Transfer Function Parameters Research Articles

The Linear Behavior of the System Middle Cerebral Artery Flow Velocity and Blood Pressure in Patients With Migraine

Migraine is considered a disorder of the autonomic nervous system. We used the frequency analysis of dynamic cerebral autoregulation to assess whether blood flow regulation disturbances can be found at the frequencies at which sympathetic and parasympathetic activity is present. We measured simultaneously mean arterial blood pressure (BP) and the mean blood velocity (V) in the middle cerebral artery using transcranial Doppler ultrasound in 33 healthy controls (mean age+/-SD; 36+/-13 years) and in 22 patients with migraine (mean age; 39+/-7 years). Apart from assessing spectral power density for BP and V, we calculated the transfer function parameters gain, phase, and coherence at the frequency range between 0.0 and 0.25 Hz. Compared with the controls, the spectral power density of BP and V exhibited a maximum magnitude of 10(26) in the migraine patients, whereas the maximum magnitude of BP and V in the controls was 10(-3). Coherence showed no difference between patients and controls. Gain between BP and V increased in the controls >0.01 Hz but was approximately 0 or negative in the migraine patients over the whole frequency range (P<0.01). The usually observed phase lead of V against BP was absent in the migraine patients in whom BP leaded V over nearly the whole frequency range (P<0.01). In terms of phase and gain, dynamic cerebral autoregulation is completely different in migraine patients compared with healthy subjects. Insofar, this can be interpreted as a lack of sympathetic and parasympathetic control of cerebral blood flow.

Damage Identification Using Sensitivity-Enhancing Control and Identified Models

This paper investigates a coherence approach for locating structural damage using modal frequencies and transfer function parameters identified from input-output data using Observer/Kalman filter identification (OKID). Autonomous damage identification using such forward methods generally require (i) a structural model by which to relate measured and predicted modal properties induced by damage, and (ii) good sensitivity of modal parameter changes to damage states. Using the coherence approach, a damage parameter vector comprised of a finite set of modal frequencies and transfer function parameters is hypothesized for each damage case using either identified or analytic structural models. Measured parameter vectors are extracted from experimental input-output data for a damaged structure using OKID and are compared to hypotheses to determine the most likely damage state. The richness of the parameter vector set, which is comprised of high-quality frequency measurements and lower-quality transfer function parameters, is evaluated in order to determine the ability to uniquely localize damage. The method is evaluated experimentally using a three-degree-of-freedom torsional system and a space-frame truss. Damage parameter hypotheses are generated from a model of the healthy structure developed by system identification in the torsional system, and an analytic model is used to generate damage hypotheses for the truss structure. Feedback control laws enhance the parameter vectors by including closed-loop modal frequencies in order to reduce noise sensitivity and improve uniqueness of parameter vector hypotheses to each damage case. Results show improvements in damage identification using damage parameter vectors comprised of open- and closed-loop modal frequencies, even when model error exists in structural models used to form damage parameter vector hypotheses.

Computation of dynamic characteristics of control systems: An Effectiveness enhancement method

The Simoy method of computation of transfer functions from transition curves is modified and improved such that selection of an optimal structure for a transfer function from synthesized variants for the channel of action of an element and computation of its parameters are implemented sequentially and automatically, and then the optimal values of computed parameters of transfer functions are determined by minimizing the approximation error. This method enhances the accuracy of computation of dynamic characteristics at least by 20%.

ACE: Automated CTF Estimation

We present a completely automated algorithm for estimating the parameters of the contrast transfer function (CTF) of a transmission electron microscope. The primary contribution of this paper is the determination of the astigmatism prior to the estimation of the CTF parameters. The CTF parameter estimation is then reduced to a 1D problem using elliptical averaging. We have also implemented an automated method to calculate lower and upper cutoff frequencies to eliminate regions of the power spectrum which perturb the estimation of the CTF parameters. The algorithm comprises three optimization subproblems, two of which are proven to be convex. Results of the CTF estimation method are presented for images of carbon support films as well as for images of single particles embedded in ice and suspended over holes in the support film. A MATLAB implementation of the algorithm, called ACE, is freely available.

A GLOBAL NONLINEAR INSTRUMENTAL VARIABLE METHOD FOR IDENTIFICATION OF CONTINUOUS-TIME SYSTEMS WITH UNKNOWN TIME DELAYS

This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived, by using stochastic global-optimization technique to avoid convergence to a local minimum. Futhermore, the GSEPNLS method is modified to a novel global separable nonlinear instrumental variable (GSEPNIV) method to yield consistent estimates if the algorithm converges to the global minimum. Simulation results show that the proposed method works quite well.

