Panigrahi, Swapnesh; Ramachandran, Hema; Alouini, Mehdi
2016-01-01
The efficiency of using intensity modulated light for estimation of scattering properties of a turbid medium and for ballistic photon discrimination is theoretically quantified in this article. Using the diffusion model for modulated photon transport and considering a noisy quadrature demodulation scheme, the minimum-variance bounds on estimation of parameters of interest are analytically derived and analyzed. The existence of a variance-minimizing optimal modulation frequency is shown and its evolution with the properties of the intervening medium is derived and studied. Furthermore, a metric is defined to quantify the efficiency of ballistic photon filtering which may be sought when imaging through turbid media. The analytical derivation of this metric shows that the minimum modulation frequency required to attain significant ballistic discrimination depends only on the reduced scattering coefficient of the medium in a linear fashion for a highly scattering medium.
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 optimisation techniques coupled with dynamic solution of the underlying model. Linear and nonlinear approaches to parameter estimation are investigated. There is also the application of maximum likelihood principles in the estimation of parameters, as well as the use of orthogonal collocation to...... generate a set of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....
A New Parameter Characterising the Ballistic Performance of Ceramics
Hoog, K.; Ernst, H.-J.; Wolf, T.
1997-01-01
Impacted by a long rod penetrator at 1800 m/s different ceramics of varying thickness and target configurations are investigated. A turning point of the residual penetration curve in the vicinity of thin ceramic layers was not found. The space equivalence factor for vanishing ceramic thickness is introduced as a target configuration independent parameter to describe the ballistic performance of materials. This factor denotes the maximum protection performance of a brittle material by assuming...
["Piggyback" shot: ballistic parameters of two simultaneously discharged airgun pellets].
Frank, Matthias; Schönekess, Holger C; Grossjohann, Rico; Ekkernkamp, Axel; Bockholdt, Britta
2014-01-01
Green and Good reported an uncommon case of homicide committed with an air rifle in 1982 (Am. J. Forensic Med. Pathol. 3: 361-365). The fatal wound was unusual in that two airgun pellets were loaded in so-called "piggyback" fashion into a single shot air rifle. Lack of further information on the ballistic characteristics of two airgun pellets as opposed to one conventionally loaded projectile led to this investigation. The mean kinetic energy (E) of the two pellets discharged in "piggyback" fashion was E = 3.6 J and E = 3.4 J, respectively. In comparison, average kinetic energy values of E = 12.5 J were calculated for conventionally discharged single diabolo pellets. Test shots into ballistic soap confirmed the findings of a single entrance wound as reported by Green and Good. While the ballistic background of pellets discharged in "piggyback" fashion could be clarified, the reason behind this mode of shooting remains unclear. PMID:24855739
Frank, Matthias; Franke, Ernst; Schönekess, Holger C; Jorczyk, Jörn; Bockholdt, Britta; Ekkernkamp, Axel
2012-03-01
Since their introduction in the 1950s in the construction and building trade, powder-actuated fastening tools (nail guns) are of forensic and traumatological importance. There are countless reports on both accidental and intentional injuries and fatalities caused by these tools in medical literature. While the ballistic parameters of so-called low-velocity fastening tools, where the expanding gases act on a captive piston that drives the fastener into the material, are well known, ballistic parameters of "high-velocity" tools, which operate like a firearm and release the energy of the propellant directly on the fastener, are unknown. Therefore, it was the aim of this work to investigate external ballistic parameters of cal. 9 and 6-mm fastening bolts discharged from four different direct-acting nail guns (Type Ideal, Record Piccolo S, Rapid Hammer R300, Titan Type 1). Average muzzle velocity ranged from 400 to 580 m/s, while average kinetic energy of the projectiles ranged from 385 to 547 J. Mean energy density of the projectiles ranged from 9 to 18 J/mm(2). To conclude, this work demonstrates that the muzzle velocity of direct-acting high-velocity tools is approximately five times higher than the muzzle velocity of piston-type tools. Hence, the much-cited comparison to the ballistic parameters of a cal. 22 handgun might be understated and a comparison to the widespread and well-known cal. 9 mm Luger might be more appropriate. PMID:21607714
