DEFF Research Database (Denmark)
Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian
2011-01-01
In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....
Ballistic model to estimate microsprinkler droplet distribution
Directory of Open Access Journals (Sweden)
Conceição Marco Antônio Fonseca
2003-01-01
Full Text Available Experimental determination of microsprinkler droplets is difficult and time-consuming. This determination, however, could be achieved using ballistic models. The present study aimed to compare simulated and measured values of microsprinkler droplet diameters. Experimental measurements were made using the flour method, and simulations using a ballistic model adopted by the SIRIAS computational software. Drop diameters quantified in the experiment varied between 0.30 mm and 1.30 mm, while the simulated between 0.28 mm and 1.06 mm. The greatest differences between simulated and measured values were registered at the highest radial distance from the emitter. The model presented a performance classified as excellent for simulating microsprinkler drop distribution.
Determining the Equation of State (EoS) Parameters for Ballistic Gelatin
2015-09-01
ARL-TR-7467 ● SEP 2015 US Army Research Laboratory Determining the Equation of State (EoS) Parameters for Ballistic Gelatin ...EoS) Parameters for Ballistic Gelatin by Yolin Huang Weapons and Materials Research Directorate, ARL Approved for...State (EoS) Parameters for Ballistic Gelatin 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Yolin Huang 5d
PESTO: Parameter EStimation TOolbox.
Stapor, Paul; Weindl, Daniel; Ballnus, Benjamin; Hug, Sabine; Loos, Carolin; Fiedler, Anna; Krause, Sabrina; Hroß, Sabrina; Fröhlich, Fabian; Hasenauer, Jan; Wren, Jonathan
2018-02-15
PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB. PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/. jan.hasenauer@helmholtz-muenchen.de. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.
Developing a Ballistic Software Kit to Estimate Vehicle Characteristics at the Draft Design Stage
Directory of Open Access Journals (Sweden)
V. I. Maiorova
2015-01-01
Full Text Available The article describes a ballistic software kit to calculate a moving vehicle trajectory in atmosphere and space. Such software gives an opportunity to accelerate the acquisition of flying vehicle’s ballistic parameters at the stage of draft design. It contributes to improving collaboration efficiency between adjacent departments involved in the project. The developed software kit includes three different programs: Trajectory-LAND© (motion in atmosphere with possible correction of a trajectory, Trajectory-SPACE© (motion in the non-central gravity field with possible simulation of maneuvers, Trajectory-LAUNCH© (launch-vehicle’s insertion into the orbit with possible defining the impact points of separated stages. Each of the software concedes the addition of computational modules to use the solution results of the basic task. Implemented mathematical models permit to take into account the influence of main perturbations on the flying vehicle during the flight. For illustration purposes, the article gives some examples of using each of the programs and their block-diagrams.The developed software implements some algorithms, which allow attaining the convergence of numerical simulation of differential equations of motion. This problem arises, for example, while determining an attitude in case the stages have already separated from the launch vehicle. The mathematical conversion from Rodriguez-Hamilton parameters into Euler’s angles disables us to obtain reliable values of attitude angles due to the limitations for existing area of inverse trigonometric functions being used. Incorrect values of pitch lead to raw and roll channels divergences. Moreover, the mistakes in attitude determination lead to mistakes in obtained values of attack angle, which is included into the forms for aerodynamic forces and torques. As a result, the solution of system of differential equations is a failure when a flying vehicle enters the height of 30-35 km. The
Aswath Damodaran
1999-01-01
Over the last three decades, the capital asset pricing model has occupied a central and often controversial place in most corporate finance analysts’ tool chests. The model requires three inputs to compute expected returns – a riskfree rate, a beta for an asset and an expected risk premium for the market portfolio (over and above the riskfree rate). Betas are estimated, by most practitioners, by regressing returns on an asset against a stock index, with the slope of the regression being the b...
Parameter estimation in food science.
Dolan, Kirk D; Mishra, Dharmendra K
2013-01-01
Modeling includes two distinct parts, the forward problem and the inverse problem. The forward problem-computing y(t) given known parameters-has received much attention, especially with the explosion of commercial simulation software. What is rarely made clear is that the forward results can be no better than the accuracy of the parameters. Therefore, the inverse problem-estimation of parameters given measured y(t)-is at least as important as the forward problem. However, in the food science literature there has been little attention paid to the accuracy of parameters. The purpose of this article is to summarize the state of the art of parameter estimation in food science, to review some of the common food science models used for parameter estimation (for microbial inactivation and growth, thermal properties, and kinetics), and to suggest a generic method to standardize parameter estimation, thereby making research results more useful. Scaled sensitivity coefficients are introduced and shown to be important in parameter identifiability. Sequential estimation and optimal experimental design are also reviewed as powerful parameter estimation methods that are beginning to be used in the food science literature.
Parameter estimation in plasmonic QED
Jahromi, H. Rangani
2018-03-01
We address the problem of parameter estimation in the presence of plasmonic modes manipulating emitted light via the localized surface plasmons in a plasmonic waveguide at the nanoscale. The emitter that we discuss is the nitrogen vacancy centre (NVC) in diamond modelled as a qubit. Our goal is to estimate the β factor measuring the fraction of emitted energy captured by waveguide surface plasmons. The best strategy to obtain the most accurate estimation of the parameter, in terms of the initial state of the probes and different control parameters, is investigated. In particular, for two-qubit estimation, it is found although we may achieve the best estimation at initial instants by using the maximally entangled initial states, at long times, the optimal estimation occurs when the initial state of the probes is a product one. We also find that decreasing the interqubit distance or increasing the propagation length of the plasmons improve the precision of the estimation. Moreover, decrease of spontaneous emission rate of the NVCs retards the quantum Fisher information (QFI) reduction and therefore the vanishing of the QFI, measuring the precision of the estimation, is delayed. In addition, if the phase parameter of the initial state of the two NVCs is equal to πrad, the best estimation with the two-qubit system is achieved when initially the NVCs are maximally entangled. Besides, the one-qubit estimation has been also analysed in detail. Especially, we show that, using a two-qubit probe, at any arbitrary time, enhances considerably the precision of estimation in comparison with one-qubit estimation.
Parameter estimation and inverse problems
Aster, Richard C; Thurber, Clifford H
2005-01-01
Parameter Estimation and Inverse Problems primarily serves as a textbook for advanced undergraduate and introductory graduate courses. Class notes have been developed and reside on the World Wide Web for faciliting use and feedback by teaching colleagues. The authors'' treatment promotes an understanding of fundamental and practical issus associated with parameter fitting and inverse problems including basic theory of inverse problems, statistical issues, computational issues, and an understanding of how to analyze the success and limitations of solutions to these probles. The text is also a practical resource for general students and professional researchers, where techniques and concepts can be readily picked up on a chapter-by-chapter basis.Parameter Estimation and Inverse Problems is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who may not have an extensive mathematical background. It is accompanied by a Web site that...
Parameter Estimation Using VLA Data
Venter, Willem C.
The main objective of this dissertation is to extract parameters from multiple wavelength images, on a pixel-to-pixel basis, when the images are corrupted with noise and a point spread function. The data used are from the field of radio astronomy. The very large array (VLA) at Socorro in New Mexico was used to observe planetary nebula NGC 7027 at three different wavelengths, 2 cm, 6 cm and 20 cm. A temperature model, describing the temperature variation in the nebula as a function of optical depth, is postulated. Mathematical expressions for the brightness distribution (flux density) of the nebula, at the three observed wavelengths, are obtained. Using these three equations and the three data values available, one from the observed flux density map at each wavelength, it is possible to solve for two temperature parameters and one optical depth parameter at each pixel location. Due to the fact that the number of unknowns equal the number of equations available, estimation theory cannot be used to smooth any noise present in the data values. It was found that a direct solution of the three highly nonlinear flux density equations is very sensitive to noise in the data. Results obtained from solving for the three unknown parameters directly, as discussed above, were not physical realizable. This was partly due to the effect of incomplete sampling at the time when the data were gathered and to noise in the system. The application of rigorous digital parameter estimation techniques result in estimated parameters that are also not physically realizable. The estimated values for the temperature parameters are for example either too high or negative, which is not physically possible. Simulation studies have shown that a "double smoothing" technique improves the results by a large margin. This technique consists of two parts: in the first part the original observed data are smoothed using a running window and in the second part a similar smoothing of the estimated parameters
Inflation and cosmological parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Hamann, J.
2007-05-15
In this work, we focus on two aspects of cosmological data analysis: inference of parameter values and the search for new effects in the inflationary sector. Constraints on cosmological parameters are commonly derived under the assumption of a minimal model. We point out that this procedure systematically underestimates errors and possibly biases estimates, due to overly restrictive assumptions. In a more conservative approach, we analyse cosmological data using a more general eleven-parameter model. We find that regions of the parameter space that were previously thought ruled out are still compatible with the data; the bounds on individual parameters are relaxed by up to a factor of two, compared to the results for the minimal six-parameter model. Moreover, we analyse a class of inflation models, in which the slow roll conditions are briefly violated, due to a step in the potential. We show that the presence of a step generically leads to an oscillating spectrum and perform a fit to CMB and galaxy clustering data. We do not find conclusive evidence for a step in the potential and derive strong bounds on quantities that parameterise the step. (orig.)
Applied parameter estimation for chemical engineers
Englezos, Peter
2000-01-01
Formulation of the parameter estimation problem; computation of parameters in linear models-linear regression; Gauss-Newton method for algebraic models; other nonlinear regression methods for algebraic models; Gauss-Newton method for ordinary differential equation (ODE) models; shortcut estimation methods for ODE models; practical guidelines for algorithm implementation; constrained parameter estimation; Gauss-Newton method for partial differential equation (PDE) models; statistical inferences; design of experiments; recursive parameter estimation; parameter estimation in nonlinear thermodynam
Data Handling and Parameter Estimation
DEFF Research Database (Denmark)
Sin, Gürkan; Gernaey, Krist
2016-01-01
literature that are mostly based on the ActivatedSludge Model (ASM) framework and their appropriate extensions (Henze et al., 2000).The chapter presents an overview of the most commonly used methods in the estimation of parameters from experimental batch data, namely: (i) data handling and validation, (ii...... and spatial scales. At full-scale wastewater treatment plants (WWTPs),mechanistic modelling using the ASM framework and concept (e.g. Henze et al., 2000) has become an important part of the engineering toolbox for process engineers. It supports plant design, operation, optimization and control applications......). Models have also been used as an integral part of the comprehensive analysis and interpretation of data obtained from a range of experimental methods from the laboratory, as well as pilot-scale studies to characterise and study wastewater treatment plants. In this regard, models help to properly explain...
Multi response optimization of wire-EDM process parameters of ballistic grade aluminium alloy
Directory of Open Access Journals (Sweden)
Ravindranadh Bobbili
2015-12-01
Full Text Available In the current investigation, a multi response optimization technique based on Taguchi method coupled with Grey relational analysis is planned for wire-EDM operations on ballistic grade aluminium alloy for armour applications. Experiments have been performed with four machining variables: pulse-on time, pulse-off time, peak current and spark voltage. Experimentation has been planned as per Taguchi technique. Three performance characteristics namely material removal rate (MRR, surface roughness (SR and gap current (GC have been chosen for this study. Results showed that pulse-on time, peak current and spark voltage were significant variables to Grey relational grade. Variation of performance measures with process variables was modelled by using response surface method. The confirmation tests have also been performed to validate the results obtained by Grey relational analysis and found that great improvement with 6% error is achieved.
Directory of Open Access Journals (Sweden)
Kyung Rok Moon
2012-12-01
Full Text Available This paper studies the problem of tracking a re-entry vehicle (RV in order to predict its impact point on the ground. Re-entry target dynamics combined with super-high speed has a complex non-linearity due to ballistic coefficient variations. However, it is difficult to construct a database for the ballistic coefficient of a unknown vehicle for a wide range of variations, thus the reliability of target tracking performance cannot be guaranteed if accurate ballistic coefficient estimation is not achieved. Various techniques for ballistic coefficient estimation have been previously proposed, but limitations exist for the estimation of non-linear parts accurately without obtaining prior information. In this paper we propose the ballistic coefficient β model-based interacting multiple model-extended Kalman filter (β-IMM-EKF for precise tracking of an RV. To evaluate the performance, other ballistic coefficient model based filters, which are gamma augmented filter, gamma bootstrapped filter were compared and assessed with the proposed β-IMM-EKF for precise tracking of an RV.
Rosenberg, Zvi
2016-01-01
This book comprehensively discusses essential aspects of terminal ballistics, combining experimental data, numerical simulations and analytical modeling. Employing a unique approach to numerical simulations as a measure of sensitivity for the major physical parameters, the new edition also includes the following features: new figures to better illustrate the problems discussed; improved explanations for the equation of state of a solid and for the cavity expansion process; new data concerning the Kolsky bar test; and a discussion of analytical modeling for the hole diameter in a thin metallic plate impacted by a shaped charge jet. The section on thick concrete targets penetrated by rigid projectiles has now been expanded to include the latest findings, and two new sections have been added: one on a novel approach to the perforation of thin concrete slabs, and one on testing the failure of thin metallic plates using a hydrodynamic ram.
Parameter Estimation in Continuous Time Domain
Directory of Open Access Journals (Sweden)
Gabriela M. ATANASIU
2016-12-01
Full Text Available This paper will aim to presents the applications of a continuous-time parameter estimation method for estimating structural parameters of a real bridge structure. For the purpose of illustrating this method two case studies of a bridge pile located in a highly seismic risk area are considered, for which the structural parameters for the mass, damping and stiffness are estimated. The estimation process is followed by the validation of the analytical results and comparison with them to the measurement data. Further benefits and applications for the continuous-time parameter estimation method in civil engineering are presented in the final part of this paper.
Chapman, G.; Kirk, D.
1974-01-01
The parameter identification scheme being used is a differential correction least squares procedure (Gauss-Newton method). The position, orientation, and derivatives of these quantities with respect to the parameters of interest (i.e., sensitivity coefficients) are determined by digital integration of the equations of motion and the parametric differential equations. The application of this technique to three vastly different sets of data is used to illustrate the versatility of the method and to indicate some of the problems that still remain.
Parameter estimation applied to physiological systems
Rideout, V.C.; Beneken, J.E.W.
Parameter estimation techniques are of ever-increasing interest in the fields of medicine and biology, as greater efforts are currently being made to describe physiological systems in explicit quantitative form. Although some of the techniques of parameter estimation as developed for use in other
Estimation of physical parameters in induction motors
DEFF Research Database (Denmark)
Børsting, H.; Knudsen, Morten; Rasmussen, Henrik
1994-01-01
Parameter estimation in induction motors is a field of great interest, because accurate models are needed for robust dynamic control of induction motors......Parameter estimation in induction motors is a field of great interest, because accurate models are needed for robust dynamic control of induction motors...
On parameter estimation in deformable models
DEFF Research Database (Denmark)
Fisker, Rune; Carstensen, Jens Michael
1998-01-01
Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian form...
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
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muhammad zahid rashid
2011-04-01
Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR, moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes
Cosmological parameter estimation using Particle Swarm Optimization
International Nuclear Information System (INIS)
Prasad, J; Souradeep, T
2014-01-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite
Cosmological parameter estimation using Particle Swarm Optimization
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Ballistic Missile Intercept from UCAV
2011-12-01
on the DPRK TPD-2 ballistic missile. A 3 degree-of-freedom ( 3DoF ) mathematical model was previously developed and used to simulate the trajectory...Characteristics(estimated) TPD-2 ICBM Data Input to Simulation(From [1]) Figure 3. Reach of TPD-2 Missile A 3DoF ballistic missile
Application of spreadsheet to estimate infiltration parameters
Directory of Open Access Journals (Sweden)
Mohammad Zakwan
2016-09-01
Full Text Available Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach for estimation of infiltration parameters often fails to estimate the infiltration parameters precisely. The generalised reduced gradient (GRG solver is reported to be a powerful tool for estimating parameters of nonlinear equations and it has, therefore, been implemented to estimate the infiltration parameters in the present paper. Field data of infiltration rate available in literature for sandy loam soils of Umuahia, Nigeria were used to evaluate the performance of GRG solver. A comparative study of graphical method and GRG solver shows that the performance of GRG solver is better than that of conventional graphical method for estimation of infiltration rates. Further, the performance of Kostiakov model has been found to be better than the Horton and Philip's model in most of the cases based on both the approaches of parameter estimation.
State and parameter estimation in bio processes
Energy Technology Data Exchange (ETDEWEB)
Maher, M.; Roux, G.; Dahhou, B. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France)]|[Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1994-12-31
A major difficulty in monitoring and control of bio-processes is the lack of reliable and simple sensors for following the evolution of the main state variables and parameters such as biomass, substrate, product, growth rate, etc... In this article, an adaptive estimation algorithm is proposed to recover the state and parameters in bio-processes. This estimator utilizes the physical process model and the reference model approach. Experimentations concerning estimation of biomass and product concentrations and specific growth rate, during batch, fed-batch and continuous fermentation processes are presented. The results show the performance of this adaptive estimation approach. (authors) 12 refs.
On Carleman estimates with two large parameters
Energy Technology Data Exchange (ETDEWEB)
Le Rousseau, Jerome, E-mail: jlr@univ-orleans.fr [Jerome Le Rousseau. Universite d' Orleans, Laboratoire Mathematiques et Applications, Physique Mathematique d' Orleans, CNRS UMR 6628, Federation Denis-Poisson, FR CNRS 2964, B.P. 6759, 45067 Orleans cedex 2 (France)
2011-04-01
We provide a general framework for the analysis and the derivation of Carleman estimates with two large parameters. For an appropriate form of weight functions strong pseudo-convexity conditions are shown to be necessary and sufficient.
Association measures and estimation of copula parameters ...
African Journals Online (AJOL)
We apply the inversion method of estimation, with several combinations of two among the four most popular association measures, to estimate the parameters of copulas in the case of bivariate distributions. We carry out a simulation study with two examples, namely Farlie-Gumbel-Morgenstern and Marshall-Olkin ...
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Estimation of Modal Parameters and their Uncertainties
DEFF Research Database (Denmark)
Andersen, P.; Brincker, Rune
1999-01-01
In this paper it is shown how to estimate the modal parameters as well as their uncertainties using the prediction error method of a dynamic system on the basis of uotput measurements only. The estimation scheme is assessed by means of a simulation study. As a part of the introduction, an example...... is given showing how the uncertainty estimates can be used in applications such as damage detection....
Reionization history and CMB parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Dizgah, Azadeh Moradinezhad; Gnedin, Nickolay Y.; Kinney, William H.
2013-05-01
We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case.
Rosenberg, Zvi
2012-01-01
This book covers the important issues of terminal ballistics in a comprehensive way combining experimental data, numerical simulations and analytical modeling. The first chapter reviews the experimental equipment which are used for ballistic tests and the diagnostics for material characterization under impulsive loading conditions. The second chapter covers essential features of the codes which are used for terminal ballistics such as the Euler vs. Lagrange schemes and meshing techniques, as well as the most popular material models. The third chapter, devoted to the penetration mechanics of rigid penetrators, brings the update of modeling in this field. The fourth chapter deals with plate perforation and the fifth chapter deals with the penetration mechanics of shaped charge jets and eroding long rods. The last two chapters discuss several techniques for the disruption and defeating of the main threats in armor design. Throughout the book the authors demonstrate the advantages of numerical simulations in unde...
Statistics of Parameter Estimates: A Concrete Example
Aguilar, Oscar
2015-01-01
© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise levels, models, or prior knowledge. But what can we say about the validity of such estimates, and the influence of these assumptions? This paper is concerned with methods to address these questions, and for didactic purposes it is written in the context of a concrete nonlinear parameter estimation problem. We will use the results of a physical experiment conducted by Allmaras et al. at Texas A&M University [M. Allmaras et al., SIAM Rev., 55 (2013), pp. 149-167] to illustrate the importance of validation procedures for statistical parameter estimation. We describe statistical methods and data analysis tools to check the choices of likelihood and prior distributions, and provide examples of how to compare Bayesian results with those obtained by non-Bayesian methods based on different types of assumptions. We explain how different statistical methods can be used in complementary ways to improve the understanding of parameter estimates and their uncertainties.
Multi-Parameter Estimation for Orthorhombic Media
Masmoudi, Nabil
2015-08-19
Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.
Parameter Estimation for Thurstone Choice Models
Energy Technology Data Exchange (ETDEWEB)
Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-04-24
We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.
Bayesian estimation of Weibull distribution parameters
International Nuclear Information System (INIS)
Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.
1994-11-01
In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs
Modelling and parameter estimation of dynamic systems
Raol, JR; Singh, J
2004-01-01
Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimization of error between the model response and actual system response. However, with the proliferation of high speed digital computers, elegant and innovative techniques like filter error method, H-infinity and Artificial Neural Networks are finding more and mor
Robust estimation of hydrological model parameters
Directory of Open Access Journals (Sweden)
A. Bárdossy
2008-11-01
Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.
MCMC for parameters estimation by bayesian approach
International Nuclear Information System (INIS)
Ait Saadi, H.; Ykhlef, F.; Guessoum, A.
2011-01-01
This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.
Parameter estimation methods for chaotic intercellular networks.
Directory of Open Access Journals (Sweden)
Inés P Mariño
Full Text Available We have investigated simulation-based techniques for parameter estimation in chaotic intercellular networks. The proposed methodology combines a synchronization-based framework for parameter estimation in coupled chaotic systems with some state-of-the-art computational inference methods borrowed from the field of computational statistics. The first method is a stochastic optimization algorithm, known as accelerated random search method, and the other two techniques are based on approximate Bayesian computation. The latter is a general methodology for non-parametric inference that can be applied to practically any system of interest. The first method based on approximate Bayesian computation is a Markov Chain Monte Carlo scheme that generates a series of random parameter realizations for which a low synchronization error is guaranteed. We show that accurate parameter estimates can be obtained by averaging over these realizations. The second ABC-based technique is a Sequential Monte Carlo scheme. The algorithm generates a sequence of "populations", i.e., sets of randomly generated parameter values, where the members of a certain population attain a synchronization error that is lesser than the error attained by members of the previous population. Again, we show that accurate estimates can be obtained by averaging over the parameter values in the last population of the sequence. We have analysed how effective these methods are from a computational perspective. For the numerical simulations we have considered a network that consists of two modified repressilators with identical parameters, coupled by the fast diffusion of the autoinducer across the cell membranes.
Parameter Estimation in Active Plate Structures
DEFF Research Database (Denmark)
Araujo, A. L.; Lopes, H. M. R.; Vaz, M. A. P.
2006-01-01
In this paper two non-destructive methods for elastic and piezoelectric parameter estimation in active plate structures with surface bonded piezoelectric patches are presented. These methods rely on experimental undamped natural frequencies of free vibration. The first solves the inverse problem...
Using Digital Filtration for Hurst Parameter Estimation
Directory of Open Access Journals (Sweden)
J. Prochaska
2009-06-01
Full Text Available We present a new method to estimate the Hurst parameter. The method exploits the form of the autocorrelation function for second-order self-similar processes and is based on one-pass digital filtration. We compare the performance and properties of the new method with that of the most common methods.
Simultaneous estimation of earthquake source parameters and ...
Indian Academy of Sciences (India)
This paper presents the simultaneous estimation of source parameters and crustal Q values for small to moderate-size aftershocks ( 2.1–5.1) of the 7.7 2001 Bhuj earthquake. The horizontal-component S-waves of 144 well located earthquakes (2001–2010) recorded at 3–10 broadband seismograph sites in the ...
Simultaneous estimation of earthquake source parameters and ...
Indian Academy of Sciences (India)
This paper presents the simultaneous estimation of source parameters and crustal Q values for small to moderate-size aftershocks (Mw 2.1–5.1) of the Mw 7.7 2001 Bhuj earthquake. The horizontal-component. S-waves of 144 well located earthquakes (2001–2010) recorded at 3–10 broadband seismograph sites in.
Parameter estimation in stochastic differential equations
Bishwal, Jaya P N
2008-01-01
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.
Sensor Placement for Modal Parameter Subset Estimation
DEFF Research Database (Denmark)
Ulriksen, Martin Dalgaard; Bernal, Dionisio; Damkilde, Lars
2016-01-01
). It is shown that the widely used Effective Independence (EI) method, which uses the modal amplitudes as surrogates for the parameters of interest, provides sensor configurations yielding theoretical lower bound variances whose maxima are up to 30 % larger than those obtained by use of the max-min approach.......The present paper proposes an approach for deciding on sensor placements in the context of modal parameter estimation from vibration measurements. The approach is based on placing sensors, of which the amount is determined a priori, such that the minimum Fisher information that the frequency...... responses carry on the selected modal parameter subset is, in some sense, maximized. The approach is validated in the context of a simple 10-DOF mass-spring-damper system by computing the variance of a set of identified modal parameters in a Monte Carlo setting for a set of sensor configurations, whose...
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Estimating RASATI scores using acoustical parameters
International Nuclear Information System (INIS)
Agüero, P D; Tulli, J C; Moscardi, G; Gonzalez, E L; Uriz, A J
2011-01-01
Acoustical analysis of speech using computers has reached an important development in the latest years. The subjective evaluation of a clinician is complemented with an objective measure of relevant parameters of voice. Praat, MDVP (Multi Dimensional Voice Program) and SAV (Software for Voice Analysis) are some examples of software for speech analysis. This paper describes an approach to estimate the subjective characteristics of RASATI scale given objective acoustical parameters. Two approaches were used: linear regression with non-negativity constraints, and neural networks. The experiments show that such approach gives correct evaluations with ±1 error in 80% of the cases.
Cosmological parameter estimation using particle swarm optimization
Prasad, Jayanti; Souradeep, Tarun
2012-06-01
Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.
Ultrasonic data compression via parameter estimation.
Cardoso, Guilherme; Saniie, Jafar
2005-02-01
Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.
Toward unbiased estimations of the statefinder parameters
Aviles, Alejandro; Klapp, Jaime; Luongo, Orlando
2017-09-01
With the use of simulated supernova catalogs, we show that the statefinder parameters turn out to be poorly and biased estimated by standard cosmography. To this end, we compute their standard deviations and several bias statistics on cosmologies near the concordance model, demonstrating that these are very large, making standard cosmography unsuitable for future and wider compilations of data. To overcome this issue, we propose a new method that consists in introducing the series of the Hubble function into the luminosity distance, instead of considering the usual direct Taylor expansions of the luminosity distance. Moreover, in order to speed up the numerical computations, we estimate the coefficients of our expansions in a hierarchical manner, in which the order of the expansion depends on the redshift of every single piece of data. In addition, we propose two hybrids methods that incorporates standard cosmography at low redshifts. The methods presented here perform better than the standard approach of cosmography both in the errors and bias of the estimated statefinders. We further propose a one-parameter diagnostic to reject non-viable methods in cosmography.
Composite likelihood estimation of demographic parameters
Directory of Open Access Journals (Sweden)
Garrigan Daniel
2009-11-01
Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable
Sequential parameter estimation for stochastic systems
Directory of Open Access Journals (Sweden)
G. A. Kivman
2003-01-01
Full Text Available The quality of the prediction of dynamical system evolution is determined by the accuracy to which initial conditions and forcing are known. Availability of future observations permits reducing the effects of errors in assessment the external model parameters by means of a filtering algorithm. Usually, uncertainties in specifying internal model parameters describing the inner system dynamics are neglected. Since they are characterized by strongly non-Gaussian distributions (parameters are positive, as a rule, traditional Kalman filtering schemes are badly suited to reducing the contribution of this type of uncertainties to the forecast errors. An extension of the Sequential Importance Resampling filter (SIR is proposed to this aim. The filter is verified against the Ensemble Kalman filter (EnKF in application to the stochastic Lorenz system. It is shown that the SIR is capable of estimating the system parameters and to predict the evolution of the system with a remarkably better accuracy than the EnKF. This highlights a severe drawback of any Kalman filtering scheme: due to utilizing only first two statistical moments in the analysis step it is unable to deal with probability density functions badly approximated by the normal distribution.
Gait parameter and event estimation using smartphones.
Pepa, Lucia; Verdini, Federica; Spalazzi, Luca
2017-09-01
The use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well. This work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland-Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects. High correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count. This study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications. Copyright © 2017 Elsevier B.V. All rights reserved.
Statistical estimation of nuclear reactor dynamic parameters
International Nuclear Information System (INIS)
Cummins, J.D.
1962-02-01
This report discusses the study of the noise in nuclear reactors and associated power plant. The report is divided into three distinct parts. In the first part parameters which influence the dynamic behaviour of some reactors will be specified and their effect on dynamic performance described. Methods of estimating dynamic parameters using statistical signals will be described in detail together with descriptions of the usefulness of the results, the accuracy and related topics. Some experiments which have been and which might be performed on nuclear reactors will be described. In the second part of the report a digital computer programme will be described. The computer programme derives the correlation functions and the spectra of signals. The programme will compute the frequency response both gain and phase for physical items of plant for which simultaneous recordings of input and output signal variations have been made. Estimations of the accuracy of the correlation functions and the spectra may be computed using the programme and the amplitude distribution of signals may also b computed. The programme is written in autocode for the Ferranti Mercury computer. In the third part of the report a practical example of the use of the method and the digital programme is presented. In order to eliminate difficulties of interpretation a very simple plant model was chosen i.e. a simple first order lag. Several interesting properties of statistical signals were measured and will be discussed. (author)
Statistical distributions applications and parameter estimates
Thomopoulos, Nick T
2017-01-01
This book gives a description of the group of statistical distributions that have ample application to studies in statistics and probability. Understanding statistical distributions is fundamental for researchers in almost all disciplines. The informed researcher will select the statistical distribution that best fits the data in the study at hand. Some of the distributions are well known to the general researcher and are in use in a wide variety of ways. Other useful distributions are less understood and are not in common use. The book describes when and how to apply each of the distributions in research studies, with a goal to identify the distribution that best applies to the study. The distributions are for continuous, discrete, and bivariate random variables. In most studies, the parameter values are not known a priori, and sample data is needed to estimate parameter values. In other scenarios, no sample data is available, and the researcher seeks some insight that allows the estimate of ...
Parameter estimation in fractional diffusion models
Kubilius, Kęstutis; Ralchenko, Kostiantyn
2017-01-01
This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is “white,” i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides s...
Ocean wave parameters estimation using backpropagation neural networks
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; SubbaRao; Raju, D.H.
is trained the ocean wave parameters can be estimated for unknown measured spectra, whereas significant wave height and spectral peak period are required to first generate the Scott spectra and then estimate other ocean wave parameters....
Determination of Parameter Estimation Errors Due to Noise and Undermodelling
DEFF Research Database (Denmark)
Knudsen, Morten
1996-01-01
A simple method for determination of the estimation error of physical parameters due to noise and undermodelling is developed.......A simple method for determination of the estimation error of physical parameters due to noise and undermodelling is developed....
Estimation of Two-Parameter Logistic Item Response Curves.
Tsutakawa, Robert K.
1984-01-01
The EM algorithm is used to derive maximum likelihood estimates for item parameters of the two-parameter logistic item response curves. The observed information matrix is then used to approximate the covariance matrix of these estimates. Simulated data are used to compare the estimated and actual item parameters. (Author/BW)
Thermoplastic composites for ballistic application
Song, John Whachong
2003-08-01
. For these reasons, samples processed at lower temperatures always gave higher energy absorption under ballistic impact. Fabric configuration was also an important parameter influencing the ballistic performance. Relatively stiff composites, KKM2/PSU 6-2 ripstop, showed better performance with smaller fragments over the other two composites. However, KM2/SP 6-2 ripstop composites, which are less stiff than KKM2/PSU 6-2 ripstop composites, exhibited better performance with larger size fragments. Fiber breakage is the major source of kinetic energy absorption upon ballistic impact. Fiber straining is the most preferred failure mechanism of the composites for maximum kinetic energy absorption upon ballistic penetration.
Application of spreadsheet to estimate infiltration parameters
Zakwan, Mohammad; Muzzammil, Mohammad; Alam, Javed
2016-01-01
Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach ...
[Statistical estimation of parameters in allometric equations].
Zotin, A A
2000-01-01
An algorithm for estimating allometric coefficients widely used in biological studies is presented. The coefficients can be estimated only when the relationship between logarithms of the approximated data meets the linearity criterion. The proposed algorithm was applied for the brain-body weight relationship in mammals and oxygen consumption rate-body weight relationship in amphibians.
Multi-objective optimization in quantum parameter estimation
Gong, BeiLi; Cui, Wei
2018-04-01
We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests...
Genetic parameter estimates for growth traits at different ages in ...
African Journals Online (AJOL)
Precise and accurate genetic parameter estimates are crucial for making sound decisions in many stages of animal improvement programme. Genetic parameter estimates for eight growth traits were determined in 308 turkeys generated from mating pure indigenous parents. PROC MIXED of SAS was used to estimate ...
Simultaneous estimation of earthquake source parameters and ...
Indian Academy of Sciences (India)
stress drop values are quite large compared to the other similar size Indian intraplate earthquakes, which can be attributed ... Earthquake source parameters; crustal Q-value; simultaneous inversion; S-wave spectra; aftershocks. J. Earth Syst. Sci. ...... 28 1339–1342. Lee W H K and Valdes C M 1985 HYP071PC: A personal.
ESTIMATION OF SHEAR STRENGTH PARAMETERS OF ...
African Journals Online (AJOL)
This research work seeks to develop models for predicting the shear strength parameters (cohesion and angle of friction) of lateritic soils in central and southern areas of Delta State using artificial neural network modeling technique. The application of these models will help reduce cost and time in acquiring geotechnical ...
Simultaneous estimation of experimental and material parameters
CSIR Research Space (South Africa)
Jansen van Rensburg, GJ
2012-07-01
Full Text Available This conference contribution focusses on the invertibility of non-ideal material tests to accurately determine material parameters. This is done by attempting to model non-ideal test cases and comparing strains as well as force history...
International Nuclear Information System (INIS)
Cassol, E.; Bonnet, J.; Porcheron, D.; Mazeron, J.J.; Peiffert, D.; Alapetite, C.
2012-01-01
This review describes the ballistic quality assurance for stereotactic intracranial irradiation treatments delivered with Gamma Knife R either dedicated or adapted medical linear accelerators. Specific and periodic controls should be performed in order to check the mechanical stability for both irradiation and collimation systems. If this step remains under the responsibility of the medical physicist, it should be done in agreement with the manufacturer's technical support. At this time, there are no recent published guidelines. With technological developments, both frequency and accuracy should be assessed in each institution according to the treatment mode: single versus hypo-fractionated dose, circular collimator versus micro-multi-leaf collimators. In addition, 'end-to-end' techniques are mandatory to find the origin of potential discrepancies and to estimate the global ballistic accuracy of the delivered treatment. Indeed, they include frames, non-invasive immobilization devices, localizers, multimodal imaging for delineation and in-room positioning imaging systems. The final precision that could be reasonably achieved is more or less 1 mm. (authors)
A Comparative Study of Distribution System Parameter Estimation Methods
Energy Technology Data Exchange (ETDEWEB)
Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup
2016-07-17
In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.
Parameter estimation and testing of hypotheses
International Nuclear Information System (INIS)
Fruhwirth, R.
1996-01-01
This lecture presents the basic mathematical ideas underlying the concept of random variable and the construction and analysis of estimators and test statistics. The material presented is based mainly on four books given in the references: the general exposition of estimators and test statistics follows Kendall and Stuart which is a comprehensive review of the field; the book by Eadie et al. contains selecting topics of particular interest to experimental physicist and a host of illuminating examples from experimental high-energy physics; for the presentation of numerical procedures, the Press et al. and the Thisted books have been used. The last section deals with estimation in dynamic systems. In most books the Kalman filter is presented in a Bayesian framework, often obscured by cumbrous notation. In this lecture, the link to classical least-squares estimators and regression models is stressed with the aim of facilitating the access to this less familiar topic. References are given for specific applications to track and vertex fitting and for extended exposition of these topics. In the appendix, the link between Bayesian decision rules and feed-forward neural networks is presented. (J.S.). 10 refs., 5 figs., 1 appendix
Estimation of a collision impact parameter
International Nuclear Information System (INIS)
Shmatov, S.V.; Zarubin, P.I.
2001-01-01
We demonstrate that the nuclear collision geometry (i.e. impact parameter) can be determined in an event-by-event analysis by measuring the transverse energy flow in the pseudorapidity region 3≤|η|≤5 with a minimal dependence on collision dynamics details at the LHC energy scale. Using the HIJING model we have illustrated our calculation by a simulation of events of nucleus-nucleus interactions at the c.m.s. energy from 1 up to 5.5 TeV per nucleon and various types of nuclei
Parameter Estimation for Improving Association Indicators in Binary Logistic Regression
Directory of Open Access Journals (Sweden)
Mahdi Bashiri
2012-02-01
Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.
Incremental parameter estimation of kinetic metabolic network models
Directory of Open Access Journals (Sweden)
Jia Gengjie
2012-11-01
Full Text Available Abstract Background An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE. Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified. Results In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates exceeds that of metabolites (chemical species. Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented. Conclusions The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.