Nonlinear Identification of a Brushless Excitation System Via Field Tests

In this paper, nonlinear identification of the excitation system (EXS) in the gas unit #2 of Rajaee power plant in Iran is presented. Two methods of modeling, i.e., grey-box and black-box modeling are used and compared. In the grey-box (classical) approach, first a block-diagram for the EXS is suggested, then a test procedure for identification of its parameters is outlined. The input-output data corresponding to each block of the system is obtained through field tests. In this approach, the only nonlinearities considered in the block diagram are the limits. The other nonlinearities are reflected in the change of parameters in the linear transfer functions at different operating conditions. In the black-box approach, identification of the system is carried out using discrete wavelet transform. A variable structure wavelet was tried to cope with system changes at different operating conditions, but a wavelet with fixed number of scaling functions or wavelet functions proved to be quite adequate. The simulation results and their comparison, show the good accuracy of both derived models. Although the model obtained through the black-box approach shows a better fit when its output is compared with the measured variables, the model obtained through the grey-box approach reflects the physical properties of the system and may be more useful for power engineers.

Identification of continuous-time systems with unknown time delays by global nonlinear least-squares method

This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. By using a digital prefilter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delays need not be an integral multiple of sampling period. Then an iterative separable nonlinear least-squares (SEPNLS) method which estimates the time delays and transfer function parameters separably is derived. Futhermore, we propose an iterative global SEPNLS method to avoid convergence to a local minimum of the SEPNLS method by using of stochastic global-optimization techniques. Simulational results show that the global SEPNLS method is able to converge to global estimate.

신경회로망을 이용한 서보 실린더의 운동제어

In this paper, a neural network controller that can be implemented in parallel with a PO controller is suggested for motion control of a hydraulic servo cylinder. By applying a self-excited oscillation method, the system design parameters of open loop transfer function of servo cylinder system are identified. Based on system design parameters, the PO gains are determined for the desired closed loop characteristics. The Neural Network is incorporated with PO control in order to compensate the inherent nonlinearities of hydraulic servo system. As an application example, a motion control using PO-NN has been performed and proved its superior performance by comparing with that of a PO control.<br/>

Semi‐empirical model for site effects on acceleration time histories at soft‐soil sites. Part 1: formulation and development

AbstractA criterion is developed for the simulation of realistic artificial ground motion histories at soft‐soil sites, corresponding to a detailed ground motion record at a reference firm‐ground site. A complex transfer function is defined as the Fourier transform of the ground acceleration time history at the soft‐soil site divided by the Fourier transform of the acceleration record at the firm‐ground site. Working with both the real and the imaginary components of the transfer function, and not only with its modulus, serves to keep the statistical information about the wave phases (and, therefore, about the time variation of amplitudes and frequencies) in the algorithm used to generate the artificial records. Samples of these transfer functions, associated with a given pair of soft‐soil and firm‐ground sites, are empirically determined from the corresponding pairs of simultaneous records. Each function included in a sample is represented as the superposition of the transfer functions of the responses of a number of oscillators. This formulation is intended to account for the contributions of trains of waves following different patterns in the vicinity of both sites. The properties of the oscillators play the role of parameters of the transfer functions. They vary from one seismic event to another. Part of the variation is systematic, and can be explained in terms of the influence of ground motion intensity on the effective values of stiffness and damping of the artificial oscillators. Another part has random nature; it reflects the random characteristics of the wave propagation patterns associated with the different events. The semi‐empirical model proposed recognizes both types of variation. The influence of intensity is estimated by means of a conventional one‐dimensional shear wave propagation model. This model is used to derive an intensity‐dependent modification of the values of the empirically determined model parameters in those cases when the firm‐ground earthquake intensity used to determine these parameters differs from that corresponding to the seismic event for which the simulated records are to be obtained. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Reliability Based Load Transfer Characteristics of Bored Precast Piles Equipped with Grouted Bulb in the Pile Toe Region

ABSTRACT End bearing load transfer characteristics of bored precast piles equipped with expanded fabric bulb in toe region is investigated based on the use of hyperbolic transfer function for shaft resistance as well as for toe resistance of the enlarged bulb portion. Based on the static loading test results, a piling method specific approach to estimate the transfer function parameters from correlation with average TV-values is proposed and its reliability investigated. The reliability investigations include the vertical load resistance aspects as well as the confidence limits for vertical movement. It is confirmed that the shaft resistance of the enlarged fabric bulb portion is much larger compared to the conventional bored precast piles and the increase is a function of the pressure applied in injecting the cement slurry into the fabric sack to form the bulb. On the average, it appears that the increase in bulb shaft resistance is of the order of 7% for every 0.1 N/mm2 increase in pressure.

