Percorrer por autor "Rosse, Higor"
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- Fairness in Designing Decision-Making Processes with Multi-Agent Systems and Human FactorsPublication . Vieira, Gustavo; Zeiner, Herwig; Paletta, Lucas; Fernandes, Rui; Rosse, Higor; Sabiri, Khadija; Paz, Juan F. De; Barbosa, JoséThis paper explores the integration of Human Factors (HF) into Multi-Agent Systems (MAS) to enhance fairness in decision-making processes in Industry 5.0 environments. We contribute with a human-centred perspective in the development of MAS by integrating the physiological aspects of workers in the manufacturing industry. This culminates in the measurement of human resilience. This paper presents an automotive manufacturing environment where wearable sensors and AI-driven analytics assess workers' physiological and psychological stress levels to calculate a human resilience score. This score, along with worker preferences, supports a dynamic worker allocation algorithm based on MAS that adapts to production demands. Our approach embodies the Industry 5.0 vision of technologies that support adaptive, transparent and, above all, fair human management. The system uses advanced technologies to meet business goals and employee needs and to promote a more inclusive, supportive and people-centred work environment.
- Improving the traceability of wood-based sheet leftovers using computer visionPublication . Guedes, Nuno; Capitanio, Iaggo; Rosse, Higor; Coelho, João Paulo; Barbosa, José; Pires, Nélio; Magalhães, JoãoBeing able to provide traceability over raw material leftovers is fundamental to reducing waste, achieving leaner production processes, and promoting overall efficiency. Even if this makes sense to virtually all industries, in the low-volume, custom-production woodworking businesses, is of paramount importance if efficient integration of leftovers in the production process should take place. However, this is easier to say than done. This paper describes a methodology that is being devised to improve traceability for small and medium carpentry industries. This approach takes place within a broader R&D project and deals with the development of a storage rack that resorts to computer vision and machine learning to facilitate data gathering and digitization. Preliminary results regarding the computer vision methodology are provided.
- Modelling with NGSI-LD: the VALLPASS project case studyPublication . Ribeiro, Tiago; Coelho, João Paulo; Jorge, Luísa; Sardão, Joaquim; Gonçalves, José; Rosse, HigorThe smart cities paradigm covers multiple domains which span from citizens' accessibility and mobility to general infrastructures and services. Hence, smart cities can be seen as an excellent showcase of heterogeneity, namely at the data level. For this reason, they are a perfect candidate for linked data and semantic web concept applications. This powerful combination leads to interoperability at the data level which is one of the ultimate goals of the Internet of Things (IoT). In this reference frame, NGSI-LD is an open framework for context information processing consisting of both a semantic information model and a RESTful Application Programming Interface (API). This paper proposes a methodology for creating semantic data models in the context of IoT, namely to represent and describe data associated with digital twins. The methodology is presented in a practical way, through the process of creating an NGSI-LD semantic data model for the VALLPASS project, inserted in the traffic domain, which is one of the most popular in smart cities.
- Pedestrian safety crosswalks in smart cities: the VALLPASS approachPublication . Rosse, Higor; Barbosa, José; Coelho, João PauloSmart Cities are the culmination of effective management regarding information collected in the urban environment. The digitization and modernization of various devices commonly present in cities and data collection for statistical purposes is one of the most aggregating technological developments from an organizational perspective. With the purpose of innovation and modernization, the project described in this paper seeks to update the concept of safety at crosswalks by implementing an IoT system. Two signalling posts located on opposite sides of each crosswalk identify the presence of pedestrians in the imminence of crossing and consequent activation of dedicated signalling to allow better visualization for drivers, especially in unfavourable weather conditions. The system design was based on modularity approaches to the plug-and-play philosophy, self-commissioning of the pedestrian identification system, and energy self-sufficiency from a renewable energy source. The communication between modules is performed via LoRa protocol which offers the advantages of low power consumption added to satisfactory data transfer rates and wide coverage capacity suitable for the designed application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.