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15th International Conference on the European Energy Market, EEM 2018

作者： Castanon, Rosendo Fernandez, Fco. Alberto Campos Martinez, Salvador Domenech Collado, Jose Villar Universidad Pontificia Comillas Instituto de Investigación Tecnológica IIT ICAI Madrid Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9781538614884

Environmental policies could accelerate the replacement of Internal Combustion Engine Vehicles (ICEV) by Plug-in-Electric Vehicles (PEV) in many countries. However, in countries where these policies are still not implemented (for example those with no significant PEV subsidies like Spain), technical and economic criteria can also be relevant to assess the future PEV penetration. This work develops a new long-term expansion model that computes the share of PEV and ICEV based on economic criteria, including the impact of PEV on the electricity price and generation mix. The model minimizes the power and transport system costs (investment, operation and maintenance costs, etc.) considering electricity and private transportation needs. Results provide insight on the combined evolution of the renewable generation and of the PEV and ICEV future fleets in Spain, considering environmental constraints such as those imposed by the European Commission. © 2018 IEEE.

关键词： Plug-in electric vehicles

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Author Correction: An interpretable machine learning system for colorectal cancer diagnosis from pathology slides

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NPJ precision oncology 2024年第1期8卷 83页

作者： Pedro C Neto Diana Montezuma Sara P Oliveira Domingos Oliveira João Fraga Ana Monteiro João Monteiro Liliana Ribeiro Sofia Gonçalves Stefan Reinhard Inti Zlobec Isabel M Pinto Jaime S Cardoso Institute for Systems and Computer Engineering Technology and Science (INESC TEC) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. pedro.d.carneiro@inesctec.pt. Faculty of Engineering University of Porto (FEUP) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. pedro.d.carneiro@inesctec.pt. IMP Diagnostics Praça do Bom Sucesso 61 sala 808 Porto 4150-146 Porto Portugal. diana.felizardo@***. Cancer Biology and Epigenetics Group Research Center of IPO Porto (CI-IPOP) / RISE@CI-IPOP (Health Research Network) Portuguese Oncology Institute of Porto (IPO Porto) / Porto Comprehensive Cancer Center (Porto.CCC) R. Dr. António Bernardino de Almeida 865 Porto 4200-072 Porto Portugal. diana.felizardo@***. Doctoral Programme in Medical Sciences School of Medicine and Biomedical Sciences - University of Porto (ICBAS-UP) R. Jorge de Viterbo Ferreira 228 Porto 4050-313 Porto Portugal. diana.felizardo@***. Institute for Systems and Computer Engineering Technology and Science (INESC TEC) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. s.oliveira@nki.nl. Faculty of Engineering University of Porto (FEUP) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. s.oliveira@nki.nl. IMP Diagnostics Praça do Bom Sucesso 61 sala 808 Porto 4150-146 Porto Portugal. Department of Pathology IPO-Porto R. Dr. António Bernardino de Almeida 865 Porto 4200-072 Porto Portugal. Institute of Pathology University of Bern Uni Bern Murtenstrasse 31 Bern 3008 Bern Switzerland. Institute for Systems and Computer Engineering Technology and Science (INESC TEC) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal. Faculty of Engineering University of Porto (FEUP) R. Dr. Roberto Frias Porto 4200-465 Porto Portugal.

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Load and electricity prices forecasting using Generalized Regression Neural Networks

Load and electricity prices forecasting using Generalized Re...

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International Conference on Smart Energy systems and technologies (SEST)

作者： José Pedro Paulos José Nuno Fidalgo INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal

Over time, the electricity price and energy consumption are increasingly growing their weight as prime foundations of the electrical sector, with their analysis and forecasts being targeted as key elements for the stable maintenance of electricity markets. The advent of smart grids is escalating the importance of forecasting because of the expected ubiquitous monitoring and growing complexity of a data-rich ever-changing milieu. So, the increasing data volatility will require forecasting tools able to rapidly readjust to a dynamic environment. The Generalized Regression Neural Network (GRNN) approach is a solution that has recently re-emerged, emphasizing good performance, fast run-times and ease of parameterization. The merging of this model with more conventional methods allows us to obtain more sturdy solutions with shortened training times, when compared to conventional Artificial Neural Networks (ANN). Overall, the performance of the GRNN, although slightly inferior to that of the ANN, is suitable, but linked to much lower training times. Ultimately, the GRNN would be a proper solution to blend with the latest smart grids features, which may require much reduced forecasting training times.

