Time Constant Parameters Research Articles

Constructing quasi-linear V̇o2 responses from nonlinear parameters

Oxygen uptake (V̇O2) kinetics have been shown to be governed by a nonlinear control system across a range of work rates. However, the linearity of the V̇O2 response to ramp incremental exercise would appear to be the result of a linear control system. This apparent contradiction could represent a balancing of changing V̇O2 kinetics parameter values across a range of work rates. To test this, six healthy men completed bouts of ramp incremental exercise at 15, 30, and 60 W/min (15R, 30R, 60R, respectively) and four bouts of an extended-step incremental exercise. V̇O2 parameter values were derived from the step exercise using two monoexponential models: one starting at time zero and encompassing the entire stage (MONO), and the other truncated to the first 5 min and allowing a time delay (5TD). The resulting parameter values were applied to an integrative model to estimate the ramp responses. As work rate increased, gain values increased (P < 0.001 for MONO and 5TD), as did mean response time (or time constant) values (MONO: P < 0.001; 5TD: P = 0.003). Up to maximal V̇O2 (V̇O(2 max)), the gains of the estimated ramp responses from both models were not different from the gains of the actual observed V̇O2 responses for 15R and 30R (15R: 11.3 ± 1.2, 11.7 ± 0.7, 10.9 ± 0.3; 30R: 10.5 ± 0.8, 11.0 ± 0.5, 10.7 ± 0.3 ml O2·min(-1)·W(-1), for actual, MONO, 5TD, respectively) but were significantly greater for 60R (8.7 ± 1.0, 9.9 ± 0.4, 10.3 ± 0.3 ml O2·min(-1)·W(-1) for actual, MONO, 5TD, respectively). Up to 80%V̇O(2 max) gain values were not significantly different for any ramp rate (P > 0.05 for all). We conclude that the apparent linearity of the V̇O2 response to ramp incremental exercise is consequent to a balancing of increasing time constant and gain parameter values.

Characterisation of the Oxygenation Response to Inspired Oxygen Adjustments in Preterm Infants

Background: Oxygen saturation (SpO₂) targeting in the preterm infant may be improved with a better understanding of the SpO₂ responses to changes in inspired oxygen (FiO₂). Objective: We investigated the first-order FiO₂-SpO₂ relationship, aiming to quantify the parameters governing that relationship, the influences on these parameters and their variability. Methods: In recordings of FiO₂ and SpO₂ from preterm infants on continuous positive airway pressure and supplemental oxygen, we identified unique FiO₂ adjustments and mapped the subsequent SpO₂ responses. For responses identified as first-order, the delay, time constant and gain parameters were determined. Clinical and physiological predictors of these parameters were sought in regression analysis, and intra- and inter-subject variability was evaluated. Results: In 3,788 h of available data from 47 infants at 31 (28-33) post-menstrual weeks [median (interquartile range)], we identified 993 unique FiO₂ adjustments followed by a first-order SpO₂ response. All response parameters differed between FiO₂ increments and decrements, with increments having a shorter delay, longer time constant and higher gain [2.9 (1.7-4.8) vs. 1.3 (0.58-2.6), p < 0.05]. Gain was also higher in less mature infants and in the setting of recent SpO₂ instability, and was diminished with increasing severity of lung dysfunction. Intra-subject variability in all parameters was prominent. Conclusions: First-order SpO₂ responses show variable gain, influenced by the direction of FiO₂ adjustment and the severity of lung disease, as well as substantial intra-subject parameter variability. These findings should be taken into account in adjustment of FiO₂ for SpO₂ targeting in preterm infants.

Physical Model of Pixels in Twisted Nematic Active-Matrix Liquid Crystal Displays

In this paper, we propose a new physical model to predict the transient responses of an active-matrix liquid crystal display (LCD) in a twisted nematic (TN) mode. For the behavioral model, three types of data are most important: 1) capacitance–voltage; 2) transmittance–voltage characteristics; and 3) time constant parameters $a_{1}$ and $a_{2}$ . We extract them from the physical properties of TN LC molecules, such as dielectric anisotropy, birefringence, viscosity, cell gap, and elastic constant. We describe the behavior of the TN-LCD by using the analog hardware description language, Verilog-A. We simulate optically rising responses of the TN-LCD panel by importing the behavioral circuit model in the circuit simulator. The simulation results show that the transient optical responses show excellent matches with the measurement ones. In addition, we can observe how the behaviors of optical responses change with varying the physical parameters, such as cell gap, dielectric anisotropy, and elastic constant.

