Abstract

Abstract
A brief review of new fiber microsphere geometries is presented. Simple microspheres working as Fabry-Perot cavities are interrogated in reflection and in transmission. Two microspheres were also spliced together, and subjected to different physical parameters. These structures are an alternative solution for load measurement and, when read in transmission, it is also possible to apply strain. Moreover, the structure is capable of being used under extreme ambient temperatures up to 900 degrees C. Random signal in cleaved microspheres was demonstrated with the possibility of using it for random laser or sensing applications. All this work was developed at the Centre for Applied Photonics, INESC TEC.

Abstract
This paper proposes a framework for the optimal operation of multi Micro Grids (multiMGs) based on Hybrid Stochastic/Robust optimization. MultiMGs with various characteristics are considered in this study. They are connected to different buses of their Up-Stream-Network (USN). Day-Ahead (DA) and Real-Time (RT) markets are contemplated. The proposed optimization structure in this paper is a bi-level one since both MGs operators' and USN operator's decisions are considered in the proposed model. The advantages of using time-of-use demand response programs on the optimal operation of USN in the presence of multiMGs are investigated. The uncertainty of different components, including wind units, photovoltaic units, plug-in electric vehicles, and DA market price is captured by using stochastic programming. In addition, robust programming is utilized for contemplating the uncertainty of the RT market price. Furthermore, the grid-connected and island modes of MGs' operation are investigated in this paper, discussing also the virtues of utilizing multiMGs over single MG. Finally, IEEE 18-bus and 30-bus test systems are considered for MGs and USN networks respectively to scrutinize the simulation results.

Abstract
Learning from data streams can only be realized by systems which are not only effective but also efficient. That is, knowledge discovery in this context is impossible without being aware of the computational resources available. Weightless artificial neural networks (WANNs) are based on an alternative principle to iterative optimization of weights employed by most mainstream artificial neural network models and related tools. WANNs explicitly manage knowledge pieces, which are stored by RAM nodes. Such foundational difference reflects on the adaptability of these models to streaming inputs: in such scenario, the application of weightless models can be considered more natural than the same for their weighted counterparts, with an ample control over learning capability as well as resources consumption. This chapter details a WANN-based approach for mining data streams, which allows the maintenance of an up-to-date data summary which can be used for several purposes. The insights and original ideas which power such model are explained as well, enabling novel applications and further development of them.

Abstract

Abstract
[No abstract available]