The ballistic electron wave swing device has previously been presented as a possible candidate for a simple power conversion technique to the THz -domain. This paper gives a simulative estimation of the power conversion efficiency. The harmonic balance simulations use an equivalent circuit model, which is also derived in this work from a mechanical model. To verify the validity of the circuit model, current waveforms are compared to Monte Carlo simulations of identical setups. Model parameters are given for a wide range of device configurations. The device configuration exhibiting the most conforming waveform is used further for determining the best conversion efficiency. The corresponding simulation setup is described. Simulation results implying a conversion efficiency of about 22% are presented. (paper)
Schildbach, Christian; Ong, Duu Sheng; Hartnagel, Hans; Schmidt, Lorenz-Peter
2016-06-01
The ballistic electron wave swing device has previously been presented as a possible candidate for a simple power conversion technique to the THz -domain. This paper gives a simulative estimation of the power conversion efficiency. The harmonic balance simulations use an equivalent circuit model, which is also derived in this work from a mechanical model. To verify the validity of the circuit model, current waveforms are compared to Monte Carlo simulations of identical setups. Model parameters are given for a wide range of device configurations. The device configuration exhibiting the most conforming waveform is used further for determining the best conversion efficiency. The corresponding simulation setup is described. Simulation results implying a conversion efficiency of about 22% are presented.
Parameter Estimation Through Ignorance
Du, Hailiang
2015-01-01
Dynamical modelling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models. Despite the importance of model parameters, there is no general method of parameter estimation outside linear systems. A new relatively simple method of parameter estimation for nonlinear systems is presented, based on variations in the accuracy of probability forecasts. It is illustrated on the Logistic Map, the Henon Map and the 12-D Lorenz96 flow, and its ability to outperform linear least squares in these systems is explored at various noise levels and sampling rates. As expected, it is more effective when the forecast error distributions are non-Gaussian. The new method selects parameter values by minimizing a proper, local skill score for continuous probability forecasts as a function of the parameter values. This new approach is easier to implement in practice than alter...
Parameter estimation through ignorance.
Du, Hailiang; Smith, Leonard A
2012-07-01
Dynamical modeling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models. Despite the importance of model parameters, there is no general method of parameter estimation outside linear systems. A relatively simple method of parameter estimation for nonlinear systems is introduced, based on variations in the accuracy of probability forecasts. It is illustrated on the logistic map, the Henon map, and the 12-dimensional Lorenz96 flow, and its ability to outperform linear least squares in these systems is explored at various noise levels and sampling rates. As expected, it is more effective when the forecast error distributions are non-Gaussian. The method selects parameter values by minimizing a proper, local skill score for continuous probability forecasts as a function of the parameter values. This approach is easier to implement in practice than alternative nonlinear methods based on the geometry of attractors or the ability of the model to shadow the observations. Direct measures of inadequacy in the model, the "implied ignorance," and the information deficit are introduced. PMID:23005513
Phenological Parameters Estimation Tool
McKellip, Rodney D.; Ross, Kenton W.; Spruce, Joseph P.; Smoot, James C.; Ryan, Robert E.; Gasser, Gerald E.; Prados, Donald L.; Vaughan, Ronald D.