Temperature-dependent behavioral model of pixels in active-matrix liquid crystal displays with fringe-field switching mode

This paper proposes a temperature-dependent behavioral circuit model to predict the optical char- acteristics of an active-matrix liquid crystal display with a fringe-field switching (FFS) mode. The optical responses of liquid crystal displays (LCDs) are strongly affected by their ambient temperature. We optimized the time-constant parameters that describe the movement of liquid crystal molecules so that simulated optical responses of FFS LCD provide the best fit to the measured responses over the temperature range of 0 to 50°C. It is found that these time-constant parameters have a linear relationship with the ambient temperature, which enables the prediction of optical responses at any given temperature. The simulation results of the transient responses show an excellent match with the measured results regardless of the ambient temperature. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.OE.54.9.093104)

Three-Dimensional Cole-Cole Model Inversion of Induced Polarization Data Based on Regularized Conjugate Gradient Method

Modeling of induced polarization (IP) phenomena is important for developing effective methods for remote sensing of subsurface geology. However, the quantitative interpretation of IP data in a complex 3-D environment is still a challenging problem of applied geophysics. This letter develops a method of determining a 3-D distribution of the four parameters of the Cole-Cole model based on surface IP data. The method takes into account the nonlinear nature of both electromagnetic induction and IP phenomena. The solution of the 3-D IP inverse problem is based on the regularized conjugate gradient method. The method was tested on a synthetic model with variable dc conductivity, intrinsic chargeability, time constant, and relaxation parameters, and it was also applied to the actual 3-D IP survey data. We demonstrate that the four parameters of the Cole-Cole model, namely, dc electrical resistivity, chargeability, time constant, and the relaxation parameter, can be recovered from the observed IP data simultaneously.

Variable range hopping (VRH) conductivity, ac conductivity and dielectric studies on Sm3+ doped Cd0.8Zn0.2S semiconductor compounds

Polycrystalline semiconductor compounds of Cd0.8Zn0.2S and Sm3+ (0.01, 0.02, 0.03, 0.04 and 0.05mol%) doped Cd0.8Zn0.2S samples have been synthesized by a controlled co-precipitation technique. The samples are characterized by X-ray diffraction (XRD). DC conductivity studies were performed in the temperature region 77–300K by using a two probe method. The dc conductivity plots show the Arrhenius behavior with three different activation energies (Ea) in three different temperature regions (I (300–220K), II (220–160K), III (160–105K), and they exhibited the variable range hopping conduction (VRH) mechanism at low temperature region (105–77K). From the impedance, and dielectric measurements bulk resistance (Rg), capacitance (Cg), relaxation time (τ) and dielectric constant (ε̕) parameter values were calculated. And explained their variations with temperature and dopant (Sm3+) concentration changes.

Resimulation of noise: a precision estimator for least square error curve-fitting tested for axial strain time constant imaging

Recent elastography techniques focus on imaging information on properties of materials which can be modeled as viscoelastic or poroelastic. These techniques often require the fitting of temporal strain data, acquired from either a creep or stress-relaxation experiment to a mathematical model using least square error (LSE) parameter estimation. It is known that the strain versus time relationships for tissues undergoing creep compression have a non-linear relationship. In non-linear cases, devising a measure of estimate reliability can be challenging. In this article, we have developed and tested a method to provide non linear LSE parameter estimate reliability: which we called Resimulation of Noise (RoN). RoN provides a measure of reliability by estimating the spread of parameter estimates from a single experiment realization. We have tested RoN specifically for the case of axial strain time constant parameter estimation in poroelastic media. Our tests show that the RoN estimated precision has a linear relationship to the actual precision of the LSE estimator. We have also compared results from the RoN derived measure of reliability against a commonly used reliability measure: the correlation coefficient (CorrCoeff). Our results show that CorrCoeff is a poor measure of estimate reliability for non-linear LSE parameter estimation. While the RoN is specifically tested only for axial strain time constant imaging, a general algorithm is provided for use in all LSE parameter estimation.

