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2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

作者： Tanzin Hoque, Tania Ferreira, Luis Faria, Luiz Martins, Constantino Mesquita, Rodrigo Conceicao, Luis Marreiros, Goreti Ramos, Carlos GECAD - Research Group on Engineering and Intelligent Computing for Innovation and Development Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781728113609

The project TheRoute (Tourism and Heritage Routes including Ambient Intelligence with Visitants' Profile Adaptation and Context Awareness) aim is to conduct studies, research and experimentation around the challenge of automatic generation of routes for visitors. The suggested routes fit the profile of visitors and groups of visitors, including aspects like emotion/mood/personality, and aware of the context (e.g. weather, security). The focus region is north of Portugal, developed around a location (urban or rural) or path (e.g. Portuguese Santiago Route or Douro) or around a theme (e.g. musician, writer). Points of Interest (POI) will be modeled. Also, the system is able to use different platforms for the tourist (desktop, notebook, tablet, Smartphone). TheRoute has been developed considering the notion of Ambient Intelligence (AmI) and Context Awareness. It consists of a context-aware touristic recommendation system which can suggest a series of locations to a user based on their personality, preferences, and location. This paper focuses on the proposed route generation processes, including examples of the overall resulting behavior. We discuss two proposed algorithms (an iterative branch-and-bound based process & sequential addition of the nearest feasible sub-route) for route generation, considering contextual model designing based on user's psychology, and point of interest and availability. The route generation tests that were performed included some of the most influential Porto points of interest. © 2018 IEEE.

关键词： Recommender systems

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A logic-based flexibility services tool to support a rich renewable distribution network operation

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Bruno Canizes Fábio Castro Vitor Silveira Zita Vale Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto 4200-072 Portugal LASI – Intelligent Systems Associate Laboratory 431 Porto 4200-072 Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto 4200-072 Portugal

In recent years, the low-voltage power distribution networks and overall power systems have been undergoing substantial transformations. What were once mere innovation trends are now solidifying into the new norm, driven by advancements in technology and the decreasing costs of manufacturing. Moreover, the role of distribution system operators has been elevated with the widespread deployment of smart meters, coupled with a growing emphasis on engaging citizens as pivotal contributors in shaping the future of energy markets and system operations. This evolving topic underscores the importance of devising innovative approaches to explore potential mechanisms for offering additional services within distribution networks, particularly at low-voltage levels. This paper presents a novel logic-based heuristic approach aimed at tackling voltage and congestion challenges in low-voltage distribution networks. The approach revolves around bolstering the involvement of small-scale users in demand response initiatives and enhancing the flexibility of dispatchable distributed renewable energy sources to serve as flexibility services. To showcase the efficacy of the proposed model, a case study was conducted on a 236-bus low-voltage distribution network, chosen to reflect real-world conditions. The results demonstrate a substantial improvement in voltage profiles and a noteworthy reduction in congestion levels, when comparing the scenarios pre- versus post-optimization, and flexibility versus no flexibility, validating the effectiveness of the approach.

关键词： Congestion management Demand flexibility Dispatchable renewable generation flexibility Low-voltage distribution network Voltage profile

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Detection of Human Falls via Computer Vision for Elderly Care – An I3D/RNN Approach 1

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20th International Symposium on Distributed computing and Artificial Intelligence, DCAI 2023

作者： Leal, João Moayyed, Hamed Vale, Zita Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (数字)9783031383335

ISBN: (纸本)9783031383328

As the population continues to age, the number of elderly individuals is increasing at an unprecedented rate as the active age group in developed countries continues to shrink. This demographic shift has resulted in a shortage of resources and limitations in the provision of adequate elderly care. Among the many health risks faced by the elderly, falls are a common and serious problem, which can lead to injuries, hospitalizations, and even death. Despite the prevalence of falls, traditional manual detection methods are often unreliable and inadequate. In response to this, this paper proposes an automatic fall detection system based on video cameras that uses a unique combination of an inflated 3D convolutional neural network (I3D) and a recurrent neural network (RNN). This model was evaluated using multiple fall detection datasets, including a newly developed dataset of simulated falls. The results of this study demonstrate that this hybrid model is highly effective at detecting falls and is on par with other state-of-the-art systems, boasting 94% F1 score and 96% recall values. This technology has the potential to significantly improve the quality of life and safety of the elderly population by providing timely and accurate detection of falls. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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Using simulation to evaluate a concept drift detector for condition based maintenance 48

