantity of dust that have been removed, sample cleanliness and also other functional parameters. Statistical analysis has been used to characterize ongoing process and full factorial analysis to establish experimental parameters dependency. The result is showing the complex dependence of samples cleanliness to each processing parameters like processing time, amplitude of oscillations, frequency of oscillations, process fluid parameters, instrumental medium, etc. Between this parameters although the most important successively the amplitude of oscillations, the frequency of oscillations the processing medium and the processing fluid depending to his considered composition, the optimal processing time can be reach only by complex combination of all this parameters every of them carry an amplify coefficient. Low frequency oscillations can be used to monitor and optimize washing and cleaning operations of paints and rusts contaminations. That guarantees process automation, its effectiveness for a large industrial application.