Physiologically Based Structure of Mean Residence Time

The Scientific World JOURNAL ◽

10.1100/2012/610631 ◽

2012 ◽

Vol 2012 ◽

pp. 1-4

Author(s):

Mária Ďurišová

Keyword(s):

Pharmacokinetic Parameter ◽

Residence Time ◽

Dynamic Systems ◽

Mean Residence Time ◽

Structural Identification ◽

Linear Dynamic Systems ◽

Linear Dynamic ◽

Physiologically Based

A mean residence time (MRT) is an important pharmacokinetic parameter. To the author's knowledge, however, a physiologically based structure of MRT (thereafter MRT structure) has not been published so far. Primarily this is because MRT structures cannot be identified by traditional pharmacokinetic methods used for the determination of MRT. Therefore, tools from the theory of linear dynamic systems were used for the structural identification of MRT in this study. The MRT structure identified is physiologically meaningful. Accordingly, it seems that the MRT structure identified may contribute to already established knowledge about MRT.

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Determination of the order of stochastically linear dynamic systems by using non-parametric estimation of a regression function

Journal of Physics Conference Series ◽

10.1088/1742-6596/1399/3/033090 ◽

2019 ◽

Vol 1399 ◽

pp. 033090

Author(s):

AV Medvedev ◽

AV Raskina ◽

EA Chzhan ◽

AA Korneeva ◽

CA Videnin

Keyword(s):

Dynamic Systems ◽

Regression Function ◽

Parametric Estimation ◽

Linear Dynamic Systems ◽

Linear Dynamic ◽

Non Parametric Estimation ◽

Non Parametric

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Discrete Time Adaptive Control of Linear Dynamic Systems with a Two-Segment Piecewise-Linear Asymmetric Nonlinearity

10.23919/acc.1983.4788177 ◽

1983 ◽

Author(s):

Min-Chio Kung ◽

Baxter F. Womack

Keyword(s):

Adaptive Control ◽

Discrete Time ◽

Dynamic Systems ◽

Piecewise Linear ◽

Linear Dynamic Systems ◽

Linear Dynamic ◽

Asymmetric Nonlinearity

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Precipitation and filtration of zinc, magnesium, copper and aluminium hydroxides

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19823362 ◽

1982 ◽

Vol 47 (12) ◽

pp. 3362-3370

Author(s):

Otakar Söhnel ◽

Eva Matějčková

Keyword(s):

Residence Time ◽

Mean Residence Time ◽

Filtration Resistance ◽

Filtration Pressure ◽

Bed Porosity ◽

The Mean ◽

Filtration Properties

Filtration properties of batchwise precipitated suspensions of Zn(OH)2, Mg(OH)2 and Cu(OH)2 and continuously precipitated Al(OH)3 were studied. For batchwise precipitated suspensions was verified the theoretically predicted dependence of specific filtration resistance on initial supersaturation and for the continuously precipitated Al(OH)3 the relation between the specific filtration resistance and the mean residence time of suspension in the reactor. Dependences were also recorded between the bed porosity and concentration of precipitated solutions, specific filtration resistance and used filtration pressure and the effect of aging of the batchwise precipitated suspension of Mg(OH)2on its filtration properties. The used CST method for determination of filtration characteristics of Zn(OH)2 suspension was also studied.

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On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models

Sensors ◽

10.3390/s21113837 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3837

Author(s):

Rafael Orellana ◽

Rodrigo Carvajal ◽

Pedro Escárate ◽

Juan C. Agüero

Keyword(s):

Probability Density Function ◽

Probability Density ◽

Density Function ◽

Dynamic Systems ◽

Gaussian Mixture ◽

Error Model ◽

Fault Detection And Diagnosis ◽

Deterministic Models ◽

Linear Dynamic Systems ◽

Linear Dynamic

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

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