Development of a monitoring system for electrical energy consumption and power quality analysis

This paper presents the development of a monitoring system for electrical energy consumption and power quality analysis, also known as power quality analyser (PQA). The internal architecture of the developed monitoring system is described in detail along the paper, highlighting the signal conditioning circuit and analogue to digital conversion (ADC) stage, the advanced RISC machine (ARM) processor, and the digital signal processor (DSP), which are used, respectively, for data acquisition, data communication and power quality calculations. This paper also describes the software developed for a Raspberry Pi, which receives the processed information from the ARM processor and presents it in real-time using a touch screen user-friendly interface. Among all the available features of the developed system, the paper presents the most relevant experimental results obtained with linear and nonlinear loads, showing the main functionalities of the different menus available in the developed user interface, mainly the menus “Scope”, “Harmonics” and “Data”.This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941.info:eu-repo/semantics/publishedVersio

First-Principles Model to Evaluate Quantitatively the Long-Life Behavior of Cellulose Acetate Polymers

UIDB/04028/2020 UIDP/04028/2020 UID/QUI/50006/2019A deep understanding of the degradation of cellulose diacetate (CDA) polymer is crucial in finding the appropriate long-term stability solution. This work presents an investigation of the reaction mechanism of hydrolysis using electronic density functional theory calculations with the B3LYP/6-31++G*∗ level of theory to determine the energetics of the degradation reactions. This information was coupled with the transition-state theory to establish the kinetics of degradation for both the acid-catalyzed and noncatalyzed degradation pathways. In this model, the dependence on water concentration of the polymer as a function of pH and the evaporation of acetic acid from the polymer is explicitly accounted for. For the latter, the dependence of the concentration of acetic acid inside the films with the partial pressure on the surrounding environment was measured by sorption isotherms, where Henry's law constant was measured as a function of temperature. The accuracy of this approach was validated through comparison with experimental results of CDA-accelerated aging experiments. This model provides a step forward for the estimation of CDA degradation dependence on environmental conditions. From a broader perspective, this method can be translated to establish degradation models to predict the aging of other types of polymeric materials from first-principles calculations.publishersversionpublishe

Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model

© The Author(s) 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.Introduction: We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported. Methods: Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0 -100 points) - previously developed and published - were measured. Results: 123 regions of interest from 117 images across 90 patients were included. There were no significant differences between lesion diameter, percentage diameter stenosis and distal border diameter between the original/segmented images. There was a statistically significant albeit minor difference [0,19 mm (0,09-0,28)] regarding proximal border diameter. Overlap accuracy ((TP + TN)/(TP + TN + FP + FN)), sensitivity (TP / (TP + FN)) and Dice Score (2TP / (2TP + FN + FP)) between original/segmented images was 99,9%, 95,1% and 94,8%, respectively. The GSS was 92 (87-96), similar to the previously obtained value in the training dataset. Conclusion: the AI model was capable of accurate CAG segmentation across multiple performance metrics, when applied to a multicentric validation dataset. This paves the way for future research on its clinical uses.Open access funding provided by FCT|FCCN (b-on). Cardiovascular Center of the University of Lisbon, INESC-ID / Instituto Superior Técnico, University of Lisbon.info:eu-repo/semantics/publishedVersio

Cannabidiol and cannabigerol exert antimicrobial activity without compromising skin microbiota

Cannabidiol (CBD) and cannabigerol (CBG) are two pharmacologically active phytocannabinoids of Cannabis sativa L. Their antimicrobial activity needs further elucidation, particularly for CBG, as reports on this cannabinoid are scarce. We investigated CBD and CBG’s antimicrobial potential, including their ability to inhibit the formation and cause the removal of biofilms. Our results demonstrate that both molecules present activity against planktonic bacteria and biofilms, with both cannabinoids removing mature biofilms at concentrations below the determined minimum inhibitory concentrations. We report for the first time minimum inhibitory and lethal concentrations for Pseudomonas aeruginosa and Escherichia coli (ranging from 400 to 3180 µM), as well as the ability of cannabinoids to inhibit Staphylococci adhesion to keratinocytes, with CBG demonstrating higher activity than CBD. The value of these molecules as preservative ingredients for cosmetics was also assayed, with CBG meeting the USP 51 challenge test criteria for antimicrobial effectiveness. Further, the exact formulation showed no negative impact on skin microbiota. Our results suggest that phytocannabinoids can be promising topical antimicrobial agents when searching for novel therapeutic candidates for different skin conditions. Additional research is needed to clarify phytocannabinoids’ mechanisms of action, aiming to develop practical applications in dermatological use.info:eu-repo/semantics/publishedVersio

Cost estimation of rail power conditioner topologies based on indirect modular multilevel converter in v/v and scott power transformers

