Active control of vibrant actuators with adaptive adjustment of the reference

An improved adaptive filter for eliminating periodic vibrations of unknown frequency in rotary machinery is presented. The proposed canceller is based on a usual bank of digital adaptive notch filters, each filter tuned in the cancellation of one harmonic. The amplitude and phase of each harmonic is adaptively adjusted by an LMS-based algorithm. Moreover, the central frequency of each notch filter is also adaptively adjusted (fine tuning). The resulting algorithm, tested in an industrial application, shows effectiveness in cancelling unknown periodic disturbances, reducing environmental noise and maintenance problems.Peer ReviewedPostprint (published version

Adaptive us-mrac for disturbance cancellation

A variable structure, model reference adaptive control (VS-MRAC) devoted to cancel interferences without the requirement of an auxiliary input is proposed. This method is an improved alternative to the strategies recently proposed in the control theory literature.Peer ReviewedPostprint (published version

Measuring Regional Cohesion Effects of Large-scale Transport Infrastructure Investments: An Accessibility Approach.

Cohesion is considered one of the main policy goals both at a EU an national level. However, there is currently a lack of a common approach to measure cohesion effects of large-scale transport infrastructure investments. Accessibility indicators have an unexploited potential in transportation assessment methodologies. Accessibility is considered an added value of locations, which represents one of the elements contributing to a region’s welfare. Therefore, spatial distribution of accessibility may be used as a proxy to assess regional cohesion. This paper suggests an approach consisting in measuring changes in the spatial distribution of four different accessibility indicators, computed and mapped using a GIS support. Cohesion is subsequently measured calculating a set of inequality indices of the resulting accessibility distribution. It is possible then to assess whether disparities in regional accessibility are increased or reduced after the implementation of a new transport infrastructure. This approach is tested assessing regional cohesion effects of road and rail network developments in Spain in the period 1992-2004. Comparing the results obtained with accessibility indicators and inequality indices allows identifying the main critical factors and sources of bias. The conclusion is that for the rode mode, cohesion has improved, while regional disparities have increased for the rail mode

Pulsations in M dwarf stars

We present the results of the first theoretical non-radial non-adiabatic pulsational study of M dwarf stellar models with masses in the range 0.1 to 0.5M_solar. We find the fundamental radial mode to be unstable due to an \epsilon mechanism caused by deuterium (D-) burning for the young 0.1 and 0.2M_solar models, by non-equilibrium He^3 burning for the 0.2 and 0.25M_solar models of 10^4Myr, and by a flux blocking mechanism for the partially convective 0.4 and 0.5M_solar models once they reach the age of 500Myr. The periods of the overstable modes excited by the D-burning are in the range 4.2 to 5.2h for the 0.1M_solar models and is of order 8.4h for the 0.2M_solar models. The periods of the modes excited by He^3 burning and flux blocking are in the range 23 to 40min. The more massive and oldest models are more promising for the observational detection of pulsations, as their ratio of instability e-folding time to age is more favourable.Comment: 5 pages, 4 figures, 2 tables. Accepted for publication in MNRAS Letters on 2011 October 11 Version 2 is the accepted manuscript with changes in boldfac

An experimental course on digital communications

In this paper a laboratory course on digital communications is presented. This course has been designed for medium degree professionals in the telecommunications field, and it is based on training equipment developed to change the usual theoretical classrooms for laboratory seminars.Peer ReviewedPostprint (published version

Dynamic selection and estimation of the digital predistorter parameters for power amplifier linearization

© © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a new technique that dynamically estimates and updates the coefficients of a digital predistorter (DPD) for power amplifier (PA) linearization. The proposed technique is dynamic in the sense of estimating, at every iteration of the coefficient's update, only the minimum necessary parameters according to a criterion based on the residual estimation error. At the first step, the original basis functions defining the DPD in the forward path are orthonormalized for DPD adaptation in the feedback path by means of a precalculated principal component analysis (PCA) transformation. The robustness and reliability of the precalculated PCA transformation (i.e., PCA transformation matrix obtained off line and only once) is tested and verified. Then, at the second step, a properly modified partial least squares (PLS) method, named dynamic partial least squares (DPLS), is applied to obtain the minimum and most relevant transformed components required for updating the coefficients of the DPD linearizer. The combination of the PCA transformation with the DPLS extraction of components is equivalent to a canonical correlation analysis (CCA) updating solution, which is optimum in the sense of generating components with maximum correlation (instead of maximum covariance as in the case of the DPLS extraction alone). The proposed dynamic extraction technique is evaluated and compared in terms of computational cost and performance with the commonly used QR decomposition approach for solving the least squares (LS) problem. Experimental results show that the proposed method (i.e., combining PCA with DPLS) drastically reduces the amount of DPD coefficients to be estimated while maintaining the same linearization performance.Peer ReviewedPostprint (author's final draft

CC and NC pion production

The disappearance searching experiments nu_mu ---> nu_x use charged current quasielastic (CCQE) reaction to detect an arriving neutrino and reconstruct its energy, while the CC1 pi^+ production can mimic the CCQE signal process. In nu_mu --->nu_e appearance experiments, the NC1 pi^0 production process can lead to a fake e^- event by the impossibility for the detector of distinguish an arriving electron or a photon. Here we present a consistent model, from the point of view of the construction of the elemental amplitude, for the mentioned pion production background processes including bounding, smearing and final state interaction (FSI) effects in a single fashion. Results are comparable with more evolved approaches based on Monte Carlo simulations.Fil: Mariano, Alejandro Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFil: Barbero, César Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; ArgentinaFil: López Castro, Gabriel. Instituto Politécnico Nacional. Centro de Investigación y de Estudios Avanzados. Departamento de Física; Méxic

Sequential Deliberation in Collective Decision-Making: The Case of the FOMC

A process of deliberation, in which policymakers exchange information prior to formal voting procedures, precedes almost every collective decision. Yet, beyond scarce evidence coming from field and laboratory experiments, few studies have analyzed the role played by sequential deliberation in policy-relevant decision-making bodies. To fill this gap, I estimate an empirical model of policy-making that incorporates social learning via deliberation. In the model, committee members speak in sequence, allowing them to weight their own information and biases against recommendations made by others. The empirical model is structurally estimated using historical transcripts from the Federal Open Market Committee (FOMC), which is the body in charge of implementing monetary policy in the United States. I find the process of deliberation significantly changes members’ behavior: a typical FOMC member would modify her policy recommendation in 36% of the meetings after listening to previous speakers, with respect to the scenario where members exclusively follow their private information. Counterfactual simulations show modest gains of modifying the order of speech on the quality of the committee’s policy choices. Incorporating sequential learning explains the pattern of individual recommendations and collective choices extremely well and improves the fit over behavioral models that ignore deliberation