997 resultados para estimated parameters


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High velocity oxi-fuel (HVOF) thermal spray process has been used in order to deposit a new alloy known as multicomponent white cast iron. The coatings were characterized in terms of macrostructure, phase composition, porosity and hardness. Coating characteristics and properties were found to be dependent on the particles size range, spray distance, gases flow rate and oxygen to propane ratio. For set of parameters utilized in this job a narrow particle size range between 20 and 45 gm with a spray distance of 200 mm and oxygen to propane ratio of 4.6 are the preferred coating parameters. Coating porosity of 0.9% and hardness of 766 HV were obtained under these conditions. (c) 2007 Elsevier B.V. All rights reserved.

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There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

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Three-dimensional modeling of piezoelectric devices requires a precise knowledge of piezoelectric material parameters. The commonly used piezoelectric materials belong to the 6mm symmetry class, which have ten independent constants. In this work, a methodology to obtain precise material constants over a wide frequency band through finite element analysis of a piezoceramic disk is presented. Given an experimental electrical impedance curve and a first estimate for the piezoelectric material properties, the objective is to find the material properties that minimize the difference between the electrical impedance calculated by the finite element method and that obtained experimentally by an electrical impedance analyzer. The methodology consists of four basic steps: experimental measurement, identification of vibration modes and their sensitivity to material constants, a preliminary identification algorithm, and final refinement of the material constants using an optimization algorithm. The application of the methodology is exemplified using a hard lead zirconate titanate piezoceramic. The same methodology is applied to a soft piezoceramic. The errors in the identification of each parameter are statistically estimated in both cases, and are less than 0.6% for elastic constants, and less than 6.3% for dielectric and piezoelectric constants.

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The practicability of estimating directional wave spectra based on a vessel`s 1st order response has been recently addressed by several researchers. Different alternatives regarding statistical inference methods and possible drawbacks that could arise from their application have been extensively discussed, with an apparent preference for estimations based on Bayesian inference algorithms. Most of the results on this matter, however, rely exclusively on numerical simulations or at best on few and sparse full-scale measurements, comprising a questionable basis for validation purposes. This paper discusses several issues that have recently been debated regarding the advantages of Bayesian inference and different alternatives for its implementation. Among those are the definition of the best set of input motions, the number of parameters required for guaranteeing smoothness of the spectrum in frequency and direction and how to determine their optimum values. These subjects are addressed in the light of an extensive experimental campaign performed with a small-scale model of an FPSO platform (VLCC hull), which was conducted in an ocean basin in Brazil. Tests involved long and short crested seas with variable levels of directional spreading and also bimodal conditions. The calibration spectra measured in the tank by means of an array of wave probes configured the paradigm for estimations. Results showed that a wide range of sea conditions could be estimated with good precision, even those with somewhat low peak periods. Some possible drawbacks that have been pointed out in previous works concerning the viability of employing large vessels for such a task are then refuted. Also, it is shown that a second parameter for smoothing the spectrum in frequency may indeed increase the accuracy in some situations, although the criterion usually proposed for estimating the optimum values (ABIC) demands large computational effort and does not seem adequate for practical on-board systems, which require expeditious estimations. (C) 2009 Elsevier Ltd. All rights reserved.

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The objective of this paper is to develop and validate a mechanistic model for the degradation of phenol by the Fenton process. Experiments were performed in semi-batch operation, in which phenol, catechol and hydroquinone concentrations were measured. Using the methodology described in Pontes and Pinto [R.F.F. Pontes, J.M. Pinto, Analysis of integrated kinetic and flow models for anaerobic digesters, Chemical Engineering journal 122 (1-2) (2006) 65-80], a stoichiometric model was first developed, with 53 reactions and 26 compounds, followed by the corresponding kinetic model. Sensitivity analysis was performed to determine the most influential kinetic parameters of the model that were estimated with the obtained experimental results. The adjusted model was used to analyze the impact of the initial concentration and flow rate of reactants on the efficiency of the Fenton process to degrade phenol. Moreover, the model was applied to evaluate the treatment cost of wastewater contaminated with phenol in order to meet environmental standards. (C) 2009 Elsevier B.V. All rights reserved.

