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A wearable system for the stress monitoring of air traffic controllers during an air traffic control refresher training and the trier social stress test: A comparative study

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Open Bioinformatics Journal 2018年第1期11卷 106-116页

作者： Rodrigues, Susana Paiva, Joana S. Dias, Duarte Aleixo, Marta Filipe, Rui Cunha, João Paulo Silva Institute for Systems Engineering and Computers – Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering University of Porto Porto Portugal Physics and Astronomy Department Sciences Faculty University of Porto Porto Portugal Navegação Aérea de Portugal (NAV) EPE Lisbon Portugal

Background: Air Traffic Control (ATC) is a complex and demanding process, exposing Air Traffic Controllers (ATCs) to high stress. Recently, efforts have been made in ATC to maintain safety and efficiency in the face of increasing air traffic demands. computer simulations have been a useful tool for ATC training, improving ATCs skills and consequently traffic safety. Objectives: This study aims to: a) evaluate psychophysiological indices of stress in an ATC simulation environment using a wearable biomonitoring platform. In order to obtain a measure of ATCs stress levels, results from an experimental study with the same participants, that included a stress-induced task were used as a stress ground truth;b) understand if there are differences in stress levels of ATCs with different job functions (“advisors” vs “operationals”) when performing an ATC Refresher Training, in a simulator environment. Methods: Two studies were conducted with ATCs: Study 1, that included a stress-induced task - the Trier Social Stress Test (TSST) and Study 2, that included an ATC simulation task. Linear Heart Rate Variability (HRV) features from ATCs were acquired using a medical grade wearable Electrocardiogram (ECG) device. Self-reports were used to measure perceived stress. Results: TSST was self-reported as being much more stressful than the simulation task. Physiological data supports these results. Results from study 2 showed more stress among the “advisors” group when comparing to the “operational” group. Conclusion: Results point to the importance of the development of quantified Occupational Health (qOHealth) devices to allow monitoring and differentiation of ATCs stress responses. © 2018 Donato and Denaro.

关键词： Air traffic controllers ECG Heart rate variability Simulation training Stress Wearable biomonitoring platform

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A New Approach for Long-Term Demand Forecasting

SSRN

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SSRN 2024年

作者： Paulos, José Pedro Azevedo, Filipe Fidalgo, José Nuno INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal The Department of Electrical Engineering ISEP IPP - School of Engineering Polytechnic Institute of Porto Portugal The Department of Electrical Engineering and Computers of the Faculty of Engineering of the University of Porto Portugal INESC TEC Porto Portugal

The methodology adopted in this article is based on the principle that load diagrams to be forecasted have two distinct components: shape and magnitude. The present work was focused on the shape component, which was essentially defined by historic data and calendar information. The implemented approach was applied to five European countries using four years for training and one year for testing. The goal was to forecast the hourly demand diagram for an entire future year. The forecasting models were based on three forecasting techniques: General Regression Neural Networks (GRNN), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN). The performance analysis showed a mean absolute percentage error roughly between 2% and 6%, depending on the country and year being forecasted. Investigating the effect of temperature on predictions was the aim of a second study, which allows us to define uncertainty margins around the forecasted demand curve. This outcome is an essential element for power system planning. © 2024, The Authors. All rights reserved.

关键词： Multiple linear regression

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An automL-based approach to multimodal image sentiment analysis

arXiv

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arXiv 2021年

作者： Ferrinho, Vasco Lopes Gaspar, António Alexandre, Luís A. Cordeiro, João NOVA LINCS Universidade da Beira Interior Portugal LIAAD INESC TEC – Institute for Systems and Computer Engineering Technology and Science Portugal

Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message polarity by creating context and connections. Multimodal sentiment analysis approaches intend to leverage information of both textual and image content to perform an evaluation. Despite recent advances, current solutions still flounder in combining both image and textual information to classify social media data, mainly due to subjectivity, inter-class homogeneity and fusion data differences. In this paper, we propose a method that combines both textual and image individual sentiment analysis into a final fused classification based on AutoML, that performs a random search to find the best model. Our method achieved state-of-the-art performance in the B-T4SA dataset, with 95.19% accuracy. © 2021, CC BY.

