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- 저자 : Varella, Luiz Eduardo (검색결과 4 건)
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The Mexican Peso and the Korean Won Real Exchange Rates: Evidence from Productivity Models
Andre Varella Mollick,Margot Quijano 중앙대학교 경제연구소 2004 Journal of Economic Development Vol.29 No.1
Using the U.S. as benchmark country, Korean data from 1970:1 to 2000:4 and Mexican data from 1983:1 to 2000:4 are decomposed into traded and non-traded sectors. We find that the traditional purchasing power parity (PPP) model performs remarkably well for the Peso and that the productivity model appears adequate for the Peso but not for the Won. As Mexican relative traded goods productivity rises, the nominal Peso appreciates (coefficients between -2.03 and -2.16). Conversely, as U.S. relative traded goods productivity rises, the Peso depreciates (coefficients between 2.06 and 2.48). Although predicting correctly the direction of change, such large magnitudes suggest only partial support for the theoretical mechanism in Mexico. Coefficients with contrary signs obtained in Korea may indicate competing models (neoclassical or Ricardian) are more appropriate to capture the relationship between productivity and exchange rates.
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Mobilization Functions of the Bacteriocinogenic Plasmid pRJ6 of Staphylococcus aureus
Marcus Lívio Varella Coelho,Hilana Ceotto,Danielle Jannuzzi Madureira,Ingolf F. Nes,Maria do Carmo de Freire Bastos 한국미생물학회 2009 The journal of microbiology Vol.47 No.3
Plasmid pRJ6 is the first known bacteriocinogenic mobilizable (Mob) plasmid of Staphylococcus aureus. Its Mob region is composed of four mob genes (mobCDAB) arranged as an operon, a genetic organization uncommon among S. aureus Mob plasmids. oriTpRJ6 was detected in a region of 431 bp, positioned immediately upstream of mobC. This region, when cloned into pCN37, was able to confer mobilization to the recombinant plasmid only in the presence of pRJ6. The entire Mob region, including oriTpRJ6, is much more similar to Mob regions from several coagulase-negative staphylococci plasmids, although some remarkable similarities with S. aureus Mob plasmids can also be noted. These similarities include the presence within oriTpRJ6 of the three mcb (MobC binding sites), firstly described in pC221 and pC223, an identical nick site also found in these same plasmids, and a nearly identical srapC223 site (sequence recognized by MobA). pRJ6 was successfully transferred to S. epidermidis by conjugation in the presence of the conjugative plasmid pGO1. Altogether these findings suggest that pRJ6 might have been originally a coagulase-negative staphylococci plasmid that had been transferred successfully to S. aureus.
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Porosity estimation by semi-supervised learning with sparsely available labeled samples
Lima, Luiz Alberto,Gö,rnitz, Nico,Varella, Luiz Eduardo,Vellasco, Marley,Mü,ller, Klaus-Robert,Nakajima, Shinichi Elsevier 2017 Computers & geosciences Vol.106 No.-
<P><B>Abstract</B></P> <P>This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, <I>Transductive Conditional Random Field Regression</I> (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A porosity estimation method with simultaneous facies classification is proposed. </LI> <LI> The method combines the benefits of ridge regression and conditional random fields. </LI> <LI> Two preprocessing techniques are introduced, inspired from image processing. </LI> </UL> </P>
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A Modified Robust Adaptive Super-twisting Sliding Mode Controller for Grid-connected Converters
Guilherme Vieira Hollweg,Wencong Su,Paulo Jefferson Dias de Oliveira Evald,Rodrigo Varella Tambara,Hilton Abílio Gründling 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.10
This work introduces the application of a new adaptive control structure, which is a modification of the robust model reference adaptive controller (RMRAC) and adaptive super-twisting sliding mode (ASTSM). This controller was previously proposed in the literature and applied to the current control of a grid-connected converter under uncertain grid environments. However, its STSM structure used the tracking error signal function as a sliding surface, which tends to impose considerable chattering in the system. The proposed controller maintains the characteristics of the known structure but replaces the signal function in the STSM equations with a sigmoid function, reducing the current tracking error and improving the system regulation since it is smoother. As the control structure lies in RMRAC theory and some core equations change, stability analysis of the adaptation algorithm is also carried out. Experimental results in a 7.5 kW converter are presented in which the known RMRAC-ASTSM controller presents 2.45% total harmonic distortion while the modified adaptive structure obtains 2.22% total harmonic distortion and better regulation performance.
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