Should blood transport kinetics be modeled in metabolic processes?

1984 ◽  
Vol 247 (5) ◽  
pp. R895-R900
Author(s):  
G. Belforte ◽  
B. Bona ◽  
G. Molino
Keyword(s):  
A Priori ◽  
Model Parameters ◽  
Physiological System ◽  
A Priori Information ◽  
Design Task ◽  
Single Compartment ◽  
Priori Information ◽  
Blood Transport ◽  
Insight Into ◽  
Drug Kinetics

We analyze the interaction between blood transport phenomena and uptake processes when drug kinetics are studied with compartmental models. Relevant advantages in the physiological interpretation of the model parameters are obtained when blood transport is explicitly included in the model. This is done by aggregating into a single compartment all the blood spaces where no exchange with extravascular spaces takes place and separating into different blood compartments those spaces where some uptake and/or return occurs. The proposed strategy extensively uses all available a priori information about the physiological system, instead of considering only the information available in the measurements. This modeling approach has three main advantages: it provides greater insight into the identified quantities; it allows the introduction of quantitative a priori information; and it facilitates the experiment design task.

BAYESIAN METHODS IN THE EVALUATION OF SCATTERING DATA

2006 ◽  
Vol 15 (02) ◽  
pp. 354-361
Author(s):  
H. J. KRAPPE
Keyword(s):  
Elastic Scattering ◽  
Bayesian Methods ◽  
Optical Model ◽  
A Priori ◽  
Scattering Data ◽  
Model Parameters ◽  
A Priori Information ◽  
Model Independent ◽  
Priori Information

It is shown how a priori information can be introduced in an optimal way to extract optical-model parameters in a model-independent way from incomplete elastic scattering data.

scholarly journals Aircraft Turbine Engine Automatic Control Based on Adaptive Dynamic Models

2020 ◽  
Vol 2020 (4) ◽  
pp. 61-70
Author(s):  
Sergiy Yepifanov
Keyword(s):  
Automatic Control ◽  
Thermodynamic Model ◽  
Dynamic Models ◽  
A Priori ◽  
Static Model ◽  
Measuring System ◽  
Model Parameters ◽  
A Priori Information ◽  
Static Models ◽  
Priori Information

AbstractOne of the most perspective development directions of the aircraft engine is the application of adaptive digital automatic control systems (ACS). The significant element of the adaptation is the correction of mathematical models of both engine and its executive, measuring devices. These models help to solve tasks of control and are a combination of static models and dynamic models, as static models describe relations between parameters at steady-state modes, and dynamic ones characterize deviations of the parameters from static values.The work considers problems of the models’ correction using parametric identification methods. It is shown that the main problem of the precise engine simulation is the correction of the static model. A robust procedure that is based on a wide application of a priori information about performances of the engine and its measuring system is proposed for this purpose. One of many variants of this procedure provides an application of the non-linear thermodynamic model of the working process and estimation of individual corrections to the engine components’ characteristics with further substitution of the thermodynamic model by approximating on-board static model. Physically grounded estimates are obtained based on a priori information setting about the estimated parameters and engine performances, using fuzzy sets.Executive devices (actuators) and the most inertial temperature sensors require correction to their dynamic models. Researches showed, in case that the data for identification are collected during regular operation of ACS, the estimates of dynamic model parameters can be strongly correlated that reasons inadmissible errors.The reason is inside the substantial limitations on transients’ intensity that contain regular algorithms of acceleration/deceleration control. Therefore, test actions on the engine are required. Their character and minimum composition are determined using the derived relations between errors in model coefficients, measurement process, and control action parameters.

