Adaptive reference model predictive control for power electronics

An adaptive reference model predictive control (ARMPC) approach is proposed as an alternative means of controlling power converters in response to the issue of steady-state residual errors presented in power converters under the conventional model predictive control (MPC). Differing from other methods of eliminating steady-state errors of MPC based control, such as MPC with integrator, the proposed ARMPC is designed to track the so-called virtual references instead of the actual references. Subsequently, additional tuning is not required for different operating conditions. In this paper, ARMPC is applied to a single-phase full-bridge voltage source inverter (VSI). It is experimentally validated that ARMPC exhibits strength in substantially eliminating the residual errors in environment of model mismatch, load change, and input voltage change, which would otherwise be present under MPC control. Moreover, it is experimentally demonstrated that the proposed ARMPC shows a consistent erasion of steady-state errors, while the MPC with integrator performs inconsistently for different cases of model mismatch after a fixed tuning of the weighting factor

Effect of blood's velocity on blood resistivity

Blood resistivity is an important quantity whose value influences the results of various methods used in the study of heart and circulation. In this paper, the relationship between blood resistivity and velocity of blood flow was evaluated and analyzed based upon a probe using six-ring electrodes and a circulatory model. The experimental results indicated that the change in blood resistivity was only ±1.1% when the velocity of blood flow changed from 2.83 to 40 cm/s and it rose to 23% when the velocity was lower than 2.83 cm/s

Improved approximate QR-LS algorithms for adaptive filtering

This paper studies a class of O(N) approximate QR-based least squares (A-QR-LS) algorithm recently proposed by Liu in 1995. It is shown that the A-QR-LS algorithm is equivalent to a normalized LMS algorithm with time-varying stepsizes and element-wise normalization of the input signal vector. It reduces to the QR-LMS algorithm proposed by Liu et al. in 1998, when all the normalization constants are chosen as the Euclidean norm of the input signal vector. An improved transform-domain approximate QR-LS (TA-QR-LS) algorithm, where the input signal vector is first approximately decorrelated by some unitary transformations before the normalization, is proposed to improve its convergence for highly correlated signals. The mean weight vectors of the algorithms are shown to converge to the optimal Wiener solution if the weighting factor w of the algorithm is chosen between 0 and 1. New Givens rotations-based algorithms for the A-QR-LS, TA-QR-LS, and the QR-LMS algorithms are proposed to reduce their arithmetic complexities. This reduces the arithmetic complexity by a factor of 2, and allows square root-free versions of the algorithms be developed. The performances of the various algorithms are evaluated through computer simulation of a system identification problem and an acoustic echo canceller. © 2004 IEEE.published_or_final_versio

Learning a Mixture of Deep Networks for Single Image Super-Resolution

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and be optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and different network architectures. Compared with other current image SR approaches, our proposed method achieves state-of-the-arts restoration results on a wide range of images consistently while allowing more flexible design choices. The source codes are available in http://www.ifp.illinois.edu/~dingliu2/accv2016

The association of HBV core promoter double mutations (A1762T and G1764A) with viral load differs between HBeAg positive and anti-HBe positive individuals: A longitudinal analysis

Background/Aims: Although there have been a few reports regarding the effect of basal core promoter (BCP) double mutations (A1762T and G1764A) on hepatitis B viral loads, the association remains uncertain. We aim to determine the association after controlling for HBeAg - a strong confounding factor.Methods: We selected randomly 190 individuals from a Chinese cohort of 2258 subjects for cross-sectional analysis and 56 of the 190 for longitudinal analysis of viral loads.Results: In multivariable analysis of the cross-sectional data, BCP double mutations are significantly associated with lower viral loads in HBeAg positive subjects but no difference was found in anti-HBe positive subjects. Triple mutations at nucleotide (nt) 1753, 1762 and 1764 and mutations between nt 1809 and 1817, precore stop mutation (nt 1896) and genotype are not associated with viral loads in either HBeAg or anti-HBe positive subjects. Analysis of the longitudinal data yielded similar results to the cross-sectional data. Viral loads differ significantly between individuals infected with wild-type and BCP double mutations prior to HBeAg seroconversion but this difference is lost after seroconversion.Conclusions: BCP double mutations are associated with lower viral loads in HBeAg positive individuals but have no effect on the viral loads of anti-HBe positive individuals. (C) 2008 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved

Low-power wind energy conversion system with variable structure control for DC grids

This paper presents a discussion on the use of variable structure control, i.e., sliding mode control, for improving the dynamic control performance of a low-power wind energy conversion system (WECS) that is connected to a DC microgrid. The sliding mode control is applied to the wind turbine system to extract the maximum possible power from the wind, thus achieving the state of maximum power point tracking to reach the maximum power generation (MPG), and also applied to the power converter to reach the maximum power injection (MPI) to the load. The amount of energy extractable from a dynamically changing wind using the WECS with sliding mode control is compared with that of the classic PI controller. Simulation results show that for a dynamically changing wind, more energy can be harvested with the sliding mode control as compared to the PI control. © 2014 IEEE.published_or_final_versio

Nonlinear Dynamic Power Tracking of Low-Power Wind Energy Conversion System

This paper addresses the use of variable structure control (i.e., sliding mode (SM) control) for improving the dynamic performance of a low-power wind energy conversion system (WECS) that is connected to a dc grid. The SM control is applied to simultaneously match 1) the maximum power generation of the wind turbine system from the wind with 2) the maximum power injection of the grid-connected power converter into the grid. The amount of energy extractable from a dynamically changing wind using the WECS with SM control is compared with that of classic PI control. Both the simulation and experimental results show that more energy can be harvested with the SM control as compared to the PI control for any dynamically changing or random wind conditions

MOTSA TOF-MRA using multi-oblique-stacks acquisition (MOSA)

One of the intrinsic advantages of current TOF MRA techniques is their insensitivity to in-plane blood flow or turbulent flow, causing hypointense signal or discontinuity in blood vessels in MRA images. To overcome this problem, a multi-oblique-stacks acquisition (MOSA) technique is proposed to improve the visualization of in-plane blood flows in MRA. The results showed that TOF-MRA obtained from MOSA was improved as compared to that of conventional MOTSA for the same amount of scan time.published_or_final_versio

Deformation of the Fermi surface in the extended Hubbard model

The deformation of the Fermi surface induced by Coulomb interactions is investigated in the t-t'-Hubbard model. The interplay of the local U and extended V interactions is analyzed. It is found that exchange interactions V enhance small anisotropies producing deformations of the Fermi surface which break the point group symmetry of the square lattice at the Van Hove filling. This Pomeranchuck instability competes with ferromagnetism and is suppressed at a critical value of U(V). The interaction V renormalizes the t' parameter to smaller values what favours nesting. It also induces changes on the topology of the Fermi surface which can go from hole to electron-like what may explain recent ARPES experiments.Comment: 5 pages, 4 ps figure