关键词： Forecasting Training Predictive models Biological neural networks Data models Smoothing methods Smart grids

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ROSM - Robotic Oil Spill Mitigations

ROSM - Robotic Oil Spill Mitigations

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OCEANS - Europe

作者： A. Dias A. P. Mucha T. Santos D. Pedrosa G. Amaral H. Ferreira A. Oliveira A. Martins J. Almeida C. M. Almeida S. Ramos C. Magalhães M. F. Carvalho E. Silva ISEP Instituto Superior de Engenharia do Porto Porto Portugal CIIMAR Centro Interdisciplinar de Investigacao Marinha e Ambiental Porto Portugal INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal

The overall aim of the ROSM project is the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). These solutions will be based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in-situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be used as the first line of the responder to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or during transport activities, in a fast, efficient and low-cost way. The paper will address the development of a team of autonomous vehicles able to carry, as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), the development of a multi-robot system able to provide a first line responses to oil spill incidents under unfavourable and harsh conditions with low human intervention, and then a decentralized cooperative planning with the ability to coordinate an efficient oil spill combat. Field tests have been performed in Leixões Harbour in Porto and Medas, Portugal, with a simulated oil spill and validated the decentralized coordinated task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV).

关键词： Oils Autonomous vehicles Microorganisms Trajectory Robots Surface treatment Chemicals

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Designing Collaborative and Coordinated Virtual Reality Training Integrated with Virtual and Physical Factories

Designing Collaborative and Coordinated Virtual Reality Trai...

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International Conference on Graphics and Interaction (ICGI)

作者： Emre Yildiz Miguel Melo Charles Møller Maximino Bessa Center for Industrial Production Aalborg University Aalborg Denmark Institute for Systems and Computer Engineering Technology and Science INESC TEC Porto Portugal University of Trá s-os-Montes e Alto Douro (UTAD) Vila Real Portugal

Rapidly changing customer demands, regulations and technologies drive the complexity of products, processes and production systems, as well as shorter product and factory lifecycles. In order to handle such complexity while decreasing the time-to-market, immersive virtual reality (VR) technologies are increasingly being used in industry to support product and factory lifecycle engineering processes, such as (re)design, validation and verification, learning and training. However, the design and development of multi-user VR training for complex and manual production processes remain a challenge for industry. The integration of VR training simulations with virtual and physical factories could support the handling of such obstacles in terms of efficiency and effectiveness by increasing the precision, accuracy and reliability of data used in VR simulations. In this study, we present a collaborative and coordinated VR training model and its data integration with virtual factory tools and manufacturing execution systems for a wind turbine assembly scenario. A demonstration has been performed and evaluated by industry experts. The preliminary evaluation results show that integrated collaborative VR training has significant potential for more efficient and effective training, as well as enabling new use cases for industry.

关键词： Training Solid modeling Collaboration Industries Data models Production facilities

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Effect of xanthan gum on physicochemical and textural properties of gluten-free batter and bread 10

Effect of xanthan gum on physicochemical and textural proper...

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10th International Conference on Simulation and Modelling in the Food and Bio-Industry, FOODSIM 2018

作者： Encina-Zelada, Christian R. Teixeira, José A. Monteiro, Fernando Gonzales-Barron, Ursula Cadavez, Vasco Centre of Biological Engineering School of Engineering University of Minho Portugal Department of Electrical Engineering School of Technology and Management INESC-TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal CIMO Mountain Research Centre School of Agriculture Polytechnic Institute of Bragança Portugal