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The influence of guar and xanthan gum and their combined use on dough proofing rate and its calorimetric properties was investigated. Fusion enthalpy, which is related to the amount of frozen water, was influenced by frozen dough formulation and storage time; specifically gum addition reduced the fusion enthalpy in comparison to control formulation, 76.9 J/g for formulation with both gums and 81.2 J/g for control, at 28th day. Other calorimetric parameters, such as T(g) and freezable water amount, were also influenced by frozen storage time. For all formulations, proofing rate of dough after freezing, frozen storage time and thawing, decreased in comparison to non-frozen dough, indicating that the freezing process itself was more detrimental to the proofing rate than storage time. For all formulations, the mean value of proofing rate was 2.97 +/- 0.24 cm(3) min(-1) per 100 g of non-frozen dough and 2.22 +/- 0.12 cm(3) min(-1) per 100 g of frozen dough. Also the proofing rate of non-frozen dough with xanthan gum decreased significantly in relation to dough without gums and dough with only guar gum. Optical microscopy analyses showed that the gas cell production after frozen storage period was reduced, which is in agreement with the proofing rate results. (C) 2008 Elsevier Ltd. All rights reserved.

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The trapezium is often a better approximation for the FinFET cross-section shape, rather than the design-intended rectangle. The frequent width variations along the vertical direction, caused by the etching process that is used for fin definition, may imply in inclined sidewalls and the inclination angles can vary in a significant range. These geometric variations may cause some important changes in the device electrical characteristics. This work analyzes the influence of the FinFET sidewall inclination angle on some relevant parameters for analog design, such as threshold voltage, output conductance, transconductance, intrinsic voltage gain (A V), gate capacitance and unit-gain frequency, through 3D numeric simulation. The intrinsic gain is affected by alterations in transconductance and output conductance. The results show that both parameters depend on the shape, but in different ways. Transconductance depends mainly on the sidewall inclination angle and the fixed average fin width, whereas the output conductance depends mainly on the average fin width and is weakly dependent on the sidewall inclination angle. The simulation results also show that higher voltage gains are obtained for smaller average fin widths with inclination angles that correspond to inverted trapeziums, i.e. for shapes where the channel width is larger at the top than at the transistor base because of the higher attained transconductance. When the channel top is thinner than the base, the transconductance degradation affects the intrinsic voltage gain. The total gate capacitances also present behavior dependent on the sidewall angle, with higher values for inverted trapezium shapes and, as a consequence, lower unit-gain frequencies.

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The temperature influence on the gate-induced floating body effect (GIFBE) in fully depleted (FD) silicon-on-insulator (SOI) nMOSFETs is investigated, based on experimental results and two-dimensional numerical simulations. The GIFBE behavior will be evaluated taking into account the impact of carrier recombination and of the effective electric field mobility degradation on the second peak in the transconductance (gm). This floating body effect is also analyzed as a function of temperature. It is shown that the variation of the studied parameters with temperature results in a ""C"" shape of the threshold voltage corresponding with the second peak in the gm curve. (C) 2008 Elsevier Ltd. All rights reserved.

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Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.

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In this paper, we devise a separation principle for the finite horizon quadratic optimal control problem of continuous-time Markovian jump linear systems driven by a Wiener process and with partial observations. We assume that the output variable and the jump parameters are available to the controller. It is desired to design a dynamic Markovian jump controller such that the closed loop system minimizes the quadratic functional cost of the system over a finite horizon period of time. As in the case with no jumps, we show that an optimal controller can be obtained from two coupled Riccati differential equations, one associated to the optimal control problem when the state variable is available, and the other one associated to the optimal filtering problem. This is a separation principle for the finite horizon quadratic optimal control problem for continuous-time Markovian jump linear systems. For the case in which the matrices are all time-invariant we analyze the asymptotic behavior of the solution of the derived interconnected Riccati differential equations to the solution of the associated set of coupled algebraic Riccati equations as well as the mean square stabilizing property of this limiting solution. When there is only one mode of operation our results coincide with the traditional ones for the LQG control of continuous-time linear systems.

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Distribution of timing signals is an essential factor for the development of digital systems for telecommunication networks, integrated circuits and manufacturing automation. Originally, this distribution was implemented by using the master-slave architecture with a precise master clock generator sending signals to phase-locked loops (PLL) working as slave oscillators. Nowadays, wireless networks with dynamical connectivity and the increase in size and operation frequency of the integrated circuits suggest that the distribution of clock signals could be more efficient if mutually connected architectures were used. Here, mutually connected PLL networks are studied and conditions for synchronous states existence are analytically derived, depending on individual node parameters and network connectivity, considering that the nodes are nonlinear oscillators with nonlinear coupling conditions. An expression for the network synchronisation frequency is obtained. The lock-in range and the transmission error bounds are analysed providing hints to the design of this kind of clock distribution system.

