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Technologies Applied to Electronics Teaching (TAEE)

作者： Ramiro S. Barbosa GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering - Polytechnic of Porto (ISEP/IPP) Porto Portugal

ISBN: (数字)9781728167329

ISBN: (纸本)9781728167336

Educational hardware kits are a good resource for reinforcing the concepts taught in the theoretical classes. This paper shares the experience of introducing an educational lab kit, called electronic thermal regulation system (SERT), in the curricular unit of Digital Control of BSc. Degree on Electrical and Computer eng.neering of Institute of eng.neering of Porto. The SERT platform is distributed to students to reinforce concepts in modeling and control. The kit proved to be useful in teaching the laboratorial part of the curricular unit, allowing illustrating and consolidating the theoretical concepts, making the classes more motivating, participatory and interesting for students.

关键词： Temperature measurement Temperature control Temperature sensors Hysteresis Matlab Mathematical model Education

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An Ontology to Promote Interoperability between Cyber-physical Security Systems in Critical Infrastructures

An Ontology to Promote Interoperability between Cyber-physic...

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International Conference on Computer and Communications (ICCC)

作者： Alda Canito Katia Aleid Isabel Praça Juan Corchado Goreti Marreiros University of Salamanca Salamanca Spain GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Critical infrastructures, like airports and hospitals, provide essential services and are considered huge investments in any country. Ensuring the security of these critical infrastructures is a necessity in every possible aspect. These infrastructures, however, currently make use of different technological solutions with distinct security levels, features and knowledge representation formats which often do not communicate directly with each other, leaving the task of processing the global view of the institution's security to the end-users. In order to have a complete view of the current cybersecurity status of a given system, interoperability between these tools must be addressed. Different ontologies have been proposed to deal with subdomains of cybersecurity. Similarly, these fail to provide a full view of cyber and physical security that would be necessary to achieve a global security approach. In this paper, we explore the current status and activities of cybersecurity as a basis for the development process, propose an integrated ontology for combined cyber and physical security. After that, we suggest several evaluation methods to be applied to the resulting ontology in order to check its usefulness in achieving the interoperability goal. The focus will be on the different systems needed for an integrated security of airports.

关键词： Ontologies Tools Airports Critical infrastructure Computer security Task analysis Interoperability

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Comparison of PV Power Generation Forecasting in a Residential Building using ANN and DNN

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IFAC-PapersOnLine 2022年第9期55卷 291-296页

作者： Inês Tavares Ricardo Manfredini José Almeida João Soares Sérgio Ramos Zahra Foroozandeh Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development School of Engineering Polytechnic of Porto Porto Portugal IFRS-Campus Farroupilha Rio Grande do Sul Brasil

Due to the fast growth of energy consumption in buildings, it is crucial to ensure sustainability demands through the use of renewable energies. The solar energies have been standing out and, as a result, the forecast of photovoltaic (PV) production has received broad attention. However, the intermittent nature of the generated power brings some uncertainty. This paper presents two PV generation forecasting methodologies based on a multi-layer feed-forward Artificial Neural Networks (ANN) and a Deep Neural Networks (DNN) combined with a Convolution Neural Network layer and a Recurrent Neural Network layer. Both techniques were implemented based on a data set of a PV production from a panel installed at a residential building. The main objective of this paper is to analyze and compare the forecasting results precision of both techniques. The accuracy of both models was evaluated through the calculated errors. The comparative analysis between the two networks demonstrated that the ANN technique is capable of predicting the PV generation with low forecasting errors.

关键词： Machine learning PV power generation forecast Artificial Neural Networks Deep Neural Networks

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Battery Reuse and Lithium Exploration: a Business Model Analysis for the Portuguese Scenario

Battery Reuse and Lithium Exploration: a Business Model Anal...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Débora De São José Pedro Faria Zita Vale Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development - GECAD Instituto Superior de Engenharia do Porto - ISEP Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This research explores the emerging market of second-life batteries and their potential, focusing on business models, challeng.s, and opportunities. The study was performed through a comprehensive literature review of existing business models for battery reuse, as well as an analysis of the challeng.s, uncertainties, and opportunities in the battery reuse market. The main findings of this research include the proposal of a hybrid business model for battery reuse, which combines elements of ownership, service-based models, and partnerships between various stakeholders in the battery reuse ecosystem. The study emphasizes the potential implications of the proposed business model, including its contribution to the circular economy, reduction of waste and reliance on raw materials, and support for the transition to a low-carbon economy. The research also highlights the need for comprehensive policies and regulations to address battery collection, testing, refurbishment, and financial incentives for businesses and individuals participating in battery reuse program.

关键词： Analytical models Uncertainty Biological system modeling Regulation Raw materials Batteries Stakeholders

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An Agent-based IoT System for intelligent Energy Monitoring in Buildings

An Agent-based IoT System for Intelligent Energy Monitoring ...

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IEEE Vehicular Technology Conference

作者： Luis Gomes Filipe Sousa Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering - Polytechnic of Porto (ISEP/IPP) Porto Portugal

The new power system paradigm demands a more active end-consumer participation in smart grids environment. To achieve this participation, new demand side management solutions should be developed and analyzed. Moreover, the massive dissemination of internet of things devices inside buildings are a reality in nowadays. This paper proposes a multi-agent system for microgrid representation that integrates internet of thing devices to boost the energy management in today's buildings. The paper will present the proposed multi-agent system as well as an environmental awareness smart plug. The case study in this paper will present the data acquisition from a real building using a combination of market internet of things smart plugs, the proposed environmental awareness smart plug and a load emulator.