Using simulation to evaluate a concept drift detector for co...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Lourenco, Afonso Fernandes, Marta Marreiros, Goreti Corchado, Juan Manuel Gecad-Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal Bisite-Research Group on Bioinformatics Intelligent Systems and Educational Technology Salamanca Spain

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Industry 4.0. has empowered the integration of real-time sensor readings and process knowledge for early identification of faults. In this paper, simulation was used to evaluate the process of classifying data streams as drifting towards defective behavior. Focusing on the challenges imposed by real-word, unlabeled and non-stationary data, the proposed method was assessed by conducting a case-study in the plastic extrusion domain. The knowledge gained from initial tests on generated synthetic data sets was used to isolate symptoms of a clogged screen pack from the concept drifts detected by the Adaptive Windowing algorithm. Experimental results for 40 days of running time of an extruder show that the proposed method helped in identifying a defective screen pack state upon the appearance of defects in the extrudate. © 2022 IEEE.

关键词： Industry 4.0

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Production and Maintenance Scheduling for Total Cost and Machine Longevity Optimization

Production and Maintenance Scheduling for Total Cost and Mac...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Mota, Bruno Faria, Pedro Canizes, Bruno Ramos, Carlos GECAD - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350332308

Production and maintenance scheduling for total cost-effective and machine longevity manufacturing is a key aspect in dealing with the ever-increasing energy prices, competitiveness, added maintenance costs, and environmental pressures that the manufacturing sector faces nowadays. This paper addresses these issues by proposing a novel intelligent production scheduling system for joint optimization of production and maintenance for overall cost minimization and machine longevity improvement. To achieve this, it is proposed a Genetic Algorithm (GA) for production and maintenance scheduling of flexible job shop manufacturing environments. The proposed GA takes into account volatile market energy prices, Renewable Energy Resources (RERs), surplus energy selling, maintenance activities, and constraints imposed on the production plan. A case study from the literature is used as a baseline scenario to validate the proposed scheduler. It uses real-production data and considers three unique machines with 275 tasks to be scheduled among them. Using the baseline scenario, it was possible to demonstrate the robustness of the proposed scheduler in reducing total costs, by taking advantage of the volatility of energy prices, as well as utilizing RERs to cover energy expenses or for selling excess energy. Furthermore, it highlights the ability to reduce the overload of single machines. Accordingly, it was possible to achieve cost reductions of up to 11.0% and improvements of 24.4% in machine longevity when compared to the baseline scenario. © 2023 IEEE.

关键词： Genetic algorithms

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PersonalFit - Fitness App with intelligent plan generator 9

PersonalFit - Fitness App with intelligent plan generator

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9th International C* Conference on Computer Science and Software engineering, C3S2E 2016

作者： Oliveira, Tiago Leite, Diogo Marreiros, Goreti Computer Science: Information and Knowledge Systems Institute of Engineering-Polytechnic of Porto Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering-Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781450340755

The growth of the mobile app market and the fitness market led to some interesting projects combining both areas to be developed over the last decade. In this paper we present PersonalFit an intelligent and adaptive prototype that helps gym clients to manage and adapt their training plans in a dynamic and personalized way, envisaging the achievement of better results. Three main modules compose PersonalFit: communications, Android Application and an intelligent plan generator that implements clustering techniques. Copyright is held by the owner/author(s).

关键词： Health

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Joint Optimization of Production and Maintenance for Effective Manufacturing Using a Genetic Algorithm

Joint Optimization of Production and Maintenance for Effecti...

