Abstract
This paper proposes an optimized dispatch control strategy for active and reactive powers delivered by a doubly fed induction generator in a wind park. In this control approach, wind turbines are supposed to operate over a deloaded maximum power extraction curve and with respond to a supervisory wind farm control after a request from a system operator for adjusting the outputs of the wind park. The definition of the active and reactive powers operating points, for each wind turbine, is defined from an optimization algorithm that uses the primal-dual predictor corrector interior point method. The control strategy used at the wind generator level exploits a combination of pitch control and control of the static converters to adjust the rotor speed for the required operation points. A small wind park is used to illustrate the effectiveness of the developed approach.

Abstract
Due to the increased availability of low Cost network technology, the use of networks to interconnect sensors, actuators and controllers is now widely accepted for the implementation of feedback control systems. To ensure the correct behaviour of such a system, the communication network must provide a reliable and timely communication service. The components with the highest impact on the communication delays are the Medium Access Control (MAC) protocol and the local communication stack. Therefore, an adequate communication stack is of utmost importance to guarantee the timing correctness of the feedback control application. I n this paper, we propose the use of state-of-the-art scheduling algorithms to manage the outgoing queue of the local communication stacks. We demonstrate that it is possible to improve the responsiveness of applications supported by the CAN communication protocol, by using just a light scheduling middleware to adequately schedule each of the outgoing queues. We also show that implementing such middleware even on top of COTS communication hardware permits a substantial reduction in the occurrence of priority inversions in the communication medium, and therefore a decrease in the number of deadline misses for highly loaded network scenarios.

Abstract
Remote sensing techniques can be used to calculate suspended sediment concentrations and to understand the flux and distribution of sediments driven by mechanisms such as tides and waves, river discharges, etc. The main objective of this study is the quantification of the Total Suspended Matter (TSM) concentration in the sea breaking zone for a particular area of the Portuguese coast, around Aveiro. The methodology used was based on in situ measurements and multi spectral satellite images. In situ experimental techniques (maritime platform, aerial platform, simulation on the beach and water sample collection in the breaking zone) were used to determine a relationship between the TSM concentration and the seawater reflectance in the breaking zone. Spectral reflectance was measured with a spectroradiometer and water samples were simultaneously collected. Empirical relationships were established between TSM concentration and the equivalent reflectance values for sensors SPOT/HRVIR, TERRA/ASTER and Landsat/TM at visible and Near Infra Red (NIR) bands computed from the experimental data. Satellite images from ASTER, SPOT HRVIR and Landsat TM were used together with the same empirical models. These satellite images were calibrated and atmospherically corrected. Equations of linear, polynomial, logarithmic, power and exponential models were tested for the satellite image bands on the visible and near infrared. The coefficients of determination (R-2) were also calculated for each model. The results obtained from the two approaches, in situ measurements and directly from the multi spectral satellite images, were analysed.

Abstract
The eagerness of Internet and Intranet computing applications has fueled the complexity of the network administration tasks. Nowadays, the network management tools have usually insufficient resources to monitor the complexity of computer networks. The majority of the agents used in computer networks are passive agents, i.e., they have no autonomy, nor participate in the decision-making procedures. We have assessed multiple artificial intelligence (AI) techniques to improve network management systems, according to the autonomic computing concept introduced by IBM. In this paper, we present a methodology for agent's development based on artificial intelligence techniques. The reported results show that the proposed methodology is adequate to support the development of active agents.

Abstract

Abstract
We propose a Multi-Agent framework to analyze the dynamics of organizational survival in cooperation networks. Firms can decide to cooperate horizontally (in the same market) or vertically with other firms that belong to the supply chain. Cooperation decisions are based on economic variables. We have defined a variant of the density dependence model to set up the dynamics of the survival in the simulation. To validate our model, we have used empirical outputs obtained in previous studies from the automobile manufacturing sector. We have observed that firms and networks proliferate in the regions with lower marginal costs, but new networks keep appearing and disappearing in regions with higher marginal costs.