2010-01-01
The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE
Estimation of ballistic block landing energy during 2014 Mount Ontake eruption
Tsunematsu, Kae; Ishimine, Yasuhiro; Kaneko, Takayuki; Yoshimoto, Mitsuhiro; Fujii, Toshitsugu; Yamaoka, Koshun
2016-05-01
The 2014 Mount Ontake eruption started just before noon on September 27, 2014. It killed 58 people, and five are still missing (as of January 1, 2016). The casualties were mainly caused by the impact of ballistic blocks around the summit area. It is necessary to know the magnitude of the block velocity and energy to construct a hazard map of ballistic projectiles and design effective shelters and mountain huts. The ejection velocities of the ballistic projectiles were estimated by comparing the observed distribution of the ballistic impact craters on the ground with simulated distributions of landing positions under various sets of conditions. A three-dimensional numerical multiparticle ballistic model adapted to account for topographic effect was used to estimate the ejection angles. From these simulations, we have obtained an ejection angle of γ = 20° from vertical to horizontal and α = 20° from north to east. With these ejection angle conditions, the ejection speed was estimated to be between 145 and 185 m/s for a previously obtained range of drag coefficients of 0.62-1.01. The order of magnitude of the mean landing energy obtained using our numerical simulation was 104 J.
Revisiting Cosmological parameter estimation
Prasad, Jayanti
2014-01-01
Constraining theoretical models with measuring the parameters of those from cosmic microwave background (CMB) anisotropy data is one of the most active areas in cosmology. WMAP, Planck and other recent experiments have shown that the six parameters standard $\\Lambda$CDM cosmological model still best fits the data. Bayesian methods based on Markov-Chain Monte Carlo (MCMC) sampling have been playing leading role in parameter estimation from CMB data. In one of the recent studies \\cite{2012PhRvD..85l3008P} we have shown that particle swarm optimization (PSO) which is a population based search procedure can also be effectively used to find the cosmological parameters which are best fit to the WMAP seven year data. In the present work we show that PSO not only can find the best-fit point, it can also sample the parameter space quite effectively, to the extent that we can use the same analysis pipeline to process PSO sampled points which is used to process the points sampled by Markov Chains, and get consistent res...
A Numerical Approach to Estimate the Ballistic Coefficient of Space Debris from TLE Orbital Data
Narkeliunas, Jonas
2016-01-01
theoretical simulations, even few continuous mode 10 kW ground-based lasers, focused by 1.5 m telescopes with adaptive optics, were enough to prevent significant amount of the debris collisions. Simulations were done by propagating all space objects in LEO by 1 year into the future and checking whether the probability of collision was high. For those space objects different ground-based lasers were used to divert them, afterwards collision probabilities were reevaluated. However, the actual accuracy of the LightForce software, which has been developed at NASA AmesResearch Center, depends on the veracity of the input parameters, one of which is the objects ballistic coefficient. It is a measure of bodys ability to overcome air resistance, which has a significant impact on the debris in LEO, and thus it is responsible for the shape of the trajectory of the debris. Having the exact values of the ballistic coefficient would make significantly better collision predictions, unfortunately, we do not know what are the values for most of the objects.In this research, we were working with part of LightForce code, which estimates the ballistic coefficient from ephemerides. Previously used method gave highly inaccurate values, when compared to known objects, and it needed to be changed. The goal of this work was to try out a different method of estimating the ballistic coefficient and to check whether or not it gives noticeable improvements.
Estimating Ancestral Population Parameters
Wakeley, J.; Hey, J.
1997-01-01
The expected numbers of different categories of polymorphic sites are derived for two related models of population history: the isolation model, in which an ancestral population splits into two descendents, and the size-change model, in which a single population undergoes an instantaneous change in size. For the isolation model, the observed numbers of shared, fixed, and exclusive polymorphic sites are used to estimate the relative sizes of the three populations, ancestral plus two descendent...
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...
Influence of pellet seating on the external ballistic parameters of spring-piston air guns.