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Postural control was studied when the subject was kneeling with erect trunk in a quiet posture and compared to that obtained during quiet standing. The analysis was based on the center of pressure motion in the sagittal plane (CPx), both in the time and in the frequency domains. One could assume that postural control during kneeling would be poorer than in standing because it is a less natural posture. This could cause a higher CPx variability. The power spectral density (PSD) of the CPx obtained from the experimental data in the kneeling position (KN) showed a significant decrease at frequencies below 0.3 Hz compared to upright (UP) (P < 0.01), which indicates less sway in KN. Conversely, there was an increase in fast postural oscillations (above 0.7 Hz) during KN compared to UP (P < 0.05). The root mean square (RMS) of the CPx was higher for UP (P < 0.01) while the mean velocity (MV) was higher during KN (P < 0.05). Lack of vision had a significant effect on the PSD and the parameters estimated from the CPx in both positions. We also sought to verify whether the changes in the PSD of the CPx found between the UP and KN positions were exclusively due to biomechanical factors (e.g., lowered center of gravity), or also reflected changes in the neural processes involved in the control of balance. To reach this goal, besides the experimental approach, a simple feedback model (a PID neural system, with added neural noise and controlling an inverted pendulum) was used to simulate postural sway in both conditions (in KN the pendulum was shortened, the mass and the moment of inertia were decreased). A parameter optimization method was used to fit the CPx power spectrum given by the model to that obtained experimentally. The results indicated that the changed anthropometric parameters in KN would indeed cause a large decrease in the power spectrum at low frequencies. However, the model fitting also showed that there were considerable changes also in the neural subsystem when the subject went from standing to kneeling. There was a lowering of the proportional and derivative gains and an increase in the neural noise power. Additional increases in the neural noise power were found also when the subject closed his eyes.

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In this paper, we deal with a generalized multi-period mean-variance portfolio selection problem with market parameters Subject to Markov random regime switchings. Problems of this kind have been recently considered in the literature for control over bankruptcy, for cases in which there are no jumps in market parameters (see [Zhu, S. S., Li, D., & Wang, S. Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean variance formulation. IEEE Transactions on Automatic Control, 49, 447-457]). We present necessary and Sufficient conditions for obtaining an optimal control policy for this Markovian generalized multi-period meal-variance problem, based on a set of interconnected Riccati difference equations, and oil a set of other recursive equations. Some closed formulas are also derived for two special cases, extending some previous results in the literature. We apply the results to a numerical example with real data for Fisk control over bankruptcy Ill a dynamic portfolio selection problem with Markov jumps selection problem. (C) 2008 Elsevier Ltd. All rights reserved.

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In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

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Grass reference evapotranspiration (ETo) is an important agrometeorological parameter for climatological and hydrological studies, as well as for irrigation planning and management. There are several methods to estimate ETo, but their performance in different environments is diverse, since all of them have some empirical background. The FAO Penman-Monteith (FAD PM) method has been considered as a universal standard to estimate ETo for more than a decade. This method considers many parameters related to the evapotranspiration process: net radiation (Rn), air temperature (7), vapor pressure deficit (Delta e), and wind speed (U); and has presented very good results when compared to data from lysimeters Populated with short grass or alfalfa. In some conditions, the use of the FAO PM method is restricted by the lack of input variables. In these cases, when data are missing, the option is to calculate ETo by the FAD PM method using estimated input variables, as recommended by FAD Irrigation and Drainage Paper 56. Based on that, the objective of this study was to evaluate the performance of the FAO PM method to estimate ETo when Rn, Delta e, and U data are missing, in Southern Ontario, Canada. Other alternative methods were also tested for the region: Priestley-Taylor, Hargreaves, and Thornthwaite. Data from 12 locations across Southern Ontario, Canada, were used to compare ETo estimated by the FAD PM method with a complete data set and with missing data. The alternative ETo equations were also tested and calibrated for each location. When relative humidity (RH) and U data were missing, the FAD PM method was still a very good option for estimating ETo for Southern Ontario, with RMSE smaller than 0.53 mm day(-1). For these cases, U data were replaced by the normal values for the region and Delta e was estimated from temperature data. The Priestley-Taylor method was also a good option for estimating ETo when U and Delta e data were missing, mainly when calibrated locally (RMSE = 0.40 mm day(-1)). When Rn was missing, the FAD PM method was not good enough for estimating ETo, with RMSE increasing to 0.79 mm day(-1). When only T data were available, adjusted Hargreaves and modified Thornthwaite methods were better options to estimate ETo than the FAO) PM method, since RMSEs from these methods, respectively 0.79 and 0.83 mm day(-1), were significantly smaller than that obtained by FAO PM (RMSE = 1.12 mm day(-1). (C) 2009 Elsevier B.V. All rights reserved.