An automated system for frequency response analysis with application to an undergraduate laboratory of electrical machines

This paper describes a LabVIEW-based automated system to carry out frequency response tests in electrical machines. The system is being used by undergraduate students in the laboratory of electrical machines, where they can experiment with the application of frequency response analysis and the characterization of mathematical models. An advantage of the system is its friendly user interface to a laboratory experiment. The system avoids the risky utilization of oscilloscopes for data acquisition and shortens the time taken to analyze the measured data. The Automated System for Frequency Analysis (ASFFA) can carry out tests within a bandwidth of 0.001-900 Hz. In addition, the ASFFA can estimate the transfer function parameters based on one- or two-port network theory. Design details of the interface, system structure, frequency response tests, electrical machine modeling, and signal processing are presented. Finally, ASFFA is applied to synchronous, induction, and direct-current machines.

On the Parameter Estimation of Pulse Transfer Function In the Presence of Input and Output Noise

Many identification methods are based on the assumption that input measurement is noise-free. However, this condition is not satisfied in most practical situations. In the presence of input noise, those methods have been shown to give erroneous results. Bias-compensated least-squares method is a consistent estimation method for unknwon parameters of pulse transfer function in the presence of input and output noise. The method is based on compensation of asymptotic bias on the least-squares estimates by making use of noise variances estimates. In this paper, a new type of noise variances estimation method is proposed to overcome drawbacks of previously proposed methods. The main feature of proposed method is to introduce a generalized least-squares type estimator in order to estimate noise variances. The results of a simulated example indicate that the proposed method provides good estimates.

手先指示に協調するパーソナルな知能機械に関する基礎的研究〜接近運動に対する心理的評価〜

In an interface that enables an intelligent machine to bring a thing to a human in response to the human's manipulation, the intelligent machine's approaching motion is required to take account of the human's state of mind to serve effectively as his or her companion. In this paper, the patterns of intelligent machine motion are determined by adopting transfer functions that consider finger position directing operation to input, the target positions of intelligent machine to output. By changing transfer function or adjustable parameters, different human psychological influences are expected. Motion properties of the intelligent machine, which is appropriate for human states of mind, are examined using SD method. As a result, it is clarified that human emotions to the intelligent machine motion are represented by the factor of “mild”. In addition, two patterns of transfer function and parameter that can form desirable movements considering human psychology are identified.

Iterative Identification Algorithms for Continuous-Time Systems with Unknown Time Delay in the Presence of Measurement Noise

This paper considers the identification problem of continuous-time systems with unknown time delay from sampled input-output data. By using a digital pre-filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of the sampling period. Then an unseparable nonlinear least-squares (UNSEPNLS) method and a separable nonlinear least-squares (SEPNLS) method for identification of transfer function parameters and time delay are derived. Furthermore, an unseparable nonlinear instrumental variable (UNSEPNIV) method and a separable nonlinear instrumental variable (SEPNIV) method are proposed, to eliminate estimate bias due to measurement noise. Simulational results show that the UNSEPNIV and SEPNIV methods yield consistent estimates in the presence of measurement noise.

Projection-based needle segmentation in 3D ultrasound images

Needles are used extensively in interventional procedures such as biopsy and brachytherapy. To deliver radioactive seeds to pre-planned positions or sample lesions from the region that may contain cancer cells, the 3D position of the needle must be determined accurately and quickly. Three-dimensional ultrasound (US) image guidance is an efficient technique used to perform this task. In this paper, we describe the development of a projection-based needle segmentation method comprising three steps. First, the 3D image is projected along an initial direction perpendicular to the approximate needle direction determined from the 3D imaging system. The needle is then segmented in a projected 2D image. Using the projection direction and the detected 2D needle direction, a plane containing the needle – called the needle plane - is determined. Secondly, the 3D image is re-projected in the direction perpendicular to the normal of the needle plane and step 1 is repeated. If the needle direction in the projected 2D image is horizontal, the needle plane is correct; otherwise, steps 1 and 2 are repeated until a correct needle plane is found. Thirdly, the 3D image is projected along the normal direction of the needle plane and the needle endpoints in the projected 2D image are determined. Using the relationship between the 3D projection and the 3D volume coordinate systems, the coordinates of the endpoints of the needle in the 3D US coordinate system are determined. Experiments with agar and turkey phantom 3D US images demonstrated that our method could segment the needle from 3D US images with an average accuracy of 0.7 mm in position and 1.2° in orientation with a speed of 13 fps on a 1.3-GHz PC. In addition, experiments illustrated that our method is robust to variations in the initial estimated needle direction, the size of the cropped volume, and the ray-casting transfer function parameters used in pre-processing.

