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19th IEEE Mediterranean Eletrotechnical Conference, MELECON 2018

作者： Oliveira, Rui Bessa, Ricardo Iranda, Vladimiro M. INESC TEC Portugal Faculty of Engineering University of Porto Porto Portugal INESC TEC Institute for Systems and Computing Engineering Technology and Science Porto Portugal INESC TEC Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9781538637388

This paper presents the concept of a tapered deep neural network, subject to unsupervised training layer by layer, under a criterion of maximum entropy, to perform the estimation of breaker status in the absence of a specific sensor signal. The almost perfect prediction power of the model confirms the conjecture that the knowledge of the topology of a network is hidden in the electric measurement values in the network. A test case is presented with computing speed accelerated by using a GPU (graphics processing unit). The comparison with a previous model illustrates the superiority of the novel approach. © 2018 IEEE.

关键词： Topology

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Optimal Prosumer Scheduling in Transactive Energy Networks Based on Energy Value Signals

Optimal Prosumer Scheduling in Transactive Energy Networks B...

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International Conference on Smart Energy systems and technologies (SEST)

作者： Mohamed Lotfi Cláudio Monteiro Mohammad S. Javadi Miadreza Shafie-khah João P.S. Catalão Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal School of Technology and Innovations University of Vaasa Vaasa Finland

We present a novel fully distributed strategy for joint scheduling of consumption and trading within transactive energy networks. The aim is maximizing social welfare, which itself is redefined and adapted for peer-to-peer prosumer-based markets. In the proposed scheme, hourly energy values are calculated to coordinate the joint scheduling of consumption and trading, taking into consideration both preferences and needs of all network participants. Electricity market prices are scaled locally based on hourly energy values of each prosumer. This creates a system where energy consumption and trading are coordinated based on the value of energy use throughout the day, rather than only the market price. For each prosumer, scheduling is done by allocating load (consumption) and supply (trading) blocks, maximizing the energy value globally and locally within the network. The proposed strategy was tested using a case study of typical residential prosumers. It was shown that the proposed model could provide potential benefits for both prosumers and the grid, albeit with a user-centered, fully distributed management model which relies solely on local scheduling in transactive energy networks.

关键词： Transactive energy Mathematical model Adaptation models Processor scheduling Distributed management Resource management Decision making

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Deep Learning techniques for Future Intelligent Cross-Media Retrieval

arXiv

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arXiv 2020年

作者： ur Rehman, Sadaqat Waqas, Muhammad Tu, Shanshan Koubaa, Anis ur Rehman, Obaid Ahmad, Jawad Hanif, Muhammad Han, Zhu Department of Computer Science Namal Institute - an associated college of University of Bradford United Kingdom Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Robotics and Internet-of-Things research lab Department of Computer Science Prince Sultan University R&D Gai-tech Robotics China CISTER/INESC TEC and ISEP-IPP Porto Portugal Department of EE Sarhad University of Science and IT Pakistan Department of Computer Science Edinburgh Napier University United Kingdom Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Hausdorff coalgebras

arXiv

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arXiv 2019年

作者： Hofmann, Dirk Nora, Pedro Center for Research and Development in Mathematics and Applications Department of Mathematics University of Aveiro Portugal HASLab INESC TEC - Institute for Systems and Computer Engineering Technology and Science University of Minho Portugal

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Wearable Glove Interaction with Robots

Wearable Glove Interaction with Robots

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2019 International Conference on engineering Applications, ICEA 2019

作者： Ferreira, N.M. Fonseca Silva, Marco Santos, Victor D.N. Santos, Frederico Miguel Santos, J. Candido B. Ferreira, Joao P. Institute for Systems and Computer Eng. Technology and Science-INESC TEC Rua Dr. Roberto Frias Porto4200-465 Portugal Coimbra Polytechnic ISEC Rua Pedro Nunes-Quinta da Nora Coimbra3030-199 Portugal Institute for Systems and Computers Engineering at Coimbra INESC Coimbra Rua Silvio Lima Polo II DEEC Coimbra3030-790 Portugal Telecommunications Institute Rua do Campo Alegre Porto4169-007 Portugal Institute of Systems and Robotics ISR DEEC Rua Sílvio Lima Pólo II Coimbra3030-290 Portugal

ISBN: (纸本)9781728129624

This paper presents the exciting use of robotics to teach technologies in an approach that motivates the students through intuitive learning. MSc degree programme students are engaged in strong team-building, while developing and testing new algorithms for mobile robotics and electronics areas. This project endows students with intuitive learning, and experimental results revealed that the integration of skills allows the students to develop applications for real scenarios. In this work it is described an application to control with hand gestures, several differential driven robots. © 2019 IEEE.