Sample Size and Item Parameter Estimation Precision When Utilizing the One-Parameter "Rasch" Model
Custer, Michael
2015-01-01
This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…
Control and Estimation of Distributed Parameter Systems
Kappel, F; Kunisch, K
1998-01-01
Consisting of 23 refereed contributions, this volume offers a broad and diverse view of current research in control and estimation of partial differential equations. Topics addressed include, but are not limited to - control and stability of hyperbolic systems related to elasticity, linear and nonlinear; - control and identification of nonlinear parabolic systems; - exact and approximate controllability, and observability; - Pontryagin's maximum principle and dynamic programming in PDE; and - numerics pertinent to optimal and suboptimal control problems. This volume is primarily geared toward control theorists seeking information on the latest developments in their area of expertise. It may also serve as a stimulating reader to any researcher who wants to gain an impression of activities at the forefront of a vigorously expanding area in applied mathematics.
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... was applied.Capture zone modelling was conducted on a synthetic stationary 3-dimensional flow problem involving river, surface and groundwater flow. Simulated capture zones were illustrated as likelihood maps and compared with a deterministic capture zones derived from a reference model. The results showed...
FUZZY SUPERNOVA TEMPLATES. II. PARAMETER ESTIMATION
International Nuclear Information System (INIS)
Rodney, Steven A.; Tonry, John L.
2010-01-01
Wide-field surveys will soon be discovering Type Ia supernovae (SNe) at rates of several thousand per year. Spectroscopic follow-up can only scratch the surface for such enormous samples, so these extensive data sets will only be useful to the extent that they can be characterized by the survey photometry alone. In a companion paper we introduced the Supernova Ontology with Fuzzy Templates (SOFT) method for analyzing SNe using direct comparison to template light curves, and demonstrated its application for photometric SN classification. In this work we extend the SOFT method to derive estimates of redshift and luminosity distance for Type Ia SNe, using light curves from the Sloan Digital Sky Survey (SDSS) and Supernova Legacy Survey (SNLS) as a validation set. Redshifts determined by SOFT using light curves alone are consistent with spectroscopic redshifts, showing an rms scatter in the residuals of rms z = 0.051. SOFT can also derive simultaneous redshift and distance estimates, yielding results that are consistent with the currently favored ΛCDM cosmological model. When SOFT is given spectroscopic information for SN classification and redshift priors, the rms scatter in Hubble diagram residuals is 0.18 mag for the SDSS data and 0.28 mag for the SNLS objects. Without access to any spectroscopic information, and even without any redshift priors from host galaxy photometry, SOFT can still measure reliable redshifts and distances, with an increase in the Hubble residuals to 0.37 mag for the combined SDSS and SNLS data set. Using Monte Carlo simulations, we predict that SOFT will be able to improve constraints on time-variable dark energy models by a factor of 2-3 with each new generation of large-scale SN surveys.
Gravity Field Parameter Estimation Using QR Factorization
Klokocnik, J.; Wagner, C. A.; McAdoo, D.; Kostelecky, J.; Bezdek, A.; Novak, P.; Gruber, C.; Marty, J.; Bruinsma, S. L.; Gratton, S.; Balmino, G.; Baboulin, M.
2007-12-01
This study compares the accuracy of the estimated geopotential coefficients when QR factorization is used instead of the classical method applied at our institute, namely the generation of normal equations that are solved by means of Cholesky decomposition. The objective is to evaluate the gain in numerical precision, which is obtained at considerable extra cost in terms of computer resources. Therefore, a significant increase in precision must be realized in order to justify the additional cost. Numerical simulations were done in order to examine the performance of both solution methods. Reference gravity gradients were simulated, using the EIGEN-GL04C gravity field model to degree and order 300, every 3 seconds along a near-circular, polar orbit at 250 km altitude. The simulation spanned a total of 60 days. A polar orbit was selected in this simulation in order to avoid the 'polar gap' problem, which causes inaccurate estimation of the low-order spherical harmonic coefficients. Regularization is required in that case (e.g., the GOCE mission), which is not the subject of the present study. The simulated gravity gradients, to which white noise was added, were then processed with the GINS software package, applying EIGEN-CG03 as the background gravity field model, followed either by the usual normal equation computation or using the QR approach for incremental linear least squares. The accuracy assessment of the gravity field recovery consists in computing the median error degree-variance spectra, accumulated geoid errors, geoid errors due to individual coefficients, and geoid errors calculated on a global grid. The performance, in terms of memory usage, required disk space, and CPU time, of the QR versus the normal equation approach is also evaluated.
Parameter estimation of an aeroelastic aircraft using neural networks
Indian Academy of Sciences (India)
https://www.ias.ac.in/article/fulltext/sadh/025/02/0181-0191. Keywords. Parameter estimation; modelling; aeroelastic aircraft; neural networks; system identification. Abstract. Application of neural networks to the problem of aerodynamic modelling and parameter estimation for aeroelastic aircraft is addressed. A neural model ...
Genetic parameter estimation of 16-month live weight and ...
African Journals Online (AJOL)
The genetic, phenotypic and environmental parameters for live weight and objectively measured wool traits were estimated for a South African Merino flock. Records of the Tygerhoek Merino resource flock were used to estimate these parameters. The database consisted of records of 4 495 animals, the progeny of 449 sires ...
Genetic parameter estimates for tick resistance in Bonsmara cattle ...
African Journals Online (AJOL)
The objectives of the study were to estimate genetic parameters for tick resistance and to evaluate the effect of the level of tick infestation on the estimates of genetic parameters for South African Bonsmara cattle. Field data of repeated tick count records (n = 11 280) on 1 176 animals were collected between 1993 and 2005 ...
Estimation of the soil strength parameters in Tertiary volcanic regolith
Indian Academy of Sciences (India)
Costly and time consuming testing techniques and the difficulties in providing undisturbed samples for these tests have led researchers to estimate strength parameters of soils with simple index tests. However, the paper focuses on estimation of strength parameters of soils as a function of the index properties. Analytical ...
Across flock genetic parameter estimation for yearling body weight ...
African Journals Online (AJOL)
Accurate genetic parameter estimates are needed upon which to perform multiple-trait across flock breed analyses. Genetic parameters for yearling body weight (BW), clean fleece weight (CFW) and mean fibre diameter (MFD) were estimated using records of 107 389 individuals (the progeny of 1 530 sires and 45 178 ...
Bayesian Parameter Estimation for Heavy-Duty Vehicles
Energy Technology Data Exchange (ETDEWEB)
Miller, Eric; Konan, Arnaud; Duran, Adam
2017-03-28
Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Parameter and state estimator for state space models.
Ding, Ruifeng; Zhuang, Linfan
2014-01-01
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
METHODS FOR ESTIMATING THE PARAMETERS OF THE POWER FUNCTION DISTRIBUTION.
Directory of Open Access Journals (Sweden)
azam zaka
2013-10-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE In this paper, we present some methods for estimating the parameters of the two parameter Power function distribution. We used the least squares method (LSM, relative least squares method (RELS and ridge regression method (RR. Sampling behavior of the estimates is indicated by a Monte Carlo simulation. The objective of identifying the best estimator amongst them we use the Total Deviation (T.D and Mean Square Error (M.S.E as performance index. We determined the best method for estimation using different values for the parameters and different sample sizes.
Kozu, Toshiaki; Nakamura, Kenji; Meneghini, Robert
1991-01-01
A method to estimate raindrop size distribution (DSD) parameters from a combined Zm profile and path-integrated attenuation is shown, and a test result of the method using the data from an aircraft experiment is presented. The 'semi' dual-parameter (SDP) measurement is employed to estimate DSD parameters using the data obtained from an aircraft experiment conducted by Communications Research Laboratory, Tokyo, in conjunction with NASA. The validity of estimated DSD parameters is examined using measured Ka-band radar reflectivities. The estimated path-averaged N(0) is consistent with the Ka/X Ze ratio, and the use of estimated DSD shows excellent agreement between the rain rates estimated from the X-band and K-band Zes. The feasibility of estimating DSD parameters from space is confirmed.
Parameter estimation and prediction of nonlinear biological systems: some examples
Doeswijk, T.G.; Keesman, K.J.
2006-01-01
Rearranging and reparameterizing a discrete-time nonlinear model with polynomial quotient structure in input, output and parameters (xk = f(Z, p)) leads to a model linear in its (new) parameters. As a result, the parameter estimation problem becomes a so-called errors-in-variables problem for which
Revisiting Boltzmann learning: parameter estimation in Markov random fields
DEFF Research Database (Denmark)
Hansen, Lars Kai; Andersen, Lars Nonboe; Kjems, Ulrik
1996-01-01
and generalization in the context of Boltzmann machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov field. The regularized adaptation produces a parameter set that closely resembles the “teacher” parameters, hence, will produce segmentations that closely reproduce...
A Novel Nonlinear Parameter Estimation Method of Soft Tissues
Directory of Open Access Journals (Sweden)
Qianqian Tong
2017-12-01
Full Text Available The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.
Modeling and Parameter Estimation of a Small Wind Generation System
Directory of Open Access Journals (Sweden)
Carlos A. Ramírez Gómez
2013-11-01
Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.
Robust Parameter and Signal Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.
This thesis deals with theories and methods for robust parameter and signal estimation in induction motors. The project originates in industrial interests concerning sensor-less control of electrical drives. During the work, some general problems concerning estimation of signals and parameters...... in nonlinear systems, have been exposed. The main objectives of this project are: - analysis and application of theories and methods for robust estimation of parameters in a model structure, obtained from knowledge of the physics of the induction motor. - analysis and application of theories and methods...... for robust estimation of the rotor speed and driving torque of the induction motor based only on measurements of stator voltages and currents. Only contimuous-time models have been used, which means that physical related signals and parameters are estimated directly and not indirectly by some discrete...
Parameter estimation for chaotic systems using improved bird swarm algorithm
Xu, Chuangbiao; Yang, Renhuan
2017-12-01
Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.
A simulation of water pollution model parameter estimation
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
Estimation of Parameters in Latent Class Models with Constraints on the Parameters.
Paulson, James A.
This paper reviews the application of the EM Algorithm to marginal maximum likelihood estimation of parameters in the latent class model and extends the algorithm to the case where there are monotone homogeneity constraints on the item parameters. It is shown that the EM algorithm can be used to obtain marginal maximum likelihood estimates of the…
Response-Based Estimation of Sea State Parameters
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam
2007-01-01
Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The sea state parameters can be estimated by Bayesian Modelling which uses complex-valued frequency response functions (FRF) to estimate the wave spectrum on the basis...... of measured ship responses. It is therefore interesting to investigate how the filtering aspect, introduced by FRF, affects the final outcome of the estimation procedures. The paper contains a study based on numerical generated time series, and the study shows that filtering has an influence...
Kinetic parameter estimation from attenuated SPECT projection measurements
International Nuclear Information System (INIS)
Reutter, B.W.; Gullberg, G.T.
1998-01-01
Conventional analysis of dynamically acquired nuclear medicine data involves fitting kinetic models to time-activity curves generated from regions of interest defined on a temporal sequence of reconstructed images. However, images reconstructed from the inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system can contain artifacts that lead to biases in the estimated kinetic parameters. To overcome this problem the authors investigated the estimation of kinetic parameters directly from projection data by modeling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated transverse slice, kinetic parameters were estimated for simple one compartment models for three myocardial regions of interest, as well as for the liver. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated data had biases ranging between 1--63%. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Predicted uncertainties (standard deviations) of the parameters obtained for 500,000 detected events ranged between 2--31% for the myocardial uptake parameters and 2--23% for the myocardial washout parameters
Models for estimating photosynthesis parameters from in situ production profiles
Kovač, Žarko; Platt, Trevor; Sathyendranath, Shubha; Antunović, Suzana
2017-12-01
The rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of
Parameter Estimation for the Thurstone Case III Model.
Mackay, David B.; Chaiy, Seoil
1982-01-01
The ability of three estimation criteria to recover parameters of the Thurstone Case V and Case III models from comparative judgment data was investigated via Monte Carlo techniques. Significant differences in recovery are shown to exist. (Author/JKS)
Dynamic Mode Decomposition based on Kalman Filter for Parameter Estimation
Shibata, Hisaichi; Nonomura, Taku; Takaki, Ryoji
2017-11-01
With the development of computational fluid dynamics, large-scale data can now be obtained. In order to model physical phenomena from such data, it is required to extract features of flow field. Dynamic mode decomposition (DMD) is a method which meets the request. DMD can compute dominant eigenmodes of flow field by approximating system matrix. From this point of view, DMD can be considered as parameter estimation of system matrix. To estimate such parameters, we propose a novel method based on Kalman filter. Our numerical experiments indicated that the proposed method can estimate the parameters more accurately if it is compared with standard DMD methods. With this method, it is also possible to improve the parameter estimation accuracy if characteristics of noise acting on the system is given.
On-Line Estimation of Allan Variance Parameters
National Research Council Canada - National Science Library
Ford, J
1999-01-01
... (Inertial Measurement Unit) gyros and accelerometers. The on-line method proposes a state space model and proposes parameter estimators for quantities previously measured from off-line data techniques such as the Allan variance graph...
Neural networks for estimation of ocean wave parameters
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Rao, S.; Raju, D.H.
as measured ocean wave spectra off Mormugao, west coast of India. The correlations of neural network and Scott spectra are also compared. Once the network is trained, the ocean wave parameters can be estimated for unknown measured spectra, whereas significant...
A distributed approach for parameters estimation in System Biology models
International Nuclear Information System (INIS)
Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.
2009-01-01
Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.
Kalman filter data assimilation: targeting observations and parameter estimation.
Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex
2014-06-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Kalman filter estimation of RLC parameters for UMP transmission line
Directory of Open Access Journals (Sweden)
Mohd Amin Siti Nur Aishah
2018-01-01
Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
Directory of Open Access Journals (Sweden)
Daigle Bernie J
2012-05-01
Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods
Estimation of Parameters of the Beta-Extreme Value Distribution
Directory of Open Access Journals (Sweden)
Zafar Iqbal
2008-09-01
Full Text Available In this research paper The Beta Extreme Value Type (III distribution which is developed by Zafar and Aleem (2007 is considered and parameters are estimated by using moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m’ & ‘n’ are real and moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m��� & ‘n’ are integers and then a Comparison between rth moments about origin when parameters are ‘m’ & ‘n’ are real and when parameters are ‘m’ & ‘n’ are integers. At the end second method, method of Maximum Likelihood is used to estimate the unknown parameters of the Beta Extreme Value Type (III distribution.
Pollen parameters estimates of genetic variability among newly ...
African Journals Online (AJOL)
Estimates of some pollen parameters where used to assess the genetic diversity among some newly selected Nigerian Roselle (Hibiscus sabdariffa L.). Standard procedures were used to determine the pollen parameters such as: percentage pollen fertility, percentage pollen sterility, pollen diameters as well as anther ...
Estimation of riverbank soil erodibility parameters using genetic ...
Indian Academy of Sciences (India)
Tapas Karmaker
2017-11-07
Nov 7, 2017 ... Abstract. Determination of the erodibility parameters, such as critical shear stress and erodibility coefficient, are necessary before estimating the annual bank erosion (or bank retreat) at river reaches. However, in many cases, the river site is inaccessible making it difficult to assess the soil parameters either ...
On the Nature of SEM Estimates of ARMA Parameters.
Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.
2002-01-01
Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…
On robust parameter estimation in brain–computer interfacing
Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert
2017-12-01
Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain–computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
2002-01-01
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of non-linear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Estimates of lactation curve parameters for Bonsmara and Nguni ...
African Journals Online (AJOL)
Despite the importance of milk production in beef cattle, little research has been done to evaluate the milk production potential of South African indigenous beef cattle. The objective of this study was to estimate average lactation curve parameters for the South African Bonsmara and Nguni cattle. Milk yield was estimated ...
Distribution Line Parameter Estimation Under Consideration of Measurement Tolerances
DEFF Research Database (Denmark)
Prostejovsky, Alexander; Gehrke, Oliver; Kosek, Anna Magdalena
2016-01-01
State estimation and control approaches in electric distribution grids rely on precise electric models that may be inaccurate. This work presents a novel method of estimating distribution line parameters using only root mean square voltage and power measurements under consideration of measurement...
Global parameter estimation methods for stochastic biochemical systems
Directory of Open Access Journals (Sweden)
Poovathingal Suresh
2010-08-01
Full Text Available Abstract Background The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data. Results Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality. Conclusions The parameter
A Note On the Estimation of the Poisson Parameter
Directory of Open Access Journals (Sweden)
S. S. Chitgopekar
1985-01-01
distribution when there are errors in observing the zeros and ones and obtains both the maximum likelihood and moments estimates of the Poisson mean and the error probabilities. It is interesting to note that either method fails to give unique estimates of these parameters unless the error probabilities are functionally related. However, it is equally interesting to observe that the estimate of the Poisson mean does not depend on the functional relationship between the error probabilities.
Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA. The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX model. The performance of the model is validated using mean squares error (MSE between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square. Based on the results obtained, MSE produce from Bat Algorithm (BA is outperformed the Least Square (LS method.
Iterative methods for distributed parameter estimation in parabolic PDE
Energy Technology Data Exchange (ETDEWEB)
Vogel, C.R. [Montana State Univ., Bozeman, MT (United States); Wade, J.G. [Bowling Green State Univ., OH (United States)
1994-12-31
The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.
A software for parameter estimation in dynamic models
Directory of Open Access Journals (Sweden)
M. Yuceer
2008-12-01
Full Text Available A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.
Improving the realism of hydrologic model through multivariate parameter estimation
Rakovec, Oldrich; Kumar, Rohini; Attinger, Sabine; Samaniego, Luis
2017-04-01
Increased availability and quality of near real-time observations should improve understanding of predictive skills of hydrological models. Recent studies have shown the limited capability of river discharge data alone to adequately constrain different components of distributed model parameterizations. In this study, the GRACE satellite-based total water storage (TWS) anomaly is used to complement the discharge data with an aim to improve the fidelity of mesoscale hydrologic model (mHM) through multivariate parameter estimation. The study is conducted in 83 European basins covering a wide range of hydro-climatic regimes. The model parameterization complemented with the TWS anomalies leads to statistically significant improvements in (1) discharge simulations during low-flow period, and (2) evapotranspiration estimates which are evaluated against independent (FLUXNET) data. Overall, there is no significant deterioration in model performance for the discharge simulations when complemented by information from the TWS anomalies. However, considerable changes in the partitioning of precipitation into runoff components are noticed by in-/exclusion of TWS during the parameter estimation. A cross-validation test carried out to assess the transferability and robustness of the calibrated parameters to other locations further confirms the benefit of complementary TWS data. In particular, the evapotranspiration estimates show more robust performance when TWS data are incorporated during the parameter estimation, in comparison with the benchmark model constrained against discharge only. This study highlights the value for incorporating multiple data sources during parameter estimation to improve the overall realism of hydrologic model and its applications over large domains. Rakovec, O., Kumar, R., Attinger, S. and Samaniego, L. (2016): Improving the realism of hydrologic model functioning through multivariate parameter estimation. Water Resour. Res., 52, http://dx.doi.org/10
Traveltime approximations and parameter estimation for orthorhombic media
Masmoudi, Nabil
2016-05-30
Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters if we relate them analytically to traveltimes. Using perturbation theory, we have developed traveltime approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2, and Δχ in inhomogeneous background media. The parameter Δχ is related to Tsvankin-Thomsen notation and ensures easier computation of traveltimes in the background model. Specifically, our expansion assumes an inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. We have used the Shanks transform to enhance the accuracy of the formulas. A homogeneous medium simplification of the traveltime expansion provided a nonhyperbolic moveout description of the traveltime that was more accurate than other derived approximations. Moreover, the formulation provides a computationally efficient tool to solve the eikonal equation of an orthorhombic medium, without any constraints on the background model complexity. Although, the expansion is based on the factorized representation of the perturbation parameters, smooth variations of these parameters (represented as effective values) provides reasonable results. Thus, this formulation provides a mechanism to estimate the three effective parameters η1, η2, and Δχ. We have derived Dix-type formulas for orthorhombic medium to convert the effective parameters to their interval values.
Federal Laboratory Consortium — The Ballistic Test Facility is comprised of two outdoor and one indoor test ranges, which are all instrumented for data acquisition and analysis. Full-size aircraft...
Small sample GEE estimation of regression parameters for longitudinal data.
Paul, Sudhir; Zhang, Xuemao
2014-09-28
Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used. Copyright © 2014 John Wiley & Sons, Ltd.
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Modeling extreme events: Sample fraction adaptive choice in parameter estimation
Neves, Manuela; Gomes, Ivette; Figueiredo, Fernanda; Gomes, Dora Prata
2012-09-01
When modeling extreme events there are a few primordial parameters, among which we refer the extreme value index and the extremal index. The extreme value index measures the right tail-weight of the underlying distribution and the extremal index characterizes the degree of local dependence in the extremes of a stationary sequence. Most of the semi-parametric estimators of these parameters show the same type of behaviour: nice asymptotic properties, but a high variance for small values of k, the number of upper order statistics to be used in the estimation, and a high bias for large values of k. This shows a real need for the choice of k. Choosing some well-known estimators of those parameters we revisit the application of a heuristic algorithm for the adaptive choice of k. The procedure is applied to some simulated samples as well as to some real data sets.
Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS and differential evolution (DE algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE. Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.
Estimation of shape model parameters for 3D surfaces
DEFF Research Database (Denmark)
Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen
2008-01-01
is applied to a database of 3D surfaces from a section of the porcine pelvic bone extracted from 33 CT scans. A leave-one-out validation shows that the parameters of the first 3 modes of the shape model can be predicted with a mean difference within [-0.01,0.02] from the true mean, with a standard deviation......Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D...... surfaces using distance maps, which enables the estimation of model parameters without the requirement of point correspondence. For applications with acquisition limitations such as speed and cost, this formulation enables the fitting of a statistical shape model to arbitrarily sampled data. The method...
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
CMB Beam Systematics: Impact on Lensing Parameter Estimation
Miller, N. J.; Shimon, M.; Keating, B. G.
2008-01-01
The CMB's B-mode polarization provides a handle on several cosmological parameters most notably the tensor-to-scalar ratio, $r$, and is sensitive to parameters which govern the growth of large scale structure (LSS) and evolution of the gravitational potential. The primordial gravitational-wave- and secondary lensing-induced B-mode signals are very weak and therefore prone to various foregrounds and systematics. In this work we use Fisher-matrix-based estimations and apply, for the first time,...
An approach of parameter estimation for non-synchronous systems
International Nuclear Information System (INIS)
Xu Daolin; Lu Fangfang
2005-01-01
Synchronization-based parameter estimation is simple and effective but only available to synchronous systems. To come over this limitation, we propose a technique that the parameters of an unknown physical process (possibly a non-synchronous system) can be identified from a time series via a minimization procedure based on a synchronization control. The feasibility of this approach is illustrated in several chaotic systems
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Distributed Systems Integration Dept.
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Visco-piezo-elastic parameter estimation in laminated plate structures
DEFF Research Database (Denmark)
Araujo, A. L.; Mota Soares, C. M.; Herskovits, J.
2009-01-01
A parameter estimation technique is presented in this article, for identification of elastic, piezoelectric and viscoelastic properties of active laminated composite plates with surface-bonded piezoelectric patches. The inverse method presented uses experimental data in the form of a set...... determining the material parameters for the best fit. The solution of the inverse problem is obtained by gradient-based optimization techniques, through constrained minimization of an error functional, which expresses the deviation of the numerical model's response with respect to the experimentally measured...... data. Results are presented for the estimation of elastic, piezoelectric and viscoelastic properties in laminated plates....
MPEG2 video parameter and no reference PSNR estimation
DEFF Research Database (Denmark)
Li, Huiying; Forchhammer, Søren
2009-01-01
to the MPEG stream. This may be used in systems and applications where the coded stream is not accessible. Detection of MPEG I-frames and DCT (discrete cosine transform) block size is presented. For the I-frames, the quantization parameters are estimated. Combining these with statistics of the reconstructed......MPEG coded video may be processed for quality assessment or postprocessed to reduce coding artifacts or transcoded. Utilizing information about the MPEG stream may be useful for these tasks. This paper deals with estimating MPEG parameter information from the decoded video stream without access...
Parameter Estimation in Stochastic Differential Equations; An Overview
DEFF Research Database (Denmark)
Nielsen, Jan Nygaard; Madsen, Henrik; Young, P. C.
2000-01-01
This paper presents an overview of the progress of research on parameter estimation methods for stochastic differential equations (mostly in the sense of Ito calculus) over the period 1981-1999. These are considered both without measurement noise and with measurement noise, where the discretely...... observed stochastic differential equations are embedded in a continuous-discrete time state space model. Every attempts has been made to include results from other scientific disciplines. Maximum likelihood estimation of parameters in nonlinear stochastic differential equations is in general not possible...
Application of genetic algorithms for parameter estimation in liquid chromatography
International Nuclear Information System (INIS)
Hernandez Torres, Reynier; Irizar Mesa, Mirtha; Tavares Camara, Leoncio Diogenes
2012-01-01
In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography
Adjustment of Sensor Locations During Thermal Property Parameter Estimation
Milos, Frank S.; Marschall, Jochen; Rasky, Daniel J. (Technical Monitor)
1996-01-01
The temperature dependent thermal properties of a material may be evaluated from transient temperature histories using nonlinear parameter estimation techniques. The usual approach is to minimize the sum of the squared errors between measured and calculated temperatures at specific locations in the body. Temperature measurements are usually made with thermocouples and it is customary to take thermocouple locations as known and fixed during parameter estimation computations. In fact, thermocouple locations are never known exactly. Location errors on the order of the thermocouple wire diameter are intrinsic to most common instrumentation procedures (e.g., inserting a thermocouple into a drilled hole) and additional errors can be expected for delicate materials, difficult installations, large thermocouple beads, etc.. Thermocouple location errors are especially significant when estimating thermal properties of low diffusively materials which can sustain large temperature gradients during testing. In the present work, a parameter estimation formulation is presented which allows for the direct inclusion of thermocouple positions into the primary parameter estimation procedure. It is straightforward to set bounds on thermocouple locations which exclude non-physical locations and are consistent with installation tolerances. Furthermore, bounds may be tightened to an extent consistent with any independent verification of thermocouple location, such as x-raying, and so the procedure is entirely consonant with experimental information. A mathematical outline of the procedure is given and its implementation is illustrated through numerical examples characteristic of light-weight, high-temperature ceramic insulation during transient heating. The efficacy and the errors associated with the procedure are discussed.
Directory of Open Access Journals (Sweden)
Nicholas W. Mitiukov
2015-12-01
Full Text Available In paper there is proposed a new method of historical research, based on analysis of derivatives coefficients of database (for example, the form factor in the database of ballistic data. This method has a much greater protection from subjectivism and direct falsification, compared with the analysis obtained directly from the source of the numerical series, as any intentional or unintentional distortion of the raw data provides a significant contrast ratio derived from the average sample values. Application of this method to the analysis of ballistic data base of naval artillery allowed to find the facts, forcing a new look at some of the events in the history data on the German naval artillery before World War I, probably overpriced for disinformation opponents of the Entente; during the First World War, Spain, apparently held secret talks with the firm Bofors ended purchase of Swedish shells; the first Russian naval rifled guns were created obvious based on the project Blackly, not Krupp as traditionally considered.
Bayesian estimation of parameters in a regional hydrological model
Directory of Open Access Journals (Sweden)
K. Engeland
2002-01-01
Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis
Targeted estimation of nuisance parameters to obtain valid statistical inference.
van der Laan, Mark J
2014-01-01
In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special
Enhancing parameter estimation precision by non-Hermitian operator process
Guo, You-neng; Fang, Mao-fa; Wang, Guo-you; Hang, Jiang; Zeng, Ke
2017-12-01
Recently, Zhong et al. (Phys Rev A 87:022337, 2013) investigated the dynamics of quantum Fisher information (QFI) in the presence of decoherence. We here reform their results and propose two schemes to enhance and preserve the QFIs for a qubit system subjected to a decoherence noisy environment by applying {non {-}Hermitian} operator process either before or after the amplitude damping noise. Resorting to the Bloch sphere representation, we derive the exact analytical expressions of the QFIs with respect to the amplitude parameter θ and the phase parameter φ , and in detail investigate the influence of {non {-}Hermitian} operator parameters on the QFIs. Compared with pure decoherence process (without non-Hermitian operator process), we find that the {post non {-}Hermitian} operator process can potentially enhance and preserve the QFIs by choosing appropriate {non {-}Hermitian} operator parameters, while with the help of the {prior non {-}Hermitian} operator process one could completely eliminate the effect of decoherence to improve the parameters estimation. Finally, a generalized non-Hermitian operator parameters effect on the parameters estimation is also discussed.
Estimation of Compaction Parameters Based on Soil Classification
Lubis, A. S.; Muis, Z. A.; Hastuty, I. P.; Siregar, I. M.
2018-02-01
Factors that must be considered in compaction of the soil works were the type of soil material, field control, maintenance and availability of funds. Those problems then raised the idea of how to estimate the density of the soil with a proper implementation system, fast, and economical. This study aims to estimate the compaction parameter i.e. the maximum dry unit weight (γ dmax) and optimum water content (Wopt) based on soil classification. Each of 30 samples were being tested for its properties index and compaction test. All of the data’s from the laboratory test results, were used to estimate the compaction parameter values by using linear regression and Goswami Model. From the research result, the soil types were A4, A-6, and A-7 according to AASHTO and SC, SC-SM, and CL based on USCS. By linear regression, the equation for estimation of the maximum dry unit weight (γdmax *)=1,862-0,005*FINES- 0,003*LL and estimation of the optimum water content (wopt *)=- 0,607+0,362*FINES+0,161*LL. By Goswami Model (with equation Y=mLogG+k), for estimation of the maximum dry unit weight (γdmax *) with m=-0,376 and k=2,482, for estimation of the optimum water content (wopt *) with m=21,265 and k=-32,421. For both of these equations a 95% confidence interval was obtained.
Procedures for parameter estimates of computational models for localized failure
Iacono, C.
2007-01-01
In the last years, many computational models have been developed for tensile fracture in concrete. However, their reliability is related to the correct estimate of the model parameters, not all directly measurable during laboratory tests. Hence, the development of inverse procedures is needed, that
Parameter estimation of an aeroelastic aircraft using neural networks
Indian Academy of Sciences (India)
Application of neural networks to the problem of aerodynamic modelling and parameter estimation for aeroelastic aircraft is addressed. A neural model capable of predicting generalized force and moment coefficients using measured motion and control variables only, without any need for conventional normal elastic ...
The observer-based synchronization and parameter estimation of a ...
Indian Academy of Sciences (India)
Haipeng Su
2017-10-31
Oct 31, 2017 ... Chaotic system; observer-based synchronization; parameter estimation; single output. PACS No. 05.45.Gg. 1. Introduction. Chaos is a widespread phenomenon occurring in many nonlinear systems, such as communication system, meteorological system etc. Since Pecora and Carroll. [1] developed a ...
On Modal Parameter Estimates from Ambient Vibration Tests
DEFF Research Database (Denmark)
Agneni, A.; Brincker, Rune; Coppotelli, B.
2004-01-01
Modal parameter estimates from ambient vibration testing are turning into the preferred technique when one is interested in systems under actual loadings and operational conditions. Moreover, with this approach, expensive devices to excite the structure are not needed, since it can be adequately...
An iterative scheme for estimating the parameters for an ...
African Journals Online (AJOL)
... parameter estimate using the least square method. It has the advantage of handling large system of equations which is difficult to handle when using the Least squares method. Some simulated data and one real life data are used to demonstrate this approach. Global Jouranl of Mathematical Sciences Vol. 6 (1) 2007: pp.
Bayesian estimation of the parameters of the birnbaum-saunders ...
African Journals Online (AJOL)
Bayesian estimation of the parameters of the birnbaum-saunders distribution. K Robert, LO Odongo. Abstract. No Abstract > East African Journal of Statistics Vol. 1 (3) 2007: pp.248-260. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT.
Estimation of object motion parameters from noisy images.
Broida, T J; Chellappa, R
1986-01-01
An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.
Revised models and genetic parameter estimates for production and ...
African Journals Online (AJOL)
Genetic parameters for production and reproduction traits in the Elsenburg Dormer sheep stud were estimated using records of 11743 lambs born between 1943 and 2002. An animal model with direct and maternal additive, maternal permanent and temporary environmental effects was fitted for traits considered traits of the ...
Cubic spline approximation techniques for parameter estimation in distributed systems
Banks, H. T.; Crowley, J. M.; Kunisch, K.
1983-01-01
Approximation schemes employing cubic splines in the context of a linear semigroup framework are developed for both parabolic and hyperbolic second-order partial differential equation parameter estimation problems. Convergence results are established for problems with linear and nonlinear systems, and a summary of numerical experiments with the techniques proposed is given.
Measuring, calculating and estimating PEP's parasitic mode loss parameters
International Nuclear Information System (INIS)
Weaver, J.N.
1981-01-01
This note discusses various ways the parasitic mode losses from a bunched beam to a vacuum chamber can be measured, calculated or estimated. A listing of the parameter, k, for the various PEP ring components is included. A number of formulas for calculating multiple and single pass losses are discussed and evaluated for several cases. 25 refs., 1 fig., 1 tab
Parameter estimates for reproductive output and product quality ...
African Journals Online (AJOL)
Parameter estimates for reproductive output and product quality traits of ostrich females within breeding seasons. ... South African Journal of Animal Science ... Egg production of young birds increased to reach a peak of approximately 4 to 5 eggs per month relatively late in the breeding season (September to December).
Parameter estimation of linear and quadratic chirps by employing ...
Indian Academy of Sciences (India)
This paper discusses some analytical results of the GTFT. We identify the eigenfunctions and eigenvalues of the GTFT. The time shift property of the GTFT is discussed. The paper describes methods for estimation of parameters of individual chirp signals on receipt of a noisy mixture of chirps. A priori knowledge of the nature ...
Parameter extraction and estimation based on the PV panel outdoor ...
African Journals Online (AJOL)
This work presents a novel approach to predict the voltage-current (V-I) characteristics of a PV panel under varying weather conditions to estimate the PV parameters. Outdoor performance of the PV module (AP-PM-15) was carried out for several times. The experimental data obtained are validated and compared with the ...
Simultaneous estimation of QTL parameters for mapping multiple traits
Indian Academy of Sciences (India)
LIANG TONG
2018-03-13
MIM) method was ... Our simulation results present the advantages of the new method in estimating parameters over an .... random error of the ith trait value of the jth subject, with mean zero and cov(eji, ejl) = σil = ρσiσl, i, l = 1,...
Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms
Berhausen, Sebastian; Paszek, Stefan
2016-01-01
In recent years, there have occurred system failures in many power systems all over the world. They have resulted in a lack of power supply to a large number of recipients. To minimize the risk of occurrence of power failures, it is necessary to perform multivariate investigations, including simulations, of power system operating conditions. To conduct reliable simulations, the current base of parameters of the models of generating units, containing the models of synchronous generators, is necessary. In the paper, there is presented a method for parameter estimation of a synchronous generator nonlinear model based on the analysis of selected transient waveforms caused by introducing a disturbance (in the form of a pseudorandom signal) in the generator voltage regulation channel. The parameter estimation was performed by minimizing the objective function defined as a mean square error for deviations between the measurement waveforms and the waveforms calculated based on the generator mathematical model. A hybrid algorithm was used for the minimization of the objective function. In the paper, there is described a filter system used for filtering the noisy measurement waveforms. The calculation results of the model of a 44 kW synchronous generator installed on a laboratory stand of the Institute of Electrical Engineering and Computer Science of the Silesian University of Technology are also given. The presented estimation method can be successfully applied to parameter estimation of different models of high-power synchronous generators operating in a power system.
Estimate of genetic and phenotypic parameters for litter size and ...
African Journals Online (AJOL)
Data on 964 and 1150 weaning weight and litter size records respectively, collected over a 10-year period on Yankasa Sheep breeding project at the National Animal Production Research Institute (N.A.P.R.I.), Zaria, were used in this study. The analysis was for estimation of genetic and phenotypic parameters for litter size ...
Estimation of genetic parameters for growth traits in Brangus cattle
African Journals Online (AJOL)
p2492989
Abstract. A combination of multiple trait and repeatability models were used to estimate genetic parameters for birth weight (BW), weaning weight (WW), yearling weight (YW), eighteen month weight (FW) and three measurements of mature weight (MW) using 23 768 records obtained from the South African Brangus.
Estimation of genetic parameters for carcass traits in Japanese quail ...
African Journals Online (AJOL)
The aim of this study was to estimate genetic parameters of some carcass characteristics in the Japanese quail. For this aim, carcass weight (Cw), breast weight (Bw), leg weight (Lw), abdominal fat weight (AFw), carcass yield (CP), breast percentage (BP), leg percentage (LP) and abdominal fat percentage (AFP) were ...
