2024
Autores
Schneider, S; Parada, E; Sengl, D; Baptista, J; Oliveira, PM;
Publicação
FRONTIERS IN SUSTAINABLE CITIES
Abstract
Despite the ubiquitous term climate neutral cities, there is a distinct lack of quantifiable and meaningful municipal decarbonization goals in terms of the targeted energy balance and composition that collectively connect to national scenarios. In this paper we present a simple but useful allocation approach to derive municipal targets for energy demand reduction and renewable expansion based on national energy transition strategies in combination with local potential estimators. The allocation uses local and regional potential estimates for demand reduction and the expansion of renewables and differentiates resulting municipal needs of action accordingly. The resulting targets are visualized and opened as a decision support system (DSS) on a web-platform to facilitate the discussion on effort sharing and potential realization in the decarbonization of society. With the proposed framework, different national scenarios, and their implications for municipal needs for action can be compared and their implications made explicit.
2024
Autores
Teixeira, FL; Soares, SP; Abreu, JP; Oliveira, PM; Teixeira, JP;
Publicação
Optimization, Learning Algorithms and Applications
Abstract
2024
Autores
Rebelo, PM; Lima, J; Soares, SP; Moura Oliveira, P; Sobreira, H; Costa, P;
Publicação
Sensors
Abstract
2024
Autores
Ribeiro, J; Pinheiro, R; Soares, S; Valente, A; Amorim, V; Filipe, V;
Publicação
Lecture Notes in Mechanical Engineering
Abstract
The manual monitoring of refilling stations in industrial environments can lead to inefficiencies and errors, which can impact the overall performance of the production line. In this paper, we present an unsupervised detection pipeline for identifying refilling stations in industrial environments. The proposed pipeline uses a combination of image processing, pattern recognition, and deep learning techniques to detect refilling stations in visual data. We evaluate our method on a set of industrial images, and the findings demonstrate that the pipeline is reliable at detecting refilling stations. Furthermore, the proposed pipeline can automate the monitoring of refilling stations, eliminating the need for manual monitoring and thus improving industrial operations’ efficiency and responsiveness. This method is a versatile solution that can be applied to different industrial contexts without the need for labeled data or prior knowledge about the location of refilling stations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
2024
Autores
Kalbermatter, RB; Franco, T; Pereira, AI; Valente, A; Soares, SP; Lima, J;
Publicação
Communications in Computer and Information Science - Optimization, Learning Algorithms and Applications
Abstract
2024
Autores
Bulganbayev, MA; Suliyev, R; Ferreira, NMF;
Publicação
ELECTRONICS
Abstract
This study provides a comprehensive overview of the automated assembly process of large-scale metal structures using industrial robots. Our research reveals that the utilization of industrial robots significantly enhances precision, speed, and cost-effectiveness in the assembly process. The main findings suggest that integrating industrial robots in metal structure assembly holds substantial promise for optimizing manufacturing processes and elevating the quality of the final products. Additionally, the research demonstrates that robotic automation in assembly operations can lead to significant improvements in resource utilization and operational consistency. This automation also offers a viable solution to the challenges of manual labor shortages and ensures a higher standard of safety and accuracy in the manufacturing environment.
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