Werner, Ronald; Schultz, Benno; Frank, Matthias
2016-09-01
In firearm examiners' and forensic specialists' casework as well as in air gun proof testing, reliable measurement of the weapon's muzzle velocity is indispensable. While there are standardized and generally accepted procedures for testing the performance of air guns, the method of seating the diabolo pellets deeper into the breech of break barrel spring-piston air guns has not found its way into standardized test procedures. The influence of pellet seating on the external ballistic parameters was investigated using ten different break barrel spring-piston air guns. Test shots were performed with the diabolo pellets seated 2 mm deeper into the breech using a pellet seater. The results were then compared to reference shots with conventionally loaded diabolo pellets. Projectile velocity was measured with a high-precision redundant ballistic speed measurement system. In eight out of ten weapons, the muzzle energy increased significantly when the pellet seater was used. The average increase in kinetic energy was 31 % (range 9-96 %). To conclude, seating the pellet even slightly deeper into the breech of spring-piston air guns might significantly alter the muzzle energy. Therefore, it is strongly recommended that this effect is taken into account when accurate and reliable measurements of air gun muzzle velocity are necessary. PMID:27448569
Pavier, Julien; Langlet, André; Eches, Nicolas; Prat, Nicolas; Bailly, Patrice; Jacquet, Jean-François
2015-07-01
The objective of the study is to better understand how blunt projectile ballistic parameters and material properties influence the events leading to injuries. The present work focuses on lateral thoracic impacts and follows an experimental approach. The projectiles are made with a soft foam nose assembled with a rigid rear plastic part. The dynamic properties of the foams were first determined using the Split Hopkinson Pressure Bar (SHPB) system. The impact forces on a rigid wall were then measured to provide reference load data. Lastly, shots were made on isolated thoraxes of porcine cadavers to investigate the response in the vicinity of the impact (wall displacements, rib accelerations and strains, rib fractures). Results show that the severity of the response appears to be mainly correlated with the impulse and with the pre-impact momentum. PMID:25951500
Estimating the shooting distance of a 9-mm Parabellum bullet via ballistic experiment.
Bresson, F; Franck, O
2009-11-20
We demonstrate here how the shooting distance of a 9-mm Parabellum FMJ bullet (115gr) has been estimated via shooting experiments. Such a bullet was found by investigators near a concrete wall, fairly distorted at its tip. The bullet carries no evidence of multiple impact and no evidence of ballistic impact on the wall has been reported. We estimated the impact velocity by comparing the questioned bullet with a set of comparison bullets hitting a wall (rigid target) with different velocities. The shooting distance was recovered from the impact velocity by studying the typical behavior of a manufactured 9 mm bullet weighting 115g (7.45g), shot in pistol or a sub-machine gun. The results demonstrated that the questioned bullet was a lost bullet. The shooting distance also helped the investigators, narrowing the range of the estimated positions of the shooter. PMID:19733457
Ballistic parameters and trauma potential of carbon dioxide-actuated arrow pistols.
Nguyen, Tien Thanh; Grossjohann, Rico; Ekkernkamp, Axel; Bockholdt, Britta; Frank, Matthias
2015-05-01
Medical literature abounds with reports of injuries and fatalities caused by arrows and crossbow bolts. Crossbows are of particular forensic and traumatological interest, because their mode of construction allows for temporary mechanical storage of energy. A newly developed type of pistol (Arcus Arrowstar), which belongs to the category of air and carbon dioxide weapons, discharges arrow-shaped bolts actuated by carbon dioxide cylinders. As, to the best of the authors' knowledge, literature contains no information on this uncommon subclass of weapons it is the aim of this work to provide the experimental data and to assess the trauma potential of these projectiles based on the ascertained physical parameters. Basic kinetic parameters of these carbon dioxide-actuated bolts (velocity v = 39 m/s, energy E = 7.2 J, energy density E' = 0.26 J/mm(2)) are similar to bolts discharged by pistol crossbows. Subsequent firing resulted in a continuous and fast decrease in kinetic energy of the arrows. Test shots into ballistic soap blocks reveal a high penetration capacity, especially when compared to conventional projectiles of equal kinetic energy values (like, e.g., airgun pellets). To conclude, these data demonstrate the high efficiency of arrow-shaped projectiles, which are also characterized by a high cross-sectional density (ratio of mass to cross-sectional area of a projectile). PMID:25246008