A constrained global inversion method using an overparameterized scheme: Application to poststack seismic data

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 613-626 ◽  
Author(s):  
Xin‐Quan Ma
Keyword(s):  
Simulated Annealing ◽  
Seismic Data ◽  
A Priori ◽  
Constant Thickness ◽  
Inversion Method ◽  
Model Parameters ◽  
A Priori Information ◽  
Initial Model ◽  
Seismic Waveform ◽  
Priori Information

A global optimization algorithm using simulated annealing has advantages over local optimization approaches in that it can escape from being trapped in local minima and it does not require a good initial model and function derivatives to find a global minimum. It is therefore more attractive and suitable for seismic waveform inversion. I adopt an improved version of a simulated annealing algorithm to invert simultaneously for acoustic impedance and layer interfaces from poststack seismic data. The earth’s subsurface is overparameterized by a series of microlayers with constant thickness in two‐way traveltime. The algorithm is constrained using the low‐frequency impedance trend and has been made computationally more efficient using this a priori information as an initial model. A search bound of each parameter, derived directly from the a priori information, reduces the nonuniqueness problem. Application of this technique to synthetic and field data examples helps one recover the true model parameters and reveals good continuity of estimated impedance across a seismic section. This approach has the capability of revealing the high‐resolution detail needed for reservoir characterization when a reliable migrated image is available with good well ties.

Blind Image Source Device Identification

2018 ◽  
Vol 12 (3) ◽  
pp. 84-99
Author(s):  
Udaya Sameer Venkata ◽  
Ruchira Naskar
Keyword(s):  
A Priori ◽  
Image Features ◽  
Practical Application ◽  
A Priori Information ◽  
Device Identification ◽  
Solution Strategies ◽  
Open Set ◽  
Priori Information ◽  
Blind Image ◽  
Insight Into

This article describes how digital forensic techniques for source investigation and identification enable forensic analysts to map an image under question to its source device, in a completely blind way, with no a-priori information about the storage and processing. Such techniques operate based on blind image fingerprinting or machine learning based modelling using appropriate image features. Although researchers till date have succeeded to achieve extremely high accuracy, more than 99% with 10-12 candidate cameras, as far as source device prediction is concerned, the practical application of the existing techniques is still doubtful. This is due to the existence of some critical open challenges in this domain, such as exact device linking, open-set challenge, classifier overfitting and counter forensics. In this article, the authors identify those open challenges, with an insight into possible solution strategies.

Improvements to elastic full-waveform inversion using cross-gradient constraints

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R105-R115 ◽  
Author(s):  
Edgar Manukyan ◽  
Hansruedi Maurer ◽  
André Nuber
Keyword(s):  
Field Data ◽  
A Priori ◽  
Waveform Inversion ◽  
Structural Similarity ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
A Priori Information ◽  
Individual Parameter ◽  
Full Waveform ◽  
Priori Information

Seismic full-waveform inversion (FWI) is potentially a powerful method for obtaining high-resolution subsurface images, but the results are often distorted by nonlinear effects and parameter trade-offs. Such distortions can be particularly severe in the case of multiparameter FWI, such as elastic FWI, in which inversion is performed simultaneously for P- and S-wave velocities and density. The problem can be alleviated by adding constraints in the form of plausible a priori information. A usually well-justified constraint includes the structural similarity of different model parameters; i.e., an anomalous body likely exhibits variations in all elastic properties, although their magnitudes may be different. To consider such types of a priori information, we have developed a structurally constrained elastic FWI, which is based on minimization of the cross products of gradients of different model parameters. Our synthetic 2D experiments show that structurally constrained FWI can significantly improve model reconstruction. It is also demonstrated that our approach still leads to improved results, even when the structural similarity between the individual parameter types is not exactly met. Inversions of field data show that in comparison to conventional FWI, structurally constrained FWI is able to match the field data equally well while requiring less structural complexity of the subsurface.

scholarly journals Interpretation of Magnetic Anomalies over 2D Fault and Sheet-Type Mineralized Structures Using Very Fast Simulated Annealing Global Optimization: An Understanding of Uncertainty and Geological Implications

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 6) ◽  
Author(s):  
Arkoprovo Biswas ◽  
Khushwant Rao
Keyword(s):  
A Priori ◽  
Theoretical Models ◽  
Magnetic Anomalies ◽  
Type Structure ◽  
Equivalent Model ◽  
Magnetic Data ◽  
Model Parameters ◽  
A Priori Information ◽  
Wide Range ◽  
Priori Information

