LMI-fuzzy control design for non-minimum-phase DC-DC converters: An application for output regulation

Indexación ScopusRobust control techniques for power converters are becoming more attractive because they can meet with most demanding control goals like uncertainties. In this sense, the Takagi-Sugeno (T-S) fuzzy controller based on linear matrix inequalities (LMI) is a linear control by intervals that has been relatively unexplored for the output-voltage regulation problem in switching converters. Through this technique it is possible to minimize the disturbance rejection level, satisfying constraints over the decay rate of state variables as well as the control effort. Therefore, it is possible to guarantee, a priori, the stability of the large-signal converters in a broad operation domain. This work presents the design of a fuzzy control synthesis based on a T-S fuzzy model for non-minimum phase dc-dc converters, such as boost and buck-boost. First, starting from the canonical bilinear converters expression, a Takagi-Sugeno (T-S) fuzzy model is obtained, allowing to define the fuzzy controller structure through the parallel distributed compensation technique (PDC). Finally, the fuzzy controller design based on LMIs is solved for the defined specification in close loop through MATLAB toolbox LMI. Simulations and experimental results of a 60 W prototype are presented to verify theoretical predictions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.https://www.mdpi.com/2076-3417/11/5/228

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213

Thermodynamics and dynamics of the formation of spherical lipidic vesicles

We propose a free energy expression accounting for the formation of spherical vesicles from planar lipidic membranes and derive a Fokker-Planck equation for the probability distribution describing the dynamics of vesicle formation. We found that formation may occur as an activated process for small membranes and as a transport process for sufficiently large membranes. We give explicit expressions for the transition rates and the characteristic time of vesicle formation in terms of the relevant physical parameters.Comment: 14pgs, 6 figures, sendo to Jour. Phys. Bio

Interstellar MHD Turbulence and Star Formation

This chapter reviews the nature of turbulence in the Galactic interstellar medium (ISM) and its connections to the star formation (SF) process. The ISM is turbulent, magnetized, self-gravitating, and is subject to heating and cooling processes that control its thermodynamic behavior. The turbulence in the warm and hot ionized components of the ISM appears to be trans- or subsonic, and thus to behave nearly incompressibly. However, the neutral warm and cold components are highly compressible, as a consequence of both thermal instability in the atomic gas and of moderately-to-strongly supersonic motions in the roughly isothermal cold atomic and molecular components. Within this context, we discuss: i) the production and statistical distribution of turbulent density fluctuations in both isothermal and polytropic media; ii) the nature of the clumps produced by thermal instability, noting that, contrary to classical ideas, they in general accrete mass from their environment; iii) the density-magnetic field correlation (or lack thereof) in turbulent density fluctuations, as a consequence of the superposition of the different wave modes in the turbulent flow; iv) the evolution of the mass-to-magnetic flux ratio (MFR) in density fluctuations as they are built up by dynamic compressions; v) the formation of cold, dense clouds aided by thermal instability; vi) the expectation that star-forming molecular clouds are likely to be undergoing global gravitational contraction, rather than being near equilibrium, and vii) the regulation of the star formation rate (SFR) in such gravitationally contracting clouds by stellar feedback which, rather than keeping the clouds from collapsing, evaporates and diperses them while they collapse.Comment: 43 pages. Invited chapter for the book "Magnetic Fields in Diffuse Media", edited by Elisabete de Gouveia dal Pino and Alex Lazarian. Revised as per referee's recommendation

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovación, Generalitat Valenciana.Peer reviewe

Monitoring of the radio galaxy M87 during a low emission state from 2012 to 2015 with MAGIC

M87 is one of the closest (z=0.00436) extragalactic sources emitting at very-high-energies (VHE, E > 100 GeV). The aim of this work is to locate the region of the VHE gamma-ray emission and to describe the observed broadband spectral energy distribution (SED) during the low VHE gamma-ray state. The data from M87 collected between 2012 and 2015 as part of a MAGIC monitoring programme are analysed and combined with multi-wavelength data from Fermi-LAT, Chandra, HST, EVN, VLBA and the Liverpool Telescope. The averaged VHE gamma-ray spectrum can be fitted from 100GeV to 10TeV with a simple power law with a photon index of (-2.41 $\pm$ 0.07), while the integral flux above 300GeV is $(1.44 \pm 0.13) \times 10^{-12} cm^{-2} s^{-1}$. During the campaign between 2012 and 2015, M87 is generally found in a low emission state at all observed wavelengths. The VHE gamma-ray flux from the present 2012-2015 M87 campaign is consistent with a constant flux with some hint of variability ($\sim3\sigma$) on a daily timescale in 2013. The low-state gamma-ray emission likely originates from the same region as the flare-state emission. Given the broadband SED, both a leptonic synchrotron self Compton and a hybrid photo-hadronic model reproduce the available data well, even if the latter is preferred. We note, however, that the energy stored in the magnetic field in the leptonic scenario is very low suggesting a matter dominated emission region

