Browsing by Author "Queiroz, Jonas"
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- Agent-based approach for decentralized data analysis in industrial cyber-physical systemsPublication . Queiroz, Jonas; Leitão, Paulo; Barbosa, José; Oliveira, EugénioThe 4th industrial revolution is marked by the use of Cyber-Physical Systems (CPSs) to achieve higher levels of flexibility and adaptation in production systems that need to cope with a demanding and ever-changing market, driven by mass customization and high quality products
- Agent-based data analysis towards the dynamic adaptation of industrial automation processesPublication . Queiroz, Jonas; Leitão, PauloIndustrial complex systems demand the dynamic adaptation and optimization of their operation to cope with operational and business changes. In order to address such requirements and challenges, cyber-physical systems promotes the development of intelligent production units and products. The realization of such concepts requires, amongst others, advanced data analysis approaches, capable to take advantage of increased availability of data, in order to overcome the inherent dynamics of industrial environments, by providing more modular, adaptable and responsiveness systems. In this context, this work introduces an agent-based data analysis approach to support the supervisory and control levels of industrial processes. It proposes to endow agents with data analysis capabilities and cooperation strategies, enabling them to perform distributed data analysis and dynamically improve their analysis capabilities, based on the aggregation of shared knowledge. Some experiments have been performed in the context of an electric micro grid to validate this approach.
- Agent-based distributed data analysis in industrial cyber-physical systemsPublication . Queiroz, Jonas; Leitão, Paulo; Barbosa, José; Oliveira, Eugenio; Garcia, GiselaCyber-Physical System (CPS) is a key concept in the fourth industrial revolution, acting as the backbone infrastructure to develop Industry 4.0 compliant solutions based on decentralized networks of cyber-physical entities. In such systems, the distribution of intelligence and data analytics capabilities by different computational layers assumes a crucial relevance to support monitoring, diagnosis, prediction, and optimization tasks. Multiagent Systems (MAS) is a suitable approach to support this distribution through vertical and horizontal dimensions. In this context, this article introduces a modular agent-based architecture to balance the distribution of data analysis capabilities in industrial CPS. The proposed MAS solution was implemented in an industrial automotive factory plant, which applicability, novelty, and benefits were recognized, e.g., through its inclusion in the Innovation Radar of the Great EU-funded Innovations due to its contribution for the implementation of Zero Defects Manufacturing strategies in multistage production systems.
- An agent-based industrial cyber-physical system deployed in an automobile multi-stage production systemPublication . Queiroz, Jonas; Leitão, Paulo; Barbosa, José; Oliveira, Eugénio; Garcia, GiselaIndustrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based on Multi-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers. © 2020, Springer Nature Switzerland AG.
- Collective intelligence in self-organized industrial cyber-physical systemsPublication . Sakurada, Lucas; Leitão, Paulo; Queiroz, JonasCyber-physical systems (CPS) play an important role in the implementation of new Industry 4.0 solutions, acting as the backbone infrastructure to host distributed intelligence capabilities and promote the collective intelligence that emerges from the interactions among individuals. This collective intelligence concept provides an alternative way to design complex systems with several benefits, such as modularity, flexibility, robustness, and reconfigurability to condition changes, but it also presents several challenges to be managed (e.g., non-linearity, self-organization, and myopia). With this in mind, this paper discusses the factors that characterize collective intelligence, particularly that associated with industrial CPS, analyzing the enabling concepts, technologies, and application sectors, and providing an illustrative example of its application in an automotive assembly line. The main contribution of the paper focuses on a comprehensive review and analysis of the main aspects, challenges, and research opportunities to be considered for implementing collective intelligence in industrial CPS. The identified challenges are clustered according to five different categories, namely decentralization, emergency, intelligent machines and products, infrastructures and methods, and human integration and ethics. Although the research indicates some potential benefits of using collective intelligence to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, such approaches are still in the early stages, with perspectives to increase in the coming years. Based on that, they need to be further developed considering some main aspects, for example, related to balancing the distribution of intelligence by the vertical and horizontal dimensions and controlling the nervousness in self-organized systems.