Abstract Identification of intraterrane dislocation zones and associated mineralized bodies is of immense importance in exploration geophysics. Understanding such structures from geophysical anomalies is challenging and cumbersome. In the present study, we present a fast and competent algorithm for interpreting magnetic anomalies from such dislocation and mineralized zones. Such dislocation and mineralized zones are well explained from 2D fault and sheet-type structures. The different parameters from 2D fault and sheet-type structures such as the intensity of magnetization (k), depth to the top (z1), depth to the bottom (z2), origin location (x0), and dip angle (θ) of the fault and sheet from magnetic anomalies are interpreted. The interpretation suggests that there is uncertainty in defining the model parameters z1 and z2 for the 2D inclined fault; k, z1, and z2 for the 2D vertical fault and finite sheet-type structure; and k and z for the infinite sheet-type structure. Here, it shows a wide range of solutions depicting an equivalent model with smaller misfits. However, the final interpreted mean model is close to the actual model with the least uncertainty. Histograms and crossplots for 2D fault and sheet-type structures also reveal the same. The present algorithm is demonstrated with four theoretical models, including the effect of noises. Furthermore, the investigation of magnetic data was also applied from three field examples from intraterrane dislocation zones (Australia), deep-seated dislocation zones (India) as a 2D fault plane, and mineralized zones (Canada) as sheet-type structures. The final estimated model parameters are in good agreement with the earlier methods applied for these field examples with a priori information wherever available in the literature. However, the present method can simultaneously interpret all model parameters without a priori information.

Interpretation of magnetic data using an enhanced local wavenumber (ELW) method

Geophysics ◽  
2005 ◽  
Vol 70 (2) ◽  
pp. L7-L12 ◽  
Author(s):  
Ahmed Salem ◽  
Dhananjay Ravat ◽  
Richard Smith ◽  
Keisuke Ushijima
Keyword(s):  
Linear Equation ◽  
Random Noise ◽  
A Priori ◽  
Magnetic Data ◽  
Model Parameters ◽  
A Priori Information ◽  
Location Parameters ◽  
Local Wavenumber ◽  
Priori Information ◽  
Theoretical Simulations

This paper presents an enhancement of the local-wavenumber method (named ELW for “enhanced local wavenumber”) for interpretation of profile magnetic data. This method uses the traditional and phase-rotated local wavenumbers to produce a linear equation as a function of the model parameters. The equation can be solved to determine the horizontal location and depth of a 2D magnetic body without specifying a priori information about the nature of the sources. Using the obtained source-location parameters, the nature of the source can then be inferred. The method was tested using theoretical simulations with random noise over a dike body. It was able to provide both the location and an index characterizing the nature of the source body. The practical utility of the method is demonstrated using field examples over dikelike bodies from Canada and Egypt.

Naturally smooth inversions with a priori information from well logs

Geophysics ◽  
1991 ◽  
Vol 56 (11) ◽  
pp. 1811-1818 ◽  
Author(s):  
M. Pilkington ◽  
J. P. Todoeschuck
Keyword(s):  
Inverse Problem ◽  
A Priori ◽  
Power Spectral Analysis ◽  
Well Logs ◽  
Noise Model ◽  
Model Parameters ◽  
Scaling Exponent ◽  
A Priori Information ◽  
Power Spectral ◽  
Priori Information

Regularization is usually necessary to guarantee a solution to a given inverse problem. When constructing a model that gives an adequate fit to the data, some suitable method of regularization which provides numerical stability can be used. When investigating the resolution and variance of the computed model parameters, the character of regularization should be specified by the a priori information available. This avoids arbitrary variation of the damping to suit the interpreter. For geophysical inverse problems we determine the appropriate level of regularization (in the form of parameter covariances) from power spectral analysis of well‐log measurements. For resistivity data, well logs indicate that the spatial variation with depth can be described adequately by a scaling noise model, that is, one in which the power spectral density is proportional to some power (α) of the frequency. We show that α, the scaling exponent, controls the smoothness of the final model. For α < 0, the model becomes smoother as α becomes more negative. As a specific example, this approach is applied to the magnetotelluric inverse problem. A synthetic example illustrates the smoothing effect of α on inversion. Comparison between the scaling noise approach and a previous Backus‐Gilbert type inversion on some field data shows that using the appropriate value of α (−1.8 for this example) results in a model which is structurally simple and contains only those features well resolved by the data.

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