Estimation of light transport parameters in biological media using ...
Indian Academy of Sciences (India)
The suitability of using the angular peak shape of the coherent backscattered light for estimating the light transport parameters of biological media has been investigated. Milk and methylene blue doped milk were used as tissue phantoms for the measurements carried out with a He–Ne laser (632.8 nm). Results indicate that ...
A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri
2014-01-01
This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...
A novel parameter estimation method for metal oxide surge arrester ...
Indian Academy of Sciences (India)
with experimental results. Keywords. Metal oxide surge arrester models; PSO; ACO; parameter estimation;. EMTP. 1. Introduction. Metal oxide (MO) surge arresters are widely used as protective devices against switching and lightning over-voltages in power systems. The proper nonlinear voltage-current characteristics,. ∗.
Estimation of light transport parameters in biological media using ...
Indian Academy of Sciences (India)
the use of the coherent backscattered peak from tissues to estimate the transport parameters has also been suggested [5–7]. ... closely resemble those of tissues and milk has been used to model light transport in brain tissue [15], to study optical imaging [16], .... The dye-doped colloids thus. Pramana – J. Phys., Vol. 54, No.
Estimation of stature from facial parameters in adult Abakaliki people ...
African Journals Online (AJOL)
This study is carried out in order to estimate the height of adult Igbo people of Abakaliki ethnic group in South-Eastern Nigeria from their facial Morphology. The parameters studied include Facial Length, Bizygomatic Diameter, Bigonial Diameter, Nasal Length, and Nasal Breadth. A total of 1000 subjects comprising 669 ...
Estimates Of Genetic Parameters Of Body Weights Of Different ...
African Journals Online (AJOL)
four (44) farrowings were used to estimate the genetic parameters (heritability and repeatability) of body weight of pigs. Results obtained from the study showed that the heritability (h2) of birth and weaning weights were moderate (0.33±0.16 ...
parameter extraction and estimation based on the pv panel outdoor
African Journals Online (AJOL)
userpc
PV panel under varying weather conditions to estimate the PV parameters. Outdoor performance of the PV module (AP-PM-15) was carried out for several times. The .... Performance. Analysis of Different Photovoltaic. Technologies Based on MATLAB. Simulation. In Northwest University. Science, Faculty of Science Annual.
Low Complexity Parameter Estimation For Off-the-Grid Targets
Jardak, Seifallah
2015-10-05
In multiple-input multiple-output radar, to estimate the reflection coefficient, spatial location, and Doppler shift of a target, a derived cost function is usually evaluated and optimized over a grid of points. The performance of such algorithms is directly affected by the size of the grid: increasing the number of points will enhance the resolution of the algorithm but exponentially increase its complexity. In this work, to estimate the parameters of a target, a reduced complexity super resolution algorithm is proposed. For off-the-grid targets, it uses a low order two dimensional fast Fourier transform to determine a suboptimal solution and then an iterative algorithm to jointly estimate the spatial location and Doppler shift. Simulation results show that the mean square estimation error of the proposed estimators achieve the Cram\\'er-Rao lower bound. © 2015 IEEE.
Parameter Estimation for a Class of Lifetime Models
Directory of Open Access Journals (Sweden)
Xinyang Ji
2014-01-01
Full Text Available Our purpose in this paper is to present a better method of parametric estimation for a bivariate nonlinear regression model, which takes the performance indicator of rubber aging as the dependent variable and time and temperature as the independent variables. We point out that the commonly used two-step method (TSM, which splits the model and estimate parameters separately, has limitation. Instead, we apply the Marquardt’s method (MM to implement parametric estimation directly for the model and compare these two methods of parametric estimation by random simulation. Our results show that MM has better effect of data fitting, more reasonable parametric estimates, and smaller prediction error compared with TSM.
Accuracy and sensitivity analysis on seismic anisotropy parameter estimation
Yan, Fuyong; Han, De-Hua
2018-04-01
There is significant uncertainty in measuring the Thomsen’s parameter δ in laboratory even though the dimensions and orientations of the rock samples are known. It is expected that more challenges will be encountered in the estimating of the seismic anisotropy parameters from field seismic data. Based on Monte Carlo simulation of vertical transversely isotropic layer cake model using the database of laboratory anisotropy measurement from the literature, we apply the commonly used quartic non-hyperbolic reflection moveout equation to estimate the seismic anisotropy parameters and test its accuracy and sensitivities to the source-receive offset, vertical interval velocity error and time picking error. The testing results show that the methodology works perfectly for noise-free synthetic data with short spread length. However, this method is extremely sensitive to the time picking error caused by mild random noises, and it requires the spread length to be greater than the depth of the reflection event. The uncertainties increase rapidly for the deeper layers and the estimated anisotropy parameters can be very unreliable for a layer with more than five overlain layers. It is possible that an isotropic formation can be misinterpreted as a strong anisotropic formation. The sensitivity analysis should provide useful guidance on how to group the reflection events and build a suitable geological model for anisotropy parameter inversion.
Estimation of parameter sensitivities for stochastic reaction networks
Gupta, Ankit
2016-01-07
Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.
Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea
Sawlan, Zaid A
2012-12-01
Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.
Dual ant colony operational modal analysis parameter estimation method
Sitarz, Piotr; Powałka, Bartosz
2018-01-01
Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.
Amiri-Simkooei, A. R.
2018-01-01
Three-dimensional (3D) coordinate transformations, generally consisting of origin shifts, axes rotations, scale changes, and skew parameters, are widely used in many geomatics applications. Although in some geodetic applications simplified transformation models are used based on the assumption of small transformation parameters, in other fields of applications such parameters are indeed large. The algorithms of two recent papers on the weighted total least-squares (WTLS) problem are used for the 3D coordinate transformation. The methodology can be applied to the case when the transformation parameters are generally large of which no approximate values of the parameters are required. Direct linearization of the rotation and scale parameters is thus not required. The WTLS formulation is employed to take into consideration errors in both the start and target systems on the estimation of the transformation parameters. Two of the well-known 3D transformation methods, namely affine (12, 9, and 8 parameters) and similarity (7 and 6 parameters) transformations, can be handled using the WTLS theory subject to hard constraints. Because the method can be formulated by the standard least-squares theory with constraints, the covariance matrix of the transformation parameters can directly be provided. The above characteristics of the 3D coordinate transformation are implemented in the presence of different variance components, which are estimated using the least squares variance component estimation. In particular, the estimability of the variance components is investigated. The efficacy of the proposed formulation is verified on two real data sets.
PhyloPars: estimation of missing parameter values using phylogeny.
Bruggeman, Jorn; Heringa, Jaap; Brandt, Bernd W
2009-07-01
A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible. The PhyloPars web server provides a statistically consistent method that combines an incomplete set of empirical observations with the species phylogeny to produce a complete set of parameter estimates for all species. It builds upon a state-of-the-art evolutionary model, extended with the ability to handle missing data. The resulting approach makes optimal use of all available information to produce estimates that can be an order of magnitude more accurate than ad-hoc alternatives. Uploading a phylogeny and incomplete feature matrix suffices to obtain estimates of all missing values, along with a measure of certainty. Real-time cross-validation provides further insight in the accuracy and bias expected for estimated values. The server allows for easy, efficient estimation of metabolic parameters, which can benefit a wide range of fields including systems biology and ecology. PhyloPars is available at: http://www.ibi.vu.nl/programs/phylopars/.
Estimation of Physical Parameters in Linear and Nonlinear Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten
for certain input in the time or frequency domain, are emphasised. Consequently, some special techniques are required, in particular for input signal design and model validation. The model structure containing physical parameters is constructed from basic physical laws (mathematical modelling). It is possible......Estimation of physical parameters is an important subclass of system identification. The specific objective is to obtain accurate estimates of the model parameters, while the objective of other aspects of system identification might be to determine a model where other properties, such as responses...... and essential to utilise this physical insight in the input design and validation procedures. This project has two objectives: 1. To develop and apply theories and techniques that are compatible with physical insight and robust to violation of assumptions and approximations, for system identification in general...
Estimating Arrhenius parameters using temperature programmed molecular dynamics
Energy Technology Data Exchange (ETDEWEB)
Imandi, Venkataramana; Chatterjee, Abhijit, E-mail: abhijit@che.iitb.ac.in [Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076 (India)
2016-07-21
Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.
Estimation of Parameters in Mean-Reverting Stochastic Systems
Directory of Open Access Journals (Sweden)
Tianhai Tian
2014-01-01
Full Text Available Stochastic differential equation (SDE is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.
Using Genetic Algorithm to Estimate Hydraulic Parameters of Unconfined Aquifers
Directory of Open Access Journals (Sweden)
Asghar Asghari Moghaddam
2009-03-01
Full Text Available Nowadays, optimization techniques such as Genetic Algorithms (GA have attracted wide attention among scientists for solving complicated engineering problems. In this article, pumping test data are used to assess the efficiency of GA in estimating unconfined aquifer parameters and a sensitivity analysis is carried out to propose an optimal arrangement of GA. For this purpose, hydraulic parameters of three sets of pumping test data are calculated by GA and they are compared with the results of graphical methods. The results indicate that the GA technique is an efficient, reliable, and powerful method for estimating the hydraulic parameters of unconfined aquifer and, further, that in cases of deficiency in pumping test data, it has a better performance than graphical methods.
Directory of Open Access Journals (Sweden)
Jonathan R Karr
2015-05-01
Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.
Estimating model parameters in nonautonomous chaotic systems using synchronization
International Nuclear Information System (INIS)
Yang, Xiaoli; Xu, Wei; Sun, Zhongkui
2007-01-01
In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation
Incorporating engineering intuition for parameter estimation in thermal sciences
Balaji, C.; Reddy, B. Konda; Herwig, H.
2013-12-01
This paper proposes a new method of incorporating priors based on engineering intuition for solving inverse problems. The thesis of this paper is that if an asymptote can be found to a problem in applied sciences or engineering, estimation of parameters can be first done for this asymptotic variant, which in principle should be simpler, since one or more parameters of the original problem may vanish for the asymptotic variant. Even so, by solving the inverse problem associated with the asymptotic variant, estimates of key parameters of the full problem can be obtained. This information can then be quantitatively incorporated as priors in the estimation of parameters for the full version of the problem which we call as prior generation through asymptotic variant. The goal is to see if this methodology will significantly reduce the uncertainties in the resulting estimates. To demonstrate this methodology, the classic problem of unsteady heat transfer from a one dimensional fin is chosen. The inverse problem is posed as the simultaneous estimation of the temperature dependent transfer coefficient (h θ ) and the thermal diffusivity ( α) of the fin material, given experimentally measured temperature-time histories at various locations along the fin. The asymptotic variant θ ( x, t) is the steady state problem where the influence of thermal diffusivity vanishes. Using surrogate temperature data generated from assumed values of h θ , first the asymptotic variant of the problem is solved using the Markov Chain Monte Carlo method in a Bayesian framework to generate an estimate of h θ . The estimate of h θ is then used as an informative prior for solving the inverse problem of determining h θ and α from θ ( x, t), and the effect of prior is quantitatively assessed by performing estimation with and without the prior. Finally, for purposes of validation, in-house experiments have been done where θ ( x, t) is generated using liquid crystal thermography and these data
Stable Parameter Estimation for Autoregressive Equations with Random Coefficients
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2014-01-01
Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross
Directory of Open Access Journals (Sweden)
A. Elsonbaty
2014-10-01
Full Text Available In this article, the adaptive chaos synchronization technique is implemented by an electronic circuit and applied to the hyperchaotic system proposed by Chen et al. We consider the more realistic and practical case where all the parameters of the master system are unknowns. We propose and implement an electronic circuit that performs the estimation of the unknown parameters and the updating of the parameters of the slave system automatically, and hence it achieves the synchronization. To the best of our knowledge, this is the first attempt to implement a circuit that estimates the values of the unknown parameters of chaotic system and achieves synchronization. The proposed circuit has a variety of suitable real applications related to chaos encryption and cryptography. The outputs of the implemented circuits and numerical simulation results are shown to view the performance of the synchronized system and the proposed circuit.
METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS
Directory of Open Access Journals (Sweden)
V. Panteleev Andrei
2017-01-01
Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and
Consistent Parameter and Transfer Function Estimation using Context Free Grammars
Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten
2017-04-01
This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a
Parameter estimation in nonlinear models for pesticide degradation
International Nuclear Information System (INIS)
Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.
1991-01-01
A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)
The Detection and Parameter Estimation of Binary Black Hole Mergers
Biwer, Christopher M.
In this dissertation we study gravitational-wave data analysis techniques for binary neutron star and black hole mergers. During its first observing run, the Advanced Laser Interferometer Gravitational-wave Observatory (Advanced LIGO) reported the first, direct observations of gravitational waves from two binary black hole mergers. We present the results from the search for binary black hole mergers which unambiguously detected the binary black hole mergers. We determine the effect of calibration errors on the detection statistic of the search. Since the search is not designed to precisely measure the astrophysical parameters of the binary neutron star and black hole mergers, we use Bayesian methods to develop a new parameter estimation analysis. We demonstrate the performance of the analysis on the binary black hole mergers detected during Advanced LIGO's first observing run. We use the parameter estimation analysis to assess the ability of gravitational-wave observatories to observe a gap in the black hole mass distribution between 52 M and 133 M due to pair-instability supernovae. Finally, we use simulated signals added to the Advanced LIGO detectors to validate the search and parameter estimation analyses used to publish the detection of the astrophysical events.
Advanced Method to Estimate Fuel Slosh Simulation Parameters
Schlee, Keith; Gangadharan, Sathya; Ristow, James; Sudermann, James; Walker, Charles; Hubert, Carl
2005-01-01
The nutation (wobble) of a spinning spacecraft in the presence of energy dissipation is a well-known problem in dynamics and is of particular concern for space missions. The nutation of a spacecraft spinning about its minor axis typically grows exponentially and the rate of growth is characterized by the Nutation Time Constant (NTC). For launch vehicles using spin-stabilized upper stages, fuel slosh in the spacecraft propellant tanks is usually the primary source of energy dissipation. For analytical prediction of the NTC this fuel slosh is commonly modeled using simple mechanical analogies such as pendulums or rigid rotors coupled to the spacecraft. Identifying model parameter values which adequately represent the sloshing dynamics is the most important step in obtaining an accurate NTC estimate. Analytic determination of the slosh model parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices and elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the equations of motion for the mechanical analog are hand-derived, evaluated, and their results are compared with the experimental results. The proposed research is an effort to automate the process of identifying the parameters of the slosh model using a MATLAB/SimMechanics-based computer simulation of the experimental setup. Different parameter estimation and optimization approaches are evaluated and compared in order to arrive at a reliable and effective parameter identification process. To evaluate each parameter identification approach, a simple one-degree-of-freedom pendulum experiment is constructed and motion is induced using an electric motor. By applying the
Parameter estimation for groundwater models under uncertain irrigation data
Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
A frequency-based parameter for rapid estimation of magnitude
Atefi, Sanam; Heidari, Reza; Mirzaei, Noorbakhsh; Siahkoohi, Hamid Reza
2017-12-01
This study introduce a new frequency parameter called τ_{fcwt}, which can be used to estimate earthquake magnitude on the basis of the first few seconds of P-waves, using the waveforms of earthquakes occurring in Japan. This new parameter is introduced using continuous wavelet transform as a tool for extracting the frequency contents carried by the first few seconds of P-wave. The empirical relationship between the logarithm of τ_{fcwt} within the initial 4 s of a waveform and magnitude was obtained. To evaluate the precision of τ_{fcwt}, we also calculated parameters τp^{ max } and τc. The average absolute values of observed and estimated magnitude differences (|M_{est} - M_{obs} |) were 0.43, 0.49, and 0.66 units of magnitude, as determined using τp^{ max }, τc, and τ_{fcwt}, respectively. For earthquakes with magnitudes greater than 6, these values were 0.34, 0.56, and 0.44 units of magnitude, as derived using τp^{ max }, τc, and τ_{fcwt}, respectively. The τ_{fcwt} parameter exhibited more precision in determining the magnitude of moderate- and small-scale earthquakes than did the τc-based approach. For a general range of magnitudes, however, the τp^{ max }-based method showed more acceptable precision than did the other two parameters.
Parameter and state estimation in nonlinear dynamical systems
Creveling, Daniel R.
This thesis is concerned with the problem of state and parameter estimation in nonlinear systems. The need to evaluate unknown parameters in models of nonlinear physical, biophysical and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. When verifying and validating these models, it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, this thesis develops a framework for presenting data to a candidate model of a physical process in a way that makes efficient use of the measured data while allowing for estimation of the unknown parameters in the model. The approach presented here builds on existing work that uses synchronization as a tool for parameter estimation. Some critical issues of stability in that work are addressed and a practical framework is developed for overcoming these difficulties. The central issue is the choice of coupling strength between the model and data. If the coupling is too strong, the model will reproduce the measured data regardless of the adequacy of the model or correctness of the parameters. If the coupling is too weak, nonlinearities in the dynamics could lead to complex dynamics rendering any cost function comparing the model to the data inadequate for the determination of model parameters. Two methods are introduced which seek to balance the need for coupling with the desire to allow the model to evolve in its natural manner without coupling. One method, 'balanced' synchronization, adds to the synchronization cost function a requirement that the conditional Lyapunov exponents of the model system, conditioned on being driven by the data, remain negative but small in magnitude. Another method allows the coupling between the data and the model to vary in time according to a specific form of differential equation. The coupling dynamics is damped to allow for a tendency toward zero coupling
Bayesian Parameter Estimation via Filtering and Functional Approximations
Matthies, Hermann G.
2016-11-25
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
Beef quality parameters estimation using ultrasound and color images.
Nunes, Jose; Piquerez, Martín; Pujadas, Leonardo; Armstrong, Eileen; Fernández, Alicia; Lecumberry, Federico
2015-01-01
Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat.
Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters.
Mathew, B; Bauer, A M; Koistinen, P; Reetz, T C; Léon, J; Sillanpää, M J
2012-10-01
Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.
Estimating parameters for probabilistic linkage of privacy-preserved datasets.
Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H
2017-07-10
Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher
Estimation of economic parameters of U.S. hydropower resources
Energy Technology Data Exchange (ETDEWEB)
Hall, Douglas G. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL); Hunt, Richard T. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL); Reeves, Kelly S. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL); Carroll, Greg R. [Idaho National Lab. (INL), Idaho Falls, ID (United States). Idaho National Engineering and Environmental Lab. (INEEL)
2003-06-01
Tools for estimating the cost of developing and operating and maintaining hydropower resources in the form of regression curves were developed based on historical plant data. Development costs that were addressed included: licensing, construction, and five types of environmental mitigation. It was found that the data for each type of cost correlated well with plant capacity. A tool for estimating the annual and monthly electric generation of hydropower resources was also developed. Additional tools were developed to estimate the cost of upgrading a turbine or a generator. The development and operation and maintenance cost estimating tools, and the generation estimating tool were applied to 2,155 U.S. hydropower sites representing a total potential capacity of 43,036 MW. The sites included totally undeveloped sites, dams without a hydroelectric plant, and hydroelectric plants that could be expanded to achieve greater capacity. Site characteristics and estimated costs and generation for each site were assembled in a database in Excel format that is also included within the EERE Library under the title, “Estimation of Economic Parameters of U.S. Hydropower Resources - INL Hydropower Resource Economics Database.”
Ballistic Phosphorene Transistor
2015-11-19
satisfactory. W911NF-14-1-0572 -II 66414-EL-II.3 TO:(1) Electronics Division (Qiu, Joe) TITLE: Final Report: Ballistic Phosphorene Transistor (x) Material... Transistor ” as a STIP award for the period 09/1/2014 through 5/31/2015. The ARO program director responsible for the grant is Dr. Joe Qiu. The PI is Prof...UU 19-11-2015 1-Sep-2014 31-May-2015 Approved for Public Release; Distribution Unlimited Final Report: Ballistic Phosphorene Transistor The views
Basic Earth's Parameters as estimated from VLBI observations
Directory of Open Access Journals (Sweden)
Ping Zhu
2017-11-01
Full Text Available The global Very Long Baseline Interferometry observation for measuring the Earth rotation's parameters was launched around 1970s. Since then the precision of the measurements is continuously improving by taking into account various instrumental and environmental effects. The MHB2000 nutation model was introduced in 2002, which is constructed based on a revised nutation series derived from 20 years VLBI observations (1980–1999. In this work, we firstly estimated the amplitudes of all nutation terms from the IERS-EOP-C04 VLBI global solutions w.r.t. IAU1980, then we further inferred the BEPs (Basic Earth's Parameters by fitting the major nutation terms. Meanwhile, the BEPs were obtained from the same nutation time series using a BI (Bayesian Inversion. The corrections to the precession rate and the estimated BEPs are in an agreement, independent of which methods have been applied.
CosmoSIS: A System for MC Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Zuntz, Joe [Manchester U.; Paterno, Marc [Fermilab; Jennings, Elise [Chicago U., EFI; Rudd, Douglas [U. Chicago; Manzotti, Alessandro [Chicago U., Astron. Astrophys. Ctr.; Dodelson, Scott [Chicago U., Astron. Astrophys. Ctr.; Bridle, Sarah [Manchester U.; Sehrish, Saba [Fermilab; Kowalkowski, James [Fermilab
2015-01-01
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. We present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in Cosmo- SIS, including camb, Planck, cosmic shear calculations, and a suite of samplers. We illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis.
Probabilistic estimation of the constitutive parameters of polymers
Directory of Open Access Journals (Sweden)
Siviour C.R.
2012-08-01
Full Text Available The Mulliken-Boyce constitutive model predicts the dynamic response of crystalline polymers as a function of strain rate and temperature. This paper describes the Mulliken-Boyce model-based estimation of the constitutive parameters in a Bayesian probabilistic framework. Experimental data from dynamic mechanical analysis and dynamic compression of PVC samples over a wide range of strain rates are analyzed. Both experimental uncertainty and natural variations in the material properties are simultaneously considered as independent and joint distributions; the posterior probability distributions are shown and compared with prior estimates of the material constitutive parameters. Additionally, particular statistical distributions are shown to be effective at capturing the rate and temperature dependence of internal phase transitions in DMA data.
PARAMETER ESTIMATION OF THE HYBRID CENSORED LOMAX DISTRIBUTION
Directory of Open Access Journals (Sweden)
Samir Kamel Ashour
2010-12-01
Full Text Available Survival analysis is used in various fields for analyzing data involving the duration between two events. It is also known as event history analysis, lifetime data analysis, reliability analysis or time to event analysis. One of the difficulties which arise in this area is the presence of censored data. The lifetime of an individual is censored when it cannot be exactly measured but partial information is available. Different circumstances can produce different types of censoring. The two most common censoring schemes used in life testing experiments are Type-I and Type-II censoring schemes. Hybrid censoring scheme is mixture of Type-I and Type-II censoring scheme. In this paper we consider the estimation of parameters of Lomax distribution based on hybrid censored data. The parameters are estimated by the maximum likelihood and Bayesian methods. The Fisher information matrix has been obtained and it can be used for constructing asymptotic confidence intervals.
On Using Exponential Parameter Estimators with an Adaptive Controller
Patre, Parag; Joshi, Suresh M.
2011-01-01
Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.
Propagation channel characterization, parameter estimation, and modeling for wireless communications
Yin, Xuefeng
2016-01-01
Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are ...
Estimation of solid earth tidal parameters and FCN with VLBI
International Nuclear Information System (INIS)
Krásná, H.
2012-01-01
Measurements of a space-geodetic technique VLBI (Very Long Baseline Interferometry) are influenced by a variety of processes which have to be modelled and put as a priori information into the analysis of the space-geodetic data. The increasing accuracy of the VLBI measurements allows access to these parameters and provides possibilities to validate them directly from the measured data. The gravitational attraction of the Moon and the Sun causes deformation of the Earth's surface which can reach several decimetres in radial direction during a day. The displacement is a function of the so-called Love and Shida numbers. Due to the present accuracy of the VLBI measurements the parameters have to be specified as complex numbers, where the imaginary parts describe the anelasticity of the Earth's mantle. Moreover, it is necessary to distinguish between the single tides within the various frequency bands. In this thesis, complex Love and Shida numbers of twelve diurnal and five long-period tides included in the solid Earth tidal displacement modelling are estimated directly from the 27 years of VLBI measurements (1984.0 - 2011.0). In this work, the period of the Free Core Nutation (FCN) is estimated which shows up in the frequency dependent solid Earth tidal displacement as well as in a nutation model describing the motion of the Earth's axis in space. The FCN period in both models is treated as a single parameter and it is estimated in a rigorous global adjustment of the VLBI data. The obtained value of -431.18 ± 0.10 sidereal days differs slightly from the conventional value -431.39 sidereal days given in IERS Conventions 2010. An empirical FCN model based on variable amplitude and phase is determined, whose parameters are estimated in yearly steps directly within VLBI global solutions. (author) [de
Estimation of parameters of interior permanent magnet synchronous motors
International Nuclear Information System (INIS)
Hwang, C.C.; Chang, S.M.; Pan, C.T.; Chang, T.Y.
2002-01-01
This paper presents a magnetic circuit model to the estimation of machine parameters of an interior permanent magnet synchronous machine. It extends the earlier work of Hwang and Cho that focused mainly on the magnetic aspects of motor design. The proposed model used to calculate EMF, d- and q-axis reactances. These calculations are compared to those from finite element analysis and measurement with good agreement
Estimation of parameters of interior permanent magnet synchronous motors
Hwang, C C; Pan, C T; Chang, T Y
2002-01-01
This paper presents a magnetic circuit model to the estimation of machine parameters of an interior permanent magnet synchronous machine. It extends the earlier work of Hwang and Cho that focused mainly on the magnetic aspects of motor design. The proposed model used to calculate EMF, d- and q-axis reactances. These calculations are compared to those from finite element analysis and measurement with good agreement.
Factorized Estimation of Partially Shared Parameters in Diffusion Networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2017-01-01
Roč. 65, č. 19 (2017), s. 5153-5163 ISSN 1053-587X R&D Projects: GA ČR(CZ) GP14-06678P; GA ČR GA16-09848S Institutional support: RVO:67985556 Keywords : Diffusion network * Diffusion estimation * Heterogeneous parameters * Multitask networks Subject RIV: BD - Theory of Information OBOR OECD: Applied mathematics Impact factor: 4.300, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/dedecius-0477044.pdf
Directory of Open Access Journals (Sweden)
Akatsuki eKimura
2015-03-01
Full Text Available Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE in a prediction or to maximize likelihood. A (local maximum of likelihood or (local minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.
Model parameters estimation and sensitivity by genetic algorithms
International Nuclear Information System (INIS)
Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca
2003-01-01
In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The
Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.
Sobhani-Tehrani, E; Talebi, H A; Khorasani, K
2014-02-01
This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.
Effects of parameter estimation on maximum-likelihood bootstrap analysis.
Ripplinger, Jennifer; Abdo, Zaid; Sullivan, Jack
2010-08-01
Bipartition support in maximum-likelihood (ML) analysis is most commonly assessed using the nonparametric bootstrap. Although bootstrap replicates should theoretically be analyzed in the same manner as the original data, model selection is almost never conducted for bootstrap replicates, substitution-model parameters are often fixed to their maximum-likelihood estimates (MLEs) for the empirical data, and bootstrap replicates may be subjected to less rigorous heuristic search strategies than the original data set. Even though this approach may increase computational tractability, it may also lead to the recovery of suboptimal tree topologies and affect bootstrap values. However, since well-supported bipartitions are often recovered regardless of method, use of a less intensive bootstrap procedure may not significantly affect the results. In this study, we investigate the impact of parameter estimation (i.e., assessment of substitution-model parameters and tree topology) on ML bootstrap analysis. We find that while forgoing model selection and/or setting substitution-model parameters to their empirical MLEs may lead to significantly different bootstrap values, it probably would not change their biological interpretation. Similarly, even though the use of reduced search methods often results in significant differences among bootstrap values, only omitting branch swapping is likely to change any biological inferences drawn from the data. Copyright 2010 Elsevier Inc. All rights reserved.
Genetic Algorithm-based Affine Parameter Estimation for Shape Recognition
Directory of Open Access Journals (Sweden)
Yuxing Mao
2014-06-01
Full Text Available Shape recognition is a classically difficult problem because of the affine transformation between two shapes. The current study proposes an affine parameter estimation method for shape recognition based on a genetic algorithm (GA. The contributions of this study are focused on the extraction of affine-invariant features, the individual encoding scheme, and the fitness function construction policy for a GA. First, the affine-invariant characteristics of the centroid distance ratios (CDRs of any two opposite contour points to the barycentre are analysed. Using different intervals along the azimuth angle, the different numbers of CDRs of two candidate shapes are computed as representations of the shapes, respectively. Then, the CDRs are selected based on predesigned affine parameters to construct the fitness function. After that, a GA is used to search for the affine parameters with optimal matching between candidate shapes, which serve as actual descriptions of the affine transformation between the shapes. Finally, the CDRs are resampled based on the estimated parameters to evaluate the similarity of the shapes for classification. The experimental results demonstrate the robust performance of the proposed method in shape recognition with translation, scaling, rotation and distortion.
Cross-hole Radio Imaging Method with Radiation Parameters Estimation
Ou, Y.; Feng, J.; Li, Y.; Jia, D.; Gao, W.
2017-12-01
To avoid distortions of the radiation pattern and source strength correction, the ray-based amplitude tomography with radiation parameters estimation for cross-hole radio-frequency electromagnetic data has been presented. It has been indicated by the numerical simulations from finite difference time domain (FDTD) method that the radiation pattern and source strength have been seriously affected by the electric material parameters along the borehole, which cannot be corrected accurately. Therefore, the radiation pattern and source strength are treated as unknown parameters with the assumption that radiation pattern changes with the ray angle and the source strength varies with the position of transmitter. The inversion algorithm applies Tikhonov regularization and imposes a variance constraint on the source strength to make the results consistent with the geological features of the boreholes revealed. The results from estimation of these radiation parameters and attenuation simultaneously have shown that an improvement in resolution of anomalies over traditional amplitude tomography can be achieved by the proposed method.
Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
Kaltenbacher, Barbara; Hasenauer, Jan
2017-01-01
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351
Impact of relativistic effects on cosmological parameter estimation
Lorenz, Christiane S.; Alonso, David; Ferreira, Pedro G.
2018-01-01
Future surveys will access large volumes of space and hence very long wavelength fluctuations of the matter density and gravitational field. It has been argued that the set of secondary effects that affect the galaxy distribution, relativistic in nature, will bring new, complementary cosmological constraints. We study this claim in detail by focusing on a subset of wide-area future surveys: Stage-4 cosmic microwave background experiments and photometric redshift surveys. In particular, we look at the magnification lensing contribution to galaxy clustering and general-relativistic corrections to all observables. We quantify the amount of information encoded in these effects in terms of the tightening of the final cosmological constraints as well as the potential bias in inferred parameters associated with neglecting them. We do so for a wide range of cosmological parameters, covering neutrino masses, standard dark-energy parametrizations and scalar-tensor gravity theories. Our results show that, while the effect of lensing magnification to number counts does not contain a significant amount of information when galaxy clustering is combined with cosmic shear measurements, this contribution does play a significant role in biasing estimates on a host of parameter families if unaccounted for. Since the amplitude of the magnification term is controlled by the slope of the source number counts with apparent magnitude, s (z ), we also estimate the accuracy to which this quantity must be known to avoid systematic parameter biases, finding that future surveys will need to determine s (z ) to the ˜5 %- 10 % level. On the contrary, large-scale general-relativistic corrections are irrelevant both in terms of information content and parameter bias for most cosmological parameters but significant for the level of primordial non-Gaussianity.
Basic MR sequence parameters systematically bias automated brain volume estimation
International Nuclear Information System (INIS)
Haller, Sven; Falkovskiy, Pavel; Roche, Alexis; Marechal, Benedicte; Meuli, Reto; Thiran, Jean-Philippe; Krueger, Gunnar; Lovblad, Karl-Olof; Kober, Tobias
2016-01-01
Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter. A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results. (orig.)
Basic MR sequence parameters systematically bias automated brain volume estimation
Energy Technology Data Exchange (ETDEWEB)
Haller, Sven [University of Geneva, Faculty of Medicine, Geneva (Switzerland); Affidea Centre de Diagnostique Radiologique de Carouge CDRC, Geneva (Switzerland); Falkovskiy, Pavel; Roche, Alexis; Marechal, Benedicte [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Meuli, Reto [University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Thiran, Jean-Philippe [LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland); Krueger, Gunnar [Siemens Medical Solutions USA, Inc., Boston, MA (United States); Lovblad, Karl-Olof [University of Geneva, Faculty of Medicine, Geneva (Switzerland); University Hospitals of Geneva, Geneva (Switzerland); Kober, Tobias [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland)
2016-11-15
Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter. A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results. (orig.)
Chloramine demand estimation using surrogate chemical and microbiological parameters.
Moradi, Sina; Liu, Sanly; Chow, Christopher W K; van Leeuwen, John; Cook, David; Drikas, Mary; Amal, Rose
2017-07-01
A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (F m ) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection. Copyright © 2017. Published by Elsevier B.V.
Biases on cosmological parameter estimators from galaxy cluster number counts
International Nuclear Information System (INIS)
Penna-Lima, M.; Wuensche, C.A.; Makler, M.
2014-01-01
Sunyaev-Zel'dovich (SZ) surveys are promising probes of cosmology — in particular for Dark Energy (DE) —, given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to hundreds of objects and maximum likelihood estimators may present biases for such sample sizes. In this work we study estimators from cluster abundance for some cosmological parameters, in particular the DE equation of state parameter w 0 , the amplitude of density fluctuations σ 8 , and the Dark Matter density parameter Ω c . We begin by deriving an unbinned likelihood for cluster number counts, showing that it is equivalent to the one commonly used in the literature. We use the Monte Carlo approach to determine the presence of bias using this likelihood and study its behavior with both the area and depth of the survey, and the number of cosmological parameters fitted. Our fiducial models are based on the South Pole Telescope (SPT) SZ survey. Assuming perfect knowledge of mass and redshift some estimators have non-negligible biases. For example, the bias of σ 8 corresponds to about 40% of its statistical error bar when fitted together with Ω c and w 0 . Including a SZ mass-observable relation decreases the relevance of the bias, for the typical sizes of current SZ surveys. Considering a joint likelihood for cluster abundance and the so-called ''distance priors'', we obtain that the biases are negligible compared to the statistical errors. However, we show that the biases from SZ estimators do not go away with increasing sample sizes and they may become the dominant source of error for an all sky survey at the SPT sensitivity. Finally, we compute the confidence regions for the cosmological parameters using Fisher matrix and profile likelihood approaches, showing that they are compatible with the Monte Carlo ones. The results of this work validate the use of the current maximum likelihood methods for
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
An approximate method of analysis is formulated for gun internal ballistics. The method is incorporated in a digital computer program which is...described. The validity of the method of analysis and computational procedure is substantiated by comparison of theoretical results with experimental
Ballistic hole magnetic microscopy
Haq, E.; Banerjee, T.; Siekman, M.H.; Lodder, J.C.; Jansen, R.
2005-01-01
A technique to study nanoscale spin transport of holes is presented: ballistic hole magnetic microscopy. The tip of a scanning tunneling microscope is used to inject hot electrons into a ferromagnetic heterostructure, where inelastic decay creates a distribution of electron-hole pairs.
Finite Sample Corrections for Parameters Estimation and Significance Testing
Directory of Open Access Journals (Sweden)
Boon Kin Teh
2018-01-01
Full Text Available An increasingly important problem in the era of Big Data is fitting data to distributions. However, many stop at visually inspecting the fits or use the coefficient of determination as a measure of the goodness of fit. In general, goodness-of-fit measures do not allow us to tell which of several distributions fit the data best. Also, the likelihood of drawing the data from a distribution can be low even when the fit is good. To overcome these limitations, Clauset et al. advocated a three-step procedure for fitting any distribution: (i estimate parameter(s accurately, (ii choosing and calculating an appropriate goodness of fit, (iii test its significance to determine how likely this goodness of fit will appear in samples of the distribution. When we perform this significance testing on exponential distributions, we often obtain low significance values despite the fits being visually good. This led to our realization that most fitting methods do not account for effects due to the finite number of elements and the finite largest element. The former produces sample size dependence in the goodness of fits and the latter introduces a bias in the estimated parameter and the goodness of fit. We propose modifications to account for both and show that these corrections improve the significance of the fits of both real and simulated data. In addition, we used simulations and analytical approximations to verify that convergence rate of the estimated parameters toward its true value depends on how fast the largest element converge to infinity, and provide fast inversion formulas to obtain p-values directly from the adjusted test statistics, in place of doing more Monte Carlo simulations.
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems
Directory of Open Access Journals (Sweden)
Banga Julio R
2006-11-01
Full Text Available Abstract Background We consider the problem of parameter estimation (model calibration in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector. In order to surmount these difficulties, global optimization (GO methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown structure (i.e. black-box models. In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.
Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R
2006-11-02
We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.
Estimating Mass of Inflatable Aerodynamic Decelerators Using Dimensionless Parameters
Samareh, Jamshid A.
2011-01-01
This paper describes a technique for estimating mass for inflatable aerodynamic decelerators. The technique uses dimensional analysis to identify a set of dimensionless parameters for inflation pressure, mass of inflation gas, and mass of flexible material. The dimensionless parameters enable scaling of an inflatable concept with geometry parameters (e.g., diameter), environmental conditions (e.g., dynamic pressure), inflation gas properties (e.g., molecular mass), and mass growth allowance. This technique is applicable for attached (e.g., tension cone, hypercone, and stacked toroid) and trailing inflatable aerodynamic decelerators. The technique uses simple engineering approximations that were developed by NASA in the 1960s and 1970s, as well as some recent important developments. The NASA Mars Entry and Descent Landing System Analysis (EDL-SA) project used this technique to estimate the masses of the inflatable concepts that were used in the analysis. The EDL-SA results compared well with two independent sets of high-fidelity finite element analyses.
Estimation of Eruption Source Parameters from Plume Growth Rate
Pouget, Solene; Bursik, Marcus; Webley, Peter; Dehn, Jon; Pavalonis, Michael; Singh, Tarunraj; Singla, Puneet; Patra, Abani; Pitman, Bruce; Stefanescu, Ramona; Madankan, Reza; Morton, Donald; Jones, Matthew
2013-04-01
The eruption of Eyjafjallajokull, Iceland in April and May, 2010, brought to light the hazards of airborne volcanic ash and the importance of Volcanic Ash Transport and Dispersion models (VATD) to estimate the concentration of ash with time. These models require Eruption Source Parameters (ESP) as input, which typically include information about the plume height, the mass eruption rate, the duration of the eruption and the particle size distribution. However much of the time these ESP are unknown or poorly known a priori. We show that the mass eruption rate can be estimated from the downwind plume or umbrella cloud growth rate. A simple version of the continuity equation can be applied to the growth of either an umbrella cloud or the downwind plume. The continuity equation coupled with the momentum equation using only inertial and gravitational terms provides another model. Numerical modeling or scaling relationships can be used, as necessary, to provide values for unknown or unavailable parameters. Use of these models applied to data on plume geometry provided by satellite imagery allows for direct estimation of plume volumetric and mass growth with time. To test our methodology, we compared our results with five well-studied and well-characterized historical eruptions: Mount St. Helens, 1980; Pinatubo, 1991, Redoubt, 1990; Hekla, 2000 and Eyjafjallajokull, 2010. These tests show that the methodologies yield results comparable to or better than currently accepted methodologies of ESP estimation. We then applied the methodology to umbrella clouds produced by the eruptions of Okmok, 12 July 2008, and Sarychev Peak, 12 June 2009, and to the downwind plume produced by the eruptions of Hekla, 2000; Kliuchevsko'i, 1 October 1994; Kasatochi 7-8 August 2008 and Bezymianny, 1 September 2012. The new methods allow a fast, remote assessment of the mass eruption rate, even for remote volcanoes. They thus provide an additional path to estimation of the ESP and the forecasting
PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
S. Kalaivani
2012-07-01
Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.
Temporal Parameters Estimation for Wheelchair Propulsion Using Wearable Sensors
Directory of Open Access Journals (Sweden)
Manoela Ojeda
2014-01-01
Full Text Available Due to lower limb paralysis, individuals with spinal cord injury (SCI rely on their upper limbs for mobility. The prevalence of upper extremity pain and injury is high among this population. We evaluated the performance of three triaxis accelerometers placed on the upper arm, wrist, and under the wheelchair, to estimate temporal parameters of wheelchair propulsion. Twenty-six participants with SCI were asked to push their wheelchair equipped with a SMARTWheel. The estimated stroke number was compared with the criterion from video observations and the estimated push frequency was compared with the criterion from the SMARTWheel. Mean absolute errors (MAE and mean absolute percentage of error (MAPE were calculated. Intraclass correlation coefficients and Bland-Altman plots were used to assess the agreement. Results showed reasonable accuracies especially using the accelerometer placed on the upper arm where the MAPE was 8.0% for stroke number and 12.9% for push frequency. The ICC was 0.994 for stroke number and 0.916 for push frequency. The wrist and seat accelerometer showed lower accuracy with a MAPE for the stroke number of 10.8% and 13.4% and ICC of 0.990 and 0.984, respectively. Results suggested that accelerometers could be an option for monitoring temporal parameters of wheelchair propulsion.
Campbell, D A; Chkrebtii, O
2013-12-01
Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.
A method for model identification and parameter estimation
International Nuclear Information System (INIS)
Bambach, M; Heinkenschloss, M; Herty, M
2013-01-01
We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)
Parameter Estimation for GRACE-FO Geometric Ranging Errors
Wegener, H.; Mueller, V.; Darbeheshti, N.; Naeimi, M.; Heinzel, G.
2017-12-01
Onboard GRACE-FO, the novel Laser Ranging Instrument (LRI) serves as a technology demonstrator, but it is a fully functional instrument to provide an additional high-precision measurement of the primary mission observable: the biased range between the two spacecraft. Its (expectedly) two largest error sources are laser frequency noise and tilt-to-length (TTL) coupling. While not much can be done about laser frequency noise, the mechanics of the TTL error are widely understood. They depend, however, on unknown parameters. In order to improve the quality of the ranging data, it is hence essential to accurately estimate these parameters and remove the resulting TTL error from the data.Means to do so will be discussed. In particular, the possibility of using calibration maneuvers, the utility of the attitude information provided by the LRI via Differential Wavefront Sensing (DWS), and the benefit from combining ranging data from LRI with ranging data from the established microwave ranging, will be mentioned.
Transport parameter estimation from lymph measurements and the Patlak equation.
Watson, P D; Wolf, M B
1992-01-01
Two methods of estimating protein transport parameters for plasma-to-lymph transport data are presented. Both use IBM-compatible computers to obtain least-squares parameters for the solvent drag reflection coefficient and the permeability-surface area product using the Patlak equation. A matrix search approach is described, and the speed and convenience of this are compared with a commercially available gradient method. The results from both of these methods were different from those of a method reported by Reed, Townsley, and Taylor [Am. J. Physiol. 257 (Heart Circ. Physiol. 26): H1037-H1041, 1989]. It is shown that the Reed et al. method contains a systematic error. It is also shown that diffusion always plays an important role for transmembrane transport at the exit end of a membrane channel under all conditions of lymph flow rate and that the statement that diffusion becomes zero at high lymph flow rate depends on a mathematical definition of diffusion.
Pedotransfer functions estimating soil hydraulic properties using different soil parameters
DEFF Research Database (Denmark)
Børgesen, Christen Duus; Iversen, Bo Vangsø; Jacobsen, Ole Hørbye
2008-01-01
Estimates of soil hydraulic properties using pedotransfer functions (PTF) are useful in many studies such as hydrochemical modelling and soil mapping. The objective of this study was to calibrate and test parametric PTFs that predict soil water retention and unsaturated hydraulic conductivity...... parameters. The PTFs are based on neural networks and the Bootstrap method using different sets of predictors and predict the van Genuchten/Mualem parameters. A Danish soil data set (152 horizons) dominated by sandy and sandy loamy soils was used in the development of PTFs to predict the Mualem hydraulic...... of the hydraulic properties of the studied soils. We found that introducing measured water content as a predictor generally gave lower errors for water retention predictions and higher errors for conductivity predictions. The best of the developed PTFs for predicting hydraulic conductivity was tested against PTFs...
Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver
Kang, Ling; Zhou, Liwei
2018-02-01
Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.
Energy parameter estimation in solar powered wireless sensor networks
Mousa, Mustafa
2014-02-24
The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.
Estimation of Aircraft Nonlinear Unsteady Parameters From Wind Tunnel Data
Klein, Vladislav; Murphy, Patrick C.
1998-01-01
Aerodynamic equations were formulated for an aircraft in one-degree-of-freedom large amplitude motion about each of its body axes. The model formulation based on indicial functions separated the resulting aerodynamic forces and moments into static terms, purely rotary terms and unsteady terms. Model identification from experimental data combined stepwise regression and maximum likelihood estimation in a two-stage optimization algorithm that can identify the unsteady term and rotary term if necessary. The identification scheme was applied to oscillatory data in two examples. The model identified from experimental data fit the data well, however, some parameters were estimated with limited accuracy. The resulting model was a good predictor for oscillatory and ramp input data.
Robustness of Modal Parameter Estimation Methods Applied to Lightweight Structures
DEFF Research Database (Denmark)
Dickow, Kristoffer Ahrens; Kirkegaard, Poul Henning; Andersen, Lars Vabbersgaard
2013-01-01
of two parameter estimation methods built into the commercial modal testing software B&K Pulse Re ex Advanced Modal Analysis. The investigations are done by means of frequency response functions generated from a nite-element model and subjected to articial noise before being analyzed with Pulse Re ex....... The ability to handle closely spaced modes and broad frequency ranges is investigated for a numerical model of a lightweight junction under dierent signal-to-noise ratios. The selection of both excitation points and response points are discussed. It is found that both the Rational Fraction Polynomial-Z method...
Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control
Eshak, Peter B.
Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller. This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored. In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to
Parameter estimation in multi-axial thermal diffusivity experiments
Davis, Sean Edgar
Thermomechanical analysis requires quantifying the thermophysical properties of thermal conductivity (or diffusivity) and specific heat. The extended flash method allows simultaneous measurement of multiple components of the thermal diffusivity tensor. The locations of the temperature sensors in such an experiment have an affect on the ability to accurately estimate the desired components of the diffusivity tensor. Here, D-optimization is applied to a simulated extended flash diffusivity experiment to improve the accuracy of the experiment through optimization of the inter-sensor distance. Results indicate that the optimal inter-sensor distance increases with an increasing ratio of inplane to out-of-plane diffusivity. The analytically determined optimal sensor positioning for an isotropic material is validated via experimental measurements on AISI 304 stainless steel, where it is shown that the accuracy of the estimated parameters improves for data sampled at the optimized locations. When modeling the anisotropic thermal response of materials, the material may be rotated such that the physical axes coincide with the principal axes of the thermal diffusivity tensor, resulting in thermal orthotropy. During measurements of such a tensor, however, the principal axes may be unknown, requiring a method to determine principal values and the orientation of the principal directions while simultaneously measuring the diffusivity. An analytical study was performed where the four non-zero components of the diffusivity tensor alpha were estimated for a material possessing random in-plane anisotropy on the order of certain manufactured or mechanically loaded elastomers. Results indicate that a four-sensor array allows sufficient sampling of the material response to permit estimation of alpha to within 1% of the reference values. When orthotropy is assumed for a material exhibiting random in-plane anisotropy, the estimated values of alphaii are resolved to within 0.4% of the
Estimating Friction Parameters in Reaction Wheels for Attitude Control
Directory of Open Access Journals (Sweden)
Valdemir Carrara
2013-01-01
Full Text Available The ever-increasing use of artificial satellites in both the study of terrestrial and space phenomena demands a search for increasingly accurate and reliable pointing systems. It is common nowadays to employ reaction wheels for attitude control that provide wide range of torque magnitude, high reliability, and little power consumption. However, the bearing friction causes the response of wheel to be nonlinear, which may compromise the stability and precision of the control system as a whole. This work presents a characterization of a typical reaction wheel of 0.65 Nms maximum angular momentum storage, in order to estimate their friction parameters. It used a friction model that takes into account the Coulomb friction, viscous friction, and static friction, according to the Stribeck formulation. The parameters were estimated by means of a nonlinear batch least squares procedure, from data raised experimentally. The results have shown wide agreement with the experimental data and were also close to a deterministic model, previously obtained for this wheel. This model was then employed in a Dynamic Model Compensator (DMC control, which successfully reduced the attitude steady state error of an instrumented one-axis air-bearing table.
Parameter estimation in space systems using recurrent neural networks
Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.
1991-01-01
The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.
Genetic Parameter Estimation in Seedstock Swine Population for Growth Performances
Directory of Open Access Journals (Sweden)
Jae Gwan Choi
2013-04-01
Full Text Available The objective of this study was to estimate genetic parameters that are to be used for across-herd genetic evaluations of seed stock pigs at GGP level. Performance data with pedigree information collected from swine breeder farms in Korea were provided by Korea Animal Improvement Association (AIAK. Performance data were composed of final body weights at test days and ultrasound measures of back fat thickness (BF, rib eye area (EMA and retail cut percentage (RCP. Breeds of swine tested were Landrace, Yorkshire and Duroc. Days to 90 kg body weight (DAYS90 were estimated with linear function of age and ADG calculated from body weights at test days. Ultrasound measures were taken with A-mode ultrasound scanners by trained technicians. Number of performance records after censoring outliers and keeping records pigs only born from year 2000 were of 78,068 Duroc pigs, 101,821 Landrace pigs and 281,421 Yorkshire pigs. Models included contemporary groups defined by the same herd and the same seasons of births of the same year, which was regarded as fixed along with the effect of sex for all traits and body weight at test day as a linear covariate for ultrasound measures. REML estimation was processed with REMLF90 program. Heritability estimates were 0.40, 0.32, 0.21 0.39 for DAYS90, ADG, BF, EMA, RCP, respectively for Duroc population. Respective heritability estimates for Landrace population were 0.43, 0.41, 0.22, and 0.43 and for Yorkshire population were 0.36, 0.38, 0.22, and 0.42. Genetic correlation coefficients of DAYS90 with BF, EMA, or RCP were estimated to be 0.00 to 0.09, −0.15 to −0.25, 0.22 to 0.28, respectively for three breeds populations. Genetic correlation coefficients estimated between BF and EMA was −0.33 to −0.39. Genetic correlation coefficient estimated between BF and RCP was high and negative (−0.78 to −0.85 but the environmental correlation coefficients between these two traits was medium and negative (near −0
Periodic orbits of hybrid systems and parameter estimation via AD.
Energy Technology Data Exchange (ETDEWEB)
Guckenheimer, John. (Cornell University); Phipps, Eric Todd; Casey, Richard (INRIA Sophia-Antipolis)
2004-07-01
Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high fidelity as periodic orbits of dynamical systems is challenging because: (1) (most) nonlinear differential equations can only be solved numerically; (2) accurate computation requires solving boundary value problems; (3) many problems and solutions are only piecewise smooth; (4) many problems require solving differential-algebraic equations; (5) sensitivity information for parameter dependence of solutions requires solving variational equations; and (6) truncation errors in numerical integration degrade performance of optimization methods for parameter estimation. In addition, mathematical models of biological processes frequently contain many poorly-known parameters, and the problems associated with this impedes the construction of detailed, high-fidelity models. Modelers are often faced with the difficult problem of using simulations of a nonlinear model, with complex dynamics and many parameters, to match experimental data. Improved computational tools for exploring parameter space and fitting models to data are clearly needed. This paper describes techniques for computing periodic orbits in systems of hybrid differential-algebraic equations and parameter estimation methods for fitting these orbits to data. These techniques make extensive use of automatic differentiation to accurately and efficiently evaluate derivatives for time integration, parameter sensitivities, root finding and optimization. The boundary value problem representing a periodic orbit in a hybrid system of differential algebraic equations is discretized via multiple-shooting using a high-degree Taylor series integration method [GM00, Phi03]. Numerical solutions to the shooting equations are then estimated by a Newton process yielding an approximate periodic orbit. A metric is defined for computing the distance
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik
1995-01-01
Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrat...
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik
1995-01-01
Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...
Estimating Mass Parameters of Doubly Synchronous Binary Asteroids
Davis, Alex; Scheeres, Daniel J.
2017-10-01
The non-spherical mass distributions of binary asteroid systems lead to coupled mutual gravitational forces and torques. Observations of the coupled attitude and orbital dynamics can be leveraged to provide information about the mass parameters of the binary system. The full 3-dimensional motion has 9 degrees of freedom, and coupled dynamics require the use of numerical investigation only. In the current study we simplify the system to a planar ellipsoid-ellipsoid binary system in a doubly synchronous orbit. Three modes are identified for the system, which has 4 degrees of freedom, with one degree of freedom corresponding to an ignorable coordinate. The three modes correspond to the three major librational modes of the system when it is in a doubly synchronous orbit. The linearized periods of each mode are a function of the mass parameters of the two asteroids, enabling measurement of these parameters based on observations of the librational motion. Here we implement estimation techniques to evaluate the capabilities of this mass measurement method. We apply this methodology to the Trojan binary asteroid system 617 Patroclus and Menoetius (1906 VY), the final flyby target of the recently announced LUCY Discovery mission. This system is of interest because a stellar occultation campaign of the Patroclus and Menoetius system has suggested that the asteroids are similarly sized oblate ellipsoids moving in a doubly-synchronous orbit, making the system an ideal test for this investigation. A number of missed observations during the campaign also suggested the possibility of a crater on the southern limb of Menoetius, the presence of which could be evaluated by our mass estimation method. This presentation will review the methodology and potential accuracy of our approach in addition to evaluating how the dynamical coupling can be used to help understand light curve and stellar occultation observations for librating binary systems.
Estimating negative binomial parameters from occurrence data with detection times.
Hwang, Wen-Han; Huggins, Richard; Stoklosa, Jakub
2016-11-01
The negative binomial distribution is a common model for the analysis of count data in biology and ecology. In many applications, we may not observe the complete frequency count in a quadrat but only that a species occurred in the quadrat. If only occurrence data are available then the two parameters of the negative binomial distribution, the aggregation index and the mean, are not identifiable. This can be overcome by data augmentation or through modeling the dependence between quadrat occupancies. Here, we propose to record the (first) detection time while collecting occurrence data in a quadrat. We show that under what we call proportionate sampling, where the time to survey a region is proportional to the area of the region, that both negative binomial parameters are estimable. When the mean parameter is larger than two, our proposed approach is more efficient than the data augmentation method developed by Solow and Smith (, Am. Nat. 176, 96-98), and in general is cheaper to conduct. We also investigate the effect of misidentification when collecting negative binomially distributed data, and conclude that, in general, the effect can be simply adjusted for provided that the mean and variance of misidentification probabilities are known. The results are demonstrated in a simulation study and illustrated in several real examples. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Estimation of Parameters of CCF with Staggered Testing
International Nuclear Information System (INIS)
Kim, Myung-Ki; Hong, Sung-Yull
2006-01-01
Common cause failures are extremely important in reliability analysis and would be dominant to risk contributor in a high reliable system such as a nuclear power plant. Of particular concern is common cause failure (CCF) that degrades redundancy or diversity implemented to improve a reliability of systems. Most of analyses of parameters of CCF models such as beta factor model, alpha factor model, and MGL(Multiple Greek Letters) model deal a system with a nonstaggered testing strategy. Non-staggered testing is that all components are tested at the same time (or at least the same shift) and staggered testing is that if there is a failure in the first component, all the other components are tested immediately, and if it succeeds, no more is done until the next scheduled testing time. Both of them are applied in the nuclear power plants. The strategy, however, is not explicitly described in the technical specifications, but implicitly in the periodic test procedure. For example, some redundant components particularly important to safety are being tested with staggered testing strategy. Others are being performed with non-staggered testing strategy. This paper presents the parameter estimator of CCF model such as beta factor model, MGL model, and alpha factor model with staggered testing strategy. In addition, a new CCF model, rho factor model, is proposed and its parameter is presented with staggered testing strategy
BOLTON-KING, Rachel; Schulze, Johan
2016-01-01
Chapter 7 of the book entitled 'Practical Veterinary Forensics' aims to introduce forensic veterinarians to the scientific concepts underpinning the field of firearms and ballistics. This introduction will enable practitioners to understand wound formation depending on the firearm and ammunition used. \\ud \\ud Various types of firearms, modern firing mechanisms and ammunition will be explained, together with an introduction to the physical concepts underpinning the four main constituents of th...
Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models
Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea
2014-05-01
Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.
Automated modal parameter estimation using correlation analysis and bootstrap sampling
Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.
2018-02-01
The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to
Learn-as-you-go acceleration of cosmological parameter estimates
International Nuclear Information System (INIS)
Aslanyan, Grigor; Easther, Richard; Price, Layne C.
2015-01-01
Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitly describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly
International Nuclear Information System (INIS)
Russell, F.M.
1989-05-01
Energetic particles moving with a solid, either from nuclear reactions or externally injected, deposit energy by inelastic scattering processes which eventually appears as thermal energy. If the transfer of energy occurs in a crystalline solid then it is possible to couple some of the energy directly to the nuclei forming the lattice by generating phonons. In this paper the transfer of energy from a compound excited nucleus to the lattice is examined by introducing a virtual particle Π. It is shown that by including a Π in the nuclear reaction a substantial amount of energy can be coupled directly to the lattice. In the lattice this particle behaves as a spatially localized phonon of high energy, the so-called supra-ballistic phonon. By multiple inelastic scattering the supra-ballistic phonon eventually thermalizes. Because both the virtual particle Π and the equivalent supra-ballistic phonon have no charge or spin and can only exist within a lattice it is difficult to detect other than by its decay into thermal phonons. The possibility of a Π removing excess energy from a compound nucleus formed by the cold fusion of deuterium is examined. (Author)
Energy Technology Data Exchange (ETDEWEB)
Prasad, M.S.R. [Koneru Lakshmiah Univ.. Dept. of Electrical and Electronics Engineering, Green fields, Vaddeswaram (India)
2012-07-01
If we consider the Statistics there is drastic increase in dependence of batteries from year to year, due to necessity of power storage equipment at homes, power generating off grid and on grid Wind, PV systems, etc.. Where wind power is leading in renewable sector, there is a need to look at its development. Considering the scenario in India, most of the wind resource areas are far away from grid and the remaining areas which are near to grid are of low wind currents which is of no use connecting these equipment directly to grid. So, there is a need for a power storage utility to be integrated, such as the BNB (Ballistic Negatron Battery). In this situation a country like India need a battery which should be reliable, cheap and which can be industrialized. So this paper presents the concept of working, design, operation, adaptability of a Ballistic Negatron Battery. Unlike present batteries with low energy density, huge size, more weight, more charging time and low resistant to wear level, this Ballistic Negatron Battery comes with, 1) High energy storage capability (many multiples more than the present most advanced battery). 2) Very compact in size. 3) Almost negligible in weight compared to present batteries. 4) Charges with in very less time. 5) Never exhibits a wear level greater than zero. Seems like inconceivable but adoptable with simple physics. This paper will explains in detail the principle, model, design, construction and practical considerations considered in making this battery. (Author)
Shoemaker, David M.
Described and listed herein with concomitant sample input and output is the Fortran IV program which estimates parameters and standard errors of estimate per parameters for parameters estimated through multiple matrix sampling. The specific program is an improved and expanded version of an earlier version. (Author/BJG)
Estimation of the Alpha Factor Parameters Using the ICDE Database
International Nuclear Information System (INIS)
Kang, Dae Il; Hwang, M. J.; Han, S. H.
2007-04-01
Detailed common cause failure (CCF) analysis generally need for the data for CCF events of other nuclear power plants because the CCF events rarely occur. KAERI has been participated at the international common cause failure data exchange (ICDE) project to get the data for the CCF events. The operation office of the ICDE project sent the CCF event data for EDG to the KAERI at December 2006. As a pilot study, we performed the detailed CCF analysis of EDGs for Yonggwang Units 3 and 4 and Ulchin Units 3 and 4 using the ICDE database. There are two onsite EDGs for each NPP. When an offsite power and the two onsite EDGs are not available, one alternate AC (AAC) diesel generator (hereafter AAC) is provided. Two onsite EDGs and the AAC are manufactured by the same company, but they are designed differently. We estimated the Alpha Factor and the CCF probability for the cases where three EDGs were assumed to be identically designed, and for those were assumed to be not identically designed. For the cases where three EDGs were assumed to be identically designed, double CCF probabilities of Yonggwang Units 3/4 and Ulchin Units 3/4 for 'fails to start' were estimated as 2.20E-4 and 2.10E-4, respectively. Triple CCF probabilities of those were estimated as 2.39E-4 and 2.42E-4, respectively. As each NPP has no experience for 'fails to run', Yonggwang Units 3/4 and Ulchin Units 3/4 have the same CCF probability. The estimated double and triple CCF probabilities for 'fails to run' are 4.21E-4 and 4.61E-4, respectively. Quantification results show that the system unavailability for the cases where the three EDGs are identical is higher than that where the three EDGs are different. The estimated system unavailability of the former case was increased by 3.4% comparing with that of the latter. As a future study, a computerization work for the estimations of the CCF parameters will be performed
Colocated MIMO Radar: Beamforming, Waveform design, and Target Parameter Estimation
Jardak, Seifallah
2014-04-01
Thanks to its improved capabilities, the Multiple Input Multiple Output (MIMO) radar is attracting the attention of researchers and practitioners alike. Because it transmits orthogonal or partially correlated waveforms, this emerging technology outperformed the phased array radar by providing better parametric identifiability, achieving higher spatial resolution, and designing complex beampatterns. To avoid jamming and enhance the signal to noise ratio, it is often interesting to maximize the transmitted power in a given region of interest and minimize it elsewhere. This problem is known as the transmit beampattern design and is usually tackled as a two-step process: a transmit covariance matrix is firstly designed by minimizing a convex optimization problem, which is then used to generate practical waveforms. In this work, we propose simple novel methods to generate correlated waveforms using finite alphabet constant and non-constant-envelope symbols. To generate finite alphabet waveforms, the proposed method maps easily generated Gaussian random variables onto the phase-shift-keying, pulse-amplitude, and quadrature-amplitude modulation schemes. For such mapping, the probability density function of Gaussian random variables is divided into M regions, where M is the number of alphabets in the corresponding modulation scheme. By exploiting the mapping function, the relationship between the cross-correlation of Gaussian and finite alphabet symbols is derived. The second part of this thesis covers the topic of target parameter estimation. To determine the reflection coefficient, spatial location, and Doppler shift of a target, maximum likelihood estimation yields the best performance. However, it requires a two dimensional search problem. Therefore, its computational complexity is prohibitively high. So, we proposed a reduced complexity and optimum performance algorithm which allows the two dimensional fast Fourier transform to jointly estimate the spatial location
Multiphase flow parameter estimation based on laser scattering
International Nuclear Information System (INIS)
Vendruscolo, Tiago P; Fischer, Robert; Martelli, Cicero; Da Silva, Marco J; Rodrigues, Rômulo L P; Morales, Rigoberto E M
2015-01-01
The flow of multiple constituents inside a pipe or vessel, known as multiphase flow, is commonly found in many industry branches. The measurement of the individual flow rates in such flow is still a challenge, which usually requires a combination of several sensor types. However, in many applications, especially in industrial process control, it is not necessary to know the absolute flow rate of the respective phases, but rather to continuously monitor flow conditions in order to quickly detect deviations from the desired parameters. Here we show how a simple and low-cost sensor design can achieve this, by using machine-learning techniques to distinguishing the characteristic patterns of oblique laser light scattered at the phase interfaces. The sensor is capable of estimating individual phase fluxes (as well as their changes) in multiphase flows and may be applied to safety applications due to its quick response time. (paper)
Multiphase flow parameter estimation based on laser scattering
Vendruscolo, Tiago P.; Fischer, Robert; Martelli, Cicero; Rodrigues, Rômulo L. P.; Morales, Rigoberto E. M.; da Silva, Marco J.
2015-07-01
The flow of multiple constituents inside a pipe or vessel, known as multiphase flow, is commonly found in many industry branches. The measurement of the individual flow rates in such flow is still a challenge, which usually requires a combination of several sensor types. However, in many applications, especially in industrial process control, it is not necessary to know the absolute flow rate of the respective phases, but rather to continuously monitor flow conditions in order to quickly detect deviations from the desired parameters. Here we show how a simple and low-cost sensor design can achieve this, by using machine-learning techniques to distinguishing the characteristic patterns of oblique laser light scattered at the phase interfaces. The sensor is capable of estimating individual phase fluxes (as well as their changes) in multiphase flows and may be applied to safety applications due to its quick response time.
Estimation of genetic parameters for reproductive traits in Shall sheep.
Amou Posht-e-Masari, Hesam; Shadparvar, Abdol Ahad; Ghavi Hossein-Zadeh, Navid; Hadi Tavatori, Mohammad Hossein
2013-06-01
The objective of this study was to estimate genetic parameters for reproductive traits in Shall sheep. Data included 1,316 records on reproductive performances of 395 Shall ewes from 41 sires and 136 dams which were collected from 2001 to 2007 in Shall breeding station in Qazvin province at the Northwest of Iran. Studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW). Test of significance to include fixed effects in the statistical model was performed using the general linear model procedure of SAS. The effects of lambing year and ewe age at lambing were significant (Psheep.
A possible estimation of atmospheric Cherenkov light parameters
International Nuclear Information System (INIS)
Aleksandrov, L.; Brankova, M.; Kirov, I.; Mavrodiev, S.Cht.; Mishev, A.; Stamenov, J.; Ushev, S.
1998-01-01
A method for analysis of extensive air showers initiated by primary gamma quanta of primary protons and nuclei in the atmosphere is presented. The method is based on measuring of Cherenkov light distribution in showers. A new approximation of the lateral and radial distributions is obtained on the basis of a nonlinear overdetermined inverse problem solution. The concrete mathematical model is created by analysis of recent measurements carried out by the Cherenkov wide angle timing telescope HOTOVO. The model is tested on simulated data obtained with the code CORSIKA. The method is applied to different sets of detectors of CELESTE array. A new detector array for optimal estimation of Cherenkov flux parameters is proposed
Total variation with automatic hyper-parameter estimation.
Nascimento, Jacinto; Sanches, João
2008-01-01
Medical diagnosis is often hampered by the quality of the images. This happens in a wide range of image modalities. Image noise reduction is a crucial step, however difficult to be accomplished. Bayesian algorithms have been commonly used with success, namely with additive white Gaussian noise (AWGN) model. In fact, the noise corrupting some of the most used medical imaging modalities is not additive neither Gaussian but multiplicative described by Poisson or Rayleigh distributions. This paper proposes a unified framework with automatic hyper parameters estimation. The proposed framework deals with AWGN but also with both Poisson and Rayleigh distributions. The algorithm proposed herein, is based on a maximum a posteriori (MAP) criterion with the edge preserving prior based on the total variation (TV), which avoids the distortion of relevant anatomical details. The denoising technique is performed via single parametric iterative scheme parameterized for each noise model considered. Tests with real data from several medical imaging modalities testify the performance of the algorithm.
Cosmological Parameter Estimation with Large Scale Structure Observations
Di Dio, Enea; Durrer, Ruth; Lesgourgues, Julien
2014-01-01
We estimate the sensitivity of future galaxy surveys to cosmological parameters, using the redshift dependent angular power spectra of galaxy number counts, $C_\\ell(z_1,z_2)$, calculated with all relativistic corrections at first order in perturbation theory. We pay special attention to the redshift dependence of the non-linearity scale and present Fisher matrix forecasts for Euclid-like and DES-like galaxy surveys. We compare the standard $P(k)$ analysis with the new $C_\\ell(z_1,z_2)$ method. We show that for surveys with photometric redshifts the new analysis performs significantly better than the $P(k)$ analysis. For spectroscopic redshifts, however, the large number of redshift bins which would be needed to fully profit from the redshift information, is severely limited by shot noise. We also identify surveys which can measure the lensing contribution and we study the monopole, $C_0(z_1,z_2)$.
Dynamic systems models new methods of parameter and state estimation
2016-01-01
This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics. Dynamic Systems Models demonstrates the use of algorithms based on polynomial approximation which have weaker requirements than already-popular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow non-differentiable elements in the vector functions being approximated. The text covers all the points necessary for the understanding and use of polynomial approximation from the mathematical fundamentals, through algorithm development to the application of the method in, for instance, aeroplane flight dynamic...
Multivariate phase type distributions - Applications and parameter estimation
DEFF Research Database (Denmark)
Meisch, David
and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate......The best known univariate probability distribution is the normal distribution. It is used throughout the literature in a broad field of applications. In cases where it is not sensible to use the normal distribution alternative distributions are at hand and well understood, many of these belonging...... to the class of phase type distributions. Phase type distributions have several advantages. They are versatile in the sense that they can be used to approximate any given probability distribution on the positive reals. There exist general probabilistic results for the entire class of phase type distributions...
Evaluation of Tropospherical Parameters Estimated In Various Routine GPS Analyses
Douza, J.
Although using always the double-differenced GPS observables and the Bernese GPS sofware processing tool, the analysis center of Geodetic observatory Pecný (AC GOP) routinely process GPS networks with different approaches for various objectives. Primarily, already 1997 GOP started to process the part of the European Reference Frame permanent network (EPN) with general delay of about 3 weeks. From the be- ginning, there was purely geodetic motivation - the precise coordinate estimation. Nevertheless, from the summer in 2001 the working group for the troposphere has started a pilot project of delivering the individual AC's ZTD products together with combining them into an unique european solution. Besides, in 1999-2000 the AC GOP was testing the near real-time (NRT) analysis for the ground-based GPS meteorology application. In 2001, the Czech Republic joined an ongoing COST-716 project focused on this topic and during the whole year, the AC GOP routinely analysed the european NRT demonstrational network. The results were delayed about 1 hour but, in addition, the GOP evaluated the same network also on post-processed basis (delayed 1-2 days) using a daily concatenated GPS data and rapid IGS orbit products. Finally, determining the subdaily precise GPS orbits, the AC GOP tested in 2000/2001 the global NRT processing on 6 hour data basis. When starting to upgrade this solution within each 3 hours (from Jan/2002), the ZTD parameters have been archived for the global NRT GPS network as well. Among these rather different analysing schemes, we can recognized important differ- ent features: e.g. the network's configurations, the use of daily or hourly GPS data, a priori products applied, reference frame realization and coordinate estimation, am- biguity resolution and general parameter handling, observation cut-off angle, data weighting and many others.In this presentation, we try to compare and evaluate all these ZTD results whenever it is possible.
Project Parameter Estimation on the Basis of an Erp Database
Directory of Open Access Journals (Sweden)
Relich Marcin
2013-12-01
Full Text Available Nowadays, more and more enterprises are using Enterprise Resource Planning (EPR systems that can also be used to plan and control the development of new products. In order to obtain a project schedule, certain parameters (e.g. duration have to be specified in an ERP system. These parameters can be defined by the employees according to their knowledge, or can be estimated on the basis of data from previously completed projects. This paper investigates using an ERP database to identify those variables that have a significant influence on the duration of a project phase. In the paper, a model of knowledge discovery from an ERP database is proposed. The presented method contains four stages of the knowledge discovery process such as data selection, data transformation, data mining and interpretation of patterns in the context of new product development. Among data mining techniques, a fuzzy neural system is chosen to seek relationships on the basis of data from completed projects stored in an ERP system.
Bayesian parameter estimation for stochastic models of biological cell migration
Dieterich, Peter; Preuss, Roland
2013-08-01
Cell migration plays an essential role under many physiological and patho-physiological conditions. It is of major importance during embryonic development and wound healing. In contrast, it also generates negative effects during inflammation processes, the transmigration of tumors or the formation of metastases. Thus, a reliable quantification and characterization of cell paths could give insight into the dynamics of these processes. Typically stochastic models are applied where parameters are extracted by fitting models to the so-called mean square displacement of the observed cell group. We show that this approach has several disadvantages and problems. Therefore, we propose a simple procedure directly relying on the positions of the cell's trajectory and the covariance matrix of the positions. It is shown that the covariance is identical with the spatial aging correlation function for the supposed linear Gaussian models of Brownian motion with drift and fractional Brownian motion. The technique is applied and illustrated with simulated data showing a reliable parameter estimation from single cell paths.
Genetic parameter estimates and identification of superior white maize populations
Directory of Open Access Journals (Sweden)
Sara Regina Silvestrin Rovaris
2017-04-01
Full Text Available In Brazil, there is a shortage of white maize cultivars and genetic studies for special maize breeding programs. This study aimed to identify populations and promising hybrid white maize for main agronomic traits and grits processing and to estimate the genetic parameters of parents and heterosis. In the 2012/13 growing season, fifteen hybrids were obtained by complete diallel crosses, and six parental and commercial check varieties were evaluated for: female flowering (FF, ear height (EH, grain yield (GY, ear length (EL, volumetric mass (VM and grits processing (GP in two locations in São Paulo State, Campinas and Mococa, using a randomized block design. Analyses of variance were carried out, and diallel crosses were performed using the Gardner and Eberhart model. The populations P3 and P6 stood out because of the estimated effects of the parents and of heterosis; the studied characters are promising for obtaining new lines and forming composites. For GP, the treatments showed no differences, implying the need to introduce new sources of germplasm.
