Abstract

Abstract
The recent growth in the use of 3D printers by independent users has contributed to a rise in interest in 3D scanners. Current 3D scanning solutions are commonly expensive due to the inherent complexity of the process. A previously proposed low-cost scanner disregarded uncertainties intrinsic to the system, associated with the measurements, such as angles and offsets. This work considers an approach to estimate these optimal values that minimize the error during the acquisition. The Particle Swarm Optimization algorithm was used to obtain the parameters to optimally fit the final point cloud to the surfaces. Three tests were performed where the Particle Swarm Optimization successfully converged to zero, generating the optimal parameters, validating the proposed methodology.

Abstract
The Natural Language Processing (NLP) community has witnessed huge improvements in the last years. However, most achievements are evaluated on benchmarked curated corpora, with little attention devoted to user-generated content and less-resourced languages. Despite the fact that recent approaches target the development of multi-lingual tools and models, they still underperform in languages such as Portuguese, for which linguistic resources do not abound. This paper exposes a set of challenges encountered when dealing with a real-world complex NLP problem, based on user-generated complaint data in Portuguese. This case study meets the needs of a country-wide governmental institution responsible for food safety and economic surveillance, and its responsibilities in handling a high number of citizen complaints. Beyond looking at the problem from an exclusively academic point of view, we adopt application-level concerns when analyzing the progress obtained through different techniques, including the need to obtain explainable decision support. We discuss modeling choices and provide useful insights for researchers working on similar problems or data.

Abstract
The ATLANTIS project aims to establish a pioneer pilot infrastructure that will allow the demonstration of key enabling robotic technologies for inspection and maintenance of offshore wind farms. The pilot will be implemented in Viana do Castelo, Portugal, and will allow for testing, validation and demonstration of technologies with a range of technology readiness level, in near-real/real environments.The demonstration of robotic technologies can promote the transition from traditional inspection and maintenance methodologies towards automated robotic strategies, that remove or reduce the need of human-in-the-loop, reducing costs and improving the safety of interventions. Eight scenarios, split into four showcases, will be used to determine the required developments for robotic integration and demonstrate the applicability in the inspection and maintenance processes. The scenarios considered were identified by end-users as key areas for robotics.

Abstract
In this work, a multi-parameter point sensor based on the combination of Fabry-Perot (FP) and the anti-resonant (AR) reflecting guidance in cascade configuration is proposed and experimentally demonstrated. This structure, based on FP interference and AR reflecting guidance, was fabricated with two different air micro-cavities. The attained experimental results showed different strain and temperature sensitivities for the antiresonance contribution. However, when analyzing the FP interference, only strain sensitivity was observed, demonstrating that this air micro-cavity was also insensitive to temperature variations.

Abstract
This paper presents three ensemble learning models for short term load forecasting. Machine learning has evolved quickly in recent years, leading to novel and advanced models that are improving the forecasting results in multiple fields. However, in highly dynamic fields such as power and energy systems, dealing with the fast acquisition of large amounts of data from multiple data sources and taking advantage from the correlation between the multiple available variables is a challenging task, for which current models are not prepared. Ensemble learning is bringing promising results in this sense, as, by combining the results and use of multiple learners, is able to find new ways for current learning models to be used and optimized. In this paper three ensemble learning models are developed and the respective results compared: gradient boosted regression trees, random forests and an adaptation of Adaboost. Results for electricity consumption forecasting in hour-ahead are presented using a case-study based on real data from an office building. Results show that the adapted Adaboost model outperforms the reference models for hour-ahead load forecasting.