A5046880702- Fault Detection and Control Systems
- Reservoir Engineering and Simulation Methods
- Mineral Processing and Grinding
- Respiratory Support and Mechanisms
- Modeling, Simulation, and Optimization
- Nonlinear Dynamics and Pattern Formation
- Oil and Gas Production Techniques
- Kawasaki Disease and Coronary Complications
- Head and Neck Anomalies
- Healthcare during COVID-19 Pandemic
- Membrane Separation Technologies
- Congenital gastrointestinal and neural anomalies
- Fluid Dynamics and Mixing
- Neural Networks and Reservoir Computing
- Machine Fault Diagnosis Techniques
- Fungal Infections and Studies
- Advanced Memory and Neural Computing
- Oceanographic and Atmospheric Processes
- Women's cancer prevention and management
- Teratomas and Epidermoid Cysts
- Medical and Biological Ozone Research
- COVID-19 Impact on Reproduction
- Aerosol Filtration and Electrostatic Precipitation
- Neural Networks and Applications
- Software Reliability and Analysis Research
Universidade Federal do Rio de Janeiro
2021-2024
Fundação de Medicina Tropical
2020
Fault detection constitutes a fundamental task for predictive maintenance, requiring mathematical models that can be conveniently provided by data-driven techniques. Autoencoders are particular type of unsupervised Artificial Neural Networks suitable fault applications. Diverse architectures might used autoencoders, resulting in different performances, which usually compared means Detection Rates fixed threshold the False Alarm Rate, limiting conclusions to cases. To improve comparability,...
This paper introduces BibMon, a Python package that provides predictive models for data-driven fault detection and diagnosis, soft sensing, process condition monitoring. Key features include regression reconstruction models, preprocessing pipelines, alarms, visualization through control charts diagnostic maps. BibMon also includes real simulated datasets benchmarking, comparative performance analysis of different hyperparameter tuning. The is designed to be highly extensible, allowing easy...
Variable selection constitutes an essential step to reduce dimensionality and improve performance of fault detection diagnosis in large scale industrial processes. For this reason, paper, variable approaches based on causality are proposed compared, terms model adjustment available data performance, with several other filter-based, wrapper-based, embedded-based methods. These applied a simulated benchmark case actual oil gas considering four different learning models. The experimental...
The present study presents, for the first time, real-time monitoring of an actual spiral-wound membrane unit used CO2 removal from natural gas in industrial offshore platform, utilizing a detailed phenomenological model. An Object-Oriented Programming (OOP) paradigm was employed to simulate separation unit, accounting diverse levels setup. A parameter estimation procedure implemented fit model real data real-time, time. In addition, estimated permeance parameters and calculated unmeasured...
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Objetivo: Relatar caso de uma paciente imunossuprimida, internada em enfermaria, evoluindo com coinfecção por fusariose e criptococose devido a aplasia após quimioterapia para tratamento leucemia mielóide aguda (LMA) refratáriaDetalhamento caso: Mulher 24 anos, previamente hígida, teve diagnóstico LMA abril 2018, foi submetida duas ocasiões, primeira citarabina idarrubicina resposta parcial remissão incompleta, sendo sucedido do protocolo as mesmas drogas altas doses, apresentando completa...
Goal: Contribute to the knowledge of medical society by identifying disease and understanding its importance, especially from point view clinical laboratory diagnosis, with emphasis on manifestations.Methods: The proposed study is a case report, observational descriptive, developed at Hospital Pronto Socorro da Criança Zona Sul, started in February 2021.The instruments used collect information were anamnesis physical examination.during period which patient remained hospitalized referred...