Estimation of fracture parameters using elastic full-waveform inversion
Zhang, Zhendong
2017-08-17
Current methodologies to characterize fractures at the reservoir scale have serious limitations in spatial resolution and suffer from uncertainties in the inverted parameters. Here, we propose to estimate the spatial distribution and physical properties of fractures using full-waveform inversion (FWI) of multicomponent surface seismic data. An effective orthorhombic medium with five clusters of vertical fractures distributed in a checkboard fashion is used to test the algorithm. A shape regularization term is added to the objective function to improve the estimation of the fracture azimuth, which is otherwise poorly constrained. The cracks are assumed to be penny-shaped to reduce the nonuniqueness in the inverted fracture weaknesses and achieve a faster convergence. To better understand the inversion results, we analyze the radiation patterns induced by the perturbations in the fracture weaknesses and orientation. Due to the high-resolution potential of elastic FWI, the developed algorithm can recover the spatial fracture distribution and identify localized “sweet spots” of intense fracturing. However, the fracture azimuth can be resolved only using long-offset data.
Burkatovskaya, Yuliya Borisovna; Kabanova, T.; Khaustov, Pavel Aleksandrovich
2016-01-01
CUSUM algorithm for controlling chain state switching in the Markov modulated Poissonprocess was investigated via simulation. Recommendations concerning the parameter choice were givensubject to characteristics of the process. Procedure of the process parameter estimation was described.
Advanced prepreg ballistic composites for military helmets
Dimeski, Dimko; Srebrenkoska, Vineta
2014-01-01
With the advancement of ballistic materials and technologies, the ballistic prepregs are becoming an essential construction technique for getting the maximum performance out of the high performance fibers. The ballistic prepregs help to maximize the engagement between fibers and high speed projectiles penetrating the ballistic material, thus reducing the amount of ballistic material required to defeat the projectiles. The backbone of lightweight ballistic materials is high perform...
Estimation of genetic parameters for reproductive traits in alpacas.
Cruz, A; Cervantes, I; Burgos, A; Morante, R; Gutiérrez, J P
2015-12-01
One of the main deficiencies affecting animal breeding programs in Peruvian alpacas is the low reproductive performance leading to low number of animals available to select from, decreasing strongly the selection intensity. Some reproductive traits could be improved by artificial selection, but very few information about genetic parameters exists for these traits in this specie. The aim of this study was to estimate genetic parameters for six reproductive traits in alpacas both in Suri (SU) and Huacaya (HU) ecotypes, as well as their genetic relationship with fiber and morphological traits. Dataset belonging to Pacomarca experimental farm collected between 2000 and 2014 was used. Number of records for age at first service (AFS), age at first calving (AFC), copulation time (CT), pregnancy diagnosis (PD), gestation length (GL), and calving interval (CI) were, respectively, 1704, 854, 19,770, 5874, 4290 and 934. Pedigree consisted of 7742 animals. Regarding reproductive traits, model of analysis included additive and residual random effects for all traits, and also permanent environmental effect for CT, PD, GL and CI traits, with color and year of recording as fixed effects for all the reproductive traits and also age at mating and sex of calf for GL trait. Estimated heritabilities, respectively for HU and SU were 0.19 and 0.09 for AFS, 0.45 and 0.59 for AFC, 0.04 and 0.05 for CT, 0.07 and 0.05 for PD, 0.12 and 0.20 for GL, and 0.14 and 0.09 for CI. Genetic correlations between them ranged from -0.96 to 0.70. No important genetic correlations were found between reproductive traits and fiber or morphological traits in HU. However, some moderate favorable genetic correlations were found between reproductive and either fiber and morphological traits in SU. According to estimated genetic correlations, some reproductive traits might be included as additional selection criteria in HU. Copyright © 2015 Elsevier B.V. All rights reserved.
Ballistic transport and electronic structure
Schep, Kees M.; Kelly, Paul J.; Bauer, Gerrit E.W.
1998-01-01
The role of the electronic structure in determining the transport properties of ballistic point contacts is studied. The conductance in the ballistic regime is related to simple geometrical projections of the Fermi surface. The essential physics is first clarified for simple models. For real
Neural Models: An Option to Estimate Seismic Parameters of Accelerograms
Alcántara, L.; García, S.; Ovando-Shelley, E.; Macías, M. A.
2014-12-01
Seismic instrumentation for recording strong earthquakes, in Mexico, goes back to the 60´s due the activities carried out by the Institute of Engineering at Universidad Nacional Autónoma de México. However, it was after the big earthquake of September 19, 1985 (M=8.1) when the project of seismic instrumentation assumes a great importance. Currently, strong ground motion networks have been installed for monitoring seismic activity mainly along the Mexican subduction zone and in Mexico City. Nevertheless, there are other major regions and cities that can be affected by strong earthquakes and have not yet begun their seismic instrumentation program or this is still in development.Because of described situation some relevant earthquakes (e.g. Huajuapan de León Oct 24, 1980 M=7.1, Tehuacán Jun 15, 1999 M=7 and Puerto Escondido Sep 30, 1999 M= 7.5) have not been registered properly in some cities, like Puebla and Oaxaca, and that were damaged during those earthquakes. Fortunately, the good maintenance work carried out in the seismic network has permitted the recording of an important number of small events in those cities. So in this research we present a methodology based on the use of neural networks to estimate significant duration and in some cases the response spectra for those seismic events. The neural model developed predicts significant duration in terms of magnitude, epicenter distance, focal depth and soil characterization. Additionally, for response spectra we used a vector of spectral accelerations. For training the model we selected a set of accelerogram records obtained from the small events recorded in the strong motion instruments installed in the cities of Puebla and Oaxaca. The final results show that neural networks as a soft computing tool that use a multi-layer feed-forward architecture provide good estimations of the target parameters and they also have a good predictive capacity to estimate strong ground motion duration and response spectra.
AUTOMATIC ESTIMATION OF SIZE PARAMETERS USING VERIFIED COMPUTERIZED STEREOANALYSIS
Directory of Open Access Journals (Sweden)
Peter R Mouton
2011-05-01
Full Text Available State-of-the-art computerized stereology systems combine high-resolution video microscopy and hardwaresoftware integration with stereological methods to assist users in quantifying multidimensional parameters of importance to biomedical research, including volume, surface area, length, number, their variation and spatial distribution. The requirement for constant interactions between a trained, non-expert user and the targeted features of interest currently limits the throughput efficiency of these systems. To address this issue we developed a novel approach for automatic stereological analysis of 2-D images, Verified Computerized Stereoanalysis (VCS. The VCS approach minimizes the need for user interactions with high contrast [high signal-to-noise ratio (S:N] biological objects of interest. Performance testing of the VCS approach confirmed dramatic increases in the efficiency of total object volume (size estimation, without a loss of accuracy or precision compared to conventional computerized stereology. The broad application of high efficiency VCS to high-contrast biological objects on tissue sections could reduce labor costs, enhance hypothesis testing, and accelerate the progress of biomedical research focused on improvements in health and the management of disease.
Model-Based Material Parameter Estimation for Terahertz Reflection Spectroscopy
Kniffin, Gabriel Paul
Many materials such as drugs and explosives have characteristic spectral signatures in the terahertz (THz) band. These unique signatures imply great promise for spectral detection and classification using THz radiation. While such spectral features are most easily observed in transmission, real-life imaging systems will need to identify materials of interest from reflection measurements, often in non-ideal geometries. One important, yet commonly overlooked source of signal corruption is the etalon effect -- interference phenomena caused by multiple reflections from dielectric layers of packaging and clothing likely to be concealing materials of interest in real-life scenarios. This thesis focuses on the development and implementation of a model-based material parameter estimation technique, primarily for use in reflection spectroscopy, that takes the influence of the etalon effect into account. The technique is adapted from techniques developed for transmission spectroscopy of thin samples and is demonstrated using measured data taken at the Northwest Electromagnetic Research Laboratory (NEAR-Lab) at Portland State University. Further tests are conducted, demonstrating the technique's robustness against measurement noise and common sources of error.
Bayesian analysis of inflation: Parameter estimation for single field models
International Nuclear Information System (INIS)
Mortonson, Michael J.; Peiris, Hiranya V.; Easther, Richard
2011-01-01
Future astrophysical data sets promise to strengthen constraints on models of inflation, and extracting these constraints requires methods and tools commensurate with the quality of the data. In this paper we describe ModeCode, a new, publicly available code that computes the primordial scalar and tensor power spectra for single-field inflationary models. ModeCode solves the inflationary mode equations numerically, avoiding the slow roll approximation. It is interfaced with CAMB and CosmoMC to compute cosmic microwave background angular power spectra and perform likelihood analysis and parameter estimation. ModeCode is easily extendable to additional models of inflation, and future updates will include Bayesian model comparison. Errors from ModeCode contribute negligibly to the error budget for analyses of data from Planck or other next generation experiments. We constrain representative single-field models (φ n with n=2/3, 1, 2, and 4, natural inflation, and 'hilltop' inflation) using current data, and provide forecasts for Planck. From current data, we obtain weak but nontrivial limits on the post-inflationary physics, which is a significant source of uncertainty in the predictions of inflationary models, while we find that Planck will dramatically improve these constraints. In particular, Planck will link the inflationary dynamics with the post-inflationary growth of the horizon, and thus begin to probe the ''primordial dark ages'' between TeV and grand unified theory scale energies.
Comparative study on parameter estimation methods for attenuation relationships
Sedaghati, Farhad; Pezeshk, Shahram
2016-12-01
In this paper, the performance and advantages and disadvantages of various regression methods to derive coefficients of an attenuation relationship have been investigated. A database containing 350 records out of 85 earthquakes with moment magnitudes of 5-7.6 and Joyner-Boore distances up to 100 km in Europe and the Middle East has been considered. The functional form proposed by Ambraseys et al (2005 Bull. Earthq. Eng. 3 1-53) is selected to compare chosen regression methods. Statistical tests reveal that although the estimated parameters are different for each method, the overall results are very similar. In essence, the weighted least squares method and one-stage maximum likelihood perform better than the other considered regression methods. Moreover, using a blind weighting matrix or a weighting matrix related to the number of records would not yield in improving the performance of the results. Further, to obtain the true standard deviation, the pure error analysis is necessary. Assuming that the correlation between different records of a specific earthquake exists, the one-stage maximum likelihood considering the true variance acquired by the pure error analysis is the most preferred method to compute the coefficients of a ground motion predication equation.
Modeling internal ballistics of gas combustion guns.
Schorge, Volker; Grossjohann, Rico; Schönekess, Holger C; Herbst, Jörg; Bockholdt, Britta; Ekkernkamp, Axel; Frank, Matthias
2016-05-01
Potato guns are popular homemade guns which work on the principle of gas combustion. They are usually constructed for recreational rather than criminal purposes. Yet some serious injuries and fatalities due to these guns are reported. As information on the internal ballistics of homemade gas combustion-powered guns is scarce, it is the aim of this work to provide an experimental model of the internal ballistics of these devices and to investigate their basic physical parameters. A gas combustion gun was constructed with a steel tube as the main component. Gas/air mixtures of acetylene, hydrogen, and ethylene were used as propellants for discharging a 46-mm caliber test projectile. Gas pressure in the combustion chamber was captured with a piezoelectric pressure sensor. Projectile velocity was measured with a ballistic speed measurement system. The maximum gas pressure, the maximum rate of pressure rise, the time parameters of the pressure curve, and the velocity and path of the projectile through the barrel as a function of time were determined according to the pressure-time curve. The maximum gas pressure was measured to be between 1.4 bar (ethylene) and 4.5 bar (acetylene). The highest maximum rate of pressure rise was determined for hydrogen at (dp/dt)max = 607 bar/s. The muzzle energy was calculated to be between 67 J (ethylene) and 204 J (acetylene). To conclude, this work provides basic information on the internal ballistics of homemade gas combustion guns. The risk of injury to the operator or bystanders is high, because accidental explosions of the gun due to the high-pressure rise during combustion of the gas/air mixture may occur.
Automated Modal Parameter Estimation of Civil Engineering Structures
DEFF Research Database (Denmark)
Andersen, Palle; Brincker, Rune; Goursat, Maurice
In this paper the problems of doing automatic modal parameter extraction of ambient excited civil engineering structures is considered. Two different approaches for obtaining the modal parameters automatically are presented: The Frequency Domain Decomposition (FDD) technique and a correlation...
Fitzgerald, R. H.; Tsunematsu, K.; Kennedy, B. M.; Breard, E. C. P.; Lube, G.; Wilson, T. M.; Jolly, A. D.; Pawson, J.; Rosenberg, M. D.; Cronin, S. J.
2014-10-01
On 6 August, 2012, Upper Te Maari Crater, Tongariro volcano, New Zealand, erupted for the first time in over one hundred years. Multiple vents were activated during the hydrothermal eruption, ejecting blocks up to 2.3 km and impacting ~ 2.6 km of the Tongariro Alpine Crossing (TAC) hiking track. Ballistic impact craters were mapped to calibrate a 3D ballistic trajectory model for the eruption. This was further used to inform future ballistic hazard. Orthophoto mapping revealed 3587 impact craters with a mean diameter of 2.4 m. However, field mapping of accessible regions indicated an average of at least four times more observable impact craters and a smaller mean crater diameter of 1.2 m. By combining the orthophoto and ground-truthed impact frequency and size distribution data, we estimate that approximately 13,200 ballistic projectiles were generated during the eruption. The 3D ballistic trajectory model and a series of inverse models were used to constrain the eruption directions, angles and velocities. When combined with eruption observations and geophysical observations, the model indicates that the blocks were ejected in five variously directed eruption pulses, in total lasting 19 s. The model successfully reproduced the mapped impact distribution using a mean initial particle velocity of 200 m/s with an accompanying average gas flow velocity over a 400 m radius of 150 m/s. We apply the calibrated model to assess ballistic hazard from the August eruption along the TAC. By taking the field mapped spatial density of impacts and an assumption that an average ballistic impact will cause serious injury or death (casualty) over an 8 m2 area, we estimate that the probability of casualty ranges from 1% to 16% along the affected track (assuming an eruption during the time of exposure). Future ballistic hazard and probabilities of casualty along the TAC are also assessed through application of the calibrated model. We model a magnitude larger eruption and illustrate
Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown
Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi
2014-01-01
When item parameter estimates are used to estimate the ability parameter in item response models, the standard error (SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the…
Single-Channel Blind Estimation of Reverberation Parameters
DEFF Research Database (Denmark)
Doire, C.S.J.; Brookes, M. D.; Naylor, P. A.
2015-01-01
The reverberation of an acoustic channel can be characterised by two frequency-dependent parameters: the reverberation time and the direct-to-reverberant energy ratio. This paper presents an algorithm for blindly determining these parameters from a single-channel speech signal. The algorithm uses...
Estimation of riverbank soil erodibility parameters using genetic ...
Indian Academy of Sciences (India)
Tapas Karmaker
2017-11-07
Nov 7, 2017 ... sion primarily depends on soil erodibility parameters and the developed shear stress. The erodibility parameters include the critical shear stress and the erodibility coeffi- cient. Critical shear stress can be defined as the limiting shear stress developed due to flowing water, if in excess, will lead to soil erosion ...
Estimating atmospheric parameters and reducing noise for multispectral imaging
Conger, James Lynn
2014-02-25
A method and system for estimating atmospheric radiance and transmittance. An atmospheric estimation system is divided into a first phase and a second phase. The first phase inputs an observed multispectral image and an initial estimate of the atmospheric radiance and transmittance for each spectral band and calculates the atmospheric radiance and transmittance for each spectral band, which can be used to generate a "corrected" multispectral image that is an estimate of the surface multispectral image. The second phase inputs the observed multispectral image and the surface multispectral image that was generated by the first phase and removes noise from the surface multispectral image by smoothing out change in average deviations of temperatures.
Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator
Pan, Xueping; Ju, Ping; Wu, Feng; Jin, Yuqing
2017-09-01
A new hierarchical parameter estimation method for doubly fed induction generator (DFIG) and drive train system in a wind turbine generator (WTG) is proposed in this paper. Firstly, the parameters of the DFIG and the drive train are estimated locally under different types of disturbances. Secondly, a coordination estimation method is further applied to identify the parameters of the DFIG and the drive train simultaneously with the purpose of attaining the global optimal estimation results. The main benefit of the proposed scheme is the improved estimation accuracy. Estimation results confirm the applicability of the proposed estimation technique.
Asymptotic Parameter Estimation for a Class of Linear Stochastic Systems Using Kalman-Bucy Filtering
Directory of Open Access Journals (Sweden)
Xiu Kan
2012-01-01
Full Text Available The asymptotic parameter estimation is investigated for a class of linear stochastic systems with unknown parameter θ:dXt=(θα(t+β(tXtdt+σ(tdWt. Continuous-time Kalman-Bucy linear filtering theory is first used to estimate the unknown parameter θ based on Bayesian analysis. Then, some sufficient conditions on coefficients are given to analyze the asymptotic convergence of the estimator. Finally, the strong consistent property of the estimator is discussed by comparison theorem.
Estimation of the petrophysical parameters of sediments from Chad ...
African Journals Online (AJOL)
Porosity was estimated from three methods, and polynomial trends having fits ranging between 0.0604 and 0.478 describe depth - porosity variations. Interpretation of the trends revealed lithology trend that agree with the trends of shaliness. Estimates of average effective porosities of formations favorably compared with ...
Simultaneous estimation of QTL parameters for mapping multiple traits
Indian Academy of Sciences (India)
Z
In the proposed algorithm, the QTL positions have closed estimating formulas, and ...... It is clear to see that the estimates obtained by the MT-MIM-NEW are very close to the corresponding true values of .... (A201207), the Science and Technology Innovation Team in Higher Education Institutions of. Heilongjiang Province ...
Robust Speed and Parameter Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.; Vadstrup, P.
1995-01-01
This paper presents a Model Reference Adaptive System (MRAS) for the estimation of the induction motor speed, based on measured terminal voltages and currents.......This paper presents a Model Reference Adaptive System (MRAS) for the estimation of the induction motor speed, based on measured terminal voltages and currents....
minimum variance estimation of yield parameters of rubber tree
African Journals Online (AJOL)
2013-03-01
Mar 1, 2013 ... year. Kalman filter, a flexible statistical estimator, is used to combine the inexact prediction of the rubber production with an equally inexact rubber yield, tree ... tapping system measurements to obtain an optimal estimate of one year ahead rubber production. ...... tation management prevision gap of 55%.
ASTROPHYSICAL PRIOR INFORMATION AND GRAVITATIONAL-WAVE PARAMETER ESTIMATION
Energy Technology Data Exchange (ETDEWEB)
Pankow, Chris; Sampson, Laura; Perri, Leah; Chase, Eve; Coughlin, Scott; Zevin, Michael; Kalogera, Vassiliki [Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) and Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208 (United States)
2017-01-10
The detection of electromagnetic counterparts to gravitational waves (GWs) has great promise for the investigation of many scientific questions. While it is well known that certain orientation parameters can reduce uncertainty in other related parameters, it was also hoped that the detection of an electromagnetic signal in conjunction with a GW could augment the measurement precision of the mass and spin from the gravitational signal itself. That is, knowledge of the sky location, inclination, and redshift of a binary could break degeneracies between these extrinsic, coordinate-dependent parameters and the physical parameters that are intrinsic to the binary. In this paper, we investigate this issue by assuming perfect knowledge of extrinsic parameters, and assessing the maximal impact of this knowledge on our ability to extract intrinsic parameters. We recover similar gains in extrinsic recovery to earlier work; however, we find only modest improvements in a few intrinsic parameters—namely the primary component’s spin. We thus conclude that, even in the best case, the use of additional information from electromagnetic observations does not improve the measurement of the intrinsic parameters significantly.
Energy Technology Data Exchange (ETDEWEB)
Meliopoulos, Sakis [Georgia Inst. of Technology, Atlanta, GA (United States); Cokkinides, George [Georgia Inst. of Technology, Atlanta, GA (United States); Fardanesh, Bruce [New York Power Authority, NY (United States); Hedrington, Clinton [U.S. Virgin Islands Water and Power Authority (WAPA), St. Croix (U.S. Virgin Islands)
2013-12-31
This is the final report for this project that was performed in the period: October1, 2009 to June 30, 2013. In this project, a fully distributed high-fidelity dynamic state estimator (DSE) that continuously tracks the real time dynamic model of a wide area system with update rates better than 60 times per second is achieved. The proposed technology is based on GPS-synchronized measurements but also utilizes data from all available Intelligent Electronic Devices in the system (numerical relays, digital fault recorders, digital meters, etc.). The distributed state estimator provides the real time model of the system not only the voltage phasors. The proposed system provides the infrastructure for a variety of applications and two very important applications (a) a high fidelity generating unit parameters estimation and (b) an energy function based transient stability monitoring of a wide area electric power system with predictive capability. Also the dynamic distributed state estimation results are stored (the storage scheme includes data and coincidental model) enabling an automatic reconstruction and “play back” of a system wide disturbance. This approach enables complete play back capability with fidelity equal to that of real time with the advantage of “playing back” at a user selected speed. The proposed technologies were developed and tested in the lab during the first 18 months of the project and then demonstrated on two actual systems, the USVI Water and Power Administration system and the New York Power Authority’s Blenheim-Gilboa pumped hydro plant in the last 18 months of the project. The four main thrusts of this project, mentioned above, are extremely important to the industry. The DSE with the achieved update rates (more than 60 times per second) provides a superior solution to the “grid visibility” question. The generator parameter identification method fills an important and practical need of the industry. The “energy function” based
Improved parameter estimation for hydrological models using weighted object functions
Stein, A.; Zaadnoordijk, W.J.
1999-01-01
This paper discusses the sensitivity of calibration of hydrological model parameters to different objective functions. Several functions are defined with weights depending upon the hydrological background. These are compared with an objective function based upon kriging. Calibration is applied to
Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints.
Salari, Autoosa; Navarro, Marco A; Milescu, Mirela; Milescu, Lorin S
2018-02-05
To understand how ion channels and other proteins function at the molecular and cellular levels, one must decrypt their kinetic mechanisms. Sophisticated algorithms have been developed that can be used to extract kinetic parameters from a variety of experimental data types. However, formulating models that not only explain new data, but are also consistent with existing knowledge, remains a challenge. Here, we present a two-part study describing a mathematical and computational formalism that can be used to enforce prior knowledge into the model using constraints. In this first part, we focus on constraints that enforce explicit linear relationships involving rate constants or other model parameters. We develop a simple, linear algebra-based transformation that can be applied to enforce many types of model properties and assumptions, such as microscopic reversibility, allosteric gating, and equality and inequality parameter relationships. This transformation converts the set of linearly interdependent model parameters into a reduced set of independent parameters, which can be passed to an automated search engine for model optimization. In the companion article, we introduce a complementary method that can be used to enforce arbitrary parameter relationships and any constraints that quantify the behavior of the model under certain conditions. The procedures described in this study can, in principle, be coupled to any of the existing methods for solving molecular kinetics for ion channels or other proteins. These concepts can be used not only to enforce existing knowledge but also to formulate and test new hypotheses. © 2018 Salari et al.
Stellar atmospheric parameter estimation using Gaussian process regression
Bu, Yude; Pan, Jingchang
2015-02-01
As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.
Retrospective forecast of ETAS model with daily parameters estimate
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Dynamic ventilation scintigraphy: a comparison of parameter estimation gating models
International Nuclear Information System (INIS)
Hack, S.N.; Paoni, R.A.; Stratton, H.; Valvano, M.; Line, B.R.; Cooper, J.A.
1988-01-01
Two procedures for providing the synchronization of ventilation scintigraphic data to create dynamic displays of the pulmonary cycle are described and compared. These techniques are based on estimating instantaneous lung volume by pneumotachometry and by scintigraphy. Twenty-three patients were studied by these two techniques. The results indicate that the estimation of the times of end-inspiration and end-expiration are equivalent by the two techniques but the morphologies of the two estimated time-volume waveforms are not equivalent. Ventilation cinescintigraphy based on time division gating but not on isovolume division gating can be equivalently generated from list mode acquired data by employing either technique described
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... for degradation modeling and failure criteria determination. The time dependent accumulated damage is assumed linearly proportional to the time dependent degradation level. It is observed that the deterministic accumulated damage at the level of unity closely estimates the characteristic fatigue life of Weibull...
Application of Parameter Estimation for Diffusions and Mixture Models
DEFF Research Database (Denmark)
Nolsøe, Kim
error models. This is obtained by constructing an estimating function through projections of some chosen function of Yti+1 onto functions of previous observations Yti ; : : : ; Yt0 . The process of interest Xti+1 is partially observed through a measurement equation Yti+1 = h(Xti+1)+ noice, where h......(:) is restricted to be a polynomial. Through a simulation study we compare for the CIR process the obtained estimator with an estimator derived from utilizing the extended Kalman filter. The simulation study shows that the two estimation methods perform equally well.......The first part of this thesis proposes a method to determine the preferred number of structures, their proportions and the corresponding geometrical shapes of an m-membered ring molecule. This is obtained by formulating a statistical model for the data and constructing an algorithm which samples...
Genetic parameter estimation of 16-month live weight and ...
African Journals Online (AJOL)
Direct heritability estimates (h²a) for 16-month live weight (LW) and objectively measured wool traits ranged from 0.20 for staple strength (SS) to 0.68 for fibre diameter (FD). Maternal heritability estimates ranged from 0.05 for LW and FD, to 0.10 for clean fleece weight (CFW). The proportion of the total phenotypic variance ...
Estimation of genetic and phenotypic parameters for sow ...
African Journals Online (AJOL)
LBWT), 21-day litter size (D21LS) and 21-day litter weight (D21LWT). Estimates of heritability for these traits were 0.07 ± 0.01, 0.11 ± 0.01, 0.03 ± 0.01 and 0.06 ± 0.01, respectively. The respective repeatability estimates for the traits were 0.15 ...
Empirical estimation of school siting parameter towards improving children's safety
Aziz, I. S.; Yusoff, Z. M.; Rasam, A. R. A.; Rahman, A. N. N. A.; Omar, D.
2014-02-01
Distance from school to home is a key determination in ensuring the safety of hildren. School siting parameters are made to make sure that a particular school is located in a safe environment. School siting parameters are made by Department of Town and Country Planning Malaysia (DTCP) and latest review was on June 2012. These school siting parameters are crucially important as they can affect the safety, school reputation, and not to mention the perception of the pupil and parents of the school. There have been many studies to review school siting parameters since these change in conjunction with this ever-changing world. In this study, the focus is the impact of school siting parameter on people with low income that live in the urban area, specifically in Johor Bahru, Malaysia. In achieving that, this study will use two methods which are on site and off site. The on site method is to give questionnaires to people and off site is to use Geographic Information System (GIS) and Statistical Product and Service Solutions (SPSS), to analyse the results obtained from the questionnaire. The output is a maps of suitable safe distance from school to house. The results of this study will be useful to people with low income as their children tend to walk to school rather than use transportation.
A Simple Method for Estimation of Parameters in First order Systems
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Miklos, Robert
2014-01-01
A simple method for estimation of parameters in first order systems with time delays is presented in this paper. The parameter estimation approach is based on a step response for the open loop system. It is shown that the estimation method does not require a complete step response, only a part of...
Efficient estimates of cochlear hearing loss parameters in individual listeners
DEFF Research Database (Denmark)
Fereczkowski, Michal; Jepsen, Morten Løve; Dau, Torsten
2013-01-01
It has been suggested that the level corresponding to the knee-point of the basilar membrane (BM) input/output (I/O) function can be used to estimate the amount of inner- and outer hair-cell loss (IHL, OHL) in listeners with a moderate cochlear hearing impairment Plack et al. (2004). According...... to Jepsen and Dau (2011) IHL + OHL = HLT [dB], where HLT stands for total hearing loss. Hence having estimates of the total hearing loss and OHC loss, one can estimate the IHL. In the present study, results from forward masking experiments based on temporal masking curves (TMC; Nelson et al., 2001...... estimates of the knee-point level. Further, it is explored whether it is possible to estimate the compression ratio using only on-frequency TMCs. 10 normal-hearing and 10 hearing-impaired listeners (with mild-to-moderate sensorineural hearing loss) were tested at 1, 2 and 4 kHz. The results showed...
Lamah, Léopold; Keita, Damany; Marie Camara, Ibrahima; Lamine Bah, Mohamed; Sory, Sidimé; Diallo, Mamadou Moustapha
2017-01-01
The objective of our study was to report the management and follow-up of a particular case of ballistic trauma and to do the literature review. Observation: A 35-year-old patient, a trader who was the victim of a firearm accident under not very clear circumstances. He was admitted to the emergency department after 3 hours. Clinically, the patient had significant bleeding in the arm and was in a state of clouding of consciousness. We could notice on the right arm, a posterior large transfixing wound of 1 cm and a 6 cm one on the antero-internal side. The limb was cold with a small and thready pulse. Sensitivity was decreased in the radial nerve area. The radiograph showed bone comminution from the middle 1/3 to the superior 1/3 of the humeral diaphysis. The treatment was orthopedic (after debridement) by scapula-brachio-ante-brachiopalmar plaster splint with thoracic strap. The wound healed in 46 days and the patient resumed his activities after 11 months and 2 weeks. Conclusion: The authors presented the value of using the scapulo-brachio-palmar plaster splints with thoracic strap in some severe upper limb trauma in the absence of the external fixator. PMID:28567155
An Introduction to Goodness of Fit for PMU Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Riepnieks, Artis; Kirkham, Harold
2017-10-01
New results of measurements of phasor-like signals are presented based on our previous work on the topic. In this document an improved estimation method is described. The algorithm (which is realized in MATLAB software) is discussed. We examine the effect of noisy and distorted signals on the Goodness of Fit metric. The estimation method is shown to be performing very well with clean data and with a measurement window as short as a half a cycle and as few as 5 samples per cycle. The Goodness of Fit decreases predictably with added phase noise, and seems to be acceptable even with visible distortion in the signal. While the exact results we obtain are specific to our method of estimation, the Goodness of Fit method could be implemented in any phasor measurement unit.
A general method of estimating stellar astrophysical parameters from photometry
Belikov, A. N.; Roeser, S.
2008-01-01
Context. Applying photometric catalogs to the study of the population of the Galaxy is obscured by the impossibility to map directly photometric colors into astrophysical parameters. Most of all-sky catalogs like ASCC or 2MASS are based upon broad-band photometric systems, and the use of broad
Measurement Error Estimation for Capacitive Voltage Transformer by Insulation Parameters
Directory of Open Access Journals (Sweden)
Bin Chen
2017-03-01
Full Text Available Measurement errors of a capacitive voltage transformer (CVT are relevant to its equivalent parameters for which its capacitive divider contributes the most. In daily operation, dielectric aging, moisture, dielectric breakdown, etc., it will exert mixing effects on a capacitive divider’s insulation characteristics, leading to fluctuation in equivalent parameters which result in the measurement error. This paper proposes an equivalent circuit model to represent a CVT which incorporates insulation characteristics of a capacitive divider. After software simulation and laboratory experiments, the relationship between measurement errors and insulation parameters is obtained. It indicates that variation of insulation parameters in a CVT will cause a reasonable measurement error. From field tests and calculation, equivalent capacitance mainly affects magnitude error, while dielectric loss mainly affects phase error. As capacitance changes 0.2%, magnitude error can reach −0.2%. As dielectric loss factor changes 0.2%, phase error can reach 5′. An increase of equivalent capacitance and dielectric loss factor in the high-voltage capacitor will cause a positive real power measurement error. An increase of equivalent capacitance and dielectric loss factor in the low-voltage capacitor will cause a negative real power measurement error.
PhyloPars: estimation of missing parameter values using phylogeny.
Bruggeman, J.; Heringa, J.; Brandt, B.W.
2009-01-01
A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible.
LIKELIHOOD ESTIMATION OF PARAMETERS USING SIMULTANEOUSLY MONITORED PROCESSES
DEFF Research Database (Denmark)
Friis-Hansen, Peter; Ditlevsen, Ove Dalager
2004-01-01
The topic is maximum likelihood inference from several simultaneously monitored response processes of a structure to obtain knowledge about the parameters of other not monitored but important response processes when the structure is subject to some Gaussian load field in space and time. The consi....... The considered example is a ship sailing with a given speed through a Gaussian wave field....
A novel parameter estimation method for metal oxide surge arrester ...
Indian Academy of Sciences (India)
Accurate modelling and exact determination of Metal Oxide (MO) surge arrester parameters are very important for arrester allocation, insulation coordination studies and systems reliability calculations. In this paper, a new technique, which is the combination of Adaptive Particle Swarm Optimization (APSO) and Ant Colony ...
Comparison of parameter estimation algorithms in hydrological modelling
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2006-01-01
for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss...
The estimation of modified non-specific solubility parameter of ...
African Journals Online (AJOL)
For apolar liquids, the modified non-specific solubility parameter ' has been correlated with a form of the Lorentz-Lorenz refractive index function and the molar energy of vaporization per unit volume, and two expressions have been developed. Using one form of these expressions, and by introducing the contribution of ...
Unconstrained parameter estimation for assessment of dynamic cerebral autoregulation
International Nuclear Information System (INIS)
Chacón, M; Nuñez, N; Henríquez, C; Panerai, R B
2008-01-01
Measurement of dynamic cerebral autoregulation (CA), the transient response of cerebral blood flow (CBF) to changes in arterial blood pressure (ABP), has been performed with an index of autoregulation (ARI), related to the parameters of a second-order differential equation model, namely gain (K), damping factor (D) and time constant (T). Limitations of the ARI were addressed by increasing its numerical resolution and generalizing the parameter space. In 16 healthy subjects, recordings of ABP (Finapres) and CBF velocity (ultrasound Doppler) were performed at rest, before, during and after 5% CO 2 breathing, and for six repeated thigh cuff maneuvers. The unconstrained model produced lower predictive error (p < 0.001) than the original model. Unconstrained parameters (K'–D'–T') were significantly different from K–D–T but were still sensitive to different measurement conditions, such as the under-regulation induced by hypercapnia. The intra-subject variability of K' was significantly lower than that of the ARI and this parameter did not show the unexpected occurrences of zero values as observed with the ARI and the classical value of K. These results suggest that K' could be considered as a more stable and reliable index of dynamic autoregulation than ARI. Further studies are needed to validate this new index under different clinical conditions
Estimation of source parameters of Chamoli Earthquake, India
Indian Academy of Sciences (India)
The devastating earthquake (mb = 6.6) at Chamoli, Garhwal Himalaya, which occurred in the morning hours on 29th March 1999, was recorded on Delhi Strong Motion Accelerograph (DSMA) Network operated by the Central Building Research Institute, Roorkee. In this paper the source parameters of this event calculated ...
Estimation of source parameters of Chamoli Earthquake, India
Indian Academy of Sciences (India)
R. Narasimhan, Krishtel eMaging Solutions
The devastating earthquake (mb = 6.6) at Chamoli, Garhwal Himalaya, which occurred in the morning hours on 29th March 1999, was recorded on Delhi Strong Motion Accelerograph (DSMA). Network operated by the Central Building Research Institute, Roorkee. In this paper the source parameters of this event calculated ...
Phase noise effects on turbulent weather radar spectrum parameter estimation
Lee, Jonggil; Baxa, Ernest G., Jr.
1990-01-01
Accurate weather spectrum moment estimation is important in the use of weather radar for hazardous windshear detection. The effect of the stable local oscillator (STALO) instability (jitter) on the spectrum moment estimation algorithm is investigated. Uncertainty in the stable local oscillator will affect both the transmitted signal and the received signal since the STALO provides transmitted and reference carriers. The proposed approach models STALO phase jitter as it affects the complex autocorrelation of the radar return. The results can therefore by interpreted in terms of any source of system phase jitter for which the model is appropriate and, in particular, may be considered as a cumulative effect of all radar system sources.
Response-Based Estimation of Sea State Parameters
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam
2007-01-01
of measured ship responses. It is therefore interesting to investigate how the filtering aspect, introduced by FRF, affects the final outcome of the estimation procedures. The paper contains a study based on numerical generated time series, and the study shows that filtering has an influence...... calculated by a 3-D time domain code and by closed-form (analytical) expressions, respectively. Based on comparisons with wave radar measurements and satellite measurements it is seen that the wave estimations based on closedform expressions exhibit a reasonable energy content, but the distribution of energy...
In Situ Parameter Estimation of Synchronous Machines Using Genetic Algorithm Method
Directory of Open Access Journals (Sweden)
Gopalakrishnan Kalarikovilagam Srinivasan
2016-01-01
Full Text Available The paper presents an in situ parameter estimation method to determine the equivalent circuit parameters of the Synchronous Machines. The parameters of synchronous generator, both cylindrical rotor and salient pole rotor, are estimated based on the circuit model. Genetic algorithm based parameter estimation technique is adopted where only one set of in-situ measured load test data is used. Conventional methods viz., EMF, MMF, Potier triangle method uses rated voltage and rated current obtained from more than one operating condition to determine the parameters. However, Genetic Algorithm (GA based method uses the working voltage and load current of a single operating point obtained from in-situ measured load test data to estimate the parameters. The test results of the GA based parameter estimation method are found to be closer to direct load test results and better than conventional methods.
Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2010-01-01
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficult...