Identification of the selected parameters of the model in the process of ballistic impact
K. Jamroziak
2011-12-01
Full Text Available Purpose: Analysis of the process of overshooting the material with high speed refers to the identification of certain properties of elasto-dissipative materials. The result of this identification is to determine the value of deformation on the basis of changes in the speed of the projectile inside the material until it stops or overshoot this material.Design/methodology/approach: On the basis of the proposed dynamic models of piercing the material using energy balance equations, dissipation of the energy of mass which strikes the shield has been described.Findings: Dependence of the values of elastic energy and dissipative energy has been derived based on the energy balance equations whose values determine the sensitivity of the analyzed parameters of the dynamic models of the overshooting process.Research limitations/implications: Dynamic models have been analyzed and the impact energy balance equations have been derived. Those equations were the basis to determine the constants and to show their mathematical and graphical interpretation.Practical implications: Derivation of the dependence for the constants, which are characteristic for the energy balance equations, allowed to describe by dependencies the selected parameters of the model, whose identification may be performed using a special quasi-statistical tests on special stand or in the manner as described.Originality/value: Presented work including the identification of piercing the ballistic shield is a part of work on the implementation of the degenerated models to describe these phenomena.
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
Effective parameters of quasi-ballistic Nb-InAs-N Josephson junctions
Correa, Juliet; Gwinn, Elisabeth; Thomas, Mason
2003-03-01
We present a study of the temperature dependence of long semiconductor-coupled Josephson junctions that are mesoscopic in length, L, but not in width, W>>L. We compare our data for the critical current, I_c(T), to a theory for long, diffusive SNS junctions. Â"LongÂ" junctions have Δ/ɛc >>1 where ɛc is the Thouless energy and Δ is the energy gap of the superconductor. Although our junctions are nominally ballistic in the length direction, we justify comparison to a diffusive theory^1 by the effects of normal reflections at the superconductor/quantum-well boundaries. Fits of the data to this theory return values of the diffusion constant that agree with the quantum well parameters, and an effective junction length, L', that is several times the lithographic length, indicating several normal reflections per Andreev reflection. ^1 P. Dubos, H. Courtois, B. Pannetier, F.K. Wilhelm, A.D. Azikin, and G. Schön, Phys. Rev. B 63, (2001).
Improving absolute gravity estimates by the $L_p$-norm approximation of the ballistic trajectory
Nagornyi, V D; Araya, A
2015-01-01
Iteratively Re-weighted Least Squares (IRLS) were used to simulate the $L_p$-norm approximation of the ballistic trajectory in absolute gravimeters. Two iterations of the IRLS delivered sufficient accuracy of the approximation, with the bias indiscernible in random noise. The simulations were performed for different samplings of the trajectory and different distributions of the data noise. On the platykurtic distributions, the simulations found $L_p$-approximation with $p\\approx 3.25$ to yield several times more precise gravity estimates than those obtained with the standard least-squares ($p=2$). The similar improvement at $p\\approx 3.5$ was observed on real data measured at the excessive noise conditions.
Load Estimation from Modal Parameters
Aenlle, Manuel López; Brincker, Rune; Fernández, Pelayo Fernández; Canteli, Alfonso Fernández
In Natural Input Modal Analysis the modal parameters are estimated just from the responses while the loading is not recorded. However, engineers are sometimes interested in knowing some features of the loading acting on a structure. In this paper, a procedure to determine the loading from a FRF...... matrix assembled from modal parameters and the experimental responses recorded using standard sensors, is presented. The method implies the inversion of the FRF which, in general, is not full rank matrix due to the truncation of the modal space. Furthermore, some ecommendations are included to improve...
Inflation and cosmological parameter estimation
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.)
