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Optimizing crowd evacuation: evaluation of strategies for safety and efficiency

Journal of Reliable Intelligent Environments 2025年第1期11卷 1-19页

作者： Oliveira, Hugo S. Faculty of Sciences University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science INESC TEC Porto Portugal

Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution. © The Author(s) 2024.

关键词： Decision making

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Enhancing Power Distribution Protection: A Comprehensive Analysis of Renewable Energy Integration Challenges and Mitigation Strategies 14

Enhancing Power Distribution Protection: A Comprehensive Ana...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Alves, Everton Reiz, Cleberton Melim, André Gouveia, Clara Institute for Systems and Computer Engineering Technology and Science - INESC TEC Porto Portugal

ISBN: (纸本)9781837242689

The integration of Distributed Energy Resources (DER) imposes challenges to the operation of distribution networks. This paper conducts a systematic assessment of the impact of DER on distribution network overcurrent protection, considering the behavior of Inverter Based Resources (IBR) during faults in the coordination of medium voltage (MV) feeders' overcurrent protection. Through a detailed analysis of various scenarios, we propose adaptive protection solutions that enhance the reliability and resilience of distribution networks in the face of growing renewable energy integration. Results highlight the advantages of using adaptive protection over traditional methods and topology changes, and delve into current protection strategies, identifying limitations and proposing mitigation strategies. © The Institution of engineering & technology 2024.

关键词： Power distribution networks

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Exploring Large Language Models for Relevance Judgments in Tetun 1

Exploring Large Language Models for Relevance Judgments in T...

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1st Workshop on Large Language Models for Evaluation in Information Retrieval, LLM4Eval 2024

作者： de Jesus, Gabriel Nunes, Sérgio INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal FEUP - Faculty of Engineering University of Porto Portugal

The Cranfield paradigm has served as a foundational approach for developing test collections, with relevance judgments typically conducted by human assessors. However, the emergence of large language models (LLMs) has introduced new possibilities for automating these tasks. This paper explores the feasibility of using LLMs to automate relevance assessments, particularly within the context of low-resource languages. In our study, LLMs are employed to automate relevance judgment tasks, by providing a series of query-document pairs in Tetun as the input text. The models are tasked with assigning relevance scores to each pair, where these scores are then compared to those from human annotators to evaluate the inter-annotator agreement levels. Our investigation reveals results that align closely with those reported in studies of high-resource languages. © 2024 CEUR-WS. All rights reserved.

关键词： Structured Query Language

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ribeiro, Lucas Oliveira, Helder P. Hu, Xiao Pereira, Tania University of Porto Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9798350337488

PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events. © 2023 IEEE.

关键词： Convolutional Neural Network Heart Rate Estimation Long Short Term Memory Motion Artifacts Photoplethysmography

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Points of interest in the city of Barcelos in Portugal through augmented reality

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 40-48页

作者： Pereira, Miguel Silva, João Carlos Pinheiro, Marisa Carvalho, Sandro Santos, Gilberto Polytechnic Institute of Cávado and Ave Barcelos Portugal 2Ai – Applied Artificial Intelligence Laboratory Barcelos Portugal INESC TEC - Institute for Systems and Computer Engineering Porto Portugal LIACC - Laboratory of Artificial Intelligence and Computer Science Porto Portugal

Barcelos is a historic city in Portugal with many tourist attractions, attracting more and more visitors who come to the city with the aim of exploring it. The main objective of this article is to boost tourism in the city of Barcelos, specifically highlighting tourist, historical and leisure spots, based on the development of a mobile application using augmented reality technologies and geolocation. This application intends to allow the users to know historical points of interest in Barcelos, as well as interact with a certain point. The results of this investigation were evaluated by testing the application by end users, with the aim of identifying whether the application meets their needs, in particular the promotion of tourist and historical points. © 2023

关键词： Augmented reality

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Optimized Design Methodology in Inductive Power Transfer systems Applied to Electric Vehicle Charging 8

Optimized Design Methodology in Inductive Power Transfer Sys...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Viera, Leonardo A. Brum Pascoal, Pedro Gelati Rech, Cassiano RS Santa Maria Brazil Inesc Tec - Institute of Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9798350321128

technologies related to the transportation electrification have been gaining attention in recent years. One technology that stands out is wireless charging, which still presents numerous challenges in terms of design and optimization of parameters. This article proposes a design methodology for optimizing the performance of an inductive power transfer (IPT) for wireless charging of electric vehicles, taking into account operating limits. The proposed methodology uses a PSO (Particle Swarm Optimization) algorithm to find the design variables that maximize the eficiency. The methodology and the development of a 3.6 kW experimental setup are presented, resulting in a power transfer efficiency of 89.4 %. © 2023 IEEE.