This paper presents a cost estimation study for several rail power conditioner (RPC) topologies based on an indirect modular multilevel converter (MMC), in which these topologies are combined with V/V or Scott power transformers. The RPC topologies under interest in this paper are: the RPC based on a full-bridge MMC (RPC based on MMC4), the RPC based on two-phase three-wire MMC (RPC based on MMC3), and the RPC based on a half-bridge MMC (RPC based on MMC2). These RPC systems operate at medium voltage levels in the interconnection to 25 kV-50 Hz catenary sections to solve power quality problems, such as the current harmonics and the negative sequence components (NSCs) of currents. Along the paper are described the V/V and the Scott power transformers, the RPC main architectures, and the estimated cost of implementation for each RPC topology considering V/V or Scott implementations. As main contribution, the presented results could help in the selection procedure of the RPC topology, giving the best economical solution according to the used power transformer (V/V or Scott).This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. This work has been supported by FCT within the Project Scope DAIPESEV – Development of Advanced Integrated Power Electronic Systems for Electric Vehicles: PTDC/EEI-EEE/30382/2017. Mohamed Tanta is supported by the doctoral scholarship with a reference PD/BD/127815/2016 granted by the Portuguese FCT agency

On-board electric vehicle battery charger with enhanced V2H operation mode

This paper proposes an on-board Electric Vehicle (EV) battery charger with enhanced Vehicle-to-Home (V2H) operation mode. For such purpose was adapted an on-board bidirectional battery charger prototype to allow the Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G) and V2H operation modes. Along the paper are presented the hardware topology and the control algorithms of this battery charger. The idea underlying to this paper is the operation of the on-board bidirectional battery charger as an energy backup system when occurs a power outages. For detecting the power outage were compared two strategies, one based on the half-cycle rms calculation of the power grid voltage, and another in the determination of the rms value based in a Kalman filter. The experimental results were obtained considering the on-board EV battery charger under the G2V, V2G, and V2H operation modes. The results show that the power outage detection is faster using a Kalman filter, up to 90% than the other strategy. This also enables a faster transition between operation modes when a power outage occurs.Fundação para a Ciência e Tecnologia (FCT), Project Scope: Pest OE/EEI/UI0319/20

Modeling, design, and experimental test of a zero‐sequence current electromagnetic suppressor

This paper presents the experimental investigation of an electromagnetic suppressor to minimize the circulation of zero‐sequence currents in three‐phase four‐wire distribution systems. The proposed zero‐sequence current electro- magnetic suppressor (ZSS) integrates two distinct electromagnetic devices, namely a zero‐sequence filter (ZSF) and a zero‐ sequence blocker (ZSB), connected in parallel and in series between the source and the load, respectively. In this paper are presented the theoretical modeling and analysis of each device, the mathematical concepts of harmonic compensation, and the procedures for the design and determination of the constructive details. The experimental results of the combined operation of the devices that integrate the ZSS demonstrate the feasibility of the proposed arrangement, by minimizing the flow of the zero‐sequence harmonic currents in the source side, enabling a significant reduction of the current in the neutral conductor, and also by improving the system power factor.This work has been supported by CNPq ‐ National Council for Scientific and Technological Development, by CAPES ‐ Coordinationfor the Improvement of Higher Education Personnel through the PDSE ‐ Doctoral Program Sandwich Abroad 7427‐12‐3, and by FCT ‐ Foundation for Science and Technology within the Project Scope: UID/CEC/00319/2019 and FCT within project PTDC/EEI‐EEE/28813/2017

Design of an intrinsically safe series-series compensation WPT system for automotive LiDAR

The earliest and simplest impedance compensation technique used in inductive wireless power transfer (WPT) design is the series-series (SS) compensation circuit, which uses capacitors in series with both primary and secondary coils of an air-gapped transformer. Despite of its simplicity at the resonant condition, this configuration exhibits a major sensitivity to variations of the load attached to the secondary, especially when higher coupling coefficients are used in the design. In the extreme situation that the secondary coil is left at open circuit, the current at the primary coil may increase above the safety limits for either the power converter driving the primary coil or the components in the primary circuit, including the coil itself. An approach often used to minimize this problem is detuning, but this also reduces the electrical efficiency of the power transfer. In low power, fixed-distance stationary WPT, a fair trade-off between efficiency and safety must be verified. This paper aims to consolidate a simple design procedure for such a SS-compensation, exemplifying its use in the prototype of a WPT system for automotive light detection and ranging (LiDAR) equipment. The guidelines herein provided should equally apply to other low power applications.This work has been supported by national funds through FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019, and also European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Autonomous vehicles are becoming central for the future of mobility, supported by advances in deep learning techniques. The performance of aself-driving system is highly dependent on the quality of the perception task. Developments in sensor technologies have led to an increased availability of 3D scanners such as LiDAR, allowing for a more accurate representation of the vehicle's surroundings, leading to safer systems. The rapid development and consequent rise of research studies around self-driving systems since early 2010, resulted in a tremendous increase in the number and novelty of object detection methods. After the first wave of works that essentially tried to expand known techniques from object detection in images, more recently there has been a notable development in newer and more adapted to LiDAR data works. This paper addresses the existing literature on object detection using LiDAR data within the scope of self-driving and brings a systematic way for analysing it. Unlike general object detection surveys, we will focus on point-cloud data, which presents specific challenges, notably its high-dimensional and sparse nature. This work introduces a common object detection pipeline and taxonomy to facilitate a thorough comparison between different techniques and, departing from it, this work will critically examine the representation of data (critical for complexity reduction), feature extraction and finally the object detection models. A comparison between performance results of the different models is included, alongside with some future research challenges.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n. 037902; Funding Reference: POCI-01-0247-FEDER-037902]