关键词： Students

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TRIDENT – technology based impact assessment tool foR sustaInable, transparent Deep sEa miNing exploraTion and exploitation: A project overview

TRIDENT – Technology based impact assessment tool foR susta...

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OCEANS

作者： E. Silva D. Viegas A. Martins J. Almeida C. Almeida B. Neves P. Madureira A.J. Wheeler G. Salavasidis A. Phillips A. Schaap B. Murton A. Berry A. Weir G. Dooly E. Omerdic D. Toal P.C. Collins M. Miranda C. Petrioli C. Barrera Rodríguez D. Demoor C. Drouet G.El Serafy S.M. Jesus J. Dañobeitia V. Tegas S. Cusi L. Lopes B. Bodo L. Beguery S. VanDam J. Dumortier L. Neves V. Srivastava T.G. Dahlgren J. Thomassen Hestetun R. Eiras R. Caldeira C. Rossi J. Spearman L. Somoza F.J. González R. Bartolomé P. Bahurel INESC TEC Porto Portugal Instituto Superior Engenharia do Porto ISEP Portugal Portuguese Task Group for the Extension of the Continental Shelf EMEPC Portugal Department of Geosciences and Institute of Earth Sciences University of Évora Portugal SFI Centre for Research in Applied Geosciences School of Biological Earth & Environmental Sciences University College Cork Ireland Marine Autonomous and Robotic Systems National Oceanography Centre NOC United Kingdom Ocean Technology and Engineering NOC United Kingdom Ocean BioGeosciences NOC United Kingdom Marine Institute Ireland University of Limerick Centre for Robotics & Intelligent Systems CRIS Ireland School of Biological Science Queen’s University Belfast Northern Ireland Instituto Português do Mar e Atmosfera IPMA Portugal WSENSE Italy Head of Vehicle Base VIMAS Consortium for the Design Equipping and Exploitation of the Oceanic Platform of the Canary Islands Spain GREENOV-ITES France Unit of Coastal and Sea Systems Stichting Deltares& TU Delft The Netherlands LARSys University of Algarve Portugal EMSO-ERIC Italy Marine Technology Unit UTM-CSIC Spain La Palma Research Centre Spain Alseamar France Agora Partners Malha Technology Israel Timelex Belgium Gesi Belgium NORCE Norwegian Research Centre Norway Forum Oceano UPTEC POLO DO MAR Portugal ARDITI Portugal Center for Automation and Robotics UPM-CSIC Universidad Politécnica de Madrid Spain HRWallingford United Kingdom Geological Survey of Spain Marine Geology Resources and Extreme Environments IGME-CSIC Spain Institute for Marine Science ICM-CSIC Spain Mercator Ocean France

By creating a dependable, transparent, and cost-effective system for forecasting and ongoing environmental impact monitoring of exploration and exploitation activities in the deep sea, TRIDENT seeks to contribute to the sustainable exploitation of seabed mineral resources. In order to operate autonomously in remote locations under harsh conditions and send real-time data to authorities in charge of granting licenses and providing oversight, this system will create and integrate new technology and innovative solutions. The efficient monitoring and inspection system that will be created will abide by national and international legal frameworks. At the sea surface, mid-water, and the bottom, TRIDENT will identify all pertinent physical, chemical, geological, and biological characteristics that must be monitored. It will also look for data gaps and suggest procedures for addressing them. These are crucial actions to take in order to produce accurate indicators of excellent environmental status, statistically robust environmental baselines, and thresholds for significant impact, allowing for the standardization of methods and tools. In order to monitor environmental parameters on mining and reference areas at representative spatial and temporal scales, the project consortium will thereafter develop and test an integrated system of stationary and mobile observatory platforms outfitted with the most recent automatic sensors and samplers. The system will incorporate high-capacity data processing pipelines able to gather, transmit, process, and display monitoring data in close to real-time to facilitate prompt actions for preventing major harm to the environment. Last but not least, it will offer systemic and technological solutions for predicting probable impacts of applying the developed monitoring and mitigation techniques.

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Joint centralized and distributed electricity generation expansion in a decarbonized scenario: The Spanish case 15

Joint centralized and distributed electricity generation exp...