关键词： Electric Vehicle Inductive Power Transfer (IPT) Optimization Design Particle Swarm Optimization (PSO) Wireless Charger Wireless Power Transfer (WPT)

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Case Studies of Development of Verified Programs with Dafny for Accessibility Assessment 1

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10th IPM International Conference on Fundamentals of Software engineering, FSEN 2023

作者： Faria, João Pascoal Abreu, Rui Faculty of Engineering of the University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal INESC ID Lisbon Portugal

ISBN: (数字)9783031424410

ISBN: (纸本)9783031424403

Formal verification techniques aim at formally proving the correctness of a computer program with respect to a formal specification, but the expertise and effort required for applying formal specification and verification techniques and scalability issues have limited their practical application. In recent years, the tremendous progress with SAT and SMT solvers enabled the construction of a new generation of tools that promise to make formal verification more accessible for software engineers, by automating most if not all of the verification process. The Dafny system is a prominent example of that trend. However, little evidence exists yet about its accessibility. To help fill this gap, we conducted a set of 10 case studies of developing verified implementations in Dafny of some real-world algorithms and data structures, to determine its accessibility for software engineers. We found that, on average, the amount of code written for specification and verification purposes is of the same order of magnitude as the traditional code written for implementation and testing purposes (ratio of 1.14) – an "overhead" that certainly pays off for high-integrity software. The performance of the Dafny verifier was impressive, with 2.4 proof obligations generated per line of code written, and 24 ms spent per proof obligation generated and verified, on average. However, we also found that the manual work needed in writing auxiliary verification code may be significant and difficult to predict and master. Hence, further automation and systematization of verification tasks are possible directions for future advances in the field. © 2023, IFIP International Federation for Information Processing.

关键词： Formal verification

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Sigma-Delta Modulation for Enhanced Underwater Optical Wireless Communication systems 23

Sigma-Delta Modulation for Enhanced Underwater Optical Wirel...

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23rd International Conference on Transparent Optical Networks, ICTON 2023

作者： Araújo, João H. Rocha, Henrique J. Tavares, Joana S. Salgado, Henrique M. Technology and Science INESC TEC - Institute for Systems and Computer Engineering Porto Portugal University of Porto Faculty of Engineering Porto Portugal

ISBN: (纸本)9798350303032

This paper presents an experimental investigation of sigma-delta modulation (SDM) as a means of improving the performance of underwater optical communication systems. The study considers the impact of the key parameters of SDM, including oversampling ratio, the system's signal-to-noise ratio, bandwidth, and optical link distance. The results of this study provide insights into the design and optimization of SDM-based underwater optical communication systems, paving the way for future research in this field. A fully digital solution, albeit operating at a lower bit rate than previously published OFDM counterparts, provides immunity against nonlinearities of the system and robustness to noise, which is relevant in harsh environments. Moreover, the proposed solution based on a first-order bandpass SDM architecture avoids the employment of a DAC at the receiver, simplifying its operation and reducing costs. An experimental investigation is carried out for the transmission of 16-QAM over SDM, and a transmission distance of 4.8 m over the underwater channel is achieved with a maximum transmission rate of 400 Mbit/s with an MER of 28 dB. © 2023 IEEE.

关键词： Delta sigma modulation

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ML-Assistant for Human Operators to Solve Faults and Classify Events Complexity in Electrical Grids 13

ML-Assistant for Human Operators to Solve Faults and Classif...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2022

作者： Campos, Vasco Andrade, José R. Bessa, Ricardo J. Gouveia, Clara INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9781839538445

Nowadays, human operators at grid control centers analyze a large volume of alarm information during outage’s events, and must act fast to restore the service. Currently, after the occurrence of short-circuit faults and its isolation via feeder protection, fault location and isolation is achieved via remotely controlled switching actions defined by operator’s experience. Despite operator’s experience and knowledge, this makes the process sub-optimal and slower. This paper proposes two novel machine learning-based algorithms to assist human operator decisions, aiming to: i) classify the complexity of a fault occurrence (Occurrences Classifier) based on its alarm events;ii) provide fast insights to the operator on how to solve it (Data2Actions). The Occurrences Classifier takes the alarm information of an occurrence and classifies it as a "simple" or "complex" occurrence. The Data2Actions takes a sequence of alarm information from the occurrence and suggests to the operator the more adequate sequence of switching actions to isolate the fault section on the overhead medium voltage line. Both algorithms were tested in real data from a Distribution System Operator between 2017 and 2020, and showed i) an accuracy of 86% for the Data2Actions, and ii) the Occurrences Classifier reached 74% accuracy for "simple" occurrences and 58% for "complex" ones, leading to an overall 65% accuracy. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Machine learning

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Reinforcement Learning Based Dispatch of Batteries

Reinforcement Learning Based Dispatch of Batteries

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Mediterranean Electrotechnical Conference (MELECON)

作者： Pedro Benedicto Ricardo Silva Clara Gouveia INESC TEC Institute for Systems and Computer Engineering Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Microgrids are poised to become the building blocks of the future control architecture of electric power systems. As the number of controllable points in the system grows exponentially, traditional control and optimization algorithms become inappropriate for the required operation time frameworks. Reinforcement learning has emerged as a potential alternative to carry out the real-time dispatching of distributed energy resources. This paper applies one of the continuous action-space algorithms, proximal policy optimization, to the optimal dispatch of a battery in a grid-connected microgrid. Our simulations show that, though suboptimal, RL presents some advantages over traditional optimization setups. Firstly, it can avoid the use of forecast data and presents a lower computational burden, therefore allowing for implementation in distributed control devices.

关键词： Electric potential Distributed databases Microgrids Reinforcement learning Dispatching Real-time systems Batteries

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