Scrutiny of entropy optimized tangent hyperbolic fluid (non-Newtonian) through perturbation and numerical methods between heated plates

Objective: Many methods have been used to maximize the capacity of heat transport. A constant pressure gradient or the motion of the wall can be used to increase the heat transfer rate and minimize entropy. The main goal of our investigation is to develop a mathematical model of a non-Newtonian fluid bounded within a parallel geometry. Minimization of entropy generation within the system also forms part of our objective. Method: Perturbation theory is applied to the nonlinear complex system of equations to obtain a series solution. The regular perturbation method is used to obtain analytical solutions to the resulting dimensionless nonlinear ordinary differential equations. A numerical scheme (the shooting method) is also used to validate the series solution obtained. Results: The flow and temperature of the fluid are accelerated as functions of the non-Newtonian parameter (via the power-law index). The pressure gradient parameter escalates the heat and volume flux fields. The energy loss due to entropy increases via the viscous heating parameter. A diminishing characteristic is predicted for the wall shear stress that occurs at the bottom plate versus the time-constant parameter. The Reynolds number suppresses the volume flux field.

Panel conditional and multinomial logit with time-varying parameters

AbstractPanel conditional logit estimators (PCLE) in the literature use mostly time-constant parameters. If the panel periods are volatile or long, however, the model parameters can change much. Hence this paper generalizes PCLE with time-constant parameters to PCLE with time-varying parameters; both static and dynamic PCLE are considered for this. The main finding is that time-varying parameters are fully allowed for static PCLE and the dynamic “pseudo” PCLE of [Bartolucci, F. and V. Nigro. 2010. “A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting an$\sqrt n$-Consistent Conditional Estimator.”Econometrica78: 719–733] that are thus recommended to practitioners. As a further generalization, static “panel conditional multinomial logit estimator (PML)” with time-varying parameters is also examined. As it turns out, time-varying parameters are also fully allowed for PML. With no error term serial correlation allowed in PCLE and dynamic PCLE’s being restrictive in their assumptions, time-varying parameters provide an alternative avenue to inject dynamics and flexibility into PCLE and PML. Since PCLE and PML converge straightforwardly in computation, allowing time-varying parameters in PCLE and PML is “computationally free.” A simulation study is also provided.

Assessment of validity and predictability of the FiO2–SpO2 transfer-function in preterm infants

In this paper an investigation of the gain, delay, and time-constant parameters of the transfer function describing the relation between fraction of inspired oxygen (FiO2) and oxygen saturation in the blood (SpO2) in preterm infants is presented. The parameters were estimated following FiO2 adjustments and goodness of fit was used to assess the validity of the model when using an assumed first-order transfer function. For responses identified to be first-order, the estimated parameters were then clustered to identify areas where they tended to be concentrated. Each group described an operating region of the transfer function; thus, predicting the right operating region could potentially assist a range-based robust inspired oxygen controller to provide more optimal control by adapting itself to different clusters. Accordingly, the samples were assigned labels based on their cluster associations and 14 features available at the time of each adjustment were used as inputs to an artificial neural network to classify the clustered samples. The validity study suggested that 37% of the adjustments were followed by first-order responses. Prediction studies on the first-order responses indicated that the clusters could be predicted with an average accuracy of 64% when the parameters were divided into two groups.

Track Estimation with Binary Derivative Observations

We focus in the work presented here on in the estimation of a target trajectory defined by whether a time constant parameter is a simple stochastic process or a random walk with binary observations. The binary observations come from binary derivative sensors, that is, the target is getting closer or moving away. Such a binary observation has a time property that will be used to ensure the quality of the velocity estimation, through single index model or classification for the constant velocity movement. In the second part of this work we present a new algorithm for target tracking within a binary sensor network when the target trajectory is assumed to be modelled by a random walk. For a given target this algorithm provides an estimation of its velocity and its position. The greatest improvements are made through a position correction and velocity analysis.

Relation of Respiratory System Elastance and Expiratory Time Constant: Are They From the Same Lung?