关键词： Sentiment analysis

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Novel Approach for Offshore Photovoltaic Panels Inspection with VTOL UAV

Novel Approach for Offshore Photovoltaic Panels Inspection w...

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OCEANS

作者： R. Morais João J. Martins P. Lima A. Dias A. Martins J. Almeida E. Silva Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Instituto Superior de Engenharia do Porto (ISEP) Porto Portugal

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Solar energy will contribute to global economic growth, increasing worldwide photovoltaic (PV) solar energy production. More recently, one of the outstanding energy achievements of the last decade has been the development of floating photovoltaic panels. These panels differ from conventional (ter-restrial) panels because they occupy space in a more environmen-tally friendly way, i.e., aquatic areas. In contrast, land areas are saved for other applications, such as construction or agriculture. Developing autonomous inspection systems using unmanned aerial vehicles (UAV s) represents a significant step forward in solar PV technology. Given the frequently remote and difficult-to-access locations, traditional inspection methods are no longer practical or suitable. Responding to these challenges, an in-novative inspection framework was developed to autonomously inspect photovoltaic plants (offshore) with a Vertical Takeoff and Landing (VTOL) UAV. This work explores two different methods of autonomous aerial inspection, each adapted to specific scenarios, thus increasing the adaptability of the inspection process. During the flight, the aerial images are evaluated in real-time for the autonomous detection of the photovoltaic modules and the detection of possible faults. This mechanism is crucial for making decisions and taking immediate corrective action. An offshore simulation environment was developed to validate the implemented system.

关键词： Economics Oceans Solar energy Production Inspection Autonomous aerial vehicles Real-time systems

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Selecting the Optimal Signals in Phasor Measurement Unit-based Power System Stabilizer Design

Selecting the Optimal Signals in Phasor Measurement Unit-bas...

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International Conference on Smart Energy systems and technologies (SEST)

作者： Mahsa Rezaei Maryam Dehghani Navid Vafamand Bita Shayanfard Mohammad Sadegh Javadi João P. S. Catalão School of Electrical and Computer Engineering Shiraz University Shiraz Iran Fars Regional Electric Company Shiraz Iran Institute for Systems and Computer Engineering Technology and Science (INESC TEC.) Porto Portugal Faculty of Engineering of the University of Porto (FEUP) and INESC TEC Porto Portugal

ISBN: (数字)9781728147017

ISBN: (纸本)9781728147024

Phasor Measurement Unit (PMU) provides beneficial information for dynamic power system stability, analysis and control. One main application of such useful information is data-driven control. This paper is devoted to presenting an approach for optimal signal selection in PMU-based power system stabilizer (PSS) design. In this paper, for selecting the optimal input and output signals for PSS, an algorithm is suggested in which the combination of clustering the generators and the buses of the system with ICA, modal analysis and PCA techniques is used. The solution for optimal PSS input-output selection is found to increase the observability and damping of the power system. This method is simulated on a 68 buses system with 16 machines. To compare the results with the previous methods, the system is simulated and the results of two previously-developed algorithms are compared with the proposed approach. The results show the benefit of the suggested method in reducing the required signals, which lowers the number of required PMUs while the system damping is not deteriorated.

关键词： Covariance matrices Phasor measurement units Generators Modal analysis Principal component analysis Observability Power system stability

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Fontes, Miguel Leite, Danilo Dallyson, João Cunha, António UTAD—University of Trás-os-Montes and Alto Douro Vila Real5001-801 Portugal INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal UFMA—Federal University of Maranhão São Luís Brazil

ISBN: (纸本)9783031606649

Artificial intelligence (AI) is playing a growing role today in several areas, especially in health, where understanding AI models and their predictions is extremely important for health professionals. In this context, Explainable AI (XAI) plays a crucial role in seeking to provide understandable explanations for these models. This article analyzes two different XAI approaches applied to analyzing gastric endoscopy images. The first, more conventional approach uses Grad CAM, while the second, even less explored but with great potential, is based on "similarity-based explanations". This example-based XAI technique aims to provide representative examples to support the decisions of AI models. In this study, we compare these two techniques applied to two different models: one based on the VGG16 architecture and the other based on ResNet50, designed to classify images from the KVASIR-capsule database. The results reveal that Grad-CAM provided intuitive explanations only for the VGG16 model, while the "similarity-based explanations" technique provided consistent explanations for both models. We conclude that exploring other XAI techniques can be a significant asset in improving the understanding of the various AI models. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Endoscopy

