55 research outputs found

    Multiple mask and boundary scoring R-CNN with cGAN data augmentation for bladder tumor segmentation in WLC videos

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    Automatic diagnosis systems capable of handling multiple pathologies are essential in clinical practice. This study focuses on enhancing precise lesion localization, classification and delineation in transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence. Despite deep learning models success, medical applications face challenges like small and limited datasets and poor image characterization, including the absence lack of color/texture modeling. To address these issues, three solutions are proposed: (1) an improved texture-constrained version of the pix2pixHD cGAN for data augmentation, addressing the tradeoff of generating high-quality images with enough stochasticity using the Fréchet Inception Distance (FID) measure. (2) Introducing the Multiple Mask and Boundary Scoring R-CNN (MM&BS R-CNN), a new mask sub-net scheme where multiple masks are generated from the different levels of the mask sub-net pipeline, improving segmentation accuracy by including a new scoring module to refine object boundaries. (3) A novel accelerated training strategy based on the SGD optimizer with the second momentum. Experimental results show significant mAP improvements: the data generation scheme improves by more than 12 %; MM&BS R-CNN proposed architecture is responsible for an improvement of about 1.25 %, and the training algorithm based on the second-order momentum increases mAP by 2–3 %. The simultaneous use of all three proposals improved the state-of-the-art mAP by 17.44 %.Fundação para a Ciência e a Tecnologia ; Ministério da Ciência, Tecnologia e Ensino Superio

    Optimal 3D printing of complex objects in a 5 - axis printer

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    Three-dimensional (3D) printing, also known as additive manufacturing (AM), has emerged in the last decades as an innovative technology to build complex structures. It enables increasing design complexity and low-cost customization with a vast range of materials. AM capabilities contributed to a widespread acceptance of 3D printing in different industries such as the aerospace and the automotive. However, important issues and limitations still need to be addressed, namely in printing complex objects where supports and material roughness surface are to be minimized. In this work we consider a 5–axis printer with the three traditional xyz movements and two additional degrees of freedom on the printer table bed. These extra degrees of freedom (table bed rotation and tilt) allow printing of more complex objects, and we propose an approach which consists on the decomposition of complex objects into simpler parts, allowing each part to be printed in an optimal way. We aim to reduce the number of supports needed and attain high final object quality due to lower material surface roughness. The optimal printing direction (or, equivalently, rotation) and sequencing of the object parts is determined by solving a combinatorial sequencing optimization problem. All the local or global optimal parts rotations are obtained by solving a global optimization sub-problem for each part, and are taken as input parameters for the sequencing optimization problem. We provide a heuristic approach for the combinatorial sequencing optimization problem, and a multistart multisplit search methodology for computing all the local or global optimal parts rotations for the sub-problems.This work was developed under the FIBR3D project titled Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI–01–0145–FEDER–016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.info:eu-repo/semantics/publishedVersio

    Dynamic FE model updating using particle swarm optimization method: A methodology to design critical mechanical composite structures

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    To increase the performance of an industrial cutting machine, this work studied the possibility of replacing its current main steel gantry by a Carbon Fibre Reinforced Polymer (CFRP) composite solution. This component strongly influences the most relevant characteristics of the equipment, namely accuracy and maxima allowed accelerations. The flexibility of composites in terms of number, thickness and orientation of layers and the challenging trade-offs between weight and stiffness motivated the development of an optimisation process. The Particle Swarm Optimisation method (PSO) was used to develop a solution able to ensure higher accelerations and the required accuracy of the equipment, by optimizing continuously the FE model algorithm input and output assessment and updating it. The process resulted in a near optimal solution allowing a 43% weight reduction and an increase of the maximum allowed acceleration in 25%, while ensuring the same accuracy.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the scholarship SFRH/BD/51106/2010

    A review of co-optimization approaches for operational and planning problems in the energy sector

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    This paper contributes to a comprehensive perspective on the application of co-optimization in the energy sector – tracking the frontiers and trends in the field and identifying possible research gaps – based on a systematic literature review of 211 related studies. The use of co-optimization is addressed from a variety of perspectives by splitting the studies into ten key categories. Research has consistently shown that co-optimization approaches can be technically challenging and it is usually a data-intensive procedure. Overall, a set of techniques such as relaxation, decomposition and linear approaches have been proposed for reducing the inherent nonlinear model's complexities. The need to coordinate the necessary data from multiples actors might increase the complexity of the problem since security and confidentiality issues would also be put on the table. The evidence from our review seems to suggest a pertinent role for addressing real-case systems in future models instead of using theoretical test cases as considered by most studies. The identified challenges for future co-optimization models include (i) dealing with the treatment of uncertainties and (ii) take into account the trade-offs among modelling fidelity, spatial granularity and geographical coverage. Although there is also a growing body of literature that recognizes the importance of co-optimization focused on integrating supply and demand-side options, there has been little work in the development of co-optimization models for long-term decision-making, intending to recognize the impact of short-term variability of both demand and RES supply and well suited to systems with a high share of RES and under different demand flexibility conditions. The research results represent a further step towards the importance of developing more comprehensive approaches for integrating short-term constraints in future co-optimized planning models. The findings provide a solid evidence base for the multi-dimensionality of the co-optimization problems and contriThis work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT – Fundaça˜o para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

