CISeD - CENTRO DE ESTUDOS EM SERVIÇOS DIGITAIS
URI permanente desta comunidade:
Navegar
Percorrer CISeD - CENTRO DE ESTUDOS EM SERVIÇOS DIGITAIS por Domínios Científicos e Tecnológicos (FOS) "Engenharia e Tecnologia"
A mostrar 1 - 8 de 8
Resultados por página
Opções de ordenação
- An integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturingPublication . Babcinschi, Mihail; Freire, Bernardo; Ferreira, Lucía; Señaris, Baltasar; Vidal, Felix; Vaz, Paulo; Neto, PedroIncreasingly, industry is looking to better integrate their industrial processes and related data. Interoperability is key since the organizations need to share data between them, between departments and the different stages of a given technological process. The problem is that many times there are no standard data formats for data exchange between heterogeneous engineering tools. In this paper we present an integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturing (MAM). Data such as the MAM robot targets and process parameters are shared and edited along the different sub-stages of the process, from Computer-Aided Design (CAD), to path planning, to multiphysics simulation, to robot simulation and production. The AutomationML neutral data format allows the implementation of optimization loops connecting different sub-stages, for example the multi-physics simulation and the path planning. A practical use case using the Direct Energy Deposition (DED) process is presented and discussed. Results demonstrated the effectiveness of the proposed AutomationML-based solution.
- Enhancing Organizational Data Integrity and Efficiency through Effective Data LineagePublication . Silva, Carlos; Abbasi, Maryam; Martins, Pedro; Silva, José; ANTUNES VAZ, PAULO JOAQUIM; Mota, DavidIn the era of exponential data growth and the growing reliance of organizations on data-driven decision-making, understanding the origin, transformation, processing, and destination of data has become crucial. Unfortunately, data arriving at organizations is often incorrect or structured differently, leading to analytical errors and compromising data integrity. Without well-documented data mapping and lineage, organizations risk making strategic decisions based on flawed or inaccurate data. Consequently, the implementation of a robust Data Lineage solution has emerged as a vital component for organizational success. Such a solution not only enables error identification, but also facilitates cost reduction by pinpointing inefficient data processing points and driving efficiency improvements. However, finding the right Data Lineage solution tailored to an organization’s specific requirements remains a challenge. Therefore, it is imperative to select and implement a suitable solution that aligns with each organization’s needs.
- Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting ConditionsPublication . Abbasi, Maryam; Silva, José; Martins, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, JoséThe visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.
- Head-to-Head Evaluation of FDM and SLA in Additive Manufacturing: Performance, Cost, and Environmental PerspectivesPublication . Abbasi, Maryam; ANTUNES VAZ, PAULO JOAQUIM; Martins, Pedro; Silva, JoséThis paper conducts a comprehensive experimental comparison of two widely used additive manufacturing (AM) processes, Fused Deposition Modeling (FDM) and Stereolithography (SLA), under standardized conditions using the same test geometries and protocols. FDM parts were printed with both Polylactic Acid (PLA) and Acryloni trile Butadiene Styrene (ABS) filaments, while SLA used a general-purpose photopolymer resin. Quantitative evaluations included surface roughness, dimensional accuracy, ten sile properties, production cost, and energy consumption. Additionally, environmental considerations and process reliability were assessed by examining waste streams, recy clability, and failure rates. The results indicate that SLA achieves superior surface quality (Ra ≈ 2 µm vs. 12–13 µm) and dimensional tolerances (±0.05 mm vs. ±0.15–0.20 mm), along with higher tensile strength (up to 70 MPa). However, FDM provides notable ad vantages in cost (approximately 60% lower on a per-part basis), production speed, and energy efficiency. Moreover, from an environmental perspective, FDM is more favorable when using biodegradable PLA or recyclable ABS, whereas SLA resin waste is hazardous. Overall, the study highlights that no single process is universally superior. FDM offers a rapid, cost-effective solution for prototyping, while SLA excels in precision and surface finish. By presenting a detailed, data-driven comparison, this work guides engineers, product designers, and researchers in choosing the most suitable AM technology for their specific needs.
- Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data IntegrationPublication . Abbasi, Maryam; Vaz, Paulo; Silva, José; Martins, Pedro; Silva, José; ANTUNES VAZ, PAULO JOAQUIMThe efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environ mental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance.
- Real-Time Gesture-Based Hand Landmark Detection for Optimized Mobile Photo Capture and SynchronizationPublication . Marques, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, Pedro; Abbasi, MaryamGesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmen tal conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recogni tion of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Ex perimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with mo tor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions.
- Sustainable Heat Production for Fossil Fuel Replacement—Life Cycle Assessment for Plant Biomass Renewable Energy SourcesPublication . Lopes Brás, Isabel Paula; Fabriccino, Massimiliano; Ferreira, José; Ferreira Silva, Maria Elisabete; Mignano, VincenzoThis study aims to assess the environmental impact of using wood-based biomass as a high-efficiency fuel alternative to fossil fuels for heat production. To achieve this, the life cycle of biomass transformation, utilization, and disposal was analyzed using the life cycle assessment (LCA) methodology with SimaPro 9.5.0.2 PhD software. The system boundaries included extraction, processing, transportation, combustion, and waste management, following a cradle-to-gate approach. A comparative analysis was conducted between natural gas, the most widely used conventional heating fuel, and two biomass-based fuels: wood pellets and wood chips. The results indicate that biomass utilization reduces greenhouse gas emissions (−19%) and fossil resource depletion (−16%) while providing environmental benefits across all assessed impact categories analyzed, except for land use (+96%). Biomass is also to be preferred for forest waste management, ease of supply, and energy independence. However, critical life cycle phases, such as raw material processing and transportation, were found to contribute significantly to human health and ecosystem well-being. To mitigate these effects, optimizing combustion efficiency, improving supply chain logistics, and promoting sustainable forestry practices are recommended. These findings highlight the potential of biomass as a viable renewable energy source and provide insights into strategies for minimizing its environmental footprint.
- The impact of Lean Manufacturing on environmental and social sustainability: a study using a concept mapping approachPublication . Silva, Cristóvão; ANTUNES VAZ, PAULO JOAQUIM; Ferreira, Luis MiguelThe concept mapping methodology consists of a structured conceptualization which can be usedby groups to develop a conceptual framework which can guide evaluation or different areas: strategic planning, product development, market analysis or decision making. In this paper we describe how the concept mapping methodology, which combines a qualitative case study approach – based on interviews, focus groups and plant visits – and quantitative methods – using software and datadriven mapping methods – was used to answer the following question: can lean manufacturing contribute to a company environmental and social sustainability? The conclusions of the research are empirically analyzed. As expected the impact of lean on productivity and process efficiency was identified but the results also demonstrates that is has a positive effect on resources and energy consumption.