Abstract
The fish farming industry is becoming widespread all over the world. By 2039 most of the fish we eat will come from the fish farming industry. In this work, we propose an autonomous robotic solution for indoor fish farming biomass estimation. Our proposed system moves silently on top of the tank borders using differential wheels and a structured light vision system (SLS). The SLS system is composed by a camera and two line lasers (projectors) equipped with a line beam that allows to obtain the fish depth profile present in the tank to perform biomass estimation. Results in laboratory and in real aquaculture environment with live fish are presented.

Abstract
The orchestration of high performance computing (HPC) services to build scientific applications is based on complex workflows. A challenging task consists of improving the reliability of such workflows, avoiding faulty behaviors that can lead to bad consequences in practice. This paper introduces a certifier component for certifying scientific workflows in a certification framework proposed for HPC Shelf, a cloud-based platform for HPC in which different kinds of users can design, deploy and execute scientific applications. This component is able to inspect the workflow description of a parallel computing system of HPC Shelf and check its consistency with respect to a number of safety and liveness properties specified by application designers and component developers.

Abstract
The implementation of automatic and real-time data processing algorithms in order to reduce the big amount of data down to a human and useful scale is often pointed out as a key step for increasing the value of Structural Health Monitoring (SHM). One of the reasons usually pointed out for the low usability of the structural monitoring data is the fact that damage events are usually masked by the environmental and/or operational effects. Indeed, the robustness and accuracy of the damage (or novelty) detection methods depend on how successfully the changes in the structural response due to damage can be discerned from the normal environmental and operational effects. The process of removing the environmental and/or operational effects from the structural response is usually termed as data normalisation. In this context, the present work describes the adopted methodologies for data normalisation and novelty detection implemented in the Corgo Bridge, a cable-stayed bridge located in northern Portugal recently opened to traffic and wherein a long-term monitoring system has been installed. Data normalisation is accomplished by means of application, alone and combined, of two well-established multivariate statistical tools: multiple linear regression analysis and principal component analysis. The Hotelling T2 control chart is used to track the existence of abnormal values. The performance of the chosen data normalisation methods are evaluated and compared. The first year of data is used to establish the multivariate models and the remaining data is used to validate the fitted models and the ability for novelty/damage detection. Since the bridge is new and sound, damage scenarios are numerically simulated.

Abstract

Abstract
The increasing penetration of distributed energy resources based on renewable energy sources in distribution systems leads to a more complex management of power systems. Consequently, ancillary services become even more important to maintain the system security and reliability. This paper proposes and evaluates a generic model for day-ahead, intraday (hour-ahead) and real-time scheduling, considering the joint optimization of energy and reserve in the scope of the virtual power player concept. The model aims to minimize the operation costs in the point of view of one aggregator agent taking into account the balance of the distribution system. For each scheduling stage, previous scheduling results and updated forecasts are considered. An illustrative test case of a distribution network with 33 buses, considering a large penetration of distribution energy resources allows demonstrating the benefits of the proposed model.

Abstract
Short-term load forecasting plays an important role to the operation of electric systems, as a key parameter for planning maintenances and to support the decision making process on the purchase and sale of electric power. A particular case in this respect is the consumption forecasting on special days, which can be a complex task as it presents unusual load behavior, when compared to regular working days. Moreover, its reduced number of samples makes it hard to properly train and validate more complex and nonlinear prediction algorithms. This paper tackles this problem by proposing a new approach to improve the accuracy of the predictions amidst existing special days, employing an Information Theoretic Learning Mean Shift algorithm for pattern discovery, classifying and densifying the available scarce consumption data. The paper describes how this methodology was applied to an electrical load forecasting problem in the northern region of Brazil, improving the previously obtained accuracy held by the power company.