Abstract
The sustainable development of power distribution systems must evolve into smart grids, where advanced automation with fast communication channels is essential. The analysis of their behavior uses the Hardware-In-the-Loop simulation for studying normal and critical operating conditions. In this work, we propose a hybrid technique for transient simulation in distribution systems by combining the high sample rate of the time domain models for voltage profile and electrical current monitoring with the processing speed of algorithms that operate the quasi-stationary, or permanent, phasor models. The proposed simulation platform is also based on the state of the art of standardized communication protocols of the power system. Its evaluation is performed using the comparison with specialized commercial software to assess the transient simulation. The time overcurrent protection function and the verification of messages exchanged between the simulator and the tested device highlights the applicability of the proposed methodology.

Abstract
Subjects change frequently in moderated debates with several participants, such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defining when a new block begins so that the task of automatically partitioning a moderated debate can focus solely on the moderator's behavior. In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal. Our results show the effectiveness of DEBACER.

Abstract

Abstract
The paper develops and investigates a novel set of constrained-output robust controllers with selectable response smoothing degree designed for an integrator-plus-dead-time (IPDT) plant model. The input-output response of the IPDT system is internally approximated by several time-delayed, possibly higher-order plant models of increasing complexity. Since they all contain a single integrator, the presented approach can be considered as a generalization of active disturbance rejection control (ADRC). Due to the input/output model used, the controller commissioning can be based on a simplified process modeling, similar to the one proposed by Ziegler and Nichols. This allows it to be compared with several alternative controllers commonly used in practice. Its main advantage is simplicity, since it uses only two identified process parameters, even when dealing with more complex systems with distributed parameters. The proposed set of controllers with increasing complexity includes the stabilizing proportional (P), proportional-derivative (PD), or proportional-derivative-acceleration (PDA) controllers. These controllers can be complemented by extended state observers (ESO) for the reconstruction of all required state variables and non-measurable input disturbances, which also cover imperfections of a simplified plant modeling. A holistic performance evaluation on a laboratory heat transfer plant shows interesting results from the point of view of the optimal least sensitive solution with smooth input and output.

Abstract
Face Recognition (FR) is a challenging task, especially when dealing with unknown identities. While Open-Set Face Recognition (OSFR) assigns a single class to all unfamiliar subjects, Open-World Face Recognition (OWFR) employs an incremental approach, creating a new class for each unknown individual. Current OWFR approaches still present limitations, mainly regarding the accuracy gap to standard closed-set approaches and execution time. This paper proposes a fast and simple mixture-based OWFR algorithm that tackles the execution time issue while avoiding accuracy decay. The proposed method uses data curve representations and Universal Background Models based on Gaussian Mixture Models. Experimental results show that the proposed approach achieves competitive performance, considering accuracy and execution time, in both closed-set and open-world scenarios.

Abstract
Soon, a new generation of Collaborative Robots embodying Human-Robot Teams (HRTs) is expected to be more widely adopted in manufacturing. The adoption of this technology requires evaluating the overall performance achieved by an HRT for a given production workflow. We study this performance by solving the underlying scheduling problem under different production settings. We formulate the problem as a Multimode Multiprocessor Task Scheduling Problem, where tasks may be executed by two different types of resources (humans and robots), or by both simultaneously. Two algorithms are proposed to solve the problem - a Constraint Programming model and a Genetic Algorithm. We also devise a new lower bound for benchmarking the methods. Computational experiments are conducted on a large set of instances generated to represent a variety of HRT production settings. General instances for the problem are also considered. The proposed methods outperform algorithms found in the literature for similar problems. For the HRT instances, we find optimal solutions for a considerable number of instances, and tight gaps to lower bounds when optimal solutions are unknown. Moreover, we derive some insights on the improvement obtained if tasks can be executed simultaneously by the HRT. The experiments suggest that collaborative tasks reduce the total work time, especially in settings with numerous precedence constraints and low robot eligibility. These results indicate that the possibility of collaborative work can shorten cycle time, which may motivate future investment in this new technology.