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2023 IEEE International Conference on Environment and Electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Mota, Bruno Faria, Pedro Ramos, Carlos GECAD - Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Laboratory Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350347432

While energy prices are seen as the major drive for competitiveness in the manufacturing field, intelligent maintenance scheduling is also one of the most widespread and significant issues plaguing the manufacturing industry. Maintenance activities can cost between 15% and 70% of the price of goods sold, thus it is essential to optimize both energy and maintenance costs. The purpose of this paper is to propose a production line optimization system that focuses on reducing the total costs (i.e., energy and maintenance costs) while also taking into account dynamic pricing, Renewable Energy Resource (RER) usage, and constraints applied in the production plan. In the proposed system, tasks and maintenance activities are scheduled by using a Genetic Algorithm. To validate the proposed system, a baseline scenario that uses real production data is considered. The obtained results show that the system is able to comply with imposed maintenance hours while also minimizing costs by shifting tasks to higher RER generation and lower energy price times, while the opposite is done to maintenance activities. © 2023 IEEE.

关键词： Genetic algorithms

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Smart city: A gecad-BISITE energy management case study 15th

Smart city: A GECAD-BISITE energy management case study

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15th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2017

作者： Canizes, Bruno Pinto, Tiago Soares, João Vale, Zita Chamoso, Pablo Santos, Daniel GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto Portugal BISITE Research Centre University of Salamanca Salamanca Spain

ISBN: (纸本)9783319615776

This paper presents the demonstration of an energy resources management approach using a physical smart city model environment. Several factors from the industry, governments and society are creating the demand for smart cities. In this scope, smart grids focus on the intelligent management of energy resources in a way that the use of energy from renewable sources can be maximized, and that the final consumers can feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This work thus presents an innovative means to enable a realistic, physical, experimentation of the impacts of novel energy resource management models, without affecting consumers. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles. © Springer International Publishing AG 2018.

关键词： Smart power grids

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A Robust Optimization for Day-ahead Microgrid Dispatch Considering Uncertainties

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IFAC-PapersOnLine 2017年第1期50卷 3350-3355页

作者： Borges N. Soares J. Vale Z. GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Rua Dr. Almeida 431 Porto 4200-072 Portugal

This paper presents a Particle Swarm Optimization (PSO) methodology to solve the problem of day-ahead microgrid (MG) dispatch with high penetration of Distributed Generation (DG) and considering uncertainties. The proposed methodology has the objective to satisfy demand aiming at obtaining the maximum profit, corresponding to the difference between the income and costs of the MG. This methodology considers the uncertainties associated with the production of electricity by the photovoltaic and wind sources. This uncertainty is modeled with the use of a robust approach in PSO. A case study is presented using a 21-bus MG from a real university campus in Portugal, and the projection of distributed energy resources based on the evolution scenario for the year 2050 managed by an aggregator. © 2017

关键词： Energy Resources Management Microgrids Particle Swarm Optimization Robust Optimization

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Multi-objective Particle Swarm Optimization to Solve Energy Scheduling with Vehicle-to-Grid in Office Buildings Considering Uncertainties

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IFAC-PapersOnLine 2017年第1期50卷 3356-3361页

作者： Borges N. Soares J. Vale Z. GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Rua Dr. Almeida 431 Porto 4200-072 Portugal

This paper presents a Multi-Objective Particle Swarm Optimization (MOPSO) methodology to solve the problem of energy resource management in buildings with a penetration of Distributed Generation (DG) and Electric Vehicles (EVs). The proposed methodology consists in a multi-objective function, in which it is intended to maximize the profit and minimize CO 2 emissions. This methodology considers the uncertainties associated with the production of electricity by the photovoltaic and wind energy sources. This uncertainty is modeled with the use of a robust optimization approach in the metaheuristic. A case study is presented using a real building facility from Portugal, in order to verify the feasibility of the implemented robust MOPSO. © 2017

关键词： Electric Vehicles Energy Resources Management Multi-Objective Optimization Robust Optimization Uncertainty

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