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Percorrer por autor "Alves, Gleifer"

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  • Development of agent-based CPS for smart parking systems
    Publication . Sakurada, Lucas; Barbosa, José; Leitão, Paulo; Alves, Gleifer; Borges, André; Botelho, Pedro
    The increase volume of vehicles circulating in large cities and the limited space for parking are factors that motivate the adoption of systems capable of dealing with such problems. In this context, smart parking systems are suitable solutions to avoid the traffic congestion, the air pollution and the long search to find a free parking spot. The inclusion of emergent ICT technologies and artificial intelligence techniques, and particularly using multi-agent systems, combined under the scope of Cyber-Physical Systems (CPS), ensure flexibility, modularity, adaptability and the decentralization of intelligence through autonomous, cooperative and proactive entities. Such smart parking systems can be easily adapted to any type of vehicle to be parked and scalable in terms of the number of parking spots and drivers/vehicles. A fundamental issue in these agent-based CPS parking systems is the interconnection between the cyber and physical counterparts, i.e. between the software agents and the physical asset controllers to access the parking spots. This paper focuses on developing an agent-based CPS for a smart parking system and particularly addressing how the software agents are interconnected with the physical asset controllers using proper Internet of Things technologies. The proposed approach was implemented in two distinct parking systems, one for bicycles and another for cars, showing an efficient, modular, adaptable and scalable operation.
    2019Comunicação em conferência Acesso restrito Ver mais
  • Integrating a multi-agent smart parking system using cloud technologies
    Publication . Boos Junior, Milton; Leitão, Paulo; Sakurada, Lucas; Alves, Paulo; Alves, Gleifer; Borges, André; Antunes, Diego
    Smart parking (SP) systems are becoming a solution to address the increasing traffic in major cities, which are related to the traffic congestion, unnecessary time spent searching for parking spots, and, consequently, environmental issues. These systems intend to help drivers that are searching for available parking spaces in a given desired location. This paper presents a cloud-based solution to integrate a Multi-Agent System (MAS) for SP, which enables the modularization, scalability and robustness of such large-scale systems. The MAS abstraction is a suitable approach to represent the dynamic features of a SP, where multiple drivers arrive, request, search, and leave the parking spots. The cloud services enable to scale up the use of a MAS, being an intermediary in the communication between the MAS and the end user, providing a broad architecture that involves database, asynchronous functions activated by events and real-time message exchange. The cloud agent-based system was depl oyed in the parking of an University campus, where users driving bicycles and cars can request and schedule parking slots that are managed in a distributed manner by the MAS. The obtained results show the user friendly interaction with the system, the scalability of the system in terms of drivers and parking spots, as well as the efficient management of the parking spots by the MAS system.
    2022Comunicação em conferência Acesso restrito Ver mais
  • Real-time rule-based monitoring tool to achieve zero defect manufacturing
    Publication . Costa, Jullo; Oliveira Júnior, Alexandre de; Barbosa, José; Alves, Gleifer; Borges, Andre; Garcia, Gisela; Pires, Rui; Leitão, Paulo
    The demands of innovative production systems are shifting from mass production to the creation of smaller quantities with a focus on high quality. To achieve these evolving demands, Zero Defect Manufacturing has emerged as a key paradigm. This approach requires an innovative architectural monitoring tool where real-time data is continuously gathered and analysed to predict defects and assess their potential impacts. It also necessitates the seamless integration of diverse data sources, advanced processing algorithms, and Digital Twins to align with industrial requirements. In this paper we present a real-time, rule-based monitoring tool applied to a real-world car manufacturing use case. The tool successfully generated early alerts for quality deviations, enabling production engineers to shift from a reactive to a proactive approach by detecting potential quality issues early in the process.
    2025Comunicação em conferência Acesso aberto Ver mais