Generalized Transfer Function Model Research Articles

Single measurement estimation of central blood pressure using an arterial transfer function

Background: Central blood pressure (BP) better reflects the loading conditions on the major organs and is more closely correlated with future cardiovascular events. The increased invasiveness and risk of infection prevents the routine measurement of central BP. Arterial transfer functions can provide central BP estimates from clinically available peripheral measurements. However, current methods are either generalized, potentially lacking the ability to adapt to inter and intra subject variability, or individualized based on additional, clinically unavailable, pulse transit time measurements. This work proposes a novel, self-contained method for individualizing an arterial transfer function from a single peripheral pressure measurement, capable of accurately estimating central BP in a range of hemodynamic conditions. Methods: Pulse wave analysis of femoral BP waves was employed to formulate initial approximations of central BP and arterial inlet flow waveforms, to serve as objective functions for the identification of all model parameters. Root mean squared error (RMSE), and systolic and pulse pressure errors were assessed with respect to invasive aortic BP measurements in a seven (7) porcine endotoxin experiments. Systolic and pulse pressure errors were analysed using Bland-Altman analysis. Method accuracy is also compared with an idealized transfer function, derived using the measured aortic-femoral pulse transit time and minimizing the RMSE of model output pressure with respect to reference aortic pressure, a generalized transfer function model, and invasive femoral pressure measurements. Results: Mean bias and limits of agreement (95% CI) for the proposed method were 1.0(-4.6, 6.7)mmHg and -1.0(-6.6, 4.6)mmHg for systolic and pulse pressure, respectively, compared to 3.6(-0.9, 8.2)mmHg and 2.7(-1.8, 7.3)mmHg for the generalized transfer function model. Mean bias and limits of agreement for femoral pressure measurements were -6.4(-15.0, 2.3)mmHg and -9.4(-18.1, -0.8)mmHg, for systolic and pulse pressure, respectively. The pooled mean and standard deviation of the RMSE produced by the single measurement method, relative to reference aortic pressure, was 4.3(1.1)mmHg, consistent with estimates produced by the idealized transfer function, 3.9(1.2)mmHg, and improving of the generalized transfer function, 4.6(1.4)mmHg. Conclusions: The proposed single measurement method provides accurate central BP estimates from routinely available peripheral pressure measurements, and nothing else. The method allows for the individualization of transfer functions on a per patient basis to better capture changes in patient condition during the progression of disease and subsequent treatment, at no additional clinical cost.

Transfer of macroeconomic shocks in stress tests modeling

In this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described employing the Bayesian approach and using the prior information provided by the impact measures. In addition, we illustrate the use of the estimated models from the credit risk data of a portfolio.

Transmission of macroeconomic shocks to risk parameters: Their uses in stress testing

AbstractIn this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described employing the Bayesian approach and using the prior information provided by the impact measures. In addition, we illustrate the use of the estimated models from the credit risk data of a portfolio.

Transmission of Macroeconomic Shocks to Risk Parameters: Their Uses in Stress Testing

In this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described employing the Bayesian approach and using the prior information provided by the impact measures. In addition, we illustrate the use of the estimated models from the credit risk data of a portfolio.

Decentralized load frequency control in deregulated environments

Decentralized load frequency control (LFC) problem for multi-area power systems in deregulated environments is studied in this paper. A decentralized PID tuning method is proposed by assuming that the tie-line power flows are disconnected. The general transfer function model of LFC for decentralized control design of deregulated power systems is derived first, and then an IMC-PID method is used to design the local load frequency controllers. A method to analyze the stability of the decentralized LFC for conventional environments is also extended to the deregulated case. The proposed method is tested on a four-area power system under various operating conditions. Simulation results show that the proposed design method is simple to follow, can achieve good performance, and results in implementable PID controllers.