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A Machine Learning Approach for Predicting Microsatellite Instability using RNA-seq

A Machine Learning Approach for Predicting Microsatellite In...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Miguel Simões Tania Pereira Francisco Silva José Carlos Machado Hélder P. Oliveira INESC TEC - Institute for Systems and Computer Engineering Technology and Science FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP - Faculty of Science University of Porto Porto Portugal IPATIMUP - Institute of Molecular Pathology and Immunology i3S - Instituto de Investigação e Inovação em Saúde FMUP - Faculty of Medicine University of Porto Porto Portugal

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

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A successful parallel implementation of NSGA-II on GPU for the energy dispatch problem on hydroelectric power plants

A successful parallel implementation of NSGA-II on GPU for t...

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Congress on Evolutionary Computation

作者： Lucas Braga de Oliveira Carolina G. Marcelino Anolan Milanés Paulo E. M. Almeida Leonel M. Carvalho Computer Engineering Departament Belo Horizonte Brasil INESC TEC Centre for Power and Energy Systems-CPES Portugal

ISBN: (纸本)9781509006243

Nowadays, hydraulic sources are responsible for most of the Brazil's energy production. Hydroelectric power plants (HPP) operators in Brazil usually distribute equally the total power required among the generator units available in the plant. However, studies show that this configuration does not guarantee that each generator unit operate close to its optimal operation point. The energy dispatch optimization problem consists in determining which generation units need to be on or off and what is their respective power-set, so that both the overall HPP costs is minimized and the power required by the plant is met. This paper presents a GPU-based parallel implementation of NSGA-II, to solve the energy dispatch problem of a HPP complaying with the real time restrictions posed by the operation of a real HPP from the reception of the power demand to the energy dispatch. Our implementation obtains better solutions than the sequential implementation currently available.

关键词： Graphics processing units Sociology Statistics Instruction sets Generators Mathematical model Kernel

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Probabilistic stereo egomotion transform

Probabilistic stereo egomotion transform

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hugo Silva Eduardo Silva Alexandre Bernardino INESC TEC (formerly INESC Porto) ISEP/IPP - School of Engineering Polytechnic Institute of Porto Institute for Systems and Robotics IST Lisbon Portugal

In this paper we propose a novel fully probabilistic solution to the stereo egomotion estimation problem. We extend the notion of probabilistic correspondence to the stereo case which allow us to compute the whole 6D motion information in a probabilistic way. We compare the developed approach against other known state-of-the-art methods for stereo egomotion estimation, and the obtained results compare favorably both for the linear and angular velocities estimation.

关键词： Estimation Probabilistic logic Cameras Angular velocity Visualization Robot sensing systems

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Grey wolf, gravitational search and particle swarm optimizers: A comparison for PID controller design 12th

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12th Portuguese Conference on Automatic Control, CONTROLO 2016

作者： Oliveira, Paulo Moura Vrančić, Damir INESC TEC—INESC Technology and Science Department of Engineering School of Sciences and Technology Universidade de Trás-os-Montes e Alto Douro UTAD Vila Real5001-801 Portugal Department of Systems and Control Jožef Stefan Institute Jamova cesta 39 Ljubljana Slovenia

ISBN: (纸本)9783319436708

Nature and biologically inspired metaheuristics can be powerful tools to design PID controllers. The grey wolf optimization is one of these promising and interesting metaheuristics, recently introduced. In this study the grey wolf optimization algorithm is proposed to design PID controllers, and the results obtained compared with the ones obtained with gravitational search and particle swarm optimization algorithms. Simulation results obtained with these three bio-inspired metaheuristics applied to a set of benchmark linear plants are presented, considering the design objective of set-point tracking. The results are also compared with two non-iterative PID tuning techniques. © Springer International Publishing Switzerland 2017.

关键词： Three term control systems

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