Time misalignments in fault detection and diagnosis

El desalineamiento temporal es la incorrespondencia de dos señales debido a una distorsión en el eje temporal. La Detección y Diagnóstico de Fallas (Fault Detection and Diagnosis-FDD) permite la detección, el diagnóstico y la corrección de fallos en un proceso. La metodología usada en FDD está dividida en dos categorías: técnicas basadas en modelos y no basadas en modelos. Esta tesis doctoral trata sobre el estudio del efecto del desalineamiento temporal en FDD. Nuestra atención se enfoca en el análisis y el diseño de sistemas FDD en caso de problemas de comunicación de datos, como retardos y pérdidas. Se proponen dos técnicas para reducir estos problemas: una basada en programación dinámica y la otra en optimización. Los métodos propuestos han sido validados sobre diferentes sistemas dinámicos: control de posición de un motor de corriente continua, una planta de laboratorio y un problema de sistemas eléctricos conocido como hueco de tensión.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Genetic least squares for system identification

The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the standard recursive least-squares algorithm using only a forgetting factor.

Modelling of surface identifying characteristics using Fourier series

Texture and small-scale surface details are widely recognised as playing an important role in the haptic identification of objects. In order to simulate realistic textures in haptic virtual environments, it has become increasingly necessary to identify a robust technique for modelling of surface profiles. This paper describes a method whereby Fourier series spectral analysis is employed in order to describe the measured surface profiles of several characteristic surfaces. The results presented suggest that a bandlimited Fourier series can be used to provide a realistic approximation to surface amplitude profiles.

Modelling and inverting complex-valued Wiener systems

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memory

Mechanism for synchronized motion between two humans in mutual tapping experiments: Transition from alternative mode to synchronization mode

We performed mutual tapping experiments between two humans to investigate the conditions required for synchronized motion. A transition from an alternative mode to a synchronization mode was discovered under the same conditions when a subject changed from a reactive mode to an anticipation mode in single tapping experiments. Experimental results suggest that the cycle time for each tapping motion is tuned by a proportional control that is based on synchronization errors and cycle time errors. As the tapping frequency increases, the mathematical model based on the feedback control in the sensory-motor closed loop predicts a discrete mode transition as the gain factors of the proportional control decease. The conditions of the synchronization were shown as a consequence of the coupled dynamics based on the subsequent feedback loop in the sensory-motor system.

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.

Flow injection analysis using carbon film resistor electrodes for amperometric determination of ambroxol

Flow injection analysis (FIA) using a carbon film sensor for amperometric detection was explored for ambroxol analysis in pharmaceutical formulations. The specially designed flow cell designed in the lab generated sharp and reproducible current peaks, with a wide linear dynamic range from 5 x 10(-7) to 3.5 x 10(-4) mol L-1, in 0.1 mol L-1 sulfuric acid electrolyte, as well as high sensitivity, 0.110 A mol(-1) L cm(-2) at the optimized flow rate. A detection limit of 7.6 x 10(-8) mol L-1 and a sampling frequency of 50 determinations per hour were achieved, employing injected volumes of 100 mu L and a flow rate of 2.0 mL min(-1). The repeatability, expressed as R.S.D. for successive and alternated injections of 6.0 x 10(-6) and 6.0 x 10(-5) mol L-1 ambroxol solutions, was 3.0 and 1.5%, respectively, without any noticeable memory effect between injections. The proposed method was applied to the analysis of ambroxol in pharmaceutical samples and the results obtained were compared with UV spectrophotometric and acid-base titrimetric methods. Good agreement between the results utilizing the three methods and the labeled values was achieved, corroborating the good performance of the proposed electrochemical methodology for ambroxol analysis. (C) 2008 Elsevier B.V. All rights reserved.

New hydrazine sensors based on electropolymerized meso-tetra (4-sulphonatephenyl)porphyrinate manganese (III)/silver nanomaterial

A new electrocatalytic active porphyrin nanocomposite material was obtained by electropolymerization of meso-tetra(4-sulphonatephenyl) porphyrinate manganese(III) complex (MnTPPS) in alkaline solutions containing sub-micromolar concentrations of silver chloride. The modified glassy carbon electrodes efficiently oxidize hydrazine at 10 mV versus Ag/AgCl, dramatically decreasing the overpotential of conventional carbon electrodes. The analytical characteristics of this amperometric sensor coupled with batch injection analysis (BIA) technique were explored. Wide linear dynamic range (2.5 x 10(-7) to 2.5 x 10(-4) mol L-1), good repeatability (R.S.D. = 0.84%, n = 30) and low detection (3.1 x 10(-8) mol L-1) and quantification (1.0 x 10(-7) mol L-1) limits, as well as very fast sampling frequency (60 determinations per hour) were achieved. (c) 2007 Elsevier B.V. All rights reserved.