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

作者： Martinez, Salvador Domenech Fernandez, Fco. Alberto Campos Abbad, Michel Rivier Collado, Jose Villar ICAI Universidad Pontificia Comillas Instituto de Investigacion Tecnologica LIT Madrid Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9781538614884

Power systems of most EU countries will face substantial changes driven mainly by the EU Clean Energy Package and the expected increasing role of customers making use of demand response and distributed self-generation and self-storage resources (self-DGS). This work aims to analyze the impact of these two factors on the long term evolution of both utility scale centralized generation and storage (CGS), and self-DGS. An hourly-resolution generation expansion model based on cost minimization for CGS and expenses' minimization for self-DGS is used. Four case studies covering the period 2018-2047 for a real sized system (the Spanish one) are presented. Results highlight a strong synergy between battery costs and solar photovoltaic investments, while wind power seems to be better complemented by peaker thermal units. © 2018 IEEE.

关键词： Wind power

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Multi-Agent Task Allocation to Minimize Costs of Energy Consumption in the Presence of a Price-Based Demand Response Program

Multi-Agent Task Allocation to Minimize Costs of Energy Cons...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Ali B. Bahgat Mohamed Lotfi Omar M. Shehata Elsayed I. Morgan Joao P. S. Catalao Multi-Robot Systems (MRS) Research Group German University in Cairo (GUC) Cairo Egypt Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Faculty of Engineering Ain Shams University Cairo Egypt

As a result of Demand Response (DR) programs implementation in the industrial sector, varying electricity prices based on Time-of-Use (ToU) rates are becoming more common, replacing traditional flate-rates per unit of energy consumption. On the other hand, increased automation of industrial facilities is gaining interest due to their reliability, flexibility, and robustness. However, it is necessary to determine a suitable task schedule in order to ensure their cost-efficiency and maximize profits. In this study, a Market-Based approach is considered to solve the Multi-Agent Task Allocation (MATA) problem for a group of homogeneous agents and tasks. While most previous studies model the problem considering flate-rates for electricity consumption, the main contribution of this study is accounting for the implementation of a DR program with varying ToU rates. The effects of optimizing the task allocation process on the costs incurred are investigated and compared to the effects of random assignment. Four different case studies are analyzed considering different-sized maps and number of tasks. The results show the computational efficiency of the proposed algorithm and its ability to massively decrease the electrical charging costs.

关键词： Task analysis Resource management Automation Load management Job shop scheduling Optimization Pricing

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Spatio-temporal assessment of whole-body center of mass coordination on standard maximum vertical jump

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Gait & Posture 2022年 97卷 S208-S209页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. Faculty of Engineering- University of Porto Biomedical Engineering Program Porto Portugal INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University- CICANT - Centre for Research in Applied Communication- Culture and New Technologies MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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HEROHE Challenge: Assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization

arXiv

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arXiv 2021年

作者： Conde-Sousa, Eduardo Vale, João Feng, Ming Xu, Kele Wang, Yin Mea, Vincenzo Della Barbera, David La Montahaei, Ehsan Baghshah, Mahdieh Soleymani Turzynski, Andreas Gildenblat, Jacob Klaiman, Eldad Hong, Yiyu Aresta, Guilherme Araújo, Teresa Aguiar, Paulo Eloy, Catarina Polónia, António I3S - Instituto de Investigação e Inovação em Saúde Universidade Do Porto Portugal INEB - Instituto de Engenharia Biomédica Universidade Do Porto Portugal Department of Pathology Ipatimup Diagnostics Institute of Molecular Pathology and Immunology University of Porto Tongji University Shanghai China National University of Defense Technology Changsha China Department of Mathematics Computer Science and Physics University of Udine Udine Italy Sharif University of Technology Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Private Group Practice for Pathology Lübeck Germany DeePathology Hatidhar 5 Raanana Israel Roche Diagnostics GmbH Nonnenwald 2 Penzberg82377 Germany Department of R&D Center Arontier Co. Ltd Seoul Korea Republic of INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal FEUP - Faculty of Engineering University of Porto Porto Portugal FMUP - Faculty of Medicine University of Porto Porto Portugal INEB/i3S Rua Alfredo Allen 208 Porto4200-135 Portugal

Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify breast cancer and define appropriate therapy, which depends on the evaluation of the expression of different biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including special techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. With the goal of reducing the number of steps and human bias in diagnosis, the HEROHE Challenge was organized, as a parallel event of the 16th European Congress on Digital Pathology, aiming to automate the assessment of the HER2 status based only on hematoxylin and eosin stained tissue sample of invasive breast cancer. Methods to assess HER2 status were presented by 21 teams worldwide and the results achieved by some of the proposed methods open potential perspectives to advance the state-of-the-art. © 2021, CC BY.

关键词： Diseases

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