- Data driven multi-agent m-health system to characterize the daily activities of elderly peoplePublication . Mendes, Solange; Queiroz, Jonas; Leitão, PauloWith the continuous growing of aging population, the society is facing new challenges, namely the implementation of healthcare services for older people, as well as the promotion of the active aging and well-being. These challenges imply the optimization of these services through biomedical, physical, psychological and socio-environmental interventions. ICT technologies can support the implementation of these healthcare services, e.g., the use of wearable devices to collect the physiological data, cloud technology to store big amounts of data and advanced data analytics algorithms to extract valuable conclusions and actionable knowledge. This work proposes a multi-agent system driven data analysis approach to characterize the daily physiological conditions of a group of elderly people institutionalized in a nursing home, supporting the healthcare professionals to monitor their behavior and promote an active aging. The individual' data were collected by a set of Fitbit Charge HR wristbands and analyzed by clustering algorithms, running in the distributed autonomous agents, allowing to identify and characterize the individuals' daily habits and physiological conditions
- Development of a smart electric motor testbed for Internet of things and big data technologiesPublication . Queiroz, Jonas; Barbosa, José; Dias, José João Cruz; Leitão, Paulo; Oliveira, EugénioSmart devices and Internet of Things (IoT) technologies are becoming each day more common. At the same time, besides the exponentially increasing demand to analyze the produced data, there is an evolving trend to perform the data analysis closer to the data sources, particularly at the Fog and Edge levels. In this sense, the development of testbeds that can, e.g., simulate smart devices in IoT environments, are important to explore and develop the technologies to enable the complete realization of such IoT concepts. This paper describes the digitization of an electric motor, through the incorporation of sensing and an analytical computational environment, towards the development of a testbed for IoT and Big Data technologies. The smart electric motor testbed provides real-time data streams, enabling a continuous monitoring of its operation along all the device life-cycle through advanced data analytics. Furthermore, the paper discusses how specific data analytics features fit the different IoT layers, while preliminary experiments demonstrate the testbed potentials.
- Distributing intelligence among cloud, fog and edge in industrial cyber-physical systemsPublication . Queiroz, Jonas; Leitão, Paulo; Barbosa, José; Oliveira, EugénioThe 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive.
- Ensino de robótica móvel através da realização de um hackathon em ROSPublication . Junior, Alexandre de Oliveira; Assumpção, Heitor Dutra; Queiroz, Jonas; Piadi, Luis; Leitão, Paulo; Pérez-Pons, María-Eugenia; Prieto, Javier; Parra-Domínguez, JavierA robótica móvel é um campo que vem crescendo constantemente nos últimos anos, possuindo aplicações em diversas áreas como a indústria, agricultura, medicina, entre outros. Sendo o ROS uma das principais plataformas de desenvolvimento de softwares desta área e visando a disseminação do conhecimento destas tecnologias, foi proposto um hackathon para fomentar a aprendizagem em ROS e robótica móvel. A decisão por essa abordagem foi devido à dificuldade de novos usuários no primeiro contato com a plataforma, por possuir conceitos complexos e ausência de front-end, o primeiro contato com ROS é difícil, levando os novos usuários a desistência. Com isso, o ensino foi feito por meio deste evento que é uma atividade imersiva e intensa para a solução de problemas na instância tecnólogica, principalmente em programação. De forma, que as primeiras barreiras fossem quebradas, o processo de aprendizagem facilitado e estimulasse os participantes a seguirem essa linha de pesquisa. O público alvo são estudantes de engenharia eletrotécnica, computação, mecânica e entusiastas em robótica. Neste paper será apresentada a estruturação do Hackathon, que compreende um nivelamento dos estudantes, para capacitá-los a programar as funcionalidades essenciais de um robô móvel e, por fim, o desafio robótico proposto. As etapas iniciais foram elaboradas com dificuldades que aumentaram progressivamente, iniciando com tarefas de controlo básico de um robô simulado até o desafio de desenvolver uma navegação autónoma em um ambiente de simulação buscando uma fonte de gás cuja posição é desconhecida.
- A fuzzy logic approach for self-managing energy efficiency in IoT nodesPublication . Melo, Victória; Funchal, Gustavo Silva; Queiroz, Jonas; Leitão, PauloThe collection and analysis of data assume a crucial importance in the digital transformation era. Internet of Things (IoT) technologies allow to gather data from heterogeneous sources and make them available for data-driven systems aiming, e.g., monitoring, diagnosis, prediction and optimization. Several applications require that these IoT nodes be located remotely without connection to the electrical grid and being powered by batteries or renewable sources, thus requiring a more efficient management of the energy consumption in their operation. This paper aims to study and develop intelligent IoT nodes that embed Artificial Intelligence techniques to optimize their operation in terms of energy consumption when operating in constrained environments and powered by energy harvesting systems. For this purpose, a Fuzzy Logic system is proposed to determine the optimal operation strategy, considering the node’s current resource demands, the current battery condition and the power charge expectation. The proposed approach was implemented in IoT nodes measuring environmental parameters and placed in a university campus with Wi-Fi coverage. The achieved results show the advantage of adjusting the operation mode taking into consideration the battery level and the weather forecasts to increase the energy efficiency without compromising the IoT nodes’ functionalities and QoS.
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