scholarly journals Joint processing of images in two spectral channels for small objects detecting

2021 ◽  
Author(s):  
V.P. Kosykh ◽  
G.I. Gromilin ◽  
N.S. Yakovenko
Keyword(s):  
Dimensional Space ◽  
Color Images ◽  
Model Parameters ◽  
Stationary Model ◽  
Joint Processing ◽  
Low Contrast ◽  
Useful Signal ◽  
Estimation Of Model Parameters ◽  
Spectral Channels ◽  
Stationary Background

The article is devoted to the problem of detecting low contrast small-sized objects in two-color images with a powerful spatially non-stationary background. An increase of the detecting reliability is achieved through a combination of three factors: attenuation of the background based on the construction of its locally stationary model; improving the estimation of model parameters by excluding statistically significant outliers from the initial data; joint processing of two-color images with a weakened background component. A method of constructing a linear boundary for detecting a useful signal in a two-dimensional space is proposed. The performance characteristics of a two-channel detector of small-sized objects are presented.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

scholarly journals Non-Stationary Model of Cerebral Oxygen Transport with Unknown Sources

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 910
Author(s):  
Andrey Kovtanyuk ◽  
Alexander Chebotarev ◽  
Varvara Turova ◽  
Irina Sidorenko ◽  
Renée Lampe
Keyword(s):  
Inverse Problem ◽  
Oxygen Transport ◽  
Model Parameters ◽  
Cerebral Oxygen ◽  
System Of Equations ◽  
Blood Oxygen ◽  
Stationary Model ◽  
Average Oxygen Concentration ◽  
The Right ◽  
The Brain

An inverse problem for a system of equations modeling oxygen transport in the brain is studied. The problem consists of finding the right-hand side of the equation for the blood oxygen transport, which is a linear combination of given functionals describing the average oxygen concentration in the neighborhoods of the ends of arterioles and venules. The overdetermination condition is determined by the values of these functionals evaluated on the solution. The unique solvability of the problem is proven without any smallness assumptions on the model parameters.

scholarly journals Mixture of Inverse Weibull and Lognormal Distributions: Properties, Estimation, and Illustration

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
K. S. Sultan ◽  
A. S. Al-Moisheer
Keyword(s):  
Information Matrix ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Component Mixture ◽  
Data Set ◽  
Hazard Functions ◽  
Lognormal Distributions ◽  
Proposed Model ◽  
Estimation Of Model Parameters

We discuss the two-component mixture of the inverse Weibull and lognormal distributions (MIWLND) as a lifetime model. First, we discuss the properties of the proposed model including the reliability and hazard functions. Next, we discuss the estimation of model parameters by using the maximum likelihood method (MLEs). We also derive expressions for the elements of the Fisher information matrix. Next, we demonstrate the usefulness of the proposed model by fitting it to a real data set. Finally, we draw some concluding remarks.

scholarly journals Modular Dynamic Biomolecular Modelling: The Unification of Stoichiometry, Thermodynamics, Kinetics and Data

2021 ◽  
Author(s):  
Peter J. Gawthrop ◽  
Michael Pan ◽  
Edmund J. Crampin
Keyword(s):  
Dynamic Models ◽  
System Development ◽  
Kinetic Modelling ◽  
Simulation Models ◽  
Bond Graph ◽  
Model Parameters ◽  
Biomolecular Systems ◽  
Genome Wide ◽  
Estimation Of Model Parameters ◽  
Genome Scale

AbstractRenewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermo-dynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of E. coli and published experimental data.

scholarly journals Calibration and validation of the Dynamic Wake Meandering model Part I: Bayesian estimation of model parameters using SpinnerLidar-derived wake characteristics

2021 ◽  
Author(s):  
Davide Conti ◽  
Nikolay Dimitrov ◽  
Alfredo Peña ◽  
Thomas Herges
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Flow Characteristics ◽  
Uncertainty Assessment ◽  
Wake Flow ◽  
Model Parameters ◽  
Wake Field ◽  
Wind Speeds ◽  
Standard Lead ◽  
Estimation Of Model Parameters

Abstract. In this first part of a two-part work, we study the calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, U.S.A. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows both for model implementation and uncertainty assessment. We validate the resulting fully-resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far wake region beyond four rotor diameters, as long as properly-calibrated parameters are used and wake meandering time series are accurately replicated. We demonstrate that the current DWM-model parameters in the IEC standard lead to conservative wake deficit predictions. Finally, we provide practical recommendations for reliable calibration procedures.

Modeling of a spiral-wound module and estimation of model parameters using numerical techniques

Desalination ◽  
2005 ◽  
Vol 173 (3) ◽  
pp. 269-286 ◽  
Author(s):  
S. Senthilmurugan ◽  
Aruj Ahluwalia ◽  
Sharad K. Gupta
Keyword(s):  
Model Parameters ◽  
Numerical Techniques ◽  
Estimation Of Model Parameters ◽  
Spiral Wound

scholarly journals GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Tomáš Brůna ◽  
Alexandre Lomsadze ◽  
Mark Borodovsky
Keyword(s):  
Prediction Accuracy ◽  
Gene Prediction ◽  
External Information ◽  
Model Parameters ◽  
Protein Database ◽  
Sequencing Project ◽  
Automated Method ◽  
Eukaryotic Gene ◽  
Estimation Of Model Parameters ◽  
Eukaryotic Genomes

Abstract We have made several steps toward creating a fast and accurate algorithm for gene prediction in eukaryotic genomes. First, we introduced an automated method for efficient ab initio gene finding, GeneMark-ES, with parameters trained in iterative unsupervised mode. Next, in GeneMark-ET we proposed a method of integration of unsupervised training with information on intron positions revealed by mapping short RNA reads. Now we describe GeneMark-EP, a tool that utilizes another source of external information, a protein database, readily available prior to the start of a sequencing project. A new specialized pipeline, ProtHint, initiates massive protein mapping to genome and extracts hints to splice sites and translation start and stop sites of potential genes. GeneMark-EP uses the hints to improve estimation of model parameters as well as to adjust coordinates of predicted genes if they disagree with the most reliable hints (the -EP+ mode). Tests of GeneMark-EP and -EP+ demonstrated improvements in gene prediction accuracy in comparison with GeneMark-ES, while the GeneMark-EP+ showed higher accuracy than GeneMark-ET. We have observed that the most pronounced improvements in gene prediction accuracy happened in large eukaryotic genomes.

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