Zobrazeno 1 - 10
of 757
pro vyhledávání: '"Araújo, Eduardo A."'
In face of climate change and increasing urbanization, the predictive mosquito-borne diseases (MBD) transmission models require constant updates. Thus, is urgent to comprehend the driving forces of this non stationary behavior, observed through spati
Externí odkaz:
http://arxiv.org/abs/2411.13680
Autor:
Collados-Rodriguez, Carlos, Spier, Daniel Westerman, Cheah-Mane, Marc, Prieto-Araujo, Eduardo, Gomis-Bellmunt, Oriol
In this paper, frequency dynamics in modern power systems with a high penetration of converter-based generation is analysed. A fundamental analysis of the frequency dynamics is performed to identify the limitations and challenges when the converter p
Externí odkaz:
http://arxiv.org/abs/2411.08161
Autor:
Ganem, Fabiana, Vacaro, Luã Bida, Araujo, Eduardo Correa, Alves, Leon Diniz, Bastos, Leonardo, Carvalho, Luiz Max, Almeida, Iasmim, de Sá, Asla Medeiros, Coelho, Flávio Codeço
Dengue is a climate-sensitive mosquito-borne disease with a complex transmission dynamic. Data related to climate, environmental and sociodemographic characteristics of the target population are important for project scenarios. Different datasets and
Externí odkaz:
http://arxiv.org/abs/2410.18945
Autor:
Mateu-Barriendos, Elia, Alican, Onur, Renedo, Javier, Collados-Rodriguez, Carlos, Martin, Macarena, Nuño, Edgar, Prieto-Araujo, Eduardo, Gomis-Bellmunt, Oriol
Inter-area oscillations have been extensively studied in conventional power systems dominated by synchronous machines, as well as methods to mitigate them. Several publications have addressed Power Oscillation Damping (POD) controllers in grid-follow
Externí odkaz:
http://arxiv.org/abs/2409.10726
In motor condition diagnosis, electrical current signature serves as an alternative feature to vibration-based sensor data, which is a more expensive and invasive method. Machine learning (ML) techniques have been emerging in diagnosing motor conditi
Externí odkaz:
http://arxiv.org/abs/2408.09649
Autor:
Piedad, Eduardo Jr, Del Rosario, Christian Ainsley, Prieto-Araujo, Eduardo, Gomis-Bellmunt, Oriol
Deep learning (DL) strategies have recently been utilized to diagnose motor faults by simply analyzing motor phase current signals, offering a less costly and non-intrusive alternative to vibration sensors. This research transforms these time-series
Externí odkaz:
http://arxiv.org/abs/2408.09644
Autor:
Araujo, Eduardo C., Codeço, Claudia T., Loch, Sandro, Vacaro, Luã B., Freitas, Laís P., Lana, Raquel M., Bastos, Leonardo S., de Almeida, Iasmim F., Valente, Fernanda, Carvalho, Luiz M., Coelho, Flávio C.
The influence of climate on mosquito-borne diseases like dengue and chikungunya is well-established, but comprehensively tracking long-term spatial and temporal trends across large areas has been hindered by fragmented data and limited analysis tools
Externí odkaz:
http://arxiv.org/abs/2407.21286
Autor:
Arévalo-Soler, Josep, Nahalparvari, Mehrdad, Gross, Dominic, Prieto-Araujo, Eduardo, Norrga, Staffan, Gomis-Bellmunt, Oriol
Interconnecting power converters (IPC) are the main elements enabling the interconnection of multiple high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) subgrids. These converters can be classified either as grid-forming o
Externí odkaz:
http://arxiv.org/abs/2404.19625
Autor:
Silva, Wenderson R. F., Monteiro, Larissa C. P., Senra, Renato L., de Araujo, Eduardo N. D., Cunha, Rafael O. R. R., Mendes, Tiago A. O., Mendes, Joaquim B. S.
Publikováno v:
Biosensors and Bioelectronics (2024) 116456
The study proposes a new efficient wireless biosensor based on magnetoelastic waves for the detection of antibodies in human plasma, aiming at the serological diagnosis of COVID-19. The biosensor was functionalized with the N antigen - nucleocapsid p
Externí odkaz:
http://arxiv.org/abs/2403.11389
In this paper, we propose a systematic closed-loop approach to provide optimal dynamic ancillary services with converter-interfaced generation systems based on local power grid perception. In particular, we structurally encode dynamic ancillary servi
Externí odkaz:
http://arxiv.org/abs/2401.17793