Simultaneous estimation of QTL parameters for mapping multiple traits
Indian Academy of Sciences (India)
Z
The analysis of quantitative trait loci (QTLs) aims at mapping and estimating the positions and effects of the genes that may affect ... Besides, Bayesian Mapping of quantitative trait loci for multiple traits was also considered by some researchers (Liu et .... random error of the ith trait value of the jth subject, with mean zero and.
Genetic parameter estimates in the South African Jersey breed
African Journals Online (AJOL)
1997-11-01
Nov 1, 1997 ... Discarding records that do not meet a minimum lactation length results in biased genetic evaluations (Norman et al., 1985), especially when the animal model is used in estimating breeding values for both male and female animals. Standard practice has been to project/extend lactations of different length to ...
Fish growth parameters are commonly estimated from the counts of ...
African Journals Online (AJOL)
spamer
total length of the fish midway during its time at liberty. If a Von Bertalanffy model describes galjoen growth. (Bennett and Griffiths 1986), then age is linearly related to the quantity ln(l∞ – TL). Therefore, the length midway between release and recovery for galjoen was estimated as follows: ln(L∞ – TLi rel) + ln(L∞ – TLi rec).
Estimation of earthquake source parameters in the Kachchh seismic ...
Indian Academy of Sciences (India)
Home; Journals; Journal of Earth System Science; Volume 126; Issue 5. Estimation of earthquake source ... SEISAN software has been used to locate the identified local earthquakes, which were recorded at least three or more stations of the Kachchh seismological network. Three component spectra of S-wave are being ...
On Structure, Family and Parameter Estimation of Hierarchical Archimedean Copulas
Czech Academy of Sciences Publication Activity Database
Górecki, J.; Hofert, M.; Holeňa, Martin
2017-01-01
Roč. 87, č. 17 (2017), s. 3261-3324 ISSN 0094-9655 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : copula estimation * goodness-of-fit * Hierarchical Archimedean copula * structure determination Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability Impact factor: 0.757, year: 2016
Estimation of genetic parameters for body weights of Kurdish sheep ...
African Journals Online (AJOL)
The data used in the present study was obtained from one research flock of Kurdistan Kurdish sheep, including 2476 animals, during the period of 1993 to 2003 (11 years). The log L estimated for all of the six models of this study showed that model 6 was the most appropriate for the analysis of data. Direct heritability values ...
Estimation of genetic and genotypic parameters for growth and ...
African Journals Online (AJOL)
Bekezela
2013-12-19
Dec 19, 2013 ... ratio (FCR), age at slaughter (AGES), lean percentage (LEAN), drip-free lean percentage (DLEAN), drip loss percentage (DRIP), carcass length (CRLTH), dressing percentage (DRESS), eye muscle area (AREA) and carcass fat (CFAT). Heritability estimates for growth traits ranged from 0.24 ± 0.03 for FCR ...
Parameters estimation for X-ray sources: positions
International Nuclear Information System (INIS)
Avni, Y.
1977-01-01
It is shown that the sizes of the positional error boxes for x-ray sources can be determined by using an estimation method which we have previously formulated generally and applied in spectral analyses. It is explained how this method can be used by scanning x-ray telescopes, by rotating modulation collimators, and by HEAO-A (author)
Estimates of selection parameters in protein mutants of spring barley
International Nuclear Information System (INIS)
Gaul, H.; Walther, H.; Seibold, K.H.; Brunner, H.; Mikaelsen, K.
1976-01-01
Detailed studies have been made with induced protein mutants regarding a possible genetic advance in selection including the estimation of the genetic variation and heritability coefficients. Estimates were obtained for protein content and protein yield. The variation of mutant lines in different environments was found to be many times as large as the variation of the line means. The detection of improved protein mutants seems therefore possible only in trials with more than one environment. The heritability of protein content and protein yield was estimated in different sets of environments and was found to be low. However, higher values were found with an increasing number of environments. At least four environments seem to be necessary to obtain reliable heritability estimates. The geneticall component of the variation between lines was significant for protein content in all environmental combinations. For protein yield some environmental combinations only showed significant differences. The expected genetic advance with one selection step was small for both protein traits. Genetically significant differences between protein micromutants give, however, a first indication that selection among protein mutants with small differences seems also possible. (author)
Revised models and genetic parameter estimates for production and ...
African Journals Online (AJOL)
The corresponding phenotypic, environmental and ewe permanent environmental correlations were all medium to high and estimated with a fair deal of accuracy according to low standard errors. The genetic relationship between weaning weight of the ewe and her lifetime reproduction (accumulated over four lambing ...
Parameter estimation of electricity spot models from futures prices
Aihara, ShinIchi; Bagchi, Arunabha; Imreizeeq, E.S.N.; Walter, E.
We consider a slight perturbation of the Schwartz-Smith model for the electricity futures prices and the resulting modified spot model. Using the martingale property of the modified price under the risk neutral measure, we derive the arbitrage free model for the spot and futures prices. We estimate
Estimating forest canopy fuel parameters using LIDAR data.
Hans-Erik Andersen; Robert J. McGaughey; Stephen E. Reutebuch
2005-01-01
Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to...
Online Parameter Estimation for a Centrifugal Decanter System
DEFF Research Database (Denmark)
Larsen, Jesper Abildgaard; Alstrøm, Preben
2014-01-01
In many processing plants decanter systems are used for separation of heterogenious mixtures, and even though they account for a large fraction of the energy consumption, most decanters just runs at a fixed setpoint. Here, multi model estimation is applied to a waste water treatment plant, and it...
The observer-based synchronization and parameter estimation of a ...
Indian Academy of Sciences (India)
Haipeng Su
2017-10-31
Oct 31, 2017 ... For exam- ple, the adaptive-impulsive synchronization and estima- tion of parameters of chaotic systems only by using discontinuous drive signals are .... c > 0,. (3) then system (2) will synchronize system (1) in the sense of limt→∞ e1 = limt→∞ e2 = limt→∞ e3 = 0, where e1 = ˆx1 − x1,e2 = ˆx2 − x2,e3 ...
Estimation of earthquake source parameters in the Kachchh seismic ...
Indian Academy of Sciences (India)
Durgada Nagamani
2017-07-25
Jul 25, 2017 ... Earthquake source parameters and crustal Q0 values for the 138 selected local events of (Mw:2.5−4.4) the 2001 Bhuj ... stress drop and static stress drop values range from 0.01 to 2.56 and 0.53 to 36.79 MPa, respectively. Our study ...... Lee W H K and Valdes C M 1985 HYP071PC: A personal computer ...
Kappa (κ): estimates, origins, and correlation to site characterisation parameters
Ktenidou, O. J.; Cotton, F.; Drouet, S.; Theodoulidis, N.; Chaljub, E. O.
2012-12-01
Knowledge of the acceleration spectral shape is important for various applications in engineering seismology. At high frequencies spectral amplitude drops rapidly. Anderson and Hough (1984) modelled this drop with the spectral decay factor κ, observing that, above a certain frequency, the acceleration spectrum decreases linearly in lin-log space. Thirty years later, and though the debate as to its source, path and site components is still on, κ constitutes a basic input parameter for the generation of stochastic ground motion and the calibration and adjustment of GMPEs. We study κ in the EUROSEISTEST site (http://euroseis.civil.auth.gr): a geologically complex site in Northern Greece, with a permanent strong motion array including surface and downhole stations. Site effects are of great importance here, and records are available from a variety of conditions ranging from soft soil to hard rock. We derive the site-related component of κ (κ0) at 16 stations following two approaches: 1. directly, measuring κ on individual S-wave spectra and regressing to zero distance as per Anderson and Hough (1984), following the procedure proposed by Ktenidou et al. (2012); 2. indirectly, deriving station-specific κ0 values from the high-frequency part of the station transfer functions, which are derived from a source-path-site inversion procedure proposed by Drouet et al. (2008). The agreement in κ0 is good. This supports the notion that κ0 is primarily a site effect, since in the second approach source and path effects are accounted for separately. The two approaches also yield similar results for anelastic attenuation within the frequency range studied: both show low regional Q, comparable to the results of crustal Q studies in Greece. We focus on κ0 values, which range from 0.02 s to 0.08 s depending on site type. As expected, κ0 increases for soft sites, but so does the scatter. Because κ0 is considered a site effect proxy, we examine its correlation with local site
PARAMETER-ESTIMATION FOR ARMA MODELS WITH INFINITE VARIANCE INNOVATIONS
MIKOSCH, T; GADRICH, T; KLUPPELBERG, C; ADLER, RJ
We consider a standard ARMA process of the form phi(B)X(t) = B(B)Z(t), where the innovations Z(t) belong to the domain of attraction of a stable law, so that neither the Z(t) nor the X(t) have a finite variance. Our aim is to estimate the coefficients of phi and theta. Since maximum likelihood
Heritability estimates of dental arch parameters in Lithuanian twins.
Švalkauskienė, Vilma; Šmigelskas, Kastytis; Šalomskienė, Loreta; Andriuškevičiūtė, Irena; Šalomskienė, Aurelija; Vasiliauskas, Arūnas; Šidlauskas, Antanas
2015-01-01
The genetic influence on dental arch morphology may be country-specific, thus it is reasonable to check the estimates of genetics across different populations. The purpose of this study was to evaluate the heredity of dental arch morphology in the sample of Lithuanian twins with accurate zygosity determination. The study sample consisted of digital dental models of 40 monozygotic (MZ) and 32 dizygotic (DZ) twin pairs. The estimates of heritability (h(2)) for dental arch breadth and length were calculated. All dental arch breadths and lengths were statistically significantly larger in men than in women. Arch length differences between genders were less expressed than largest breadth differences. In the upper jaw the largest genetic effect was found on the arch breadth between lateral incisors. The heritability of dental arch length demonstrated similar differences between upper and lower jaw with mandible dental arch length being more genetically determined. The largest genetic impact was found on the upper dental arch breadth between lateral incisors. Similar, but lower heritability is inherent for canines and first premolars of the upper jaw and first premolars of the lower jaw. It also can be noted, that arch breadths between posterior teeth show lower heritability estimates than between anterior teeth on both jaws. The dental arch in the upper jaw has more expressed genetic component than in the lower jaw.
Marker-based estimation of genetic parameters in genomics.
Directory of Open Access Journals (Sweden)
Zhiqiu Hu
Full Text Available Linear mixed model (LMM analysis has been recently used extensively for estimating additive genetic variances and narrow-sense heritability in many genomic studies. While the LMM analysis is computationally less intensive than the Bayesian algorithms, it remains infeasible for large-scale genomic data sets. In this paper, we advocate the use of a statistical procedure known as symmetric differences squared (SDS as it may serve as a viable alternative when the LMM methods have difficulty or fail to work with large datasets. The SDS procedure is a general and computationally simple method based only on the least squares regression analysis. We carry out computer simulations and empirical analyses to compare the SDS procedure with two commonly used LMM-based procedures. Our results show that the SDS method is not as good as the LMM methods for small data sets, but it becomes progressively better and can match well with the precision of estimation by the LMM methods for data sets with large sample sizes. Its major advantage is that with larger and larger samples, it continues to work with the increasing precision of estimation while the commonly used LMM methods are no longer able to work under our current typical computing capacity. Thus, these results suggest that the SDS method can serve as a viable alternative particularly when analyzing 'big' genomic data sets.
Directory of Open Access Journals (Sweden)
Azam Zaka
2014-10-01
Full Text Available This paper is concerned with the modifications of maximum likelihood, moments and percentile estimators of the two parameter Power function distribution. Sampling behavior of the estimators is indicated by Monte Carlo simulation. For some combinations of parameter values, some of the modified estimators appear better than the traditional maximum likelihood, moments and percentile estimators with respect to bias, mean square error and total deviation.
Estimation of the Randomized Complete Block Design Parameters with Fuzzy Goal Programming
Kula, Kamile; Apaydin, Ayşen
2011-01-01
Since goal programming was introduced by Charnes, Cooper and Ferguson (1955), goal programming has been widely studied and applied in various areas. Parameter estimation is quite important in many areas. Recently, many researches have been studied in fuzzy estimation. In this study, fuzzy goal programming was proposed by Hannan (1981) adapted to estimation of randomized complete block design parameters. Suggested fuzzy goal programming is used for estimation of randomized complete block desig...
Comparing Three Estimation Methods for the Three-Parameter Logistic IRT Model
Lamsal, Sunil
2015-01-01
Different estimation procedures have been developed for the unidimensional three-parameter item response theory (IRT) model. These techniques include the marginal maximum likelihood estimation, the fully Bayesian estimation using Markov chain Monte Carlo simulation techniques, and the Metropolis-Hastings Robbin-Monro estimation. With each…
Parameter estimation via conditional expectation: a Bayesian inversion
Matthies, Hermann G.
2016-08-11
When a mathematical or computational model is used to analyse some system, it is usual that some parameters resp. functions or fields in the model are not known, and hence uncertain. These parametric quantities are then identified by actual observations of the response of the real system. In a probabilistic setting, Bayes’s theory is the proper mathematical background for this identification process. The possibility of being able to compute a conditional expectation turns out to be crucial for this purpose. We show how this theoretical background can be used in an actual numerical procedure, and shortly discuss various numerical approximations.
Parameters influencing deposit estimation when using water sensitive papers
Directory of Open Access Journals (Sweden)
Emanuele Cerruto
2013-10-01
Full Text Available The aim of the study was to assess the possibility of using water sensitive papers (WSP to estimate the amount of deposit on the target when varying the spray characteristics. To identify the main quantities influencing the deposit, some simplifying hypotheses were applied to simulate WSP behaviour: log-normal distribution of the diameters of the drops and circular stains randomly placed on the images. A very large number (4704 of images of WSPs were produced by means of simulation. The images were obtained by simulating drops of different arithmetic mean diameter (40-300 μm, different coefficient of variation (0.1-1.5, and different percentage of covered surface (2-100%, not considering overlaps. These images were considered to be effective WSP images and then analysed using image processing software in order to measure the percentage of covered surface, the number of particles, and the area of each particle; the deposit was then calculated. These data were correlated with those used to produce the images, varying the spray characteristics. As far as the drop populations are concerned, a classification based on the volume median diameter only should be avoided, especially in case of high variability. This, in fact, results in classifying sprays with very low arithmetic mean diameter as extremely or ultra coarse. The WSP image analysis shows that the relation between simulated and computed percentage of covered surface is independent of the type of spray, whereas impact density and unitary deposit can be estimated from the computed percentage of covered surface only if the spray characteristics (arithmetic mean and coefficient of variation of the drop diameters are known. These data can be estimated by analysing the particles on the WSP images. The results of a validation test show good agreement between simulated and computed deposits, testified by a high (0.93 coefficient of determination.
Estimating canopy fuel parameters for Atlantic Coastal Plain forest types.
Energy Technology Data Exchange (ETDEWEB)
Parresol, Bernard, R.
2007-01-15
Abstract It is necessary to quantify forest canopy characteristics to assess crown fire hazard, prioritize treatment areas, and design treatments to reduce crown fire potential. A number of fire behavior models such as FARSITE, FIRETEC, and NEXUS require as input four particular canopy fuel parameters: 1) canopy cover, 2) stand height, 3) crown base height, and 4) canopy bulk density. These canopy characteristics must be mapped across the landscape at high spatial resolution to accurately simulate crown fire. Currently no models exist to forecast these four canopy parameters for forests of the Atlantic Coastal Plain, a region that supports millions of acres of loblolly, longleaf, and slash pine forests as well as pine-broadleaf forests and mixed species broadleaf forests. Many forest cover types are recognized, too many to efficiently model. For expediency, forests of the Savannah River Site are categorized as belonging to 1 of 7 broad forest type groups, based on composition: 1) loblolly pine, 2) longleaf pine, 3) slash pine, 4) pine-hardwood, 5) hardwood-pine, 6) hardwoods, and 7) cypress-tupelo. These 7 broad forest types typify forests of the Atlantic Coastal Plain region, from Maryland to Florida.
a Stochastic Realisation Algorithm with Application to Modal Parameter Estimation
Lardiès, Joseph
2001-03-01
This paper presents procedures that allow us to estimate the frequencies and damping coefficients of a vibrating system in time domain, from multioutput data only. The system is excited by a random force and a stochastic state space model is considered. Two algorithms are used to determine the transition matrix of the system which contains all modal information. The first algorithm uses a forward-innovation representation, and the transition matrix is connected to the controllability matrix of the system. The second algorithm uses a backward-innovation representation and the transition matrix is used to the observability matrix of the system. A numerical example is treated and compare these algorithms.
Estimation of atomic interaction parameters by quantum measurements
DEFF Research Database (Denmark)
Kiilerich, Alexander Holm; Mølmer, Klaus
Quantum systems, ranging from atomic systems to field modes and mechanical devices are useful precision probes for a variety of physical properties and phenomena. Measurements by which we extract information about the evolution of single quantum systems yield random results and cause a back actio...... strategies, we address the Fisher information and the Cramér-Rao sensitivity bound. We investigate monitoring by photon counting, homodyne detection and frequent projective measurements respectively, and exemplify by Rabi frequency estimation in a driven two-level system....
Estimations of parameters in Pareto reliability model in the presence of masked data
International Nuclear Information System (INIS)
Sarhan, Ammar M.
2003-01-01
Estimations of parameters included in the individual distributions of the life times of system components in a series system are considered in this paper based on masked system life test data. We consider a series system of two independent components each has a Pareto distributed lifetime. The maximum likelihood and Bayes estimators for the parameters and the values of the reliability of the system's components at a specific time are obtained. Symmetrical triangular prior distributions are assumed for the unknown parameters to be estimated in obtaining the Bayes estimators of these parameters. Large simulation studies are done in order: (i) explain how one can utilize the theoretical results obtained; (ii) compare the maximum likelihood and Bayes estimates obtained of the underlying parameters; and (iii) study the influence of the masking level and the sample size on the accuracy of the estimates obtained
Directory of Open Access Journals (Sweden)
Chuii Khim Chong
2012-06-01
Full Text Available This paper introduces an improved Differential Evolution algorithm (IDE which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE in this paper is a hybrid of a Differential Evolution algorithm (DE and a Kalman Filter (KF. The outcome of IDE is proven to be superior than Genetic Algorithm (GA and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms
Ballistic heat conduction and mass disorder in one dimension
International Nuclear Information System (INIS)
Ong, Zhun-Yong; Zhang, Gang
2014-01-01
It is well-known that in the disordered harmonic chain, heat conduction is subballistic and the thermal conductivity (κ) scales asymptotically as lim L→∞ κ∝L 0.5 where L is the chain length. However, using the nonequilibrium Green's function (NEGF) method and analytical modelling, we show that there exists a critical crossover length scale (L C ) below which ballistic heat conduction (κ∝L) can coexist with mass disorder. This ballistic-to-subballistic heat conduction crossover is connected to the exponential attenuation of the phonon transmittance function Ξ i.e. Ξ(ω, L) = exp[−L/λ(ω)], where λ is the frequency-dependent attenuation length. The crossover length can be determined from the minimum attenuation length, which depends on the maximum transmitted frequency. We numerically determine the dependence of the transmittance on frequency and mass composition as well as derive a closed form estimate, which agrees closely with the numerical results. For the length-dependent thermal conductance, we also derive a closed form expression which agrees closely with numerical results and reproduces the ballistic to subballistic thermal conduction crossover. This allows us to characterize the crossover in terms of changes in the length, mass composition and temperature dependence, and also to determine the conditions under which heat conduction enters the ballistic regime. We describe how the mass composition can be modified to increase ballistic heat conduction. (paper)
Comparison of parameter estimation algorithms in hydrological modelling
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2006-01-01
Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well...... for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss......-Marquardt-Levenberg algorithm (implemented in the PEST software), when applied to a steady-state and a transient groundwater model. The results show that PEST can have severe problems in locating the global optimum and in being trapped in local regions of attractions. The global SCE procedure is, in general, more effective...
Improving Distribution Resiliency with Microgrids and State and Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Tuffner, Francis K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Williams, Tess L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Schneider, Kevin P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Elizondo, Marcelo A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sun, Yannan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Liu, Chen-Ching [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Xu, Yin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Gourisetti, Sri Nikhil Gup [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2015-09-30
Modern society relies on low-cost reliable electrical power, both to maintain industry, as well as provide basic social services to the populace. When major disturbances occur, such as Hurricane Katrina or Hurricane Sandy, the nation’s electrical infrastructure can experience significant outages. To help prevent the spread of these outages, as well as facilitating faster restoration after an outage, various aspects of improving the resiliency of the power system are needed. Two such approaches are breaking the system into smaller microgrid sections, and to have improved insight into the operations to detect failures or mis-operations before they become critical. Breaking the system into smaller sections of microgrid islands, power can be maintained in smaller areas where distribution generation and energy storage resources are still available, but bulk power generation is no longer connected. Additionally, microgrid systems can maintain service to local pockets of customers when there has been extensive damage to the local distribution system. However, microgrids are grid connected a majority of the time and implementing and operating a microgrid is much different than when islanded. This report discusses work conducted by the Pacific Northwest National Laboratory that developed improvements for simulation tools to capture the characteristics of microgrids and how they can be used to develop new operational strategies. These operational strategies reduce the cost of microgrid operation and increase the reliability and resilience of the nation’s electricity infrastructure. In addition to the ability to break the system into microgrids, improved observability into the state of the distribution grid can make the power system more resilient. State estimation on the transmission system already provides great insight into grid operations and detecting abnormal conditions by leveraging existing measurements. These transmission-level approaches are expanded to using
Ballistic materials in MR imaging
International Nuclear Information System (INIS)
Smith, A.S.; Hurst, G.C.; Duerk, J.L.; Diaz, P.J.
1990-01-01
This paper reports on the most common ballistic materials available in the urban setting studied for deflection force, rotation, heating, and imaging artifact at 1.5 T to determine potential efficacy and safety for imaging patients with ballistic injuries. Twenty-eight missiles were tested, covering the range of bullet types and materials suggested by the Cleveland Police Department. Deflection force was measured by the New method. Rotation was studied by evaluating bullets in a 10% (W/W) ballistic gelating after 30 minutes with the long axis of the bullet placed parallel and perpendicular to the z axis. Heating was measured with alcohol thermometers imaged for 1 hour alternately with FESUM and spin-echo sequences (RF absorption w/Kg and 0.033 w/Kg). Image artifact evaluation of routine sequences was performed
Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation
DEFF Research Database (Denmark)
Fyhn, Karsten; Duarte, Marco F.; Jensen, Søren Holdt
2015-01-01
We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non...... to attain good estimation precision and keep the computational complexity low. Our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency-estimation problem followed by a super...... interpolation increases the estimation precision....
ESTIMATION OF HUMAN BODY SHAPE PARAMETERS USING MICROSOFT KINECTSENCOR
Directory of Open Access Journals (Sweden)
D. M. Vasilkov
2017-01-01
Full Text Available In the paper a human body shape estimation technology based on scan data acquired from sensor controller Microsoft Kinect is described. This device includes an RGB camera and a depth sensor that provides, for each pixel of the image,a distance from the camera focus to the object. A scan session produces a triangulated high-density surface noised with oscillations, isolated fragments and holes. When scanning a human, additional noise comes from garment folds and wrinkles. An algorithm of creating a sparse and regular 3D human body model (avatar free of these defects, which approximates shape, posture and basic metrics of the scanned body is proposed. This solution finds application in individual clothing industry and computer games, as well.
Estimation of CE–CVM energy parameters from miscibility gap data
Indian Academy of Sciences (India)
Home; Journals; Bulletin of Materials Science; Volume 28; Issue 2. Estimation of CE–CVM energy parameters from miscibility gap data. G Srinivasa Gupta G ... As a starting point, a method has been devised to estimate the values of energy parameters from consolute point (miscibility gap maximum) data. Empirical relations ...
Estimating the parameters of globular cluster M 30 (NGC 7099) from time-series photometry
DEFF Research Database (Denmark)
Kains, N.; Bramich, D.M.; Figuera Jaimes, R.
2013-01-01
values for the cluster's parameters. Methods. We used difference image analysis to reduce our data to obtain high-precision light curves of variable stars. We then estimated the cluster parameters by performing a Fourier decomposition of the light curves of RR Lyrae stars for which a good period estimate...
Item Parameter Estimation via Marginal Maximum Likelihood and an EM Algorithm: A Didactic.
Harwell, Michael R.; And Others
1988-01-01
The Bock and Aitkin Marginal Maximum Likelihood/EM (MML/EM) approach to item parameter estimation is an alternative to the classical joint maximum likelihood procedure of item response theory. This paper provides the essential mathematical details of a MML/EM solution and shows its use in obtaining consistent item parameter estimates. (TJH)
On the estimation of water pure compound parameters in association theories
DEFF Research Database (Denmark)
Grenner, Andreas; Kontogeorgis, Georgios; Michelsen, Michael Locht
2007-01-01
Determination of the appropriate number of association sites and estimation of parameters for association (SAFT-type) theories is not a trivial matter. Building further on a recently published manuscript by Clark et al., this work investigates aspects of the parameter estimation for water using two...... different association theories. Their performance for various properties as well as against the results presented earlier is demonstrated....
Adaptive neuro-fuzzy estimation of optimal lens system parameters
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
Search Space Calculation to Improve Parameter Estimation of Excitation Control Systems
Directory of Open Access Journals (Sweden)
Andrés J. Saavedra-Montes
2013-11-01
Full Text Available A method to calculate the search space for each parameter in an excitation control system is presented in this paper. The calculated search space is intended to reduce the number of parameter solution sets that can be found by an estimation algorithm, reducing its processing time. The method considers a synchronous generator time constant range between 4s and 10s, an excitation control system performance index, a controller design technique, and the excitation control system model structure. When the obtained search space is used to estimate the parameters, less processing time is used by the algorithm. Also the estimated parameters are closer to the reference ones.
Estimation of power feedback parameters of pulse reactor IBR-2M on transients
International Nuclear Information System (INIS)
Pepyolyshev, Yu.N.; Popov, A.K.
2013-01-01
Parameters of the IBR-2M reactor power feedback (PFB) on a model of the reactor dynamics by mathematical treatment of two registered transients are estimated. Frequency characteristics and the pulse transient characteristics corresponding to these PFB parameters are calculated. PFB parameters received thus can be considered as their express tentative estimation as real measurements in this case occupy no more than 30 minutes. Total PFB is negative at 1 and 2 MW. At the received estimations of PFB parameters in a self-regulation mode it is possible to consider the stability margins of the IBR-2M reactor satisfactory
Model-based parameter estimation using cardiovascular response to orthostatic stress
Heldt, T.; Shim, E. B.; Kamm, R. D.; Mark, R. G.
2001-01-01
This paper presents a cardiovascular model that is capable of simulating the short-term (response to gravitational stress and a gradient-based optimization method that allows for the automated estimation of model parameters from simulated or experimental data. We perform a sensitivity analysis of the transient heart rate response to determine which parameters of the model impact the heart rate dynamics significantly. We subsequently include only those parameters in the estimation routine that impact the transient heart rate dynamics substantially. We apply the estimation algorithm to both simulated and real data and showed that restriction to the 20 most important parameters does not impair our ability to match the data.
Control of an Aerial Manipulator using On-line Parameter Estimator for an Unknown Payload
Lee, Hyeonbeom; Kim, Suseong; Kim, H. Jin
2016-01-01
This paper presents an estimation and control algorithm for an aerial manipulator using a hexacopter with a 2-DOF robotic arm. The unknown parameters of a payload are estimated by an on-line estimator based on parametrization of the aerial manipulator dynamics. With the estimated mass information and the augmented passivity-based controller, the aerial manipulator can fly with the unknown object. Simulation for an aerial manipulator is performed to compare estimation performance between the p...
Parameter estimation in a simple stochastic differential equation for phytoplankton modelling
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Madsen, Henrik; Carstensen, Jacob
2011-01-01
The use of stochastic differential equations (SDEs) for simulation of aquatic ecosystems has attracted increasing attention in recent years. The SDE setting also provides the opportunity for statistical estimation of ecosystem parameters. We present an estimation procedure, based on Kalman...... filtering and likelihood estimation, which has proven useful in other fields of application. The estimation procedure is presented and the development from ordinary differential equations (ODEs) to SDEs is discussed with emphasis on autocorrelated residuals, commonly encountered with ODEs. The estimation...
Directory of Open Access Journals (Sweden)
Shaolong Chen
2016-01-01
Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.
Zayane, Chadia
2014-06-01
In this paper, we address a special case of state and parameter estimation, where the system can be put on a cascade form allowing to estimate the state components and the set of unknown parameters separately. Inspired by the nonlinear Balloon hemodynamic model for functional Magnetic Resonance Imaging problem, we propose a hierarchical approach. The system is divided into two subsystems in cascade. The state and input are first estimated from a noisy measured signal using an adaptive observer. The obtained input is then used to estimate the parameters of a linear system using the modulating functions method. Some numerical results are presented to illustrate the efficiency of the proposed method.
Mazas, Franck; Hamm, Luc; Garat, Philippe
2013-04-01
For a decade, the methodology for determining extreme values of environmental variables has converged towards the so-called GPD-Poisson model. The Peaks-Over-Threshold (POT) method is used for extracting extreme i.i.d. data from a time series. Excesses above a statistical meaningful threshold are fit to a 2-parameter Generalized Pareto Distribution (GPD), often with the Maximum Likelihood Estimator (MLE). Last, extreme values for desired return periods (quantiles) and confidence intervals are derived. In this approach, the GPD plays the role of an asymptotic approximation of the true law of excesses; hence it is valid when the threshold is high enough: a correct determination of the threshold value is thus crucial. Besides, a close examination of the sensitivity of the estimations with respect to the threshold shows that the use of 2-parameters (scale and shape) distributions yields unstable results. Considering the GPD (results are similar with other distributions), we explain in this presentation the need for a location parameter, distinct from the threshold, and the non-optimality of ML estimators. When the threshold is allowed to vary between two consecutive data values of the extreme sample, the ML estimates of the scale and shape parameters of the distribution do not remain constant. In other words, a slight translation of the sample may lead to a significant change in the estimated quantiles, although the same physical events are extrapolated. The likelihood of the 2-parameter GPD model is continuously increasing when the threshold tends to the first value of the sample of the excesses, with non-null derivatives. When extending the model to a 3-parameter GPD (adding a location parameter), it can be shown that the maximum likelihood is reached at the open upper bound of the interval of validity of this parameter, with non-null derivatives. Still, the asymptotic properties of the MLE require that the maximum be reached on an interior point of an open set. Thus
Automatic smoothing parameter selection in GAMLSS with an application to centile estimation.
Rigby, Robert A; Stasinopoulos, Dimitrios M
2014-08-01
A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each distribution parameter to estimate its smoothing parameters. This provides a fast method for estimating multiple smoothing parameters. The method is applied to centile estimation where all four parameters of a distribution for the response variable are modelled as smooth functions of a transformed explanatory variable x This allows smooth modelling of the location, scale, skewness and kurtosis parameters of the response variable distribution as functions of x. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Analysis of behind the armor ballistic trauma.
Wen, Yaoke; Xu, Cheng; Wang, Shu; Batra, R C
2015-05-01
The impact response of body armor composed of a ceramic plate with an ultrahigh molecular weight polyethylene (UHMWPE) fiber-reinforced composite and layers of UHMWPE fibers shielding a block of ballistic gelatin has been experimentally and numerically analyzed. It is a surrogate model for studying injuries to human torso caused by a bullet striking body protection armor placed on a person. Photographs taken with a high speed camera are used to determine deformations of the armor and the gelatin. The maximum depth of the temporary cavity formed in the ballistic gelatin and the peak pressure 40mm behind the center of the gelatin front face contacting the armor are found to be, respectively, ~34mm and ~15MPa. The Johnson-Holmquist material model has been used to simulate deformations and failure of the ceramic. The UHMWPE fiber-reinforced composite and the UHMWPE fiber layers are modeled as linear elastic orthotropic materials. The gelatin is modeled as a strain-rate dependent hyperelastic material. Values of material parameters are taken from the open literature. The computed evolution of the temporary cavity formed in the gelatin is found to qualitatively agree with that seen in experiments. Furthermore, the computed time histories of the average pressure at four points in the gelatin agree with the corresponding experimentally measured ones. The maximum pressure at a point and the depth of the temporary cavity formed in the gelatin can be taken as measures of the severity of the bodily injury caused by the impact; e.g. see the United States National Institute of Justice standard 0101.06-Ballistic Resistance of Body Armor. Copyright © 2015 Elsevier Ltd. All rights reserved.
Bayesian estimation of regularization parameters for deformable surface models
Energy Technology Data Exchange (ETDEWEB)
Cunningham, G.S.; Lehovich, A.; Hanson, K.M.
1999-02-20
In this article the authors build on their past attempts to reconstruct a 3D, time-varying bolus of radiotracer from first-pass data obtained by the dynamic SPECT imager, FASTSPECT, built by the University of Arizona. The object imaged is a CardioWest total artificial heart. The bolus is entirely contained in one ventricle and its associated inlet and outlet tubes. The model for the radiotracer distribution at a given time is a closed surface parameterized by 482 vertices that are connected to make 960 triangles, with nonuniform intensity variations of radiotracer allowed inside the surface on a voxel-to-voxel basis. The total curvature of the surface is minimized through the use of a weighted prior in the Bayesian framework, as is the weighted norm of the gradient of the voxellated grid. MAP estimates for the vertices, interior intensity voxels and background count level are produced. The strength of the priors, or hyperparameters, are determined by maximizing the probability of the data given the hyperparameters, called the evidence. The evidence is calculated by first assuming that the posterior is approximately normal in the values of the vertices and voxels, and then by evaluating the integral of the multi-dimensional normal distribution. This integral (which requires evaluating the determinant of a covariance matrix) is computed by applying a recent algorithm from Bai et. al. that calculates the needed determinant efficiently. They demonstrate that the radiotracer is highly inhomogeneous in early time frames, as suspected in earlier reconstruction attempts that assumed a uniform intensity of radiotracer within the closed surface, and that the optimal choice of hyperparameters is substantially different for different time frames.
DEFF Research Database (Denmark)
Ditlevsen, Susanne; Yip, Kay-Pong; Holstein-Rathlou, N.-H.
2005-01-01
A key parameter in the understanding of renal hemodynamics is the gain of the feedback function in the tubuloglomerular feedback mechanism. A dynamic model of autoregulation of renal blood flow and glomerular filtration rate has been extended to include a stochastic differential equations model...... analyzed, and the parameters characterizing the gain and the delay have been estimated. There was good agreement between the estimated values, and the values obtained for the same parameters in independent, previously published experiments....
Use of timesat to estimate phenological parameters in Northwestern Patagonia
Oddi, Facundo; Minotti, Priscilla; Ghermandi, Luciana; Lasaponara, Rosa
2015-04-01
Under a global change context, ecosystems are receiving high pressure and the ecology science play a key role for monitoring and assessment of natural resources. To achieve an effective resources management to develop an ecosystem functioning knowledge based on spatio-temporal perspective is useful. Satellite imagery periodically capture the spectral response of the earth and remote sensing have been widely utilized as classification and change detection tool making possible evaluate the intra and inter-annual plant dynamics. Vegetation spectral indices (e.g., NDVI) are particularly suitable to study spatio-temporal processes related to plant phenology and remote sensing specific software, such as TIMESAT, has been developed to carry out time series analysis of spectral indexes. We used TIMESAT software applied to series of 25 years of NDVI bi-monthly composites (240 images covering the period 1982-2006) from the NOAA-AVHRR sensor (8 x 8 km) to assessment plant pheonology over 900000 ha of shrubby-grasslands in the Northwestern of Patagonia, Argentina. The study area corresponds to a Mediterranean environment and is part of a gradient defined by a sharp drop west-east in the precipitation regime (600 mm to 280 mm). We fitted the temporal series of NDVI data to double logistic functions by least-squares methods evaluating three seasonality parameters: a) start of growing season, b) growing season length, c) NDVI seasonal integral. According to fitted models by TIMESAT, start average of growing season was the second half of September (± 10 days) with beginnings latest in the east (dryer areas). The average growing season length was 180 days (± 15 days) without a clear spatial trend. The NDVI seasonal integral showed a clear trend of decrease in west-east direction following the precipitation gradient. The temporal and spatial information allows revealing important patterns of ecological interest, which can be of great importance to environmental monitoring. In this
Shrivastava, Akash; Mohanty, A. R.
2018-03-01
This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise.
Estimation of delays and other parameters in nonlinear functional differential equations
Banks, H. T.; Lamm, P. K. D.
1983-01-01
A spline-based approximation scheme for nonlinear nonautonomous delay differential equations is discussed. Convergence results (using dissipative type estimates on the underlying nonlinear operators) are given in the context of parameter estimation problems which include estimation of multiple delays and initial data as well as the usual coefficient-type parameters. A brief summary of some of the related numerical findings is also given.
