Selection of measurement frequencies for optimal extraction of tissue impedance model parameters

1999 ◽  
Vol 37 (6) ◽  
pp. 699-703 ◽  
Author(s):  
S. Kun ◽  
R. A. Peura
Keyword(s):  
Model Parameters ◽  
Tissue Impedance ◽  
Impedance Model ◽  
Selection Of

Real-time extraction of tissue impedance model parameters for electrical impedance spectrometer

1999 ◽  
Vol 37 (4) ◽  
pp. 428-432 ◽  
Author(s):  
S. Kun ◽  
B. Ristic ◽  
R. A. Peura ◽  
R. M. Dunn
Keyword(s):  
Real Time ◽  
Electrical Impedance ◽  
Model Parameters ◽  
Tissue Impedance ◽  
Impedance Model

scholarly journals Selection of catchment descriptors for the physical similarity approach. Part II: Application

2013 ◽  
Vol 8 (No. 4) ◽  
pp. 186-194
Author(s):  
M. Heřmanovský ◽  
P. Pech
Keyword(s):  
Land Cover ◽  
A Priori ◽  
Model Parameters ◽  
Physical Similarity ◽  
Model Parameter ◽  
Soil Moisture Accounting ◽  
Estimated Parameters ◽  
Selection Of

This paper demonstrates an application of the previously published method for selection of optimal catchment descriptors, according to which similar catchments can be identified for the purpose of estimation of the Sacramento – Soil Moisture Accounting (SAC-SMA) model parameters for a set of tested catchments, based on the physical similarity approach. For the purpose of the analysis, the following data from the Model Parameter Estimation Experiment (MOPEX) project were taken: a priori model parameter sets used as reference values for comparison with the newly estimated parameters, and catchment descriptors of four categories (climatic descriptors, soil properties, land cover and catchment morphology). The inverse clustering method, with Andrews’ curves for a homogeneity check, was used for the catchment grouping process. The optimal catchment descriptors were selected on the basis of two criteria, one comparing different subsets of catchment descriptors of the same size (MIN), the other one evaluating the improvement after addition of another catchment descriptor (MAX). The results suggest that the proposed method and the two criteria used may lead to the selection of a subset of conditionally optimal catchment descriptors from a broader set of them. As expected, the quality of the resulting subset of optimal catchment descriptors is mainly dependent on the number and type of the descriptors in the broader set. In the presented case study, six to seven catchment descriptors (two climatic, two soil and at least two land-cover descriptors) were identified as optimal for regionalisation of the SAC-SMA model parameters for a set of MOPEX catchments.

Numerical estimation of Fricke–Morse impedance model parameters using single-frequency sinusoidal excitation

2019 ◽  
Vol 40 (9) ◽  
pp. 09NT01 ◽  
Author(s):  
Fu Zhang ◽  
Benjamin Sanchez ◽  
Seward B Rutkove ◽  
Yuxiang Yang ◽  
Haowen Zhong ◽  
...  
Keyword(s):  
Single Frequency ◽  
Model Parameters ◽  
Numerical Estimation ◽  
Impedance Model ◽  
Sinusoidal Excitation

scholarly journals Initial value selection of the model parameters in the curve fitting phase of the dynamic SPECT imaging

2018 ◽  
Vol 51 (27) ◽  
pp. 241-246
Author(s):  
Zsófia Barna ◽  
Ákos Szlávecz ◽  
Gábor Hesz ◽  
Péter Somogyi ◽  
Katalin Kovács ◽  
...  
Keyword(s):  
Curve Fitting ◽  
Spect Imaging ◽  
Model Parameters ◽  
Dynamic Spect ◽  
Initial Value ◽  
Selection Of

A Mathematical Model of Evaporation and Dissolution From Oil Spills on Ice, Land, Water and Under Ice

1975 ◽  
Vol 10 (1) ◽  
pp. 132-141 ◽  
Author(s):  
P.J. Leinonen ◽  
D. Mackay
Keyword(s):  
Mass Transfer ◽  
Crude Oil ◽  
Transfer Coefficient ◽  
Mass Transfer Coefficient ◽  
Oil Spills ◽  
Aquatic Toxicity ◽  
Eddy Diffusivity ◽  
Model Parameters ◽  
Evaporation Flux ◽  
Selection Of

Abstract Mathematical models are presented which quantify the processes of evaporation and dissolution of components of crude oil in three situations: a spill on water, a spill on ice, and a spill under ice cover in which the oil lies between the water and ice phases. Constant spill area is assumed. The evaporation flux is calculated using a mass transfer coefficient based on windspeed and spill dimensions. The dissolution flux can be calculated from two models, a mass transfer coefficient approach and an eddy diffusivity approach involving the integration of a set of partial differential equations in depth and time. The selection of model parameters is discussed. For the three physical situations, using a synthetic crude oil, results are presented giving the relative rates of evaporation and dissolution and the aqueous phase concentration of selected hydrocarbons. The implications of the results for clean-up technology and aquatic toxicity are discussed, particularly with regard to spills under ice.

Automatic selection of arterial input function in DSC-MRI measurements for calculation of brain perfusion parameters using parametric modelling

2018 ◽  
Vol 13 (6) ◽  
pp. 58
Author(s):  
Seweryn Lipiński ◽  
Renata Kalicka
Keyword(s):  
Arterial Input Function ◽  
Regression Function ◽  
Input Function ◽  
Model Parameters ◽  
Parametric Modelling ◽  
Automatic Selection ◽  
Dsc Mri ◽  
Mri Measurements ◽  
Selection Of

A novel method and algorithm of automatic selection of arterial input function (AIF) is presented and its efficiency is proved using exemplary DSC-MRI measurements. The method chooses AIF devoted to a particular purpose, which is calculation of perfusion parameters with the use of parametric modelling of DSC-MRI data. The quality of medical diagnosis made on the basis of perfusion parameters depends on the quality of these parameters, which in turn is determined by the quality of the AIF signal. The proposed algorithm combines physiological requirements for AIF with mathematical criteria. The choice of parametric approach, instead of black-box modelling, allows better understanding of the investigated system functioning, as model parameters may be credited with physical interpretation. Furthermore, using multi-compartmental model of the DSC-MRI data with AIF regression function in an exponential form, gives direct, analytic results concerning the basic descriptors of AIF. The method chooses candidates for AIF on the basis of the descriptors quality. This step allows rejecting measurements which do not fulfil fundamental requirements concerning AIF from the physiological point of view. As these requirements are met, the next criterion can be adopted, that is the quality of fitting the regression function to measurements. The final step is choosing the AIF for calculating perfusion parameters with the best accuracy, which is attainable thanks to implementing the AIF devoted particularly to parametric modelling.

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