Identification of 5-Axis Machine Tools Feed Drive Systems for Contouring Simulation

An identification technique is introduced for identifying closed loop transfer function of machine tool’s feed drive systems to be used in simulation of the tracking and contouring performance of Computer Numerical Controlled (CNC) machine tools. The identification is performed from air-cutting tests utilizing only standard G-codes containing linear motion commands. A general transfer function model is derived for representing the closed loop tracking response of the feed drive system. The model considers the drive to be controlled by commonly used controller schemes such as P-PI Cascade, PID or the Sliding Mode Controller (SMC) with feed-forward dynamic and friction compensation. The parameters of the model transfer function are fitted tominimize the discrepancy between the actual and predicted axis position on the axis. In order to guarantee the stability of the identified model transfer function, bounds on the pole locations are imposed. The resultant constrained non-linear optimization problem is solved efficiently using the Particle Swarm Optimization (PSO) method. For achieving reliable convergence of the stochastic PSO algorithm, a parameter tuning strategy is presented. Simulation and experimental studies show that the identified feed drive model captures the fundamental dynamics of the drives system accurately for simulating their closed loop response. Combined with the kinematics of the machine, contouring errors of 5-axis CNC machine tools during simultaneous multi-axis motion are predicted.

Load Frequency Control of an Isolated Small Hydro Power Plant Using Multi-pipe Scheme

In this article, a load frequency control technique for an isolated small hydro power plant based on a multiple flow control system is proposed. In general, the frequency of the system is controlled by employing a dump load whose rating is equal to the rated power output of the plant. The proposed scheme reduces the size of the dump load to 50%, as 50% input power of the hydro plant is controlled by an on/off control. A generalized transfer function model for the system is developed with on/off control logic, and the parameters were optimized using the integral time absolute error technique. The on/off control linearly raises or lowers the generation. Finally, transient responses are shown for low, medium, and high head installations with different loading conditions.

Daily river flow forecasting using wavelet ANN hybrid models

Advance time step stream flow forecasting is of paramount importance in controlling flood damage. During the past few decades, artificial neural network (ANN) techniques have been used extensively in stream flow forecasting and have proven to be a better technique than other forecasting methods such as multiple regression and general transfer function models. This study uses discrete wavelet transformation functions to preprocess the time series of the flow data into wavelet coefficients of different frequency bands. Effective wavelet coefficients are selected from the correlation analysis of the decomposed wavelet coefficients of all frequency bands with the observed flow data. Neural network models are proposed for 1-, 2- and 3-day flow forecasting at a site of Brahmani River, India. The effective wavelet coefficients are used as input to the neural network models. Both the wavelet and ANN techniques are employed to form a loose type of wavelet ANN hybrid model (NW). The hybrid models are trained using Levenberg–Marquart (LM) algorithm and the results are compared with simple ANN models. The results revealed that the predictabilities of NW models are significantly superior to conventional ANN models. The peak flow conditions are predicted with better accuracy using NW models than compared to ANN models.

A new load frequency control technique for an isolated small hydropower plant

In this paper a new load frequency control technique for an isolated small hydropower plant is proposed. In general, the frequency of the system is controlled by employing a dump load, whose rating is equal to the rated power output of the plant. The scheme proposed reduces the size of the dump load to 50 per cent, as 50 per cent input power of the hydro plant is controlled by an on/off control. A generalized transfer function model for the system is developed with an on/off control logic, which linearly raises or lowers the generation. Finally, transient responses are shown for low, medium, and high head installations with different loading conditions.

Load Frequency Control of an Isolated Small-Hydro Power Plant With Reduced Dump Load

In this paper, a novel technique for load frequency control of an isolated small-hydro plant is presented. In general, the frequency is controlled by using a dump load, whose rating is equal to the rated power output of the plant. The new scheme proposed reduces the size of the dump load by controlling input power of the hydro power plant using on/off controls. The water flowing through the penstock is rerouted in smaller pipes, two or three fitted with motor operated valves. The opening or closing of the valves is achieved by on/off controls. The on/off control linearly raises or lowers the generation. A generalized transfer function model for the system is developed with an on/off control logic. Finally, the transient performance of the system is compared for the two-pipe case (50% dump load) and the three-pipe case (30% dump load)