Modeling the respiratory system can aid clinical decision making during mechanical ventilation (MV) in the intensive care. However, most lung models only use airway pressure and flow data during inspiration phase of the breathing, and the data from expiration are often neglected. In this study, we hypothesized that the airway pressure and flow data during expiration have equal important information as the inspiratory data for respiratory mechanics modeling. In particular, expiratory time constant parameter, K, can provide unique information and relation with respiratory system elastance in MV patients. Two different respiratory models were investigated, with one model focusing on data from inspiratory cycle (Single compartment model) and the other focusing on expiratory cycle (Time constant model). The expiratory time constant model and single compartment model were evaluated based on 22 retrospective datasets of acute respiratory distress syndrome (ARDS) patients from two different cohorts. The expiration time constant model captured a moderate correlation with the lung elastance with R2= 0.568 in Cohort 1 and weak correlation with R2= 0.184 in Cohort 2, resulting in an overall of R2 = 0.435. Significant variations in lung resistance may lead to a poor correlation between K and lung elastance, Elung. Thus, the application of K as a surrogate to the respiratory elastance warrants further investigation.

Multiparameters Model of the Initial SOC Considering the Relaxation Effect

To improve the accuracy of the initial state-of-charge (SOC0) estimation, an enhanced SOC0 model-based ampere-hour (Ah) model of a LiFePO4 battery is presented for application in a battery energy storage system. It varies as a function of the open circuit voltage (OCV), rates, and relaxation effect. Here, the Thevenin equivalent circuit model is selected to model the LiFePO4 battery, and its mathematic equations are deduced to determine the inherent trade-off between the time constant and main parameter of the battery, which can address the dilemma of the conventional SOC0 method (OCV model) as the relaxation effect and how long the battery takes to reach a steady state. To illustrate advantages of the enhanced estimation model, a comparison analysis is performed on the enhanced model with the former model under the dynamic stress test. Furthermore, the experimental results indicate that the correction of SOC0 can significantly decrease the estimation error. The enhanced SOC0 model is expected to help determine operational parameters for batteries of variable power sources.

Visual fatigue effects on vergence dynamics in asymptomatic individuals

To evaluate objectively changes in vergence dynamics following intentional visual fatiguing tasks using congruent (C) and non-congruent (NC) vergence stimulus demands. Pre- and post-task vergence dynamics to a 10.3° disparity stimulus were recorded objectively in 12 adult, asymptomatic individuals for both convergence and divergence. There were two fatigue-inducing tasks: Congruent or C-task: Subjects altered bifixation (50 cycles) between reduced Snellen charts at 2 m and at 20 cm every 3 s. Non-congruent or NC task: Subjects performed 50 cycles of vergence flipper (7Δ BO/BI), while bifixating a reduced Snellen chart at 40 cm. Pre- and post-task mean amplitude, time constant, peak velocity, and steady-state response variability were compared. There was a significant increase in mean post-task, steady-state response variability for both the C and NC tasks. However, there were no significant group differences for either convergence or divergence between the pre- and post-mean amplitude, time constant, and peak velocity parameters for both the C and NC tasks, although post-task peak velocity was consistently reduced. Steady-state vergence response variability was found to be the critical objective parameter to demonstrate significant fatigue effects in the group. Peak velocity was also consistently reduced to a small extent in the NC task. We speculate that these fatigue effects are of a central, and not of peripheral, origin.

遺伝的アルゴリズムを用いたパラメータ推定による南国果実の熟度判定法

This paper presents a method of maturity detection of the tropical fruits using a smell sensor. We investigate many sensing data of different fruits and find out that the data of the fruits are characterized as a first-order lag element. The first-order lag element has two parameters: gain and time constant. Knowing the gain and time constant of the smell data is useful for detecting the maturity of the fruits. In this paper, we apply a genetic algorithm to identify the gain and time constant parameters for the sensing data. Experiments of the maturity detection for monkey bananas and guavas show good results.

Spectrum Comparative Analysis of the Frequency Domain Complex Cole-Cole Mode

Using MATLAB the spectrum comparative analysis was gived between complex Cole-Cole model and basic Cole-Cole model when theirs parameters change. The results show that the complex resistivity curve shape of complex model is limitted by total variation of two same kind parameters of complex model. The variation rule of the curve shape is consistent with basic mode when same kind parameters change. The influent of polarization rate and frequency correlation coefficient is bigger than time constant parameter. The resistivity curve of parallel complex model and series complex model is very similar. Then points out the difficulties and problems faced by the extraction of IP parameters.