P. Pappas, George; A. Zohdy, Mohamed
2017-01-01
In this paper accurate estimation of parameters, higher order state space prediction methods and Extended Kalman filter (EKF) for modeling shadow power in wireless mobile communications are developed. Path-loss parameter estimation models are compared and evaluated. Shadow power estimation methods in wireless cellular communications are very important for use in power control of mobile device and base station. The methods are validated and compared to existing methods, Kalman Filter (KF) with...
Estimation of the Malthusian parameter in an stochastic epidemic model using martingale methods.
Lindenstrand, David; Svensson, Åke
2013-12-01
Data, on the number of infected, gathered from a large epidemic outbreak can be used to estimate parameters related to the strength and speed of the spread. The Malthusian parameter, which determines the initial growth rate of the epidemic is often of crucial interest. Using a simple epidemic SEIR model with known generation time distribution, we define and analyze an estimate, based on martingale methods. We derive asymptotic properties of the estimate and compare them to the results from simulations of the epidemic. The estimate uses all the information contained in the epidemic curve, in contrast to estimates which only use data from the start of the outbreak.
Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo.
Sharifi, Soroosh; Murthy, Sudhir; Takács, Imre; Massoudieh, Arash
2014-03-01
One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters. Copyright © 2013 Elsevier Ltd. All rights reserved.
BALLISTIC RESISTANT ARTICLES COMPRISING TAPES
VAN DER EEM, JORIS; HARINGS, JULES; JANSE, GERARDUS; TJADEN, HENDRIK
2015-01-01
The invention pertains to a ballistic-resistant moulded article comprising a compressed stack of sheets comprising reinforcing tapes having a tensile strength of at least 1.0 GPa, a tensile modulus of at least 40 GPa, and a tensile energy-to-break of at least 15 J/g, the direction of the tapes
Optimization theory for ballistic conversion
Xie, Yanbo; Versluis, Andreas Michel; van den Berg, Albert; Eijkel, Jan C.T.
2016-01-01
The growing demand of renewable energy stimulates the exploration of new materials and methods for clean energy. We recently demonstrated a high efficiency and power density energy conversion mechanism by using jetted charged microdroplets, termed as ballistic energy conversion. Hereby, we model and
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-01
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO-ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO-ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO-ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO-ACO is a very powerful tool for parameter estimation with high accuracy and low deviations.
An inverse method for estimating the electromechanical parameters of moving-coil loudspeakers.
Tsai, Yu-Ting; Wang, Chi-Chang; Huang, Jin H
2013-11-01
This article presents an inverse method for estimating the electromechanical parameters of a moving-coil loudspeaker with or without the eddy current and suspension creep effects. With known voice-coil displacement, voice-coil current, and stimulus signal as inputs, four calculation procedures for the direct problem, adjoint problem, sensitivity problem, and conjugate gradient method are involved in inversely solving the unknown electromechanical parameters. The proposed method features high efficiency in solving the direct problem through a hybrid spline difference method. It requires a small number of iterations for the computational algorithm, while offering excellent accuracy in parameter estimations. Analysis results demonstrate small differences between the estimated and measured electromechanical parameters under a variety of stimulus signals, excitation times, and initial guesses. The results are also confirmed by experimental measurements. These results indicate that the proposed method has a strong potential for estimating the electromechanical parameters of moving-coil loudspeakers.
Directory of Open Access Journals (Sweden)
Peter R. J. North
2013-03-01
Full Text Available Radiative transfer models predicting the bidirectional reflectance factor (BRF of leaf canopies are powerful tools that relate biophysical parameters such as leaf area index (LAI, fractional vegetation cover fV and the fraction of photosynthetically active radiation absorbed by the green parts of the vegetation canopy (fAPAR to remotely sensed reflectance data. One of the most successful approaches to biophysical parameter estimation is the inversion of detailed radiative transfer models through the construction of Look-Up Tables (LUTs. The solution of the inverse problem requires additional information on canopy structure, soil background and leaf properties, and the relationships between these parameters and the measured reflectance data are often nonlinear. The commonly used approach for optimization of a solution is based on minimization of the least squares estimate between model and observations (referred to as cost function or distance; here we will also use the terms “statistical distance” or “divergence” or “metric”, which are common in the statistical literature. This paper investigates how least-squares minimization and alternative distances affect the solution to the inverse problem. The paper provides a comprehensive list of different cost functions from the statistical literature, which can be divided into three major classes: information measures, M-estimates and minimum contrast methods. We found that, for the conditions investigated, Least Square Estimation (LSE is not an optimal statistical distance for the estimation of biophysical parameters. Our results indicate that other statistical distances, such as the two power measures, Hellinger, Pearson chi-squared measure, Arimoto and Koenker–Basset distances result in better estimates of biophysical parameters than LSE; in some cases the parameter estimation was improved by 15%.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
Energy Technology Data Exchange (ETDEWEB)
Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-11
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
International Nuclear Information System (INIS)
Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.
2016-01-01
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Directory of Open Access Journals (Sweden)
Ibrahim M. Safwat
2017-11-01
Full Text Available State-of-charge (SOC estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.
Ballistic Limit Equation for Single Wall Titanium
Ratliff, J. M.; Christiansen, Eric L.; Bryant, C.
2009-01-01
Hypervelocity impact tests and hydrocode simulations were used to determine the ballistic limit equation (BLE) for perforation of a titanium wall, as a function of wall thickness. Two titanium alloys were considered, and separate BLEs were derived for each. Tested wall thicknesses ranged from 0.5mm to 2.0mm. The single-wall damage equation of Cour-Palais [ref. 1] was used to analyze the Ti wall's shielding effectiveness. It was concluded that the Cour-Palais single-wall equation produced a non-conservative prediction of the ballistic limit for the Ti shield. The inaccurate prediction was not a particularly surprising result; the Cour-Palais single-wall BLE contains shield material properties as parameters, but it was formulated only from tests of different aluminum alloys. Single-wall Ti shield tests were run (thicknesses of 2.0 mm, 1.5 mm, 1.0 mm, and 0.5 mm) on Ti 15-3-3-3 material custom cut from rod stock. Hypervelocity impact (HVI) tests were used to establish the failure threshold empirically, using the additional constraint that the damage scales with impact energy, as was indicated by hydrocode simulations. The criterion for shield failure was defined as no detached spall from the shield back surface during HVI. Based on the test results, which confirmed an approximately energy-dependent shield effectiveness, the Cour-Palais equation was modified.
Nam, Kanghyun
2015-11-11
This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle's cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.
On Drift Parameter Estimation in Models with Fractional Brownian Motion by Discrete Observations
Directory of Open Access Journals (Sweden)
Yuliya Mishura
2014-06-01
Full Text Available We study a problem of an unknown drift parameter estimation in a stochastic differen- tial equation driven by fractional Brownian motion. We represent the likelihood ratio as a function of the observable process. The form of this representation is in general rather complicated. However, in the simplest case it can be simplified and we can discretize it to establish the a. s. convergence of the discretized version of maximum likelihood estimator to the true value of parameter. We also investigate a non-standard estimator of the drift parameter showing further its strong consistency.
Directory of Open Access Journals (Sweden)
S. Nie
2011-08-01
Full Text Available The performance of the ensemble Kalman filter (EnKF in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD scenarios, the narrow IPD (NIPD scenario and the wide IPD (WIPD scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method
Estimation of metallurgical parameters of flotation process from froth visual features
Directory of Open Access Journals (Sweden)
Mohammad Massinaei
2015-06-01
Full Text Available The estimation of metallurgical parameters of flotation process from froth visual features is the ultimate goal of a machine vision based control system. In this study, a batch flotation system was operated under different process conditions and metallurgical parameters and froth image data were determined simultaneously. Algorithms have been developed for measuring textural and physical froth features from the captured images. The correlation between the froth features and metallurgical parameters was successfully modeled, using artificial neural networks. It has been shown that the performance parameters of flotation process can be accurately estimated from the extracted image features, which is of great importance for developing automatic control systems.
Estimation of the Shape Parameter of Ged Distribution for a Small Sample Size
Directory of Open Access Journals (Sweden)
Purczyński Jan
2014-06-01
Full Text Available In this paper a new method of estimating the shape parameter of generalized error distribution (GED, called ‘approximated moment method’, was proposed. The following estimators were considered: the one obtained through the maximum likelihood method (MLM, approximated fast estimator (AFE, and approximated moment method (AMM. The quality of estimator was evaluated on the basis of the value of the relative mean square error. Computer simulations were conducted using random number generators for the following shape parameters: s = 0.5, s = 1.0 (Laplace distribution s = 2.0 (Gaussian distribution and s = 3.0.
Estimation of multi-frequency signal parameters by frequency domain non-linear least squares
Zhu, Li-Min; Li, Han-Xiong; Ding, Han
2005-09-01
This paper presents a frequency domain method for estimating the parameters of a multi-frequency signal from the discrete-time observations corrupted by additive noise. With two weak restrictions on the window function used, a concise non-linear least squares-based parameter estimation model, which exploits the joint information carried by the spectral samples nearby each spectrum peak, is established, and utilising its particular structure an efficient two-step iterative algorithm is developed to solve it. The derived analytical expressions of the estimator variances indicate that this approach has superior accuracy over other computationally efficient frequency domain estimation methods. Simulation results confirm the validity of the presented method.
Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm.
Bock, R. Darrell; Aitkin, Murray
1981-01-01
The practicality of using the EM algorithm for maximum likelihood estimation of item parameters in the marginal distribution is presented. The EM procedure is shown to apply to general item-response models. (Author/JKS)
Measurement-Based Transmission Line Parameter Estimation with Adaptive Data Selection Scheme
DEFF Research Database (Denmark)
Li, Changgang; Zhang, Yaping; Zhang, Hengxu
2017-01-01
Accurate parameters of transmission lines are critical for power system operation and control decision making. Transmission line parameter estimation based on measured data is an effective way to enhance the validity of the parameters. This paper proposes a multi-point transmission line parameter...... of the proposed model. Some 500kV transmission lines from a provincial power system of China are estimated to demonstrate the applicability of the presented model. The superiority of the proposed model over fixed data selection schemes is also verified....... estimation model with an adaptive data selection scheme based on measured data. Data selection scheme, defined with time window and number of data points, is introduced in the estimation model as additional variables to optimize. The data selection scheme is adaptively adjusted to minimize the relative...
Parameter Estimation of a Closed Loop Coupled Tank Time Varying System using Recursive Methods
International Nuclear Information System (INIS)
Basir, Siti Nora; Yussof, Hanafiah; Shamsuddin, Syamimi; Selamat, Hazlina; Zahari, Nur Ismarrubie
2013-01-01
This project investigates the direct identification of closed loop plant using discrete-time approach. The uses of Recursive Least Squares (RLS), Recursive Instrumental Variable (RIV) and Recursive Instrumental Variable with Centre-Of-Triangle (RIV + COT) in the parameter estimation of closed loop time varying system have been considered. The algorithms were applied in a coupled tank system that employs covariance resetting technique where the time of parameter changes occur is unknown. The performances of all the parameter estimation methods, RLS, RIV and RIV + COT were compared. The estimation of the system whose output was corrupted with white and coloured noises were investigated. Covariance resetting technique successfully executed when the parameters change. RIV + COT gives better estimates than RLS and RIV in terms of convergence and maximum overshoot
A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence
Grauer, Jared A.; Morelli, Eugene A.
2015-01-01
A new formulation of the filter-error method for estimating aerodynamic parameters in nonlinear aircraft dynamic models during turbulence was developed and demonstrated. The approach uses an estimate of the measurement noise covariance to identify the model parameters, their uncertainties, and the process noise covariance, in a relaxation method analogous to the output-error method. Prior information on the model parameters and uncertainties can be supplied, and a post-estimation correction to the uncertainty was included to account for colored residuals not considered in the theory. No tuning parameters, needing adjustment by the analyst, are used in the estimation. The method was demonstrated in simulation using the NASA Generic Transport Model, then applied to the subscale T-2 jet-engine transport aircraft flight. Modeling results in different levels of turbulence were compared with results from time-domain output error and frequency- domain equation error methods to demonstrate the effectiveness of the approach.
Methodology to estimate parameters of an excitation system based on experimental conditions
Energy Technology Data Exchange (ETDEWEB)
Saavedra-Montes, A.J. [Carrera 80 No 65-223, Bloque M8 oficina 113, Escuela de Mecatronica, Universidad Nacional de Colombia, Medellin (Colombia); Calle 13 No 100-00, Escuela de Ingenieria Electrica y Electronica, Universidad del Valle, Cali, Valle (Colombia); Ramirez-Scarpetta, J.M. [Calle 13 No 100-00, Escuela de Ingenieria Electrica y Electronica, Universidad del Valle, Cali, Valle (Colombia); Malik, O.P. [2500 University Drive N.W., Electrical and Computer Engineering Department, University of Calgary, Calgary, Alberta (Canada)
2011-01-15
A methodology to estimate the parameters of a potential-source controlled rectifier excitation system model is presented in this paper. The proposed parameter estimation methodology is based on the characteristics of the excitation system. A comparison of two pseudo random binary signals, two sampling periods for each one, and three estimation algorithms is also presented. Simulation results from an excitation control system model and experimental results from an excitation system of a power laboratory setup are obtained. To apply the proposed methodology, the excitation system parameters are identified at two different levels of the generator saturation curve. The results show that it is possible to estimate the parameters of the standard model of an excitation system, recording two signals and the system operating in closed loop with the generator. The normalized sum of squared error obtained with experimental data is below 10%, and with simulation data is below 5%. (author)
Online Estimation of Model Parameters of Lithium-Ion Battery Using the Cubature Kalman Filter
Tian, Yong; Yan, Rusheng; Tian, Jindong; Zhou, Shijie; Hu, Chao
2017-11-01
Online estimation of state variables, including state-of-charge (SOC), state-of-energy (SOE) and state-of-health (SOH) is greatly crucial for the operation safety of lithium-ion battery. In order to improve estimation accuracy of these state variables, a precise battery model needs to be established. As the lithium-ion battery is a nonlinear time-varying system, the model parameters significantly vary with many factors, such as ambient temperature, discharge rate and depth of discharge, etc. This paper presents an online estimation method of model parameters for lithium-ion battery based on the cubature Kalman filter. The commonly used first-order resistor-capacitor equivalent circuit model is selected as the battery model, based on which the model parameters are estimated online. Experimental results show that the presented method can accurately track the parameters variation at different scenarios.
A robust methodology for kinetic model parameter estimation for biocatalytic reactions
DEFF Research Database (Denmark)
Al-Haque, Naweed; Andrade Santacoloma, Paloma de Gracia; Lima Afonso Neto, Watson
2012-01-01
Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme-catalyzed reactions generally include several...... parameters, which are strongly correlated with each other. State-of-the-art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver-Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely...... lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches...
Parameter estimation of activated sludge process based on an improved cuckoo search algorithm.
Du, Xianjun; Wang, Junlu; Jegatheesan, Veeriah; Shi, Guohua
2018-02-01
It is essential to use appropriate values for kinetic parameters in activated sludge model when the model is applied for wastewater treatment processes under different environments. An improved cuckoo search (ICS) algorithm was proposed in this paper for the estimation of kinetic parameters used in Activated Sludge Model No. 1 (ASM1). ICS is tested for its speed and accuracy in reaching solution by searching global minima of six standard functions. Cyclical adjustment strategy was employed into the detected probability to increase searching ability. Meanwhile, the searching step was adaptively adjusted based on the optimal nest of the last generation and the current iteration numbers. Subsequently, ICS is used to estimate 7 sensitive parameters in ASM1 for practical applications. Field data are used to validate prediction accuracy of ASM1 with estimated parameters. Predicted results of the model are closer to the actual data with adjusted parameters. Copyright © 2017 Elsevier Ltd. All rights reserved.
A new method to estimate parameters of linear compartmental models using artificial neural networks
International Nuclear Information System (INIS)
Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.
1998-01-01
At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)
Directory of Open Access Journals (Sweden)
Shengyu eJiang
2016-02-01
Full Text Available Likert types of rating scales in which a respondent chooses a response from an ordered set of response options are used to measure a wide variety of psychological, educational, and medical outcome variables. The most appropriate item response theory model for analyzing and scoring these instruments when they provide scores on multiple scales is the multidimensional graded response model (MGRM. A simulation study was conducted to investigate the variables that might affect item parameter recovery for the MGRM. Data were generated based on different sample sizes, test lengths, and scale intercorrelations. Parameter estimates were obtained through the flexiMIRT software. The quality of parameter recovery was assessed by the correlation between true and estimated parameters as well as bias and root- mean-square-error. Results indicated that for the vast majority of cases studied a sample size of N = 500 provided accurate parameter estimates, except for tests with 240 items when 1,000 examinees were necessary to obtain accurate parameter estimates. Increasing sample size beyond N = 1,000 did not increase the accuracy of MGRM parameter estimates.
Luo, Rutao; Piovoso, Michael J.; Martinez-Picado, Javier; Zurakowski, Ryan
2012-01-01
Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3–5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients. PMID:22815727
Bayesian parameter estimation in dynamic population model via particle Markov chain Monte Carlo
Directory of Open Access Journals (Sweden)
Meng Gao
2012-12-01
Full Text Available In nature, population dynamics are subject to multiple sources of stochasticity. State-space models (SSMs provide an ideal framework for incorporating both environmental noises and measurement errors into dynamic population models. In this paper, we present a recently developed method, Particle Markov Chain Monte Carlo (Particle MCMC, for parameter estimation in nonlinear SSMs. We use one effective algorithm of Particle MCMC, Particle Gibbs sampling algorithm, to estimate the parameters of a state-space model of population dynamics. The posterior distributions of parameters are derived given the conjugate prior distribution. Numerical simulations showed that the model parameters can be accurately estimated, no matter the deterministic model is stable, periodic or chaotic. Moreover, we fit the model to 16 representative time series from Global Population Dynamics Database (GPDD. It is verified that the results of parameter and state estimation using Particle Gibbs sampling algorithm are satisfactory for a majority of time series. For other time series, the quality of parameter estimation can also be improved, if prior knowledge is constrained. In conclusion, Particle Gibbs sampling algorithm provides a new Bayesian parameter inference method for studying population dynamics.
Application of Joint Parameter Identification and State Estimation to a Fault-Tolerant Robot System
DEFF Research Database (Denmark)
Sun, Zhen; Yang, Zhenyu
2011-01-01
, it would further simplify the reconfigurable design task and possibly speed up the system recovery, if the system state information under the new operating circumstance can be available along with faulty parameter information. The joint parameter identification and state estimation using the combined......The joint parameter identification and state estimation technique is applied to develop a fault-tolerant space robot system. The potential faults in the considered system are abrupt parametric faults, which indicate that some system parameters will immediately deviate from their nominal values...... if a fault happens. The concerned system parameters consist of deterministic parts as well as those describing the stochastic features in the system. Due to the purpose for design of reconfigurable control, these deviated system parameters need to be identified as precisely and quickly as possible. Meanwhile...
Basin, M.; Maldonado, J. J.; Zendejo, O.
2016-07-01
This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.
Directory of Open Access Journals (Sweden)
Y. H. Lee
2006-12-01
Full Text Available In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.
Parameter Estimation and Prediction of a Nonlinear Storage Model: an algebraic approach
Doeswijk, T.G.; Keesman, K.J.
2005-01-01
Generally, parameters that are nonlinear in system models are estimated by nonlinear least-squares optimization algorithms. In this paper, if a nonlinear discrete-time model with a polynomial quotient structure in input, output, and parameters, a method is proposed to re-parameterize the model such
DEFF Research Database (Denmark)
Röttger, Richard; Kalaghatgi, Prabhav; Sun, Peng
2013-01-01
: all clustering tools need a density parameter that adjusts the number and size of the clusters. This parameter is crucial but hard to estimate without gold standard data at hand. Developing a gold standard, however, is a difficult and time consuming task. Having a reliable method for detecting...
Dynamics of a scrapie outbreak in a flock of Romanov sheep-estimation of transmission parameters
Hagenaars, T.H.J.; Donelly, C.A.; Ferguson, N.M.; Anderson, R.M.
2003-01-01
Knowledge of epidemiological mechanisms and parameters underlying scrapie transmission in sheep flocks remains very limited at present. Here we introduce a method for fitting stochastic transmission models to outbreak data to estimate bounds on key transmission parameters. We apply this method to
Book review: Estimation of parameters for animal populations: A primer for the rest of us
Post van der Burg, Max
2016-01-01
No abstract available.Estimation of Parameters for Animal Populations: A Primer for the Rest of Us. Larkin A. Powell and George A. Gale. 2015. Caught Napping Publications, Lincoln, Nebraska, USA. 239 pages. (http://larkinpowell.wixsite.com/larkinpowell/estimationof-parameters-for-animal-pop). ISBN: 978-329-06151-4.
The Chameleonic Behavior of Ionic Liquids and its Impact on the Solubility Parameters Estimation
DEFF Research Database (Denmark)
Batista, Marta; Neves, Catarina S; Carvalho, Pedro Jorge
2011-01-01
The possibility to develop a solubility parameter scale, with the purpose of predicting the performance and help the selection of ILs, is here evaluated. For the estimation of solubility parameters infinite dilution activity coefficient data is used. The results allowed the identification of a cu...
Ballistic Evaluation of 2060 Aluminum
2016-05-24
compared with other ballistic- grade AAs, namely AA2195 and AA2139. The results of these experiments were used to derive the acceptance tables for AA2060...contributions of the following people who made this work possible: program lead Brian Placzankis of ARL’s Coatings, Corrosion and Engineered Polymers Branch...properties as the AA2195 alloy and therefore became the basis for a fiscal year 2012 OSD-funded DAC program to fully validate and ultimately transition
Kohno, Iichiro; Nishigaki, Makoto; Takeshita, Yuji
1989-01-01
The numerical feasibility of determining soil water retention and hydraulic conductivity functions simultaneously from one-dimensional transient flow experiments in the laboratory by parameter estimation method is evaluated. Soil hydraulic properties are assumed to be represented by van Genuchten's closed-form expressions involving two unknown parameters: coefficients α and n . These parameters are evaluated by nonlinear least-squares fitting of predicted and observed pressure head with...
Zernin, M. V.; Mishin, A. V.; Rybkin, N. N.
2017-10-01
Expert evaluation of damage the connecting rod bearing shells UNP55-250 pump unit was performed. The finite elements method is used to calculate oil flow in the bearing clearance. Influence of operating defects on the parameters of hydrodynamics is also estimated. Calculations for various designs of connecting rod bearings are carried out and impact of various surface compliances is estimated.
A Note on Parameter Estimation for Lazarsfeld's Latent Class Model Using the EM Algorithm.
Everitt, B. S.
1984-01-01
Latent class analysis is formulated as a problem of estimating parameters in a finite mixture distribution. The EM algorithm is used to find the maximum likelihood estimates, and the case of categorical variables with more than two categories is considered. (Author)
Recursive Estimation of π-Line Parameters for Electric Power Distribution Grids
DEFF Research Database (Denmark)
Prostejovsky, Alexander; Gehrke, Oliver; Kosek, Anna Magdalena
2016-01-01
Electrical models of power distribution grids are used in applications such as state estimation and Optimal Power Flow (OPF), the reliability of which depends on the accuracy of the model. This work presents an approach for estimating distribution line parameters from Remote Terminal Unit (RTU...
In this paper, we present methods for estimating Freundlich isotherm fitting parameters (K and N) and their joint uncertainty, which have been implemented into the freeware software platforms R and WinBUGS. These estimates were determined by both Frequentist and Bayesian analyse...
A Fast Algorithm for Maximum Likelihood Estimation of Harmonic Chirp Parameters
DEFF Research Database (Denmark)
Jensen, Tobias Lindstrøm; Nielsen, Jesper Kjær; Jensen, Jesper Rindom
2017-01-01
. A statistically efficient estimator for extracting the parameters of the harmonic chirp model in additive white Gaussian noise is the maximum likelihood (ML) estimator which recently has been demonstrated to be robust to noise and accurate --- even when the model order is unknown. The main drawback of the ML...
Parametric estimation of R&M parameters during the conceptual design of space vehicles
Ebeling, Charles E.
1992-01-01
Reliability and maintainability parameters of proposed space vehicles are estimated based on a comparability analysis of similar aircraft subsystems. Using multiple regression techniques, parametric equations are developed for each subsystem to predict mean flying hours between failure as a function of vehicle design and performance specifications. These estimates are then adjusted to account for reliability growth, environmental differences, and new technologies. Overall vehicle mission reliability may then be computed from subsystem reliability estimates.
The performance of simulated annealing in parameter estimation for vapor-liquid equilibrium modeling
Directory of Open Access Journals (Sweden)
A. Bonilla-Petriciolet
2007-03-01
Full Text Available In this paper we report the application and evaluation of the simulated annealing (SA optimization method in parameter estimation for vapor-liquid equilibrium (VLE modeling. We tested this optimization method using the classical least squares and error-in-variable approaches. The reliability and efficiency of the data-fitting procedure are also considered using different values for algorithm parameters of the SA method. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. However, in difficult problems it still can converge to local optimums of the objective function.
A Consistent Methodology Based Parameter Estimation for a Lactic Acid Bacteria Fermentation Model
DEFF Research Database (Denmark)
Spann, Robert; Roca, Christophe; Kold, David
2017-01-01
Lactic acid bacteria are used in many industrial applications, e.g. as starter cultures in the dairy industry or as probiotics, and research on their cell production is highly required. A first principles kinetic model was developed to describe and understand the biological, physical, and chemical...... mechanisms in a lactic acid bacteria fermentation. We present here a consistent approach for a methodology based parameter estimation for a lactic acid fermentation. In the beginning, just an initial knowledge based guess of parameters was available and an initial parameter estimation of the complete set...
Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation
2016-08-29
In this note, the joint state and parameters estimation problem for nonlinear multi-input multi-output descriptor systems is considered. Asymptotic convergence of the adaptive descriptor observer is established by a sufficient set of linear matrix inequalities for the noise-free systems. The noise corrupted systems are also considered and it is shown that the state and parameters estimation errors are bounded for bounded noises. In addition, if the noises are bounded and have zero mean, then the estimation errors asymptotically converge to zero in the mean. The performance of the proposed adaptive observer is illustrated by a numerical example.
Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes
Kandidayeni, M.; Macias, A.; Amamou, A. A.; Boulon, L.; Kelouwani, S.; Chaoui, H.
2018-03-01
Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et al. and Amphlett et al. Ultimately, the achieved results and future challenges are discussed.
A framework for scalable parameter estimation of gene circuit models using structural information
Kuwahara, Hiroyuki
2013-06-21
Motivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. The Author 2013.
Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics
Kukreja, Sunil L.; Boyle, Richard D.
2014-01-01
Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.
A new M w estimation parameter for use in earthquake early warning systems
Wang, Zijun; Zhao, Boming
2018-01-01
We propose a method that employs the squared displacement integral ( ID2) to estimate earthquake magnitudes in real time for use in earthquake early warning (EEW) systems. Moreover, using τ c and P d for comparison, we establish formulas for estimating the moment magnitudes of these three parameters based on the selected aftershocks (4.0 ≤ M s ≤ 6.5) of the 2008 Wenchuan earthquake. In this comparison, the proposed ID2 method displays the highest accuracy. Furthermore, we investigate the applicability of the initial parameters to large earthquakes by estimating the magnitude of the Wenchuan M s 8.0 mainshock using a 3-s time window. Although these three parameters all display problems with saturation, the proposed ID2 parameter is relatively accurate. The evolutionary estimation of ID2 as a function of the time window shows that the estimation equation established with ID2 Ref determined from the first 8-s of P wave data can be directly applicable to predicate the magnitudes of 8.0. Therefore, the proposed ID2 parameter provides a robust estimator of earthquake moment magnitudes and can be used for EEW purposes.
Energy Technology Data Exchange (ETDEWEB)
Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA
2016-10-01
The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.
PARAMETER ESTIMATION AND MODEL SELECTION FOR INDOOR ENVIRONMENTS BASED ON SPARSE OBSERVATIONS
Directory of Open Access Journals (Sweden)
Y. Dehbi
2017-09-01
Full Text Available This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.
Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations
Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.
2017-09-01
This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.
Zhan, Hanyu; Voelz, David G.
2016-12-01
The polarimetric bidirectional reflectance distribution function (pBRDF) describes the relationships between incident and scattered Stokes parameters, but the familiar surface-only microfacet pBRDF cannot capture diffuse scattering contributions and depolarization phenomena. We propose a modified pBRDF model with a diffuse scattering component developed from the Kubelka-Munk and Le Hors et al. theories, and apply it in the development of a method to jointly estimate refractive index, slope variance, and diffuse scattering parameters from a series of Stokes parameter measurements of a surface. An application of the model and estimation approach to experimental data published by Priest and Meier shows improved correspondence with measurements of normalized Mueller matrix elements. By converting the Stokes/Mueller calculus formulation of the model to a degree of polarization (DOP) description, the estimation results of the parameters from measured DOP values are found to be consistent with a previous DOP model and results.
See, J. J.; Jamaian, S. S.; Salleh, R. M.; Nor, M. E.; Aman, F.
2018-04-01
This research aims to estimate the parameters of Monod model of microalgae Botryococcus Braunii sp growth by the Least-Squares method. Monod equation is a non-linear equation which can be transformed into a linear equation form and it is solved by implementing the Least-Squares linear regression method. Meanwhile, Gauss-Newton method is an alternative method to solve the non-linear Least-Squares problem with the aim to obtain the parameters value of Monod model by minimizing the sum of square error ( SSE). As the result, the parameters of the Monod model for microalgae Botryococcus Braunii sp can be estimated by the Least-Squares method. However, the estimated parameters value obtained by the non-linear Least-Squares method are more accurate compared to the linear Least-Squares method since the SSE of the non-linear Least-Squares method is less than the linear Least-Squares method.
Analysis of the earthquake data and estimation of source parameters in the Kyungsang basin
Energy Technology Data Exchange (ETDEWEB)
Seo, Jeong-Moon; Lee, Jun-Hee [Korea Atomic Energy Research Institute, Taejeon (Korea)
2000-04-01
The purpose of the present study is to determine the response spectrum for the Korean Peninsula and estimate the seismic source parameters and analyze and simulate the ground motion adequately from the seismic characteristics of Korean Peninsula and compare this with the real data. The estimated seismic source parameters such as apparent seismic stress drop is somewhat unstable because the data are insufficient. When the instrumental earthquake data were continuously accumulated in the future, the modification of these parameters may be developed. Although equations presented in this report are derived from the limited data, they can be utilized both in seismology and earthquake engineering. Finally, predictive equations may be given in terms of magnitude and hypocentral distances using these parameters. The estimation of the predictive equation constructed from the simulation is the object of further study. 34 refs., 27 figs., 10 tabs. (Author)
Dynamic State Estimation and Parameter Calibration of DFIG based on Ensemble Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Fan, Rui; Huang, Zhenyu; Wang, Shaobu; Diao, Ruisheng; Meng, Da
2015-07-30
With the growing interest in the application of wind energy, doubly fed induction generator (DFIG) plays an essential role in the industry nowadays. To deal with the increasing stochastic variations introduced by intermittent wind resource and responsive loads, dynamic state estimation (DSE) are introduced in any power system associated with DFIGs. However, sometimes this dynamic analysis canould not work because the parameters of DFIGs are not accurate enough. To solve the problem, an ensemble Kalman filter (EnKF) method is proposed for the state estimation and parameter calibration tasks. In this paper, a DFIG is modeled and implemented with the EnKF method. Sensitivity analysis is demonstrated regarding the measurement noise, initial state errors and parameter errors. The results indicate this EnKF method has a robust performance on the state estimation and parameter calibration of DFIGs.
Parameter Estimation of a Ground Moving Target Using Image Sharpness Optimization.
Yu, Jing; Li, Yaan
2016-06-30
Motion parameter estimation of a ground moving target is an important issue in synthetic aperture radar ground moving target indication (SAR-GMTI) which has significant applications for civilian and military. The SAR image of a moving target may be displaced and defocused due to the radial and along-track velocity components, respectively. The sharpness cost function presents a measure of the degree of focus of the image. In this work, a new ground moving target parameter estimation algorithm based on the sharpness optimization criterion is proposed. The relationships between the quadratic phase errors and the target's velocity components are derived. Using two-dimensional searching of the sharpness cost function, we can obtain the velocity components of the target and the focused target image simultaneously. The proposed moving target parameter estimation method and image sharpness metrics are analyzed in detail. Finally, numerical results illustrate the effective and superior velocity estimation performance of the proposed method when compared to existing algorithms.
DEFF Research Database (Denmark)
Frontczak, Monika Joanna
The main objective of the Ph.D. study was to examine occupants’ perception of comfort and self-estimated job performance in non-industrial buildings (homes and offices), in particular how building occupants understand comfort and which parameters, not necessarily related to indoor environments...... and storage, noise level and visual privacy. However, if job performance is considered, then satisfaction with the main indoor environmental parameters should be addressed first as they affected self-estimated job performance to the highest extent. The present study showed that overall satisfaction...... with personal workspace affected significantly the self-estimated job performance. Increasing overall satisfaction with the personal workspace by about 15% would correspond to an increase of self-estimated job performance by 3.7%. Among indoor environmental parameters and building features, satisfaction...
Monopulse joint parameter estimation of multiple unresolved targets within the radar beam
Yuan, Hui; Wang, Chunyang; An, Lei; Li, Xin
2017-06-01
Aiming at the problem of the parameter estimation of multiple unresolved targets within the radar beam, using the joint bin processing model, a method of jointly estimating the number and the position of the targets is proposed based on reversible jump Markov Chain Monte Carlo (RJ-MCMC). Reasonable assumptions of the prior distributions and Bayesian theory are adopted to obtain the posterior probability density function of the estimated parameters from the conditional likelihood function of the observation, and then the acceptance ratios of the birth, death and update moves are given. During the update move, a hybrid Metropolis-Hastings (MH) sampling algorithm is used to make a better exploration of the parameter space. The simulation results show that this new method outperforms the method of ML-MLD [11] proposed by X.Zhang for similar estimation accuracy is achieved while fewer sub-pulses are needed.
Directory of Open Access Journals (Sweden)
Marcus Henrique Victor Júnior
Full Text Available Abstract Introduction: This work concerns the assessment of a novel system for mechanical ventilation and a parameter estimation method in a bench test. The tested system was based on a commercial mechanical ventilator and a personal computer. A computational routine was developed do drive the mechanical ventilator and a parameter estimation method was utilized to estimate positive end-expiratory pressure, resistance and compliance of the artificial respiratory system. Methods The computational routine was responsible for establishing connections between devices and controlling them. Parameters such as tidal volume, respiratory rate and others can be set for standard and noisy ventilation regimes. Ventilation tests were performed directly varying parameters in the system. Readings from a calibrated measuring device were the basis for analysis. Adopting a first-order linear model, the parameters could be estimated and the outcomes statistically analysed. Results Data acquisition was effective in terms of sample frequency and low noise content. After filtering, cycle detection and estimation took place. Statistics of median, mean and standard deviation were calculated, showing consistent matching with adjusted values. Changes in positive end-expiratory pressure statistically imply changes in compliance, but not the opposite. Conclusion The developed system was satisfactory in terms of clinical parameters. Statistics exhibited consistent relations between adjusted and estimated values, besides precision of the measurements. The system is expected to be used in animals, with a view to better understand the benefits of noisy ventilation, by evaluating the estimated parameters and performing cross relations among blood gas, ultrasonography and electrical impedance tomography.
Kahl, Gunnar M; Sidorenko, Yury; Gottesbüren, Bernhard
2015-04-01
As an option for higher-tier leaching assessment of pesticides in Europe according to FOCUS, pesticide properties can be estimated from lysimeter studies by inversely fitting parameter values (substance half-life DT50 and sorption coefficient to organic matter kom ). The aim of the study was to identify adequate methods for inverse modelling. Model parameters for the PEARL (Pesticide Emission Assessment at Regional and Local scales) model were estimated with different inverse optimisation algorithms - the Levenberg-Marquardt (LM), PD_MS2 (PEST Driver Multiple Starting Points 2) and SCEM (Shuffled Complex Evolution Metropolis) algorithms. Optimisation of crop factors and hydraulic properties was found to be an ill-posed problem, and all algorithms failed to identify reliable global minima for the hydrological parameters. All algorithms performed equally well in estimating pesticide sorption and degradation parameters. SCEM was in most cases the only algorithm that reliably calculated uncertainties. The most reliable approach for finding the best parameter set in the stepwise approach of optimising evapotranspiration, soil hydrology and pesticide parameters was to run only SCEM or a combined approach with more than one algorithm using the best fit of each step for further processing. PD_MS2 was well suited to a quick parameter search. The linear parameter uncertainty intervals estimated by LM and PD_MS2 were usually larger than by the non-linear method used by SCEM. With the suggested methods, parameter optimisation, together with reliable estimation of uncertainties, is possible also for relatively complex systems. © 2014 Society of Chemical Industry.
Parameter estimation and determinability analysis applied to Drosophila gap gene circuits
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Jaeger Johannes
2008-09-01
Full Text Available Abstract Background Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. Results In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. Conclusion Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous.