The 2010 very high energy gamma-ray flare & 10 years of multi-wavelength observations of M 87

Abridged: The giant radio galaxy M 87 with its proximity, famous jet, and very massive black hole provides a unique opportunity to investigate the origin of very high energy (VHE; E>100 GeV) gamma-ray emission generated in relativistic outflows and the surroundings of super-massive black holes. M 87 has been established as a VHE gamma-ray emitter since 2006. The VHE gamma-ray emission displays strong variability on timescales as short as a day. In this paper, results from a joint VHE monitoring campaign on M 87 by the MAGIC and VERITAS instruments in 2010 are reported. During the campaign, a flare at VHE was detected triggering further observations at VHE (H.E.S.S.), X-rays (Chandra), and radio (43 GHz VLBA). The excellent sampling of the VHE gamma-ray light curve enables one to derive a precise temporal characterization of the flare: the single, isolated flare is well described by a two-sided exponential function with significantly different flux rise and decay times. While the overall variability pattern of the 2010 flare appears somewhat different from that of previous VHE flares in 2005 and 2008, they share very similar timescales (~day), peak fluxes (Phi(>0.35 TeV) ~= (1-3) x 10^-11 ph cm^-2 s^-1), and VHE spectra. 43 GHz VLBA radio observations of the inner jet regions indicate no enhanced flux in 2010 in contrast to observations in 2008, where an increase of the radio flux of the innermost core regions coincided with a VHE flare. On the other hand, Chandra X-ray observations taken ~3 days after the peak of the VHE gamma-ray emission reveal an enhanced flux from the core. The long-term (2001-2010) multi-wavelength light curve of M 87, spanning from radio to VHE and including data from HST, LT, VLA and EVN, is used to further investigate the origin of the VHE gamma-ray emission. No unique, common MWL signature of the three VHE flares has been identified.Comment: 19 pages, 5 figures; Corresponding authors: M. Raue, L. Stawarz, D. Mazin, P. Colin, C. M. Hui, M. Beilicke; Fig. 1 lightcurve data available online: http://www.desy.de/~mraue/m87

Search for a vector-like quark T′ → tH via the diphoton decay mode of the Higgs boson in proton-proton collisions at s \sqrt{s} = 13 TeV

A search for the electroweak production of a vector-like quark T′, decaying to a top quark and a Higgs boson is presented. The search is based on a sample of proton-proton collision events recorded at the LHC at = 13 TeV, corresponding to an integrated luminosity of 138 fb−1. This is the first T′ search that exploits the Higgs boson decay to a pair of photons. For narrow isospin singlet T′ states with masses up to 1.1 TeV, the excellent diphoton invariant mass resolution of 1–2% results in an increased sensitivity compared to previous searches based on the same production mechanism. The electroweak production of a T′ quark with mass up to 960 GeV is excluded at 95% confidence level, assuming a coupling strength κT = 0.25 and a relative decay width Γ/MT′ < 5%

Measurement of the Higgs boson inclusive and differential fiducial production cross sections in the diphoton decay channel with pp collisions at s \sqrt{s} = 13 TeV

The measurements of the inclusive and differential fiducial cross sections of the Higgs boson decaying to a pair of photons are presented. The analysis is performed using proton-proton collisions data recorded with the CMS detector at the LHC at a centre-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 137 fb$^{−1}$. The inclusive fiducial cross section is measured to be $σ_{fid}$=73.4$^{+5.4}_{−5.3}$(stat)$^{+2.4}_{−2.2}$(syst) fb, in agreement with the standard model expectation of 75.4 ± 4.1 fb. The measurements are also performed in fiducial regions targeting different production modes and as function of several observables describing the diphoton system, the number of additional jets present in the event, and other kinematic observables. Two double differential measurements are performed. No significant deviations from the standard model expectations are observed