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928 results on '"Rodriguez J."'

8. Context-Dependent Services Selection in Smart Environments

Author

Flores-Martin D., Garcia-Alonso J., Berrocal J., Foschini L., Rodriguez J. M. M., Flores-Martin D., Garcia-Alonso J., Berrocal J., Foschini L., and Rodriguez J.M.M.

Subjects
Service, Smart devices, Characterization, People, Context, Selection
Abstract

The current trend of smart environments is leading towards a world where everything is considered as a service. Internet-connected smart devices make these environments largely manageable and controllable through services. In these environments, not only devices offer services, but lately, people through their smartphones can also offer services such as personal information provided by the name, the preferences, or the location, promoting the offer of almost anything as a service. However, this high supply of services makes it more difficult for IoT systems to identify which services to use to solve a particular need. This paper proposes a solution to characterize services homogeneously and a service selection mechanism is outlined considering the properties of the services and the context in which they are found. With this proposal, services are defined commonly to facilitate a smart selection by IoT applications.

Published
2021

9. Reluctance-Based Circuit for High-Temperature Superconductor Generator Lumped-Parameter Model.

Author

Baez-Munoz, A., Trillaud, F., Rodriguez-Rodriguez, J. R., Castro, Luis M., and Escarela-Perez, R.

Subjects
HIGH temperature superconductors, SYNCHRONOUS generators, MAGNETIC flux density, MAGNETIC circuits, FINITE element method, MAGNETIC flux
Abstract

The present work addresses the ongoing development of a mesh-based magnetic equivalent circuit (MEC) to estimate the magnetic flux distribution in a superconducting hybrid rotating synchronous machine, superconducting (SC) rotor, and conventional stator. The MEC is to be integrated in an existing thermoelectric lumped-parameter machine model for the transient simulation of High Temperature Superconductor (HTS) field coil synchronous generator (SG) in the dq0 frame of reference. It will allow the inclusion of the magnetic flux density inside the definition of the non-linear resistance of the superconductor in addition to the already-present temperature and current. The objective is to gain further accuracy on the coupled thermal and electromagnetic behavior of superconducting hybrid machines during partial or total loss of the superconducting state. In the present work, the MEC is benchmarked against a Finite Element Model (FEM). It is shown that the MEC can provide proper magnitude of the magnetic flux density over the HTS winding achieving a good estimation of the magnetic response of the field winding by taking into account the penetration of current within the superconductor. The case study is a 100-MVA hydroelectric HTS field coil SG considering a field current ramp. [ABSTRACT FROM AUTHOR]

Published
2022
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30. Modeling and Simulation of the R5912 Photomultiplier for the LAGO Project.

Author

Pena-Rodriguez, J., Hernandez-Barajas, S., Leon-Carreno, Y., and Nunez, L. A.

Abstract

We present the results of the Hamamatsu R5912 photomultiplier tube modelling and simulating. The model can be adapted to any photomultiplier architecture by changing the number of electrodes, voltage distribution, and intrinsic PMT parameters. Our approach solves the use of tapered voltage dividers and allows photomultiplier simulations under stimulation conditions. The model implementation is not limited to SPICE-based software but also be used with compiled and interpreted programming languages. The modeled pulse charge of a vertical muon differs about 4% from the data. The resistive divider model tested reproduces the voltage distribution along the dynodes with a variance of ~3.5%. We compare the photomultiplier model linearity with the data from the operating LAGO’s (Latin American Giant Observatory) water Cherenkov detectors installed at Bucaramanga-Colombia and Bariloche-Argentina. [ABSTRACT FROM AUTHOR]

Published
2021
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40. COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images.

Author

Tabik, S., Gomez-Rios, A., Martin-Rodriguez, J. L., Sevillano-Garcia, I., Rey-Area, M., Charte, D., Guirado, E., Suarez, J. L., Luengo, J., Valero-Gonzalez, M. A., Garcia-Villanova, P., Olmedo-Sanchez, E., and Herrera, F.

Subjects
COVID-19, X-ray imaging, FORECASTING, CONVOLUTIONAL neural networks, COMMUNICABLE diseases
Abstract

Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building COVID-19 triage systems and detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This article is three-fold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Clínico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from normal with Positive RT-PCR, Mild, Moderate to Severe. COVIDGR-1.0 contains 426 positive and 426 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. Our approach reaches good and stable results with an accuracy of 97.72% ± 0.95%, 86.90% ± 3.20%, 61.80% ± 5.49% in severe, moderate and mild COVID-19 severity levels. Our approach could help in the early detection of COVID-19. COVIDGR-1.0 along with the severity level labels are available to the scientific community through this link https://dasci.es/es/transferencia/open-data/covidgr/. [ABSTRACT FROM AUTHOR]

Published
2020
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41. Characterization of DC N2O Plasma Discharge Using Optical Emission Spectroscopy.

Author

Yousif, F. B., Lopez, Juan, Rodriguez, J., Fuentes, B. E., and Martinez, H. M.

Subjects
EMISSION spectroscopy, OPTICAL spectroscopy, PLASMA flow, NITROUS oxide, OXYGEN, INDUCTIVELY coupled plasma atomic emission spectrometry
Abstract

We report in this article the optical emission spectroscopy (OES) measurements for N2O dc discharge in the pressure range between 0.1 and 0.6 mbar. The emission bands were observed and measured in the wavelength range of 200–1100 nm. The intensity of the principal species observed for N2, N2+, O+, O2, O2+, NO, and N2O+ was investigated as a function of pressure and discharge power. The origin of emission bands and lines was discussed. N2+ and N2 were observed to be the dominant emission lines, on one hand, while no emission lines were observed for atomic oxygen and nitrogen. [ABSTRACT FROM AUTHOR]

Published
2020
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48. TRITIUM - A Real-Time Tritium Monitor System for Water Quality Surveillance

Author

Azevedo, C. D. R., primary, Baeza, A., additional, Bras, M., additional, Camara, T., additional, Cerna, C., additional, Chauveau, E., additional, Gil, J. M., additional, Corbacho, J. A., additional, Delgado, V., additional, Diaz, J., additional, Domange, J., additional, Marquet, C., additional, Martinez-Roig, M., additional, Moreno, A., additional, Piquemal, F., additional, Rodriguez, A., additional, Rodriguez, J., additional, Roldan, C., additional, Veloso, J. F. C. A., additional, and Yahlali, N., additional

Published
2018
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