Estimation of Synchronous Machine Parameters
Oddvar Hallingstad
1980-01-01
Full Text Available The present paper gives a short description of an interactive estimation program based on the maximum likelihood (ML method. The program may also perform identifiability analysis by calculating sensitivity functions and the Hessian matrix. For the short circuit test the ML method is able to estimate the q-axis subtransient reactance x''q, which is not possible by means of the conventional graphical method (another set of measurements has to be used. By means of the synchronization and close test, the ML program can estimate the inertial constant (M, the d-axis transient open circuit time constant (T'do, the d-axis subtransient o.c.t.c (T''do and the q-axis subtransient o.c.t.c (T''qo. In particular, T''qo is difficult to estimate by any of the methods at present in use. Parameter identifiability is thoroughly examined both analytically and by numerical methods. Measurements from a small laboratory machine are used.
Parameter estimation and inverse problems
Aster, Richard C; Thurber, Clifford H
2011-01-01
Parameter Estimation and Inverse Problems, 2e provides geoscience students and professionals with answers to common questions like how one can derive a physical model from a finite set of observations containing errors, and how one may determine the quality of such a model. This book takes on these fundamental and challenging problems, introducing students and professionals to the broad range of approaches that lie in the realm of inverse theory. The authors present both the underlying theory and practical algorithms for solving inverse problems. The authors' treatment is approp
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
SURFACE VOLUME ESTIMATES FOR INFILTRATION PARAMETER ESTIMATION
Volume balance calculations used in surface irrigation engineering analysis require estimates of surface storage. These calculations are often performed by estimating upstream depth with a normal depth formula. That assumption can result in significant volume estimation errors when upstream flow d...
Data Handling and Parameter Estimation
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...... 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...
S. N. Asthana; S. R. Gore; M. V. Vaidya; Venkatesan, K; P.G. Shrotri
1991-01-01
This paper reports the influence of important process parameters, namely mixing time and batch size; on the mechanical properties and ballistics of nitramine-based advanced CMDB propellants. Considerable improvement to the tune of 67 per cent in tensile strength was observed at a mixing time increase of 60-135 min. Scaling up of batch size from 8 to 25 kg resulted in 30 per cent higher tensile strength. Recorded enhancement of burning rate was of the order of 8 per cent in both the set...
Jeon, Jongwook; Kang, Myounggon
2016-05-01
In this work, we investigated the noise source and noise parameters of a quasi-ballistic MOSFET at the high-frequency regime. We presented the shot noise properties in the measured drain current noise and its impact on the induced gate noise and the noise parameters of 10-nm-scale n-/p-type MOS (N/PMOS) devices for the first time. The measured noise sources and noise parameters were carefully analyzed with the shot and thermal noise models in all operation regions. On the basis of the results, new noise parameter models are proposed and the noise performance improvement in the quasi-ballistic regime is shown.
Review Of Parameter Estimation Using Adaptive Filtering
LALITA RANI, SHALOO KIKAN
2013-07-01
Full Text Available In this paper, a comparative study of different adaptive filter algorithm for channel parameter estimation is described. We presented different parameter estimation approaches of adaptive filtering. An extended Kalman filter is then applied as a near-optimal solution to the adaptive channel parameter estimation problem. Kalman filtering is applied for motion parameters resulting in optimal pose estimation. A parallel Kalman filter is applied for joint estimation of code delay, multipath gains and Doppler shift. In this paper, a complete review of parameter estimation using adaptive filtering is explained.
Estimation of Synchronous Machine Parameters
Oddvar Hallingstad
1980-01-01
The present paper gives a short description of an interactive estimation program based on the maximum likelihood (ML) method. The program may also perform identifiability analysis by calculating sensitivity functions and the Hessian matrix. For the short circuit test the ML method is able to estimate the q-axis subtransient reactance x''q, which is not possible by means of the conventional graphical method (another set of measurements has to be used). By means of the synchronization and close...