关键词： Plugs Buildings Energy management Protocols Microgrids intelligent sensors

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S3PHER: Secure and Searchable System for Patient-driven HEalth data shaRing

arXiv

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arXiv 2024年

作者： Costa, Ivan Amorim, Ivone Maia, Eva Barbosa, Pedro Praça, Isabel GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal IPP - Instituto Politécnico do Porto Porto Portugal

Healthcare data contains some of the most sensitive information about an individual, yet sharing this data with healthcare practitioners can significantly enhance patient care and support research efforts. However, current systems for sharing health data between patients and caregivers do not fully address the critical security requirements of privacy, confidentiality, and consent management. Furthermore, compliance with regulatory laws such as GDPR and HIPAA is often deficient, largely because patients typically are asked to provide general consent for healthcare entities to access their data. Recognizing the limitations of existing systems, we present S3PHER, a novel approach to sharing health data that provides patients with control over who accesses their data, what data is accessed, and when. Our system ensures end-to-end privacy by integrating a Proxy Re-Encryption Scheme with a Searchable Encryption Scheme, utilizing Homomorphic Encryption to enable healthcare practitioners to privately search and access patients’ documents. The practicality and benefits of S3PHER are further validated through end-to-end deployment and use-case analyses, with tests on real datasets demonstrating promising execution times. © 2024, CC BY.

关键词： Information management

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A New Hybrid-Adaptive Differential Evolution for a Smart Grid Application Under Uncertainty

A New Hybrid-Adaptive Differential Evolution for a Smart Gri...

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Congress on Evolutionary Computation

作者： Fernando Lezama Joao Soares Ricardo Faia Tiago Pinto Zita Vale GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation Development School of Engineering Porto Portugal BISITE Research Center University of Salamanca Salamanca Spain

Power systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four state-of-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new “DE/target - to - perturbed_best/1” strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.

关键词： Uncertainty Smart grids Mathematical model Complexity theory Optimization Energy resources Weather forecasting

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Secure, Searchable, and Consent-Driven Healthcare Data Sharing System

Secure, Searchable, and Consent-Driven Healthcare Data Shari...

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IEEE International Symposium on Network computing and Applications

作者： Ivan Costa Ivone Amorim Eva Maia Pedro Barbosa Isabel Praça GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal IPP - Instituto Politécnico do Porto Porto Portugal

ISBN: (数字)9798331510183

ISBN: (纸本)9798331510190

Healthcare data contains some of the most sensitive information about an individual, yet sharing this data with healthcare practitioners can significantly enhance patient care and support research efforts. However, current systems for sharing health data between patients and caregivers do not fully address the critical security requirements of privacy, confidentiality, and consent management. Furthermore, compliance with regulatory laws such as GDPR and HIPAA is often deficient, largely because patients typically are asked to provide general consent for healthcare entities to access their data. Recognizing the limitations of existing systems, we present a novel approach to sharing health data that provides patients with control over who accesses their data, what data is accessed, and when. Our system ensures end-to-end privacy by integrating a Proxy ReEncryption Scheme with a Searchable Encryption Scheme, utilizing Homomorphic Encryption to enable healthcare practitioners to easily and securely search and access patients’ documents. A time performance analysis is also presented, which allowed us to observe that the number of keywords has a much greater impact on the running time of the different processes than the number of files.

关键词： Data privacy Privacy Medical services Performance analysis Security Homomorphic encryption

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Generation of intelligent reports for ubiquitous group decision support systems

Generation of intelligent reports for ubiquitous group decis...

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International Global Information Infrastructure Symposium (GIIS)

作者： Luís Conceição João Carneiro Diogo Martinho Goreti Marreiros Paulo Novais GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal Algoritmi Centre University of Minho Guimarães Portugal

Supporting group decision-making is a complex process, especially when decision-makers have no opportunity to gather at the same place and at the same time. Besides that, finding solutions may be difficult in case agents representing decision-makers are not able to understand the process and support them accordingly. In this work we present some topics of information that can be reported to decision-makers to improve their perception about the negotiation process. We classified those topics according to two dimensions and we defined an algorithm to select which information will be built for each report.

关键词： Decision making Context Decision support systems Proposals Organizations Data mining Complexity theory

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Detailed analysis of a deep learning energy forecast model considering different input units and dimensions

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IFAC-PapersOnLine 2024年第13期58卷 308-313页

作者： Bruno Ribeiro Rafael Silva Luis Gomes Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 4249-015 Porto Portugal

The use of smart buildings technologies in residential houses, commonly referred to as smart homes, can benefit the operation and management of the buildings as well and its users. The application of energy related models in smart buildings can improve building sustainability and efficiency, but, usually, requires the existence of energy forecasting models capable of providing accurate and precise information. In this paper, it was studied how different input configurations of an energy forecasting model impact its performance by considering multiple error metrics. The conducted study allowed a detailed analysis of each input configuration to see which one is better suitable to be integrated with the smart grid management platform developed by the authors, which is also presented in this paper. The results demonstrate that the inputs’ units and dimensions have impact on the forecast error, indicating that accumulative Wh values are potentially better for the energy forecast model considered.

关键词： Energy forecasting error analysis forecast evaluation IoT-based smart building smart buildings

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