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Projeto de investigação
Centre of Technology and Systems
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Developing energy flexibility in clusters of buildings
Publication . Le Dréau, Jérôme; Lopes, Rui Amaral; O'Connell, Sarah; Finn, Donal; Hu, Maomao; Queiroz, Humberto; Alexander, Dani; Satchwell, Andrew; Österreicher, Doris; Polly, Ben; Arteconi, Alessia; de Andrade Pereira, Flávia; Hall, Monika; Kırant-Mitić, Tuğçin; Cai, Hanmin; Johra, Hicham; Kazmi, Hussain; Li, Rongling; Liu, Aaron; Nespoli, Lorenzo; Saeed, Muhammad Hafeez; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; DEE - Departamento de Engenharia Electrotécnica e de Computadores; Elsevier Science B.V., Amsterdam.
This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts.
Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System
Publication . Lourenço, Miguel; Estima, Diogo; Oliveira, Henrique; Oliveira, Luís; Mora, André; DEE - Departamento de Engenharia Electrotécnica e de Computadores; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; Molecular Diversity Preservation International (MDPI)
To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.
Unilateral Laplace Transforms on Time Scales
Publication . Şan, Müfit; Ortigueira, Manuel D.; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; DEE - Departamento de Engenharia Electrotécnica e de Computadores; MDPI - Multidisciplinary Digital Publishing Institute
We review the direct and inverse Laplace transforms on non-uniform time scales. We introduce full backward-compatible unilateral Laplace transforms and studied their properties. We also present the corresponding inverse integrals and some examples.
Cyber-Physical-Social Systems
Publication . Pasandideh, Shabnam; Pereira, Pedro; Gomes, Luís; CTS - Centro de Tecnologia e Sistemas; DEE - Departamento de Engenharia Electrotécnica e de Computadores; DEE2010-B1 Energia; DEE2010-C1 Sistemas Digitais e Percepcionais; Institute of Electrical and Electronics Engineers (IEEE)
A Cyber-Physical-Social System (CPSS) is a novel paradigm of cyber-physical and cyber-social systems with a highly inhomogeneous and distributed nature integrating dynamic stochastic hybrid systems including computation, communication, sensing and actuation, and social systems.Their usage can be traced to homes such as smart homes, manufacturers in Industry 5.0, critical infrastructures, smart cities, medicines, healthcare systems, and many other examples. They can simplify and speed up tasks and provide a higher level of control and accessibility. In this study, we provide a systematic review of the definition of CPSS,and an uniform definition of CPSS. We propose a novel taxonomy to define CPSS to help future researches and system designing. The CPSS taxonomy aims to provide a comprehensive and understating of CPSS characteristics and aspects from system-to-system point of view. Furthermore, we discuss about social integration in CPSS and their relationships with CPS. We mention issues and opportunities in CPSS in the designing and implementing phases.
MD Automatic epicardial fat segmentation and volume quantification on non-contrast Cardiac Computed Tomography
Publication . Rebelo, Ana Filipa; Ferreira, António Miguel; Fonseca, José M.; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; CTS - Centro de Tecnologia e Sistemas; DEE2010-C1 Sistemas Digitais e Percepcionais; DEE - Departamento de Engenharia Electrotécnica e de Computadores; DF – Departamento de Física; Elsevier
Epicardial Fat Volume (EFV) represents a valuable predictor of cardio- and cerebrovascular events. However, the manual procedures for EFV calculation, diffused in clinical practice, are highly time-consuming for technicians or physicians and often involve significant intra- or inter-observer variances. To reduce the processing time and improve results repeatability, we propose a computer-assisted tool that automatically performs epicardial fat segmentation and volume quantification on non-contrast cardiac Computed Tomography (CT). The proposed algorithm prioritizes the use of basic image techniques, promoting lower computational complexity. The heart region is selected using Otsu’s Method, Template Matching and Connected Component Analysis. Then, to refine the pericardium delineation, convex hull is applied. Lastly, epicardial fat is segmented by thresholding. In addition to the algorithm, an intuitive software (HARTA) was developed for clinical use, allowing human intervention for adjustments. A set of 878 non-contrast cardiac CT images was used to validate the method. Using HARTA, the average time to segment the epicardial fat on a CT was 15.5 ± 2.42 s, while manually 10 to 26 min were required. Epicardial fat segmentation was evaluated obtaining an accuracy of 98.83% and a Dice Similarity Coefficient of 0.7730. EFV automatic quantification presents Pearson and Spearman correlation coefficients of 0.9366 and 0.8773, respectively. The proposed tool presents potential to be used in clinical contexts, assisting cardiologists to achieve faster and accurate EFV, leading towards personalized diagnosis and therapy. The human intervention component can also improve the automatic results and insure the credibility of this diagnostic support system. The software hereby presented is available for public access at GitHub.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/00066/2020