Parameter estimation and sensitivity analysis for a mathematical model with time delays of leukemia
Cândea, Doina; Halanay, Andrei; Rǎdulescu, Rodica; Tǎlmaci, Rodica
2017-01-01
We consider a system of nonlinear delay differential equations that describes the interaction between three competing cell populations: healthy, leukemic and anti-leukemia T cells involved in Chronic Myeloid Leukemia (CML) under treatment with Imatinib. The aim of this work is to establish which model parameters are the most important in the success or failure of leukemia remission under treatment using a sensitivity analysis of the model parameters. For the most significant parameters of the model which affect the evolution of CML disease during Imatinib treatment we try to estimate the realistic values using some experimental data. For these parameters, steady states are calculated and their stability is analyzed and biologically interpreted.
Applying fuzzy logic to estimate the parameters of the length-weight relationship
Directory of Open Access Journals (Sweden)
S. D. Bitar
Full Text Available Abstract We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system.
Applying fuzzy logic to estimate the parameters of the length-weight relationship.
Bitar, S D; Campos, C P; Freitas, C E C
2016-05-03
We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system.
Schoups, Gerrit; Vrugt, Jasper A.
2010-05-01
Estimation of parameter and predictive uncertainty of hydrologic models usually relies on the assumption of additive residual errors that are independent and identically distributed according to a normal distribution with a mean of zero and a constant variance. Here, we investigate to what extent estimates of parameter and predictive uncertainty are affected when these assumptions are relaxed. Parameter and predictive uncertainty are estimated by Monte Carlo Markov Chain sampling from a generalized likelihood function that accounts for correlation, heteroscedasticity, and non-normality of residual errors. Application to rainfall-runoff modeling using daily data from a humid basin reveals that: (i) residual errors are much better described by a heteroscedastic, first-order auto-correlated error model with a Laplacian density characterized by heavier tails than a Gaussian density, and (ii) proper representation of the statistical distribution of residual errors yields tighter predictive uncertainty bands and more physically realistic parameter estimates that are less sensitive to the particular time period used for inference. The latter is especially useful for regionalization and extrapolation of parameter values to ungauged basins. Application to daily rainfall-runoff data from a semi-arid basin shows that allowing skew in the error distribution yields improved estimates of predictive uncertainty when flows are close to zero.
Implementation of D-Spline-Based Incremental Performance Parameter Estimation Method with ppOpen-AT
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Teruo Tanaka
2014-01-01
Full Text Available In automatic performance tuning (AT, a primary aim is to optimize performance parameters that are suitable for certain computational environments in ordinary mathematical libraries. For AT, an important issue is to reduce the estimation time required for optimizing performance parameters. To reduce the estimation time, we previously proposed the Incremental Performance Parameter Estimation method (IPPE method. This method estimates optimal performance parameters by inserting suitable sampling points that are based on computational results for a fitting function. As the fitting function, we introduced d-Spline, which is highly adaptable and requires little estimation time. In this paper, we report the implementation of the IPPE method with ppOpen-AT, which is a scripting language (set of directives with features that reduce the workload of the developers of mathematical libraries that have AT features. To confirm the effectiveness of the IPPE method for the runtime phase AT, we applied the method to sparse matrix–vector multiplication (SpMV, in which the block size of the sparse matrix structure blocked compressed row storage (BCRS was used for the performance parameter. The results from the experiment show that the cost was negligibly small for AT using the IPPE method in the runtime phase. Moreover, using the obtained optimal value, the execution time for the mathematical library SpMV was reduced by 44% on comparing the compressed row storage and BCRS (block size 8.
Ait-El-Fquih, Boujemaa
2016-08-12
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.
Bayesian parameter estimation and interpretation for an intermediate model of tree-ring width
Directory of Open Access Journals (Sweden)
S. E. Tolwinski-Ward
2013-07-01
Full Text Available We present a Bayesian model for estimating the parameters of the VS-Lite forward model of tree-ring width for a particular chronology and its local climatology. The scheme also provides information about the uncertainty of the parameter estimates, as well as the model error in representing the observed proxy time series. By inferring VS-Lite's parameters independently for synthetically generated ring-width series at several hundred sites across the United States, we show that the algorithm is skillful. We also infer optimal parameter values for modeling observed ring-width data at the same network of sites. The estimated parameter values covary in physical space, and their locations in multidimensional parameter space provide insight into the dominant climatic controls on modeled tree-ring growth at each site as well as the stability of those controls. The estimation procedure is useful for forward and inverse modeling studies using VS-Lite to quantify the full range of model uncertainty stemming from its parameterization.
The estimation of parameter compaction values for pavement subgrade stabilized with lime
Lubis, A. S.; Muis, Z. A.; Simbolon, C. A.
2018-02-01
The type of soil material, field control, maintenance and availability of funds are several factors that must be considered in compaction of the pavement subgrade. In determining the compaction parameters in laboratory desperately requires considerable materials, time and funds, and reliable laboratory operators. If the result of soil classification values can be used to estimate the compaction parameters of a subgrade material, so it would save time, energy, materials and cost on the execution of this work. This is also a clarification (cross check) of the work that has been done by technicians in the laboratory. The study aims to estimate the compaction parameter values ie. maximum dry unit weight (γdmax) and optimum water content (Wopt) of the soil subgrade that stabilized with lime. The tests that conducted in the laboratory of soil mechanics were to determine the index properties (Fines and Liquid Limit/LL) and Standard Compaction Test. Soil samples that have Plasticity Index (PI) > 10% were made with additional 3% lime for 30 samples. By using the Goswami equation, the compaction parameter values can be estimated by equation γd max # = -0,1686 Log G + 1,8434 and Wopt # = 2,9178 log G + 17,086. From the validation calculation, there was a significant positive correlation between the compaction parameter values laboratory and the compaction parameter values estimated, with a 95% confidence interval as a strong relationship.
Plumb, John M.; Moffitt, Christine M.
2015-01-01
Researchers have cautioned against the borrowing of consumption and growth parameters from other species and life stages in bioenergetics growth models. In particular, the function that dictates temperature dependence in maximum consumption (Cmax) within the Wisconsin bioenergetics model for Chinook Salmon Oncorhynchus tshawytscha produces estimates that are lower than those measured in published laboratory feeding trials. We used published and unpublished data from laboratory feeding trials with subyearling Chinook Salmon from three stocks (Snake, Nechako, and Big Qualicum rivers) to estimate and adjust the model parameters for temperature dependence in Cmax. The data included growth measures in fish ranging from 1.5 to 7.2 g that were held at temperatures from 14°C to 26°C. Parameters for temperature dependence in Cmax were estimated based on relative differences in food consumption, and bootstrapping techniques were then used to estimate the error about the parameters. We found that at temperatures between 17°C and 25°C, the current parameter values did not match the observed data, indicating that Cmax should be shifted by about 4°C relative to the current implementation under the bioenergetics model. We conclude that the adjusted parameters for Cmax should produce more accurate predictions from the bioenergetics model for subyearling Chinook Salmon.
Directory of Open Access Journals (Sweden)
Haiwen Li
2018-01-01
Full Text Available The estimation speed of positioning parameters determines the effectiveness of the positioning system. The time of arrival (TOA and direction of arrival (DOA parameters can be estimated by the space-time two-dimensional multiple signal classification (2D-MUSIC algorithm for array antenna. However, this algorithm needs much time to complete the two-dimensional pseudo spectral peak search, which makes it difficult to apply in practice. Aiming at solving this problem, a fast estimation method of space-time two-dimensional positioning parameters based on Hadamard product is proposed in orthogonal frequency division multiplexing (OFDM system, and the Cramer-Rao bound (CRB is also presented. Firstly, according to the channel frequency domain response vector of each array, the channel frequency domain estimation vector is constructed using the Hadamard product form containing location information. Then, the autocorrelation matrix of the channel response vector for the extended array element in frequency domain and the noise subspace are calculated successively. Finally, by combining the closed-form solution and parameter pairing, the fast joint estimation for time delay and arrival direction is accomplished. The theoretical analysis and simulation results show that the proposed algorithm can significantly reduce the computational complexity and guarantee that the estimation accuracy is not only better than estimating signal parameters via rotational invariance techniques (ESPRIT algorithm and 2D matrix pencil (MP algorithm but also close to 2D-MUSIC algorithm. Moreover, the proposed algorithm also has certain adaptability to multipath environment and effectively improves the ability of fast acquisition of location parameters.
Response-based estimation of sea state parameters - Influence of filtering
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam
2007-01-01
Reliable estimation of the on-site sea state parameters is essential to decision support systems for safe navigation of ships. The wave spectrum can be estimated from procedures based on measured ship responses. The paper deals with two procedures—Bayesian Modelling and Parametric Modelling...... parameters—are carried out for a large container vessel. The study shows that filtering has an influence on the estimations, since high-frequency components of the wave excitations are not estimated as accurately as lower frequency components....
Estimating Parameters in Physical Models through Bayesian Inversion: A Complete Example
Allmaras, Moritz
2013-02-07
All mathematical models of real-world phenomena contain parameters that need to be estimated from measurements, either for realistic predictions or simply to understand the characteristics of the model. Bayesian statistics provides a framework for parameter estimation in which uncertainties about models and measurements are translated into uncertainties in estimates of parameters. This paper provides a simple, step-by-step example-starting from a physical experiment and going through all of the mathematics-to explain the use of Bayesian techniques for estimating the coefficients of gravity and air friction in the equations describing a falling body. In the experiment we dropped an object from a known height and recorded the free fall using a video camera. The video recording was analyzed frame by frame to obtain the distance the body had fallen as a function of time, including measures of uncertainty in our data that we describe as probability densities. We explain the decisions behind the various choices of probability distributions and relate them to observed phenomena. Our measured data are then combined with a mathematical model of a falling body to obtain probability densities on the space of parameters we seek to estimate. We interpret these results and discuss sources of errors in our estimation procedure. © 2013 Society for Industrial and Applied Mathematics.
Parameter estimation techniques and uncertainty in ground water flow model predictions
International Nuclear Information System (INIS)
Zimmerman, D.A.; Davis, P.A.
1990-01-01
Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs
Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications
Directory of Open Access Journals (Sweden)
Jufeng Yang
2016-12-01
Full Text Available This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.
Confidence interval based parameter estimation--a new SOCR applet and activity.
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Nicolas Christou
Full Text Available Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and fundamental principles for computing point and interval estimates. Efficient and reliable parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. In this manuscript, we demonstrate simulation, construction, validation and interpretation of confidence intervals, under various assumptions, using the interactive web-based tools provided by the Statistics Online Computational Resource (http://www.SOCR.ucla.edu. Specifically, we present confidence interval examples for population means, with known or unknown population standard deviation; population variance; population proportion (exact and approximate, as well as confidence intervals based on bootstrapping or the asymptotic properties of the maximum likelihood estimates. Like all SOCR resources, these confidence interval resources may be openly accessed via an Internet-connected Java-enabled browser. The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interest on the corresponding confidence interval. Two applications of the new interval estimation computational library are presented. The first one is a simulation of confidence interval estimating the US unemployment rate and the second application demonstrates the computations of point and interval estimates of hippocampal surface complexity for Alzheimers disease patients, mild cognitive impairment subjects and asymptomatic controls.
Morandage, Shehan; Schnepf, Andrea; Vanderborght, Jan; Javaux, Mathieu; Leitner, Daniel; Laloy, Eric; Vereecken, Harry
2017-04-01
Root traits are increasingly important in breading of new crop varieties. E.g., longer and fewer lateral roots are suggested to improve drought resistance of wheat. Thus, detailed root architectural parameters are important. However, classical field sampling of roots only provides more aggregated information such as root length density (coring), root counts per area (trenches) or root arrival curves at certain depths (rhizotubes). We investigate the possibility of obtaining the information about root system architecture of plants using field based classical root sampling schemes, based on sensitivity analysis and inverse parameter estimation. This methodology was developed based on a virtual experiment where a root architectural model was used to simulate root system development in a field, parameterized for winter wheat. This information provided the ground truth which is normally unknown in a real field experiment. The three sampling schemes coring, trenching, and rhizotubes where virtually applied to and aggregated information computed. Morris OAT global sensitivity analysis method was then performed to determine the most sensitive parameters of root architecture model for the three different sampling methods. The estimated means and the standard deviation of elementary effects of a total number of 37 parameters were evaluated. Upper and lower bounds of the parameters were obtained based on literature and published data of winter wheat root architectural parameters. Root length density profiles of coring, arrival curve characteristics observed in rhizotubes, and root counts in grids of trench profile method were evaluated statistically to investigate the influence of each parameter using five different error functions. Number of branches, insertion angle inter-nodal distance, and elongation rates are the most sensitive parameters and the parameter sensitivity varies slightly with the depth. Most parameters and their interaction with the other parameters show
International Nuclear Information System (INIS)
Li Xiang-Tao; Yin Ming-Hao
2012-01-01
We study the parameter estimation of a nonlinear chaotic system, which can be essentially formulated as a multidimensional optimization problem. In this paper, an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy. Experiments are conducted on the Lorenz system and the Chen system. The proposed algorithm is used to estimate the parameters for these two systems. Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained. (general)
DEFF Research Database (Denmark)
Pedersen, Leif Toudal; Tonboe, Rasmus T.; Høyer, Jacob
.e. horizontal and vertical polarization at channels between 6 and 89 GHz as a function of a limited set of physical parameters, i.e. atmospheric water vapor, cloud liquid water, wind speed, surface and air temperature. This type of model is ideal for optimal estimation applications because of its limited set...... channels as well as the combination of data from multiple sources such as microwave radiometry, scatterometry and numerical weather prediction. Optimal estimation is data assimilation without a numerical model for retrieving physical parameters from remote sensing using a multitude of available information....... The methodology is observation driven and model innovation is limited to the translation between observation space and physical parameter space Over open water we use a semi-empirical radiative transfer model developed by Meissner & Wentz that estimates the multispectral AMSR brightness temperatures, i...
Murphy, K. A.
1990-01-01
A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.
Directory of Open Access Journals (Sweden)
He Wang
2018-01-01
Full Text Available An effective method is proposed to estimate the parameters of a dynamic grain flow model (DGFM. To this end, an improved artificial bee colony (IABC algorithm is used to estimate unknown parameters of DGFM with minimizing a given objective function. A comparative study of the performance of the IABC algorithm and the other ABC variants on several benchmark functions is carried out, and the results present a significant improvement in performance over the other ABC variants. The practical application performance of the IABC is compared to that of the nonlinear least squares (NLS, particle swarm optimization (PSO, and genetic algorithm (GA. The compared results demonstrate that IABC algorithm is more accurate and effective for the parameter estimation of DGFM than the other algorithms.
A parameter estimation for DC servo motor by using optimization process
International Nuclear Information System (INIS)
Arjoni Amir
2010-01-01
Modeling and simulation parameters of DC servo motor using Matlab Simulink software have been done. The objective to define the DC servo motor parameter estimation is to get DC servo motor parameter values (B, La, Ra, Km, J) which are significant value that can be used for actuation process of control systems. In the analysis of control systems DC the servo motor expressed by transfer function equation to make faster to be analyzed as a component of the actuator. To obtain the data model parameters and initial conditions of the DC servo motors is then carried out the processor modeling and simulation in which the DC servo motor combined with other components. To obtain preliminary data of the DC servo motor parameters as estimated venue, it is obtained from the data factory of the DC servo motor. The initial data parameters of the DC servo motor are applied for the optimization process by using nonlinear least square algorithm and minimize the cost function value so that the DC servo motors parameter values are obtained significantly. The result of the optimization process of the DC servo motor parameter values are B = 0.039881, J= 1.2608e-007, Km = 0.069648, La = 2.3242e-006 and Ra = 1.8837. (author)
a Comparison Between Two Ols-Based Approaches to Estimating Urban Multifractal Parameters
Huang, Lin-Shan; Chen, Yan-Guang
Multifractal theory provides a new spatial analytical tool for urban studies, but many basic problems remain to be solved. Among various pending issues, the most significant one is how to obtain proper multifractal dimension spectrums. If an algorithm is improperly used, the parameter spectrums will be abnormal. This paper is devoted to investigating two ordinary least squares (OLS)-based approaches for estimating urban multifractal parameters. Using empirical study and comparative analysis, we demonstrate how to utilize the adequate linear regression to calculate multifractal parameters. The OLS regression analysis has two different approaches. One is that the intercept is fixed to zero, and the other is that the intercept is not limited. The results of comparative study show that the zero-intercept regression yields proper multifractal parameter spectrums within certain scale range of moment order, while the common regression method often leads to abnormal multifractal parameter values. A conclusion can be reached that fixing the intercept to zero is a more advisable regression method for multifractal parameters estimation, and the shapes of spectral curves and value ranges of fractal parameters can be employed to diagnose urban problems. This research is helpful for scientists to understand multifractal models and apply a more reasonable technique to multifractal parameter calculations.
Directory of Open Access Journals (Sweden)
Teresa eLehnert
2015-06-01
Full Text Available Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM, because this level of model complexity allows estimating textit{a priori} unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e. least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment.
Masterlark, Timothy; Donovan, Theodore; Feigl, Kurt L.; Haney, Matt; Thurber, Clifford H.; Tung, Sui
2016-01-01
The eruption cycle of a volcano is controlled in part by the upward migration of magma. The characteristics of the magma flux produce a deformation signature at the Earth's surface. Inverse analyses use geodetic data to estimate strategic controlling parameters that describe the position and pressurization of a magma chamber at depth. The specific distribution of material properties controls how observed surface deformation translates to source parameter estimates. Seismic tomography models describe the spatial distributions of material properties that are necessary for accurate models of volcano deformation. This study investigates how uncertainties in seismic tomography models propagate into variations in the estimates of volcano deformation source parameters inverted from geodetic data. We conduct finite element model-based nonlinear inverse analyses of interferometric synthetic aperture radar (InSAR) data for Okmok volcano, Alaska, as an example. We then analyze the estimated parameters and their uncertainties to characterize the magma chamber. Analyses are performed separately for models simulating a pressurized chamber embedded in a homogeneous domain as well as for a domain having a heterogeneous distribution of material properties according to seismic tomography. The estimated depth of the source is sensitive to the distribution of material properties. The estimated depths for the homogeneous and heterogeneous domains are 2666 ± 42 and 3527 ± 56 m below mean sea level, respectively (99% confidence). A Monte Carlo analysis indicates that uncertainties of the seismic tomography cannot account for this discrepancy at the 99% confidence level. Accounting for the spatial distribution of elastic properties according to seismic tomography significantly improves the fit of the deformation model predictions and significantly influences estimates for parameters that describe the location of a pressurized magma chamber.
Artifacts that mimic ballistic magnetoresistance
International Nuclear Information System (INIS)
Egelhoff, W.F. . E-mail : egelhoff@nist.gov; Gan, L.; Ettedgui, H.; Kadmon, Y.; Powell, C.J.; Chen, P.J.; Shapiro, A.J.; McMichael, R.D.; Mallett, J.J.; Moffat, T.P.; Stiles, M.D.; Svedberg, E.B.
2005-01-01
We have investigated the circumstances underlying recent reports of very large values of ballistic magnetoresistance (BMR) in nanocontacts between magnetic wires. We find that the geometries used are subject to artifacts due to motion of the wires that distort the nanocontact thereby changing its electrical resistance. Since these nanocontacts are often of atomic scale, reliable experiments would require stability on the atomic scale. No method for achieving such stability in macroscopic wires is apparent. We conclude that macroscopic magnetic wires cannot be used to establish the validity of the BMR effect
Brito, Paula M.; Antunes, Fernando
2014-01-01
The lack of kinetic data concerning the biological effects of reactive oxygen species is slowing down the development of the field of redox signaling. Herein, we deduced and applied equations to estimate kinetic parameters from typical redox signaling experiments. H2O2-sensing mediated by the oxidation of a protein target and the switch-off of this sensor, by being converted back to its reduced form, are the two processes for which kinetic parameters are determined. The experimental data requ...
Implementation of the EM Algorithm in the Estimation of Item Parameters: The BILOG Computer Program.
Mislevy, Robert J.; Bock, R. Darrell
This paper reviews the basic elements of the EM approach to estimating item parameters and illustrates its use with one simulated and one real data set. In order to illustrate the use of the BILOG computer program, runs for 1-, 2-, and 3-parameter models are presented for the two sets of data. First is a set of responses from 1,000 persons to five…
Frontczak, Monika Joanna; Wargocki, Pawel
2011-01-01
The main objective of the Ph.D. study was to examine occupants’ perception of comfort and self-estimated job performance in non-industrial buildings (homes and offices), in particular how building occupants understand comfort and which parameters, not necessarily related to indoor environments, influence the perception of comfort.To meet the objective, the following actions were taken: (1) a literature survey exploring which indoor environmental parameters (thermal, acoustic, visualenvironmen...
Estimating Parameters for the PVsyst Version 6 Photovoltaic Module Performance Model
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
2015-10-01
We present an algorithm to determine parameters for the photovoltaic module perf ormance model encoded in the software package PVsyst(TM) version 6. Our method operates on current - voltage (I - V) measured over a range of irradiance and temperature conditions. We describe the method and illustrate its steps using data for a 36 cell crystalli ne silicon module. We qualitatively compare our method with one other technique for estimating parameters for the PVsyst(TM) version 6 model .
Automated estimator parameter selection for an IBM head/disk assembly.
Thein, May-Win L; Rendon, Thomas; Misawa, Eduardo A
2005-07-01
This paper presents the application of a discrete adaptive observer (DAO) to an IBM head/disk assembly system. Because of the difficulties in tuning, a genetic algorithm is implemented off-line to obtain optimal observer parameters for the DAO. Simulations show that the genetic algorithm is successful in choosing appropriate observer gains. Furthermore, as a result of these optimal gains, the observer state and parameter estimates converge accurately and quickly.
Plasma parameter estimations for the Large Helical Device based on the gyro-reduced Bohm scaling
International Nuclear Information System (INIS)
Okamoto, Masao; Nakajima, Noriyoshi; Sugama, Hideo.
1991-10-01
A model of gyro-reduced Bohm scaling law is incorporated into a one-dimensional transport code to predict plasma parameters for the Large Helical Device (LHD). The transport code calculations reproduce well the LHD empirical scaling law and basic parameters and profiles of the LHD plasma are calculated. The amounts of toroidal currents (bootstrap current and beam-driven current) are also estimated. (author)
Estimating Parameter Uncertainty in Binding-Energy Models by the Frequency-Domain Bootstrap
Bertsch, G. F.; Bingham, Derek
2017-12-01
We propose using the frequency-domain bootstrap (FDB) to estimate errors of modeling parameters when the modeling error is itself a major source of uncertainty. Unlike the usual bootstrap or the simple χ2 analysis, the FDB can take into account correlations between errors. It is also very fast compared to the Gaussian process Bayesian estimate as often implemented for computer model calibration. The method is illustrated with a simple example, the liquid drop model of nuclear binding energies. We find that the FDB gives a more conservative estimate of the uncertainty in liquid drop parameters than the χ2 method, and is in fair accord with more empirical estimates. For the nuclear physics application, there are no apparent obstacles to apply the method to the more accurate and detailed models based on density-functional theory.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals....
International Nuclear Information System (INIS)
Looney, B.B.; Grant, M.W.; King, C.M.
1987-03-01
Geochemical parameter estimates to be used in assessing the subsurface transport of chemicals from Savannah River Plant (SRP) waste sites are presented. Specifically, reference values for soil-solution distribution coefficients, solubility, leach rates, and retardation coefficients are estimated for 31 inorganic chemicals (assuming speciation is governed by reasonable assumptions about controlling variables such as Eh and pH) and 36 organic compounds. Additionally, facilitated transport (the association of chemicals with inorganic and organic ligands or colloids resulting in relatively high mobility) was estimated using field data to derive a fraction of the disposal mass which was assumed to be mobile. Hydrologic parameters such as dispersion coefficient, average moisture content in vadose zone, bulk density, and effective porosity are also presented. The estimates are based on site-specific studies when available, combined with technical literature
International Nuclear Information System (INIS)
Ridzikova, A; Fronka, A.; Maly, B.; Moucka, L.
2003-01-01
In the present investigation, we will be study the dose relevant factors from continual monitoring in real homes into account getting more accurate estimation of 222 Rn the effective dose. The dose relevant parameters include the radon concentration, the equilibrium factor (f), the fraction (fp) of unattached radon decay products and real time occupancy people in home. The result of the measurement are the time courses of radon concentration that are based on estimation effective doses together with assessment of the real time occupancy people indoor. We found out by analysis that year effective dose is lower than effective dose estimated by ICRP recommendation from the integral measurement that included only average radon concentration. Our analysis of estimation effective doses using measurement of several physical parameters was made only in one case and for the better specification is important to measure in different real occupancy houses. (authors)
Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal
Directory of Open Access Journals (Sweden)
Chen Ying
2016-10-01
Full Text Available To overcome the disadvantages of a non-cooperative frequency hopping communication system, such as a high sampling rate and inadequate prior information, parameter estimation based on Blind Compressed Sensing (BCS is proposed. The signal is precisely reconstructed by the alternating iteration of sparse coding and basis updating, and the hopping frequencies are directly estimated based on the results. Compared with conventional compressive sensing, blind compressed sensing does not require prior information of the frequency hopping signals; hence, it offers an effective solution to the inadequate prior information problem. In the proposed method, the signal is first modeled and then reconstructed by Orthonormal Block Diagonal Blind Compressed Sensing (OBD-BCS, and the hopping frequencies and hop period are finally estimated. The simulation results suggest that the proposed method can reconstruct and estimate the parameters of noncooperative frequency hopping signals with a low signal-to-noise ratio.
Multiple Moving Targets Detection and Parameters Estimation in Strong Reverberation Environments
Directory of Open Access Journals (Sweden)
Ge Yu
2016-01-01
Full Text Available This paper considers the problem of multiple moving targets detection and parameters estimation (direction of arrival and range in strong reverberation environments. As reverberation has a strong correlation with target echo, the performance of target detection and parameters estimation is significantly degraded in practical underwater environments. In this paper, we utilize two uniform circular arrays to receive plane wave of the linear frequency modulation signal reflected from far-field targets. On the basis of received signal, we build a variance matrix of multiple beams by using modal decomposition, conventional beamforming, and fractional Fourier transform (FrFT. We then propose a novel detection method and an estimation method of parameters based on the constructed image. A significant feature of the proposed methods is that our design does not involve any a priori knowledge about targets number and parameters of marine environments. Finally, we demonstrate via numerical simulation examples that the detection probability and the accuracy of estimated parameters of the proposed method are higher than the existing methods in both low signal-to-reverberation ratio and signal-to-noise ratio environment.
Energy Technology Data Exchange (ETDEWEB)
Mukhopadhyay, S.; Tsang, Y.; Finsterle, S.
2009-01-15
A simple conceptual model has been recently developed for analyzing pressure and temperature data from flowing fluid temperature logging (FFTL) in unsaturated fractured rock. Using this conceptual model, we developed an analytical solution for FFTL pressure response, and a semianalytical solution for FFTL temperature response. We also proposed a method for estimating fracture permeability from FFTL temperature data. The conceptual model was based on some simplifying assumptions, particularly that a single-phase airflow model was used. In this paper, we develop a more comprehensive numerical model of multiphase flow and heat transfer associated with FFTL. Using this numerical model, we perform a number of forward simulations to determine the parameters that have the strongest influence on the pressure and temperature response from FFTL. We then use the iTOUGH2 optimization code to estimate these most sensitive parameters through inverse modeling and to quantify the uncertainties associated with these estimated parameters. We conclude that FFTL can be utilized to determine permeability, porosity, and thermal conductivity of the fracture rock. Two other parameters, which are not properties of the fractured rock, have strong influence on FFTL response. These are pressure and temperature in the borehole that were at equilibrium with the fractured rock formation at the beginning of FFTL. We illustrate how these parameters can also be estimated from FFTL data.
The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.
Directory of Open Access Journals (Sweden)
Andrew White
2016-12-01
Full Text Available We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model's discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system-a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model.
A Note on Parameter Estimation in the Composite Weibull–Pareto Distribution
Directory of Open Access Journals (Sweden)
Enrique Calderín-Ojeda
2018-02-01
Full Text Available Composite models have received much attention in the recent actuarial literature to describe heavy-tailed insurance loss data. One of the models that presents a good performance to describe this kind of data is the composite Weibull–Pareto (CWL distribution. On this note, this distribution is revisited to carry out estimation of parameters via mle and mle2 optimization functions in R. The results are compared with those obtained in a previous paper by using the nlm function, in terms of analytical and graphical methods of model selection. In addition, the consistency of the parameter estimation is examined via a simulation study.
Estimation of Temperature Dependent Parameters of a Batch Alcoholic Fermentation Process
de Andrade, Rafael Ramos; Rivera, Elmer Ccopa; Costa, Aline C.; Atala, Daniel I. P.; Filho, Francisco Maugeri; Filho, Rubens Maciel
In this work, a procedure was established to develop a mathematical model considering the effect of temperature on reaction kinetics. Experiments were performed in batch mode in temperatures from 30 to 38°C. The microorganism used was Saccharomyces cerevisiae and the culture media, sugarcane molasses. The objective is to assess the difficulty in updating the kinetic parameters when there are changes in fermentation conditions. We conclude that, although the re-estimation is a time-consuming task, it is possible to accurately describe the process when there are changes in raw material composition if a re-estimation of parameters is performed.
A coherent structure approach for parameter estimation in Lagrangian Data Assimilation
Maclean, John; Santitissadeekorn, Naratip; Jones, Christopher K. R. T.
2017-12-01
We introduce a data assimilation method to estimate model parameters with observations of passive tracers by directly assimilating Lagrangian Coherent Structures. Our approach differs from the usual Lagrangian Data Assimilation approach, where parameters are estimated based on tracer trajectories. We employ the Approximate Bayesian Computation (ABC) framework to avoid computing the likelihood function of the coherent structure, which is usually unavailable. We solve the ABC by a Sequential Monte Carlo (SMC) method, and use Principal Component Analysis (PCA) to identify the coherent patterns from tracer trajectory data. Our new method shows remarkably improved results compared to the bootstrap particle filter when the physical model exhibits chaotic advection.
Comparison of Two New Robust Parameter Estimation Methods for the Power Function Distribution.
Shakeel, Muhammad; Haq, Muhammad Ahsan Ul; Hussain, Ijaz; Abdulhamid, Alaa Mohamd; Faisal, Muhammad
2016-01-01
Estimation of any probability distribution parameters is vital because imprecise and biased estimates can be misleading. In this study, we investigate a flexible power function distribution and introduced new two methods such as, probability weighted moments, and generalized probability weighted methods for its parameters. We compare their results with L-moments, trimmed L-moments by a simulation study and a real data example based on performance measures such as, mean square error and total deviation. We concluded that all the methods perform well in the case of large sample size (n>30), however, the generalized probability weighted moment method performs better for small sample size.
Comparison of Two New Robust Parameter Estimation Methods for the Power Function Distribution.
Directory of Open Access Journals (Sweden)
Muhammad Shakeel
Full Text Available Estimation of any probability distribution parameters is vital because imprecise and biased estimates can be misleading. In this study, we investigate a flexible power function distribution and introduced new two methods such as, probability weighted moments, and generalized probability weighted methods for its parameters. We compare their results with L-moments, trimmed L-moments by a simulation study and a real data example based on performance measures such as, mean square error and total deviation. We concluded that all the methods perform well in the case of large sample size (n>30, however, the generalized probability weighted moment method performs better for small sample size.
Micro-motion Parameter Estimation in Non-Gaussian Noise via Mutual Correntropy
Directory of Open Access Journals (Sweden)
Xiong Dingding
2017-06-01
Full Text Available This study considered parameter estimations for micro-motion targets embedded in non-Gaussian noise with a Single Input Multiple Output (SIMO radar. A novel estimation algorithm based on mutual correntropy was presented and used to derive the micro-perturbation parameters by exploiting the second and higher-order knowledge of the return signals among multiple channels. Compared with a conventional Fourier Transform (FT method, the method proposed herein had a much higher Signal to Noise Ratio (SNR gain. In addition, the location was derived by employing the Phase-Comparison Monopulse (PCM technique. Finally, several numerical results were provided and discussed.
Directory of Open Access Journals (Sweden)
Fachruddin Fachruddin
2017-07-01
Full Text Available Software Effort Estimation adalah proses estimasi biaya perangkat lunak sebagai suatu proses penting dalam melakukan proyek perangkat lunak. Berbagai penelitian terdahulu telah melakukan estimasi usaha perangkat lunak dengan berbagai metode, baik metode machine learning maupun non machine learning. Penelitian ini mengadakan set eksperimen seleksi atribut pada parameter proyek menggunakan teknik k-nearest neighbours sebagai estimasinya dengan melakukan seleksi atribut menggunakan information gain dan mutual information serta bagaimana menemukan parameter proyek yang paling representif pada software effort estimation. Dataset software estimation effort yang digunakan pada eksperimen adalah yakni albrecht, china, kemerer dan mizayaki94 yang dapat diperoleh dari repositori data khusus Software Effort Estimation melalui url http://openscience.us/repo/effort/. Selanjutnya peneliti melakukan pembangunan aplikasi seleksi atribut untuk menyeleksi parameter proyek. Sistem ini menghasilkan dataset arff yang telah diseleksi. Aplikasi ini dibangun dengan bahasa java menggunakan IDE Netbean. Kemudian dataset yang telah di-generate merupakan parameter hasil seleksi yang akan dibandingkan pada saat melakukan Software Effort Estimation menggunakan tool WEKA . Seleksi Fitur berhasil menurunkan nilai error estimasi (yang diwakilkan oleh nilai RAE dan RMSE. Artinya bahwa semakin rendah nilai error (RAE dan RMSE maka semakin akurat nilai estimasi yang dihasilkan. Estimasi semakin baik setelah di lakukan seleksi fitur baik menggunakan information gain maupun mutual information. Dari nilai error yang dihasilkan maka dapat disimpulkan bahwa dataset yang dihasilkan seleksi fitur dengan metode information gain lebih baik dibanding mutual information namun, perbedaan keduanya tidak terlalu signifikan.
Integration based profile likelihood calculation for PDE constrained parameter estimation problems
Boiger, R.; Hasenauer, J.; Hroß, S.; Kaltenbacher, B.
2016-12-01
Partial differential equation (PDE) models are widely used in engineering and natural sciences to describe spatio-temporal processes. The parameters of the considered processes are often unknown and have to be estimated from experimental data. Due to partial observations and measurement noise, these parameter estimates are subject to uncertainty. This uncertainty can be assessed using profile likelihoods, a reliable but computationally intensive approach. In this paper, we present the integration based approach for the profile likelihood calculation developed by (Chen and Jennrich 2002 J. Comput. Graph. Stat. 11 714-32) and adapt it to inverse problems with PDE constraints. While existing methods for profile likelihood calculation in parameter estimation problems with PDE constraints rely on repeated optimization, the proposed approach exploits a dynamical system evolving along the likelihood profile. We derive the dynamical system for the unreduced estimation problem, prove convergence and study the properties of the integration based approach for the PDE case. To evaluate the proposed method, we compare it with state-of-the-art algorithms for a simple reaction-diffusion model for a cellular patterning process. We observe a good accuracy of the method as well as a significant speed up as compared to established methods. Integration based profile calculation facilitates rigorous uncertainty analysis for computationally demanding parameter estimation problems with PDE constraints.
The Cooperative Ballistic Missile Defence Game
Evers, L.; Barros, A.I.; Monsuur, H.
2013-01-01
The increasing proliferation of ballistic missiles and weapons of mass destruction poses new risks worldwide. For a threatened nation and given the characteristics of this threat a layered ballistic missile defence system strategy appears to be the preferred solution. However, such a strategy
Utilising temperature differences as constraints for estimating parameters in a simple climate model
International Nuclear Information System (INIS)
Bodman, Roger W; Karoly, David J; Enting, Ian G
2010-01-01
Simple climate models can be used to estimate the global temperature response to increasing greenhouse gases. Changes in the energy balance of the global climate system are represented by equations that necessitate the use of uncertain parameters. The values of these parameters can be estimated from historical observations, model testing, and tuning to more complex models. Efforts have been made at estimating the possible ranges for these parameters. This study continues this process, but demonstrates two new constraints. Previous studies have shown that land-ocean temperature differences are only weakly correlated with global mean temperature for natural internal climate variations. Hence, these temperature differences provide additional information that can be used to help constrain model parameters. In addition, an ocean heat content ratio can also provide a further constraint. A pulse response technique was used to identify relative parameter sensitivity which confirmed the importance of climate sensitivity and ocean vertical diffusivity, but the land-ocean warming ratio and the land-ocean heat exchange coefficient were also found to be important. Experiments demonstrate the utility of the land-ocean temperature difference and ocean heat content ratio for setting parameter values. This work is based on investigations with MAGICC (Model for the Assessment of Greenhouse-gas Induced Climate Change) as the simple climate model.