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20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022

作者： Vieira, Ana Conceição, Luís Faria, Luiz Novais, Paulo Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto Porto Portugal ALGORITMI Centre University of Minho Braga Portugal

ISBN: (纸本)9783031186967

To address the current burdens in the healthcare of patients with vascular diseases, the Portuguese consortium of the Inno4Health project is currently developing a remote monitoring platform that aims to support the self-management of patients with vascular diseases. With the continuous remote monitoring of patients, it is intended that the platform supports both patients and health professionals. Patients will be supported through the presentation of personalized recommendations of activities to perform and health professionals will be supported in the clinical decision making through the presentation of meaningful insights. As the platform is composed by several components, including monitoring sensors, data processing modules and user applications, theressss is a need for the standardization of the data used. This work presents the implementation of a Fast Healthcare Interoperability Resources (FHIR) specification in the remote monitoring platform to ensure the standardization of the data that will be collected and exchanged between the several components of the platform. With this work, it will be possible to easily and securely exchange data, as well as integrating new monitoring sensors and user applications in the platform. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Standardization

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Production Line Energy Cost Optimization with Renewable Energy Resource Usage in a Flexible Job Shop Configuration

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IFAC-PapersOnLine 2023年第2期56卷 7972-7977页

作者： Bruno Mota Pedro Faria Carlos Ramos GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto 4249-015 Portugal

Ever-increasing electricity prices due to inflation has become one of the main problems for manufacturing industries to deal with, mainly in a competitive market. The usage of Renewable Energy Resources (RER)s can be a great way to mitigate the energy costs of the electricity market, by not only covering the energy consumption but also turning a profit with the surplus. This paper aims to address these issues by proposing an intelligent production line scheduling system, in a flexible job shop configuration, that focuses on reducing energy costs. It considers dynamic pricing, RERs usage and surplus selling, and complex constraints applied in the production plan. To achieve these results, a Genetic Algorithm (GA) is employed. A case study from the literature, that uses real production data, is presented which does not take into account energy selling. Results show that with the proposed GA energy costs can be further reduced by 24.6% when considering RERs surplus selling.

关键词： Energy cost optimization Evolutionary algorithm Job shop scheduling

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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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Demand Response Event Participants Selection Using Classification Methods

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IFAC-PapersOnLine 2023年第2期56卷 10917-10922页

作者： Cátia Silva Pedro Faria Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto (P.PORTO) Porto Portugal

The active consumers' role in the power and energy market is changing. To deal with the volatile behavior from the Distributed Generation (DG) resources, load flexibility must be provided from the demand side. So, Demand Response (DR) events are triggered. To reduce the response uncertainty, the authors proposed a Trustworthy Rate (TR) to classify the performance of the active consumers in a community. For comparison with previous works' approaches, the innovation from the present study introduces classification methods for deciding the best approach for selecting the DR program participants (load shifting) for specific contexts and reduction goals. From the results, the Decision Tree model created had a lower accuracy performance than the Artificial Neural Network one. To evaluate the effectiveness of the proposed model, the mean absolute error (MAE) was used by the authors.

关键词： Artificial Neural Network Decision Tree Demand Response Smart Grids

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Demand Response Programs for Peak Reduction Using Consumers Aggregation

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IFAC-PapersOnLine 2023年第2期56卷 10941-10946页

作者： Modar Zheiry Pedro Faria Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group for Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto (P.PORTO) Porto Portugal

Many studies have examined the potential of the flexible loads within the power systems, taking advantage of Demand Response (DR) programs and Data Mining (DM) to optimize the system response and achieve sustainability through load management. The authors propose aggregation models to study their impact on the power system, using unsupervised DM technique which is Clustering (k-means). The scientific contribution of this paper is related to providing peak-reduction using clustering for load aggregation. A case study has been provided, consisting of three scenarios based on the load aggregation model. The results indicate the system responses to the different scenarios and illustrate the features of each load aggregation model. Furthermore, the results demonstrate how using DR programs combined with DM can effectively provide benefits to the system stability.

关键词： Energy Management Demand Response Peak Reduction Load Aggregation Clustering

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Building Energy Consumption Forecast under Different Anticipations on a Green Computation Perspective

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IFAC-PapersOnLine 2023年第2期56卷 10923-10928页

作者： Daniel Ramos Pedro Faria Luis Gomes Zita Vale Intelligent Systems Associated Laboratory (LASI) Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Polytechnic of Porto (P.PORTO) Porto Portugal

Electrical buildings are composed by smart grids technologies intended on improving the energy efficiency. Nowadays, forecasting algorithms are crucial to formulate advance decisions resulting in lower energy costs. This paper uses two forecasting algorithms known as artificial neural networks and k-nearest neighbors to obtain accurate energy predictions in a target week with the support of an annual historic with energy and auxiliary sensors devices data. Green computing is also addressed in this paper to value the environmental sustainability of computing devices. This is possible by reducing the computational effort of the GPU device dedicated on forecasting activities. Therefore, the historic and predictions of this paper are contextualized in five minutes periods and hour schedules with energy activity behaviors.

关键词： building energy efficiency forecasting green computing

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Demand Response-based Model for an Energy Community Considering Members and Electric Vehicles Participation

Demand Response-based Model for an Energy Community Consider...

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International Conference on Environment and Electrical eng.neering (EEEIC)

作者： Ruben Barreto Luis Gomes Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI Polytechnic of Porto Porto Portugal

This paper proposes a demand response-based model to manage the energy resources of an energy community, considering the data privacy of the respective community members and electric vehicles. First, through a day-ahead forecasting algorithm, the implemented model identifies possible demand response events to be launched in the energy community. Then, the community members and electric vehicles are ranked to identify which will be selected to participate in the respective demand response event. This selection is based on several metrics created to assess different types of community members' data through clustering algorithms. Furthermore, this model also considers real-time monitoring and evaluation of the demand response event. Finally, the implemented model was tested with energy community data containing 750 possible candidates to participate in the demand response event with their flexibility, where these candidates are households and several different electric vehicles. The results show that the implemented model enables a saving of 13.66 EUR while reducing the CO 2 emissions by 32.34 kg in a single demand response event.

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Risk assessment model based on centrifugal governors and artificial neural networks

Risk assessment model based on centrifugal governors and art...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Francisco Oliveira Fernando Lezama Luis Gomes João Soares Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Portugal

In today’s industry, old machines, that were not manufactured according to Industry 4.0 standards, may not be equipped with sophisticated sensors for monitoring critical values and ensuring the machine's proper health and operation. As a result, third-party sensors, such as thermometers and vibration sensors, are often integrated into these machines. Unfortunately, despite being able to obtain effective measurements, such sensors lack relativization of these values to the contextual values of each machine. This paper proposes a risk assessment model that digitally mimics a real-life centrifugal governor's operation. The system combines machine learning and data analysis and uses a context-aware algorithm that can work with single or multiple sensors to output aggregated information on a machine’s health.

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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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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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