    Integrating supply and demand-side management in renewable-based energy systems

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    Demand-Response (DR) has emerged as a valuable resource option for balancing electricity supply and demand. However, traditional power system models have neglected to include DR within long-term expansion problems. We can summarize our scientific contributions in the following aspects: (i) design of a new integrated co-optimization planning model for supply and demand coordination; (ii) assessment of the technical and economic impact of DR for systems with a high share of Renewable Energy Sources (RES) and (iii) proposal of the ‘opportunity cost’ concept for computing the price of not meeting the demand. Findings of this research support the hypothesis that DR scenarios reveal a high potential for delaying future investments in power capacity compared to scenario BAU (Business as Usual). However, it was found a limited potential of DR to integrate additional renewable plants. This research has provided further evidence concerning the potential of DR to decrease the levels of CO2 emissions that is strictly related to the reduced need for fossil fuel thermal power plants. Given the high RES share, uncertainties related to future weather conditions must be however highlighted. This study concludes on the importance of DR for power systems planning and lays the groundwork for future research.This work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT e Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

    On the optimal object orientation in additive manufacturing

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    Additive manufacturing (AM) has emerged in the last decades as an alternative to traditional subtractive and forming manufacturing processes. AM is a process where 3D objects are built by adding material in consecutive layers. Typical AM is characterized by four processing stages: model orientation, creation of supports, slicing, and path planning. In this paper, we will focus on how to use mathematical optimization to address some limitations that arise from the model orientation, creation of supports, and slicing processing stages. Model orientation and creation of supports are usually related, since the best orientation of the object to be built can result in lower building time and lower need for creation of supports, leading to an improvement of the surface quality. Slicing comprises the object division by layers and the major difficulty is the staircase effect, which becomes more critical for objects with high slopes and curvatures, resulting in high roughness surfaces. In this paper, we show how to take advantage of a state-of-the-art optimization solver to optimize object building orientation and the need for supports generation in additive manufacturing, thus leading to better object surface accuracy and smoothness.This work was developed under the FIBR3D project Hybrid processes based on additive manufacturing of composites with long or short fibers reinforced thermoplastic matrix (POCI-01-0145-FEDER-016414), supported by the Lisbon Regional Operational Programme 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).Support was also given by the FCT project grants UID/MAT/00324/2013, and P2020SAICTPAC/0011/2015

    A scheduling application to a molding injection machine: a challenge addressed on the 109th European Study Group with Industry

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    This paper addresses a scheduling optimization problem applied to a molding injection machine. This optimization problem was posed as a challenge to the mathematical community present at the 109th European Study Group with Industry (ESGI), held in Portugal in 2015. We propose a mathematical model for the scheduling optimization problem, which was coded in a widely known modeling language. The model is validated through a set of numerical results obtained with a state-of-the-art solver.info:eu-repo/semantics/publishedVersio

    Generation expansion planning with high share of renewables of variable output

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    This study presents a new generation expansion planning (GEP) incorporating the effects of renewables variable generation on thermal power plants efficiency. An hourly unit commitment problem was integrated in the GEP problem with the overall goal of supporting the selection of future mixes of power plants through long term planning. The problem resulted in a binary mixed integer non-linear cost optimization model. The model application was demonstrated for the design of electricity plans for a 10 year planning period under different CO 2 assumptions for a thermal, hydroelectric and wind power system. The results were compared with the ones obtained using a traditional GEP model, which assumed average operating conditions for thermal power plants. The scenario analysis shows that the impact of renewables variability on the performance of thermal power plants and on the generation expansion planning is non-negligible. The results suggest that assuming average operating conditions can result on the underestimation of the system costs which highlights the importance of the proposed integrated model to strategic decision making

    Optimization modeling to support renewables integration in power systems

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    This study focuses on the problem of generation expansion planning and the integration of an increasing share of renewable energy sources (RES) technologies in the power grid. A survey of papers addressing the use of optimization models for electricity generation planning is presented. From this, an electricity planning model able to integrate thermal and RES power plants was proposed. An analysis of different electricity scenarios for a mixed hydro-thermal-wind power system is presented using the proposed mixed integer optimization model. The results show the importance of these tools to support the strategic energy policy decision making under different regulatory or political scenarios. The expected impacts in terms of costs and CO 2 emissions are evaluated for a 10 year planning period, and a set of optimal scenarios is analyzed. The use of the model to obtain and characterize close to optimal scenarios is shown to be strategically useful. In particular the impact of different wind power scenarios is addressed, demonstrating the relevance of assessing other possible strategies that, despite not being original Pareto solutions, may be worth considering by the decision makers.Uminho - Universidade do Minho(PEst- OE/UID/CEC00319/2013)The authors wish to acknowledge the support of ALGORITMI, a research Centre at the University of Minho. This work is supported by National Funds through FCT - Foundation for Science and Technology, under the project PEst- OE/UID/CEC00319/2013info:eu-repo/semantics/publishedVersio

    Combined tools for surgical case packages contents and cost optimization: a preliminary study

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    This paper presents a solution proposal based on mathematical and statistical tools to optimize Surgical Case Packages of an Operating Room (OR) in a Portuguese public hospital that it is the most complex environment in a hospital. In this particular hospital, more than 27000 surgeries/year are performed, employing, sometimes, misadjusted composition of standard surgical packages and non-optimized grouping of surgical instruments. Problem consequences are, among others, high transport of various surgical cases packages; high number of open cases and delays in surgical times following surgery. These type of problems are waste that do not add value to the service in the context of Lean Healthcare and must be eliminated using the most suitable tools. After the analysis, different tools were used: combinatorial analysis to optimize surgical cases composition and statistical analysis to identify the instruments usage and surgical basic case patterns. An optimization model was developed which produced a sterilizing initial solution of 135.24€. By identifying the most commonly employed instruments, it was concluded that some instruments have never been used and others rarely and some patterns were identified. The results achieved were based on minor sample and in a form of data collection that needs some adjustments.The authors want to acknowledge the Portuguese public hospital involved and to the ESGI initiative. The authors also would like to express their acknowledgments to national funds by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
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