A temporal model of neural activity and VSD response in V1

Mechanistic developmental models of the primary visual cortex (V1) in mammals have been able to replicate many of the large-scale spatial features of V1 neurons from experiments, such as their spatial receptive fields and the spatial organization into maps in V1 (reviewed in ref [1]). However, the models have previously been formulated at a very abstract level that does not account for the detailed, transient time course of neural responses. Conversely, there are a number of detailed, large-scale spiking models of the adult visual cortex, but these have not explained the development of feature preferences and feature maps, relying instead on prespecified patterns of connectivity. Here we present a new temporally and spatially calibrated model of cortical activity using ratebased units that could help unify these different types of explanation and levels of modelling. The model is called TCAL (Temporally CALibrated), and is a small variant on the GCAL model from the LISSOM family [1]. Compared to GCAL, the only change to the model mechanisms is to add hysteresis to the model LGN and V1 units. Hysteresis allows the damping of temporal responses to be controlled with one time-constant parameter per sheet. These two new parameters were set first for the LGN and then for V1to match results from electrophysiological recordings. Both onset and offset responses are matched against experimentally recorded peristimulus time histograms (PSTHs) for LGN [2] and cortical [3] neurons using the Invariant Response Description model. Despite the two orders of magnitude difference in time scales between GCAL and TCAL and

Model Free Adaptive Predictive Control for Main Stream Pressure System of Power Plant

This paper is concerned with an improved model free adaptive predictive control (MFAPC) schemer. The controlled system is linearized dynamically to build a predictive model by using the notion of pseudo-partial-derivative. The new control method is applied to the main steam pressure cascade control system, which is a typical controlled process with large time constant and large time-delay parameters in a thermal power plant. The main control loop is implemented by the MFAPC algorithm and the inner control loop is implemented by a conventional PID controller. Simulation results have convinced that the MFAPC-PI cascade control method has good adaptability to overcome disturbances and track point.

Application of RBF Network in Rotor Time Constant Adaptation

The paper presents the results of the rotor time constant adaptation method with the application of artificial neural network. Theestimation of the rotor time constant for adaptive model of MRAS is realized by the help of PI-controller and then is replaced by theRadial Basis Function network. The estimated rotor time constant is then used in the vector control of electrical drive. There werediscussed the different architectures of RBF network in the field of adaptation of rotor time constant parameter. Simulations have beenperformed in the Matlab-Simulink. Ill. 20, bibl. 8 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.113.7.606

Neuronal model with distributed delay: analysis and simulation study for gamma distribution memory kernel

A single neuronal model incorporating distributed delay (memory)is proposed. The stochastic model has been formulated as a Stochastic Integro-Differential Equation (SIDE) which results in the underlying process being non-Markovian. A detailed analysis of the model when the distributed delay kernel has exponential form (weak delay) has been carried out. The selection of exponential kernel has enabled the transformation of the non-Markovian model to a Markovian model in an extended state space. For the study of First Passage Time (FPT) with exponential delay kernel, the model has been transformed to a system of coupled Stochastic Differential Equations (SDEs) in two-dimensional state space. Simulation studies of the SDEs provide insight into the effect of weak delay kernel on the Inter-Spike Interval(ISI) distribution. A measure based on Jensen-Shannon divergence is proposed which can be used to make a choice between two competing models viz. distributed delay model vis-á-vis LIF model. An interesting feature of the model is that the behavior of (CV(t))((ISI)) (Coefficient of Variation) of the ISI distribution with respect to memory kernel time constant parameter η reveals that neuron can switch from a bursting state to non-bursting state as the noise intensity parameter changes. The membrane potential exhibits decaying auto-correlation structure with or without damped oscillatory behavior depending on the choice of parameters. This behavior is in agreement with empirically observed pattern of spike count in a fixed time window. The power spectral density derived from the auto-correlation function is found to exhibit single and double peaks. The model is also examined for the case of strong delay with memory kernel having the form of Gamma distribution. In contrast to fast decay of damped oscillations of the ISI distribution for the model with weak delay kernel, the decay of damped oscillations is found to be slower for the model with strong delay kernel.