ISBN: (纸本)9789492859013

The objective of this study was to evaluate the effect of xanthan gum (XG) on physicochemical, rheological and textural properties of gluten-free batter and bread. Batches of gluten-free bread used a base formulation of rice, corn and quinoa flour, and different levels of XG (1.5, 2.5 and 3.5%) and water content (WC;90, 100 and 110%) in a full factorial design. Although in interaction with water, higher XG doses tended to produce batters of lower stickiness, work of adhesion, strength-cohesiveness;which, when baked, produced loaves of lower specific volume and baking loss;and bread crumb of lower Aw, pH, hardness, springiness, firmness, mean cell area, void fraction, mean cell aspect ratio, and higher firmness, consistency, cohesiveness, adhesiveness, chewiness, resilience, cell density, cell size uniformity and mean cell compactness (p © 2018 EUROSIS-ETI.

关键词： Aspect ratio

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A smartphone accelerometer data-driven approach to recognize activities of daily life: A comparative study

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Smart Health 2023年 30卷

作者： Hussain, Faisal Goncalves, Norberto Jorge Alexandre, Daniel Coelho, Paulo Jorge Albuquerque, Carlos Quietinho Leithardt, Valderi Reis Pires, Ivan Miguel Al-Khwarizmi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore 54890 Pakistan Escola de Ciências e Tecnologia University of Trás-os-Montes e Alto Douro Quinta de Prados Vila Real 5001-801 Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) DEEC Pólo II Coimbra 3030-290 Portugal Polytechnic of Leiria Leiria 2411-901 Portugal Health Sciences Research Unit: Nursing (UICISA: E) Nursing School of Coimbra (ESEnfC) Coimbra 3004-011 Portugal Higher School of Health Polytechnic Institute of Viseu Viseu 3500-843 Portugal Child Studies Research Center (CIEC) University of Minho Braga 4710-057 Portugal Instituto Superior de Engenharia de Lisboa (ISEL) Instituto Politécnico de Lisboa Lisbon 1959-007 Portugal Instituto de Telecomunicações Covilhã 6201-001 Portugal Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda 3750-127 Portugal

Smartphones have become an indispensable part of our everyday life, influencing various aspects of our routines, from wake-up alarms to managing daily life activities. Nowadays, almost every smartphone has a built-in accelerometer sensor. Motivated by the notable increase in smartphone usage in our everyday life, in this research, we focus on harnessing the potential of smartphone accelerometers to recognize human daily life activities, aiming to leverage the usability and convenience of smartphones. We used smartphone accelerometer data from data collection to daily life activity recognition. To accomplish this, we first collected the smartphone's accelerometer data while performing five activities of daily living (ADLs) namely: moving downstairs, upstairs, running, standing, and walking, from 25 volunteers through a mobile application. After this, we extracted 15 statistical features from the smartphone's accelerometer data to efficiently classify the five referred ADLs. We then applied data pre-processing techniques, i.e., data cleaning and feature extraction. Afterward, we trained nine commonly used machine learning models to recognize five ADLs. Finally, we evaluated and compared the performance of all nine ML models to recognize each activity and analyzed the performance of these trained ML models to identify all five ADLs. The evaluated results revealed that the Adaboost (AB) classifier outperformed all other ML models with 100% area under the curve (AUC), precision, recall, accuracy, and F1-score for recognizing the five ADLs. © 2023 The Authors

关键词： Activities recognition Daily life activities Human activities detection Human activities recognition Machine learning Wearable sensors

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Using nuclear observations to improve climate research and GHG emission estimates – the NuClim project

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EPJ - Nuclear sciences & technologies 2025年 11卷 14-14页

