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Neural Networks for Condition Monitoring of Wind Turbines Gearbox

Journal of Energy and Power engineering 2012年第4期6卷 638-644页

作者： Roque Filipe Mesquita Brandao Jose Antonio Beleza Carvalho Fernando Pires Maciel Barbosa Electrical Engineering Department School of Engineering Porto Polytechnic Institute Porto 4200-072 Portugal Institute for Systems and Computer Engineering of Porto （INESC TEC）＆ Engineering Faculty Porto University Porto 4200-465 Portugal

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

关键词： Condition monitoring maintenance neural networks wind energy.

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GEOPHYSICAL EXPLORATION OF ANTIMONY MINERALIZATIONS: ALTO DO SOBRIDO AND RIBEIRO DA SERRA MINES (PORTUGAL) 22

GEOPHYSICAL EXPLORATION OF ANTIMONY MINERALIZATIONS: ALTO DO...

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22nd International Multidisciplinary Scientific Geoconference: Science and technologies in Geology, Exploration and Mining, SGEM 2022

作者： Carvalho, Ana Ribeiro, Ricardo Moura, Rui Lima, Alexandre Department of Geosciences Environment and Spatial Planning Faculty of Sciences University of Porto Portugal Institute of Earth Sciences Pole of University of Porto Portugal INESC TEC Centre for Robotics and Autonomous Systems Superior Institute of Engineering of Porto Portugal

ISBN: (纸本)9786197603484

The Alto do Sobrido and Ribeiro da Serra Mines, are two antimony-gold mines located in Gondomar, Portugal, and are part of the Dúrico-Beirão Mining District (DBMD). In this work, we use four geophysical methods, such as gravity, radiometry, magnetic and electromagnetic, to improve the knowledge about these mineralizations and the lithologies and structures associated with them. These four methods allowed us to study these mineralizations through different physical properties. The gravimetric method allowed us to add more data to the hypothesis that the gold-antimony mineralizations of the DBMD could be genetically associated with a non-outcropping granite intrusion, which functioned either as a heat engine that promoted the circulation of fluids and/or as a source of fluids and metals itself. A 2D model was proposed, created with both modelling and inversion, to show that the presence of this granite could indeed fit the response of the negative anomalies found in the residual anomaly. The radiometric method was used mainly as a mapping tool, where we were able to distinguish between some of the lithologies of the area, like the Lower Ordovician quartzites and the metasediments of the Middle Ordovician to the Carboniferous, but also to observe different element concentrations within the Pre-Ordovician Schist-Greywacke Complex (SGC). These variations within the SGC lithology don’t have a clear explanation as to why they occur. Still, we believe that they could occur due to the influence of the abovementioned non-outcropping granite. With the very low frequency (VLF) electromagnetic method, it was possible to locate areas of lower resistivities that correspond to the location of several known mineralized veins, as well as the probable location for a new vein. It was also possible to observe some weathered rocks near faults and fractures associated with the Dúrico-Beirão Shear Zone (DBSZ). With the magnetic method, it is possible to contribute to areas with the prevale

关键词： Radiometry

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Evaluation of the Economic Feasibility of Price Arbitrage Operations in the Iberian Electricity Market

Evaluation of the Economic Feasibility of Price Arbitrage Op...

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International Conference on European Electricity Market, EEM

作者： Francisco Lobo João Tomé Saraiva FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

This paper describes a study developed to analyse the interest in investing in Li-ion batteries to perform price arbitrage in the power system of Portugal. In this context, it was developed a methodology to identify the most suitable hours for charging and discharging the energy, and the new market prices were estimated for these hours. It was concluded that at current investment costs in this storage technology, and current market prices, this investment would not be viable in the lifetime of the batteries despite the recent rise of electricity market prices and also the larger price spread. This spread is now larger given the depression of prices at sunny hours that is getting typical in the Iberian electricity market.

关键词： Lithium-ion batteries Visualization Costs Europe Aging Electricity supply industry Depression

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Novel RSSI-based Localization System for Cattle and Animal Tracking

Novel RSSI-based Localization System for Cattle and Animal T...

