Percorrer por autor "Mendes, H."
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- Development of a responsive fisheries management system for the Portuguese crustacean bottom trawl fishery: Lessons learntPublication . Silva, C.; Mendes, H.; Rangel, Mafalda; Wise, L.; Erzini, Karim; Borges, M. D.; Ballesteros, M.; Santiago, Jose Luis; Campos, Aida; Vioarsson, J.; Nielsen, K. NA prototype for a Responsive Fisheries Management System (RFMS) was developed in the context of the European FP7 project EcoFishMan and tested on the Portuguese crustacean trawl fishery. Building on Results Based Management principles, RFMS involves the definition of specific and measurable objectives for a fishery by the relevant authorities but allows resource users the freedom to find ways to achieve the objectives and to provide adequate documentation. Taking into account the main goals of the new Common Fisheries Policy, such as sustainable utilization of the resources, end of discards and unwanted catches, a management plan for the Portuguese crustacean trawl fishery was developed in cooperation with the fishing industry, following the process and design laid out in the RFMS concept. The plan considers biological, social and economic goals and assigns a responsibility for increased data collection to the resource users. The performance of the plan with regard to selected indicators was evaluated through simulations. In this paper the process towards a RFMS is described and the lessons learnt from the interaction with stakeholders in the development of an alternative management plan are discussed. (C) 2014 Elsevier Ltd. All rights reserved.
- MOGA design of temperature and relative humidity models for predictive thermal comfortPublication . Ruano, Antonio; Ferreira, P. M.; Mendes, H.The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. One of these applications is predictive HVAC control, which aims to maintain thermal comfort while simultaneously minimizing the energy spent, within a specified prediction horizon. Thermal comfort depends on several variables; among them inside temperature and relative humidity are key factors. In this paper the design of predictive neural network models for these two climate variables is discussed. The design approach uses a Multi-Objective Genetic Algorithms (MOGA) to determine the structure of the network, together with an efficient derivative-based estimation algorithm. Simulations with real weather and climate data show that excellent predictive models can be obtained with this methodology.