作者： Susana Barbosa Scott Chambers Wlodzimierz Pawlak Krzysztof Fortuniak Jussi Paatero Annette Röttger Stefan Röttger Xuemeng Chen Anca Melintescu Damien Martin Dafina Kikaj Angelina Wenger Kieran Stanley Joana Barcelos Ramos Juha Hatakka Timo Anttila Hermanni Aaltonen Nuno Dias Maria Eduarda Silva João Castro Markku Kulmala INESC TEC – Institute for Systems and Computer Engineering Technology and Science Rua Dr Roberto Frias Porto Portugal ANSTO Australian Nuclear Science and Technology Organisation New Illawarra Road Lucas Heights Australia University of Lodz Department of Meteorology and Climatology Narutowicza 88 Lodz Poland Finnish Meteorological Institute PO Box 503 FI-00101 Helsinki Finland Physikalisch-Technische Bundesanstalt (PTB) Bundesallee 100 38116 Braunschweig Germany Institute for Atmospheric and Earth System Research Faculty of Science University of Helsinki Helsinki Finland Horia Hulubei National Institute for Physics and Nuclear Engineering 30 Reactorului St. POB MG-6 RO-077125 Bucharest-Magurele Romania University of Galway Ryan Institutes School of Physics University Road H91 TK33 Galway Ireland National Physical Laboratory Teddington UK School of Chemistry University of Bristol Cantocks Closed BS81TS Bristol UK UAc University of the Azores IITAA Rua Capitão d’Ávila São Pedro PT-9700-042 Angra do Heroísmo Portugal

Project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) aims to use high-quality measurements of atmospheric radon activity concentration and ambient radioactivity to advance climate science and improve radiation protection and nuclear surveillance capabilities. It is supported by new metrological capabilities developed in the EMPIR project 19ENV01 traceRadon. This work reviews the scientific objectives of project NuClim in terms of both climate science and radiological protection, and provides an overview of the NuClim field campaign and the various nuclear measurements being implemented within the scope of the project.

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A Kernel Principal Component Regressor for LPV System Identification ⁎

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IFAC-PapersOnLine 2019年第28期52卷 7-12页

作者： Paulo Lopes dos Santos T-P Azevedo Perdicoúlis INESC TEC - Institute for Systems and Computer Engineering Technology and Science and FEUP - Faculty of Engineering University of Porto Rua Dr Roberto Frias s/n 4200-464 Porto Portugal Universidade de Trás-os-Montes e Alto Douro Vila Real and Instituto de Sistemas e Robótica (ISR) Coimbra both in Portugal

This article describes a Kernel Principal Component Regressor (KPCR) to identify Auto Regressive eXogenous (ARX) Linear Parmeter Varying (LPV) models. The new method differs from the Least Squares Support Vector Machines (LS-SVM) algorithm in the regularisa-tion of the Least Squares (LS) problem, since the KPCR only keeps the principal components of the Gram matrix while LS-SVM performs the inversion of the same matrix after adding a regularisation factor. Also, in this new approach, the LS problem is formulated in the primal space but it ends up being solved in the dual space overcoming the fact that the regressors are unknown. The method is assessed and compared to the LS-SVM approach through 2 Monte Carlo (MC) experiments. Every experiment consists of 100 runs of a simulated example, and a different noise level is used in each experiment, with Signal to Noise Ratios of 20db and 10db, respectively. The obtained results are twofold, first the performance of the new method is comparable to the LS-SVM, for both noise levels, although the required calculations are much faster for the KPCR. Second, this new method reduces the dimension of the primal space and may convey a way of knowing the number of basis functions required in the Kernel. Furthermore, having a structure very similar to LS-SVM makes it possible to use this method in other types of models, e.g. the LPV state-space model identification.

关键词： System Identification LPV systems Arx Kernel Kernel Regression Least squares Principal Components Least Squares Support Vector Machines

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Turning social news curation into microtask crowdsourcing: a vision and research agenda

Turning social news curation into microtask crowdsourcing: a...

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IEEE International Conference on systems, Man and Cybernetics

作者： Daniel Schneider António Correia Ramon Chaves Ana Paula Pimentel Marcio Antelio Edson Mello Lucas Marcos Antonio de Almeida Luiz Oliveira Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Graduate School of Engineering CEFET-RJ Rio de Janeiro Brazil Polytechnic Institute IPRJ/UERJ Rio de Janeiro Brazil Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Federal Institute of Rio de Janeiro (IFRJ) Rio de Janeiro Brazil

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.

关键词： Crowdsourcing Industries Conferences Government Collaboration Turning Cybernetics

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