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2019 International Conference on engineering Applications, ICEA 2019

作者： Santos, Victor D.N. Neves, Bernardino Ferreira, N.M. Fonseca Coimbra Polytechnic ISEC Rua Pedro Nunes-Quinta da Nora Coimbra3030-199 Portugal Institute for Systems and Computers Engineering at Coimbra INESC Coimbra Rua Sílvio Lima Pólo II DEEC Coimbra3030-790 Portugal Technology and Science-INESC TEC Institute for Systems and Computer Eng. Rua Dr. Roberto Frias Porto4200-465 Portugal

ISBN: (纸本)9781728129624

This paper presents a novel radio localization system based on radio signal strength measurements to estimate the location of animals on a farm. The system uses low-cost mobile devices based on a microcontroller, a radio transceiver and an antenna. The developed solution works on the 433 MHz band and comprises innovative protocols that use the radio wake-up concept in order to minimize the mobile unit energy consumption, promoting that way its autonomy. A software application was developed to track the animals on a real-time basis using the Internet of Things paradigm. © 2019 IEEE.

关键词： Animals

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WalkTogether – Mobile Application to Enhance Blind People Accessibility: System Design

WalkTogether – Mobile Application to Enhance Blind People A...

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International Conference on Computational Science and its Applications (ICCSA)

作者： Tiago Castro J.C. Silva Marisa Pinheiro School of Technology Polytechnic Institute of Cávado and Ave Barcelos Portugal INESC TEC Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9781665458443

This paper aims to describe an approach to help blind people getting more accessibility when performing common tasks such as using public transport or going shopping. The main objective is to aid blind people when they are disoriented and without having minimally viable alternatives to ask for help in useful time. The design of a mobile application will be described focusing the advantages of using an app by blind people to request face-to-face help from a volunteer. Using a telephone contact, audio, video and sharing coordinates, volunteer in the same geographic area can be automatically asked to help blind people.

关键词： Scientific computing Focusing Blindness Telephone sets Mobile applications Task analysis Audio-visual systems

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Data Augmented Rule-based Expert System to Control a Hybrid Storage System

Data Augmented Rule-based Expert System to Control a Hybrid ...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Ricardo J. Bessa Francisco Lobo Francisco Fernandes Bernardo Silva Centre for Power and Energy Systems (CPES) INESC TEC Porto Portugal CPES & Department of Electrical and Computer Engineering INESC TEC & Faculty of Engineering University of Porto Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Li-ion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.

关键词： Training Renewable energy sources Power system measurements Reinforcement learning Microgrids Redox Power system stability

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Robust Mobile Robot Localization Based on Security Laser Scanner

Robust Mobile Robot Localization Based on Security Laser Sca...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Héber Sobreira A. Paulo Moreira Paulo Gomes Costa José Lima INESC TEC (formerly INESC PORTO) - Robotics and Intelligent Systems Faculty of Engineering of University Porto Portugal Portugal INESC TEC (formerly INESC PORTO) - Robotics and Intelligent Systems Polytechnic Institute of Bragança Bragança Portugal

This paper addresses the development of a new localization system based on a security laser presented on most AGVs for safety reasons. An enhanced artificial beacons detection algorithm is applied with a combination of a Kalman filter and an outliers rejection method in order to increase the robustness and precision of the system. This new robust approach allows to implement such system in current AGVs. Real results in industrial environment validate the proposed methodology.

关键词： Lasers Measurement by laser beam Security Filtering algorithms Kalman filters Mobile robots

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Chest Radiography Few-Shot Image Synthesis for Automated Pathology Screening Applications

Chest Radiography Few-Shot Image Synthesis for Automated Pat...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Martim Quintas E Sousa João Pedrosa Joana Rocha Sofia Cardoso Pereira Ana Maria Mendonça Aurélio Campilho Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal

