Abstract

Abstract

Abstract
We present a giant sensitivity displacement sensor combining the push-pull method and enhanced Vernier effect. The displacement sensor consists in two interferometers that are composed by two cleaved standard optical fibers coupled by a 3 dB coupler and combined with a double-sided mirror. The push pull-method is applied to the mirror creating a symmetrical change to the length of each interferometer. Furthermore, we demonstrate that the Vernier effect has a maximum sensitivity of two-fold that obtained with a single interferometer. The combination of the push-pull method and the Vernier effect in the displacement sensors allows a sensitivity of 60 +/- 1 nm/mu m when compared with a single interferometer working in the same free spectral range. In addition, exploring the maximum performance of the displacement sensors, a sensitivity of 254 +/- 6 nm/mu m is achieved, presenting a M-factor of 1071 and M-Vernier of 1.9 corresponding to a resolution of 79 pm. This new solution allows the implementation of giant-sensitive displacement measurement for a wide range of applications.

Abstract
The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, stream-based learning algorithms require careful hyperparameter settings. However, this problem is exacerbated in online learning settings, especially with the occurrence of concept drifts, which frequently require the reconfiguration of hyperparameters. In this article, we present SSPT, an extension of the Self Parameter Tuning (SPT) optimisation algorithm for data streams. We apply the Nelder-Mead algorithm to dynamically-sized samples, converging to optimal settings in a single pass over data while using a relatively small number of hyperparameter configurations. In addition, our proposal automatically readjusts hyperparameters when concept drift occurs. To assess the effectiveness of SSPT, the algorithm is evaluated with three different machine learning problems: recommendation, regression, and classification. Experiments with well-known data sets show that the proposed algorithm can outperform previous hyperparameter tuning efforts by human experts. Results also show that SSPT converges significantly faster and presents at least similar accuracy when compared with the previous double-pass version of the SPT algorithm.

Abstract
Data-driven decision support systems rely on increasing amounts of information that needs to be converted into actionable knowledge in business intelligence processes. The latter have been applied to diverse business areas, including governmental organizations, where they can be used effectively. The Portuguese Food and Economic Safety Authority (ASAE) is one example of such organizations. Over its years of operation, a rich dataset has been collected which can be used to improve their activity regarding prevention in the areas of food safety and economic enforcement. ASAE needs to inspect Economic Operators all over the country, and the efficient and effective generation of optimized and flexible inspection routes is a major concern. The focus of this paper is, thus, the generation of optimized inspection routes, which can then be flexibly adapted towards their operational accomplishment. Each Economic Operator is assigned an inspection utility - an indication of the risk it poses to public health and food safety, to business practices and intellectual property as well as to security and environment. Optimal inspection routes are then generated typically by seeking to maximize the utility gained from inspecting the chosen Economic Operators. The need of incorporating constraints such as Economic Operators' opening hours and multiple departure/arrival spots has led to model the problem as a Multi-Depot Periodic Vehicle Routing Problem with Time Windows. Exact and meta-heuristic methods were implemented to solve the problem and the Genetic Algorithm showed a high performance with realistic solutions to be used by ASAE inspectors. The hybrid approach that combined the Genetic Algorithm with the Hill Climbing also showed to be a good manner of enhancing the solution quality.

Abstract
This paper investigates the issue of robust frequency regulation of single-area alternating current (AC) power applications. The robust stability and disturbance rejection performance criteria are considered in the design procedure of an output feedback controller. Four cases of single-area AC power systems, which comprise the different types of governors and generators, are considered. These components are modeled by first- and second-order transfer functions and exhibit non(minimum) phase behavior. Based on the uncertain linear transfer functions of the governors and generators, the resilient controller against uncertainties and unknown power load demand is designed numerically. Several numerical simulations are carried out to show the merits of the developed controller. Also, the effects of different types of governors and generators on the AC MG frequency deviation are also investigated.