Square wave adsorptive cathodic stripping voltarnmetry automated by sequential injection analysis - Potentialities and limitations exemplified by the determination of methyl parathion in water samples

This paper describes the development and evaluation of a sequential injection method to automate the determination of methyl parathion by square wave adsorptive cathodic stripping voltammetry exploiting the concept of monosegmented flow analysis to perform in-line sample conditioning and standard addition. Accumulation and stripping steps are made in the sample medium conditioned with 40 mmol L-1 Britton-Robinson buffer (pH 10) in 0.25 mol L-1 NaNO3. The homogenized mixture is injected at a flow rate of 10 mu Ls(-1) toward the flow cell, which is adapted to the capillary of a hanging drop mercury electrode. After a suitable deposition time, the flow is stopped and the potential is scanned from -0.3 to -1.0 V versus Ag/AgCl at frequency of 250 Hz and pulse height of 25 mV The linear dynamic range is observed for methyl parathion concentrations between 0.010 and 0.50 mgL(-1), with detection and quantification limits of 2 and 7 mu gL(-1), respectively. The sampling throughput is 25 h(-1) if the in line standard addition and sample conditioning protocols are followed, but this frequency can be increased up to 61 h(-1) if the sample is conditioned off-line and quantified using an external calibration curve. The method was applied for determination of methyl parathion in spiked water samples and the accuracy was evaluated either by comparison to high performance liquid chromatography with UV detection, or by the recovery percentages. Although no evidences of statistically significant differences were observed between the expected and obtained concentrations, because of the susceptibility of the method to interference by other pesticides (e.g., parathion, dichlorvos) and natural organic matter (e.g., fulvic and humic acids), isolation of the analyte may be required when more complex sample matrices are encountered. (C) 2007 Elsevier B.V. All rights reserved.

Evaluation of monolithic columns for determination of formaldehyde and acetaldehyde in sugar cane spirits by High-Performance Liquid Chromatography

High-Performance Liquid Chromatography (HPLC) conditions are described for separation of 2,4-dinitrophenylhydrazone (2,4-DNPH) derivatives of carbonyl compounds in a 10 cm long C-18 reversed phase monolithic column. Using a linear gradient from 40 to 77% acetonitrile (acetonitrile-water system), the separation was achieved in about 10 min-a time significantly shorter than that obtained with a packed particles column. The method was applied for determination of formaldehyde and acetaldehyde in Brazilian sugar cane spirits. The linear dynamic range was between 30 and 600 mu g L-1, and the detection limits were 8 and 4 mu g L-1 for formaldehyde and acetaldehyde, respectively.

THE POTENTIALITIES OF USING A GRAPHITE-SILICONE RUBBER COMPOSITE ELECTRODE IN THE DETERMINATION OF PROPRANOLOL

A graphite silicone-rubber composite electrode (GSR) was used for the determination of propranolol in drug formulation. Cyclic voltammetry (CV) at the GSR presented an irreversible oxidation peak at + 0.8V vs. SCE, in Britton Robinson (B-R) buffer pH 7.4. The quantitative determination was carried out using differential pulse voltammetry (DPV). Under optimized parameters a linear dynamic range from 5.0 to 80.6 mu mol L(-1) with a detection limit of 1.1 mu mol L(-1) was observed. A repeatability of 4.5 +/- 0.1 mu A (n = 10) peak current was found after 10 successive DPV voltammograms of propranolol in the same solution after surface renovations. Using the proposed electrode, propranolol was quantified in a pharmaceutical formulation with results that agreed within 95% confidence level (t-test) with those from an official method.

Analytical Potentialities of Carbon Nanotube/Silicone Rubber Composite Electrodes: Determination of Propranolol

A new composite electrode based on multiwall carbon nanotubes (MWCNT) and silicone-rubber (SR) was developed and applied to the determination of propranolol in pharmaceutical formulations. The effect of using MWCNT/graphite mixtures in different proportions was also investigated. Cyclic voltammetry and electrochemical impedance spectroscopy were used for electrochemical characterization of different electrode compositions. Propranolol was determined using MWCNT/SR 70% (m/m) electrodes with linear dynamic ranges up to 7.0 mu molL(-1) by differential pulse and up to 5.4 mu molL(-1) by square wave voltammetry, with LODs of 0.12 and 0.078 mu molL(-1), respectively. Analysis of commercial samples agreed with that obtained by the official spectrophotometric method. The electrode is mechanically robust and presented reproducible results and a long useful life.

Consumo de bens duráveis e poupança em uma nova trajetória de comportamento do consumidor brasileiro

O trabalho avalia a dinâmica descrita pelo consumo de bens duráveis e poupança dos consumidores brasileiros entre setembro de 2005 e abril de 2011 e contribui com a literatura ao utilizar como ferramenta de análise um modelo autoregressivo com valor limite endógeno e dados qualitativos da pesquisa Sondagem de Expectativas do Consumidor Brasileiro, da FGV. Indicadores qualitativos para essas duas variáveis foram calculados e a metodologia proposta permitiu investigar, simultaneamente, a linearidade e estacionaridade de suas trajetórias. Os resultados sugerem, em ambos os casos, uma dinâmica não-linear com raiz unitária parcial. Adicionalmente, a estacionaridade constatada a partir de um valor limite estimado de 3,3 pontos percentuais para o Indicador de Compras de Bens Duráveis e de 3,6 pontos percentuais para o Indicador de Poupança permitem classificar seus históricos com indícios de saturação da capacidade de poupança e consumo dos indivíduos.

A time-varying markov-switching model for economic growth

This paper investigates economic growth’s pattern of variation across and within countries using a Time-Varying Transition Matrix Markov-Switching Approach. The model developed follows the approach of Pritchett (2003) and explains the dynamics of growth based on a collection of different states, each of which has a sub-model and a growth pattern, by which countries oscillate over time. The transition matrix among the different states varies over time, depending on the conditioning variables of each country, with a linear dynamic for each state. We develop a generalization of the Diebold’s EM Algorithm and estimate an example model in a panel with a transition matrix conditioned on the quality of the institutions and the level of investment. We found three states of growth: stable growth, miraculous growth, and stagnation. The results show that the quality of the institutions is an important determinant of long-term growth, whereas the level of investment has varying roles in that it contributes positively in countries with high-quality institutions but is of little relevance in countries with medium- or poor-quality institutions.