ISBN: (纸本)9781665429825

Chest radiography is one of the most ubiquitous imaging modalities, playing an essential role in screening, diagnosis and disease management. However, chest radiography interpretation is a time-consuming and complex task, requiring the availability of experienced radiologists. As such, automated diagnosis systems for pathology detection have been proposed aiming to reduce the burden on radiologists and reduce variability in image interpretation. While promising results have been obtained, particularly since the advent of deep learning, there are significant limitations in the developed solutions, namely the lack of representative data for less frequent pathologies and the learning of biases from the training data, such as patient position, medical devices and other markers as proxies for certain pathologies. The lack of explainability is also a challenge for the adoption of these solutions in clinical *** adversarial networks could play a significant role as a solution for these challenges as they allow to artificially create new realistic images. This way, new synthetic chest radiography images could be used to increase the prevalence of less represented pathology classes and decrease model biases as well as improving the explainability of automatic decisions by generating samples that serve as examples or counter-examples to the image being analysed, ensuring patient *** this study, a few-shot generative adversarial network is used to generate synthetic chest radiography images. A minimum Fréchet Inception Distance score of 17.83 was obtained, allowing to generate convincing synthetic images. Perceptual validation was then performed by asking multiple readers to classify a mixed set of synthetic and real images. An average accuracy of 83.5% was obtained but a strong dependency on reader experience level was observed. While synthetic images showed structural irregularities, the overall image sharpness was a major factor in the decision of rea

关键词： Training Pathology Privacy Medical devices Image synthesis Training data Detectors

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Deep Learning Strategies For Rare Drug Mechanism of Action Prediction

Deep Learning Strategies For Rare Drug Mechanism of Action P...

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International Joint Conference on Neural Networks (IJCNN)

作者： Gonçalo Ferreira Marco Teixeira Raquel Belo Wilson Silva Jaime S. Cardoso FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

The application of machine learning algorithms to predict the mechanism of action (MoA) of drugs can be highly valuable and enable the discovery of new uses for known molecules. The developed methods are usually evaluated with small subsets of MoAs with large support, leading to deceptively good generalization. However, these datasets may not accurately represent a practical use, due to the limited number of target MoAs. Accurate predictions for these rare drugs are important for drug discovery and should be a point of focus. In this work, we explore different training strategies to improve the performance of a well established deep learning model for rare drug MoA prediction. We explored transfer learning by first learning a model for common MoAs, and then using it to initialize the learning of another model for rarer MoAs. We also investigated the use of a cascaded methodology, in which results from an initial model are used as additional inputs to the model for rare MoAs. Finally, we proposed and tested an extension of Mixup data augmentation for multilabel classification. The baseline model showed an AUC of 73.2% for common MoAs and 62.4% for rarer classes. From the investigated methods, Mixup alone failed to improve the performance of a baseline classifier. Nonetheless, the other proposed methods outperformed the baseline for rare classes. Transfer Learning was preferred in predicting classes with less than 10 training samples, while the cascaded classifiers (with Mixup) showed better predictions for MoAs with more than 10 samples. However, the performance for rarer MoAs still lags behind the performance for frequent MoAs and is not sufficient for the reliable prediction of rare MoAs.

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A systematic review of artificial intelligence applications in education: Emerging trends and challenges

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Decision Analytics Journal 2025年 15卷

作者： Matos, Tomás Santos, Walter Zdravevski, Eftim Coelho, Paulo Jorge Pires, Ivan Miguel Madeira, Filipe Escola Superior de Gestão e Tecnologia Polytechnic University of Santarém 2001-904 Portugal Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal Faculty of Computer Science and Engineering University Ss Cyril and Methodius Skopje 1000 North Macedonia School of Technology and Management Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal Digital Literacy and Social Inclusion Research Hub from CIAC Polytechnic University of Santarém 2001-904 Portugal

The academic world is becoming increasingly interested in the applications of Artificial Intelligence technology in education. A systematic review examines AI applications in education, focusing on their effectiveness, challenges, and implications. A comprehensive analysis of studies published between 2011 and 2024 encompassed 45 research articles from major databases, such as PubMed Central, IEEE Xplore, Elsevier, Springer, MDPI, ACM, and PMC. The findings highlight the predominant use of generative AI tools like ChatGPT (30%), followed by other advanced technologies, such as GPT-4, machine learning, and virtual reality. Research across global regions, particularly in Canada (18%), the United States (12%), and China (8%), highlights the multifaceted applications of AI in enhancing personalized learning, fostering critical thinking, and supporting professional education. Tools such as ChatGPT have demonstrated strong performance in theoretical knowledge delivery and medical education, while augmented and virtual reality excels in practical skill development. Despite these advances, challenges such as data privacy concerns, algorithmic bias, and the need for specialized educator training remain critical. © 2025 The Author(s)

关键词： Adaptive learning Artificial intelligence ChatGPT Educational technology Machine learning Systematic review

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