Identification of Continuous-Time Systems with Unknown Time Delay by Nonlinear Least-Squares and Instrumental Variable Methods

This paper considers the identification problem of continuous-time systems with unknown time delay from sampled input-output data. By using a digital prefilter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of the sampling period. Then an iterative separable nonlinear least-squares (SEPNLS) method which estimates the time delay and transfer function parameters separably is derived. Furthermore, through investigation of the properties of the SEPNLS estimate, a novel separable nonlinear instrumental variable (SEPNIV) method is also proposed. Simulational results show that the SEPNIV method yields consistent estimates.

Optimal Sampled-Data Reconstruction of Stochastic Signals With Preview

The problem of H2-optimal reconstruction of a continuous-time signal using a stable sampled-data filter is considered. The signal is corrupted by additive noise and is measured with preview of τ, i.e., it is known in advance over the interval τ. This problem is equivalent to delayed signal reconstruction. A rigorous solution of the problem is presented on the basis of the parametric transfer function approach. Explicit expressions are given for the order of the optimal filter. A lower bound is obtained for the cost function for τ→∞, and it is shown that this bound depends on the ratio of the preview interval to the sampling period.

Identification and Tuning of Integrating Processes with Deadtime and Inverse Response

The classical example of an integrating process with inverse response is level control of a boiler steam drum. The “boiler swell” problem can lead to a transfer function between the drum level and boiler feedwater flow rate that contains a pure integrator and a positive zero, in addition to some deadtime and lags. This paper presents a procedure for identifying the transfer-function parameters for this type of system from step response data. A proportional−integral controller tuning procedure is also presented. Because the process contains an integrator and the proportional−integral controller also contains an integrator, controller tuning is somewhat complex. The proposed method determines the smallest possible value for integral time. Then, using this value, the controller gain that gives a +2 dB maximum closed-loop log modulus is calculated. Simple Matlab programs are given for performing these calculations. The use of proportional−integral−derivative control is also briefly studied, but the typically ...

Composite transfer functions for karst aquifers

Linear transfer functions have been extensively used in hydrological studies. Generally, we support this conclusion: rainfall-runoff models based on the convolution between rainfall rates and a nonparametric transfer function (NTF) are not successful at simulating karst spring discharges during long recession periods. The tails of identified transfer functions have irregular shapes and they are not accurate physical representation of the transport through a karst system. Irregularities are the result of unavoidable errors in input and output time series and simplifications made by considering the system as linear and time invariant. This paper deals with a new form of the transfer functions for karst aquifers, the so-called composite transfer function (CTF). The CTF simulates discharges by two transfer functions adapted for the quick flow and the slow flow hydrograph component modeling. NTF is responsible for the quick flow component. The slow flow component is modeled by a parametric transfer function that is an instantaneous unit hydrograph mathematically formulated and defined from a conceptual model. By using the CTF, the irregular shape of the tail of the identified transfer function can be avoided, and the simulation of long recession periods as well as the simulation of a complete hydrograph becomes more successful. The NTF, the Nash model, the Zoch model and other similar conceptual models can be considered separately as simplified forms of the CTF. The rainfall-runoff model based on the convolution between rainfall rates and the CTF was tested on the Jadro Spring in Croatia. The results of the application are compared with the results obtained by applying NTFs independently.

Use of prior bounds on time constants and steady‐state gain in recursive parameter bounding

AbstractComputation of parameter bounds of a linear dynamical system, given input–output observations and bounds on model‐output error, has been developed as an alternative to classical parameter estimation using least squares, maximum likelihood or the prediction error method. When bounds on time‐domain plant behaviour are known in advance, they can be used to develop prior parameter bounds for discrete‐time rational transfer‐function parameters. These bounds can be used to initialize standard parameter‐bounding algorithms which process input–output observations to update the exact polytope feasible set or one of its outer bounding approximations such as an ellipsoid, orthotope or parallelotope. This paper presents a method to compute such prior bounds from bounds on time constants and steady‐state (dc) gain, often available from the physics of the system or from previous experience. The method finds subsets making up the prior feasible parameter set, recursively in model order, for any configuration of the pole ranges. An analysis leading to measures of the value of prior bounds, in terms of their chances of remaining active when new bounds derived from observations are imposed, is presented. A simulation study compares polytope updating with and without such initial bounds. The simulations investigate the influence of the tightness of time‐constant and steady‐state‐gain bounds in reducing the volume of the feasible sets obtained as observations are processed. The effects of initial bound tightness and signal‐to‐noise ratio on survival time of the prior bounds are also examined. Copyright © 2002 John Wiley &amp; Sons, Ltd.