Purpose-This paper aims to report on a case study concerning the development of sustainable energy partnerships involving engineering faculty and undergraduate students at the
Abstract-In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-Means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-Means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach.
This paper outlines the online performance of a control law based on PID (proportional-integral-derivative) controllers and MPC (model predictive control) for mobile robot local path-following. Both techniques share the use of a set of different dynamic models. PID controllers are used for controlling the speed of the robot's wheels, while high level algorithms compute the necessary wheel speeds in order to generate a motion that approaches the vehicle towards the desired path. Meanwhile, local MPC is implemented by computing the horizon of suitable coordinates that arise from the set of command input combinations. Therefore, command speeds that correspond to the desired point are obtained by minimizing a cost function in which the population of the available coordinates is taken into accoun
Abstract. Offshore wind energy is one of the fastest growing powers in the field of renewable energy. An offshore wind farm situated sufficiently far away from the coast can generate more wind power and will have a longer operation life since the wind is stronger and more consistent than that on or near the coast. It can also avoid some major problems of the traditional wind farms like the visual and noise impacts and potential damage to wildlife. From the technical point of view, it is difficult to anchor the wind turbines directly on the seabed in deep water. Thus, new constructive solutions based on floating support structures are proposed. One of the main challenges is to reduce the fatigue of a floating offshore wind turbine so as to guarantee its proper functioning under the constraints imposed by the floating support structures subject to a greater range of motion than that of the fixed-bottom support structures. This paper analyzes the loads and dynamic response of floating support structures and proposes the smart control strategies for mitigating the dynamic wind and wave loads on floating wind turbines.
Key wordsFloating wind turbines, offshore wind energy, smart structural control systems, analysis of dynamic loads and vibration control.
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