Robust Estimation of the Generalized Solute Transfer Function Parameters

A method is presented to mathematically characterize a wide range of solute transport processes in soils from time‐domain‐reflectometry (TDR)‐based breakthrough curve (BTC) measurements. To do this, we combined the flexible generalized transfer function model (GTF) with the definition of the time normalized resident concentrations Crt* The GTF is a four‐parameter flexible transfer function able to describe both the convection‐dispersive (CD) and the stochastic‐convective (SC) process of dispersion in a soil. In addition, it allows other dispersion processes typical for heterogeneous soils to be modeled. To obtain robust estimations of the GTF model parameters, closed‐form expressions of the GTF time normalized resident concentrations Crt* and temporal moments of Crt* were defined. These expressions allow the transport parameters to be estimated from TDR‐based BTCs without relying on problematic probe calibrations and mass recovery assumptions. Since GTF is a four‐parameter model, the robustness of the parameterization procedure was tested by fitting the Crt* of the GTF model to numerically generated, error‐contaminated BTCs. Using the least‐square optimization technique, robust estimates of the GTF parameters could be obtained from TDR observations at two different soil depths. Application of the proposed method was also tested for a real undisturbed sandy subsoil and compared with the convective lognormal transfer (CLT) and CD models. For this case, it was shown that the GTF model improved greatly the goodness of prediction of the BTCs. The proposed method offers a new powerful tool for analyzing transport processes of nonreactive solutes in soil.

Robust Estimation of the Generalized Solute Transfer Function Parameters

A method is presented to mathematically characterize a wide range of solute transport processes in soils from time-domain-reflectometry (TDR)-based breakthrough curve (BTC) measurements. To do this, we combined the flexible generalized transfer function model (GTF) with the definition of the time normalized resident concentrations Crt* The GTF is a four-parameter flexible transfer function able to describe both the convection-dispersive (CD) and the stochastic-convective (SC) process of dispersion in a soil. In addition, it allows other dispersion processes typical for heterogeneous soils to be modeled. To obtain robust estimations of the GTF model parameters, closed-form expressions of the GTF time normalized resident concentrations Crt* and temporal moments of Crt* were defined. These expressions allow the transport parameters to be estimated from TDR-based BTCs without relying on problematic probe calibrations and mass recovery assumptions. Since GTF is a four-parameter model, the robustness of the parameterization procedure was tested by fitting the Crt* of the GTF model to numerically generated, error-contaminated BTCs. Using the least-square optimization technique, robust estimates of the GTF parameters could be obtained from TDR observations at two different soil depths. Application of the proposed method was also tested for a real undisturbed sandy subsoil and compared with the convective lognormal transfer (CLT) and CD models. For this case, it was shown that the GTF model improved greatly the goodness of prediction of the BTCs. The proposed method offers a new powerful tool for analyzing transport processes of nonreactive solutes in soil.

Generalized Transfer Function Model for Solute Transport in Heterogeneous Soils

The convection‐dispersion (CDE) equation and stochastic–convective models are the most commonly used process representations for predicting solute transport in the field. The convection–dispersion equation assumes that the solute is perfectly mixing in the lateral direction, whereas the stochastic–convective model assumes that the solute moves at different velocities in isolated stream tubes without lateral mixing. However, solute transport in heterogeneous porous media cannot always be conceptualized as being either a convective–dispersive or a stochastic–convective process. In this study, a generalized transfer function model (GTF) was proposed to describe various solute transport processes in heterogenous soils. The model is similar to the convective lognormal transfer function model, but two parameters, λμ and λσ, are introduced to characterize the depth‐dependency of the mean (μ) and standard deviation (σ) of the logarithm of travel time, respectively. The GTF can describe well the two extremes of solute dispersion, the convective–dispersive and stochastic–convective processes, and transport processes between the two extremes. In addition, the GTF can be used to characterize other solute transport processes in heterogeneous soils, such as those in which the mean of travel time increases with depth nonlinearly, and those in which the dispersivity is a scale‐dependent function of the travel distance with any power values.

Simulation and modeling-SPICE2 voltage-programmable transfer function modeling

A general transfer function model with voltage-programmable coefficients is developed, from which first- and second-order transfer function models are derived. In these models, the voltage-programmable feature is achieved by making use of multiplier or product terms in the multidimensional polynomial controlled voltage sources provided in SPICE2 where, by definition, one variable in a product pair is the voltage analog of a coefficient value. The utility of the voltage-programmable transfer function models lies in their ability to simulate a multiple of transfer functions using only one or two subcircuits and cascade the models to represent more complicated transfer functions. An example application to the simulation of a motor speed regulator control system is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>