Automation for the Maritime Industries by Edited by: J Aranda, M A Armada, J M de la Cruz

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By Edited by: J Aranda, M A Armada, J M de la Cruz

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S. Real time reconciliation of mineral processing plant data using bilinear material balance equations coupled to empirical dynamic models. Int. Journal of Mineral Processing , 48: pp. 245-264. [11] Bakiotis C. et all. 1979. Parameter and Discriminant Analysis for jet engine mechanical state diagnosis. Proc. Of the IEEE Conf. On Decision and Control, pp 11-1. Piscataway, New jersey. IEEE Press. [12] Isserman R 1998. : A survey. 404. [13] Isserman R 1993. -Tutorial paper. Automatica,29: pp. 815-835.

For this controlled passenger’ flow, there are two different options: one member of the crew acts as a “leader” for a group of passengers, or different crewmembers are placed in critical points of the evacuation route in order to guide the passengers and regulate their flow. As a consequence of the mentioned above, the need of specific models and tools for the ship evacuation analysis is clear. 3, requested the Sub-Committee on Fire Protection (FP) to 27 Automation for the Maritime Industries develop guidelines also on evacuation analysis for passenger ships in general and for highspeed passenger crafts.

This is a typical result when using GA to approximate the optimal solution. It is easy to refine this solution with a local optimisation algorithm, obtaining smoother curves. For the solution described in figure 6, the cog trajectory of the AUV is as depicted in figure 4. The curve is smooth because the marine craft inertia integrates the forces. Figure 7 shows the evolution of the surge, heave and pitch speeds due to the application of the forces exerted. It can see that the AUV ends with zero final speed in each degree of freedom (DOF).

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