M. Jauhari; Bandyopadhyay, A
1980-01-01
The paper suggests an impact test which can be used to evaluate the deformation energies of small arm projectiles. Such an evaluation is of significance in wound ballistics studies while determining the amount of energy actually consumed in causing cavitation. Various sources of error inherent in the test have been discussed and it has been concluded that although approximate, the test can serve the useful purpose of providing a basis for interpreting the energy loss figures in gel on a ratio...
Estimation of physical parameters in induction motors
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...
Postprocessing MPEG based on estimated quantization parameters
Forchhammer, Søren
2009-01-01
the case where the coded stream is not accessible, or from an architectural point of view not desirable to use, and instead estimate some of the MPEG stream parameters based on the decoded sequence. The I-frames are detected and the quantization parameters are estimated from the coded stream and used...
Estimation for large non-centrality parameters
Inácio, Sónia; Mexia, João; Fonseca, Miguel; Carvalho, Francisco
2016-06-01
We introduce the concept of estimability for models for which accurate estimators can be obtained for the respective parameters. The study was conducted for model with almost scalar matrix using the study of estimability after validation of these models. In the validation of these models we use F statistics with non centrality parameter τ =‖λ/‖2 σ2 when this parameter is sufficiently large we obtain good estimators for λ and α so there is estimability. Thus, we are interested in obtaining a lower bound for the non-centrality parameter. In this context we use for the statistical inference inducing pivot variables, see Ferreira et al. 2013, and asymptotic linearity, introduced by Mexia & Oliveira 2011, to derive confidence intervals for large non-centrality parameters (see Inácio et al. 2015). These results enable us to measure relevance of effects and interactions in multifactors models when we get highly statistically significant the values of F tests statistics.
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
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
Translational Motion Compensation for Ballistic Targets Based on Delayed Conjugated Multiplication
He Si-san
2014-10-01
Full Text Available The micro-motion is combined with the high velocity of translation motion for ballistic targets. The translation motion should be compensated for micro-Doppler information extraction. A new method based on delay conjugate multiplication is proposed to compensate the translation motion of ballistic target. By delay conjugate multiplication of the received signal, the micro-Doppler information are canceled out and the translation motion parameters estimation problem is transformed as an multi-polynomial phase signal parameters estimation problem. Thus, the translation parameters can be estimated. Simulation results suggest that the proposed algorithm can achieve high-precision compensation for ballistic targets under low SNR.
Distributed Parameter Estimation in Probabilistic Graphical Models
Mizrahi, Yariv Dror; Denil, Misha; De Freitas, Nando
2014-01-01
This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimators are consistent.
Cosmological parameter estimation using Particle Swarm Optimization
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
M. Jauhari
1980-04-01
Full Text Available The paper suggests an impact test which can be used to evaluate the deformation energies of small arm projectiles. Such an evaluation is of significance in wound ballistics studies while determining the amount of energy actually consumed in causing cavitation. Various sources of error inherent in the test have been discussed and it has been concluded that although approximate, the test can serve the useful purpose of providing a basis for interpreting the energy loss figures in gel on a rational and scientific basis.
State and parameter estimation in bio processes
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.
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.
DEB parameters estimation for Mytilus edulis
Saraiva, S.; van der Meer, J.; Kooijman, S. A. L. M.; Sousa, T.
2011-11-01
The potential of DEB theory to simulate an organism life-cycle has been demonstrated at numerous occasions. However, its applicability requires parameter estimates that are not easily obtained by direct observations. During the last years various attempts were made to estimate the main DEB parameters for bivalve species. The estimation procedure was by then, however, rather ad-hoc and based on additional assumptions that were not always consistent with the DEB theory principles. A new approach has now been developed - the covariation method - based on simultaneous minimization of the weighted sum of squared deviations between data sets and model predictions in one single procedure. This paper presents the implementation of this method to estimate the DEB parameters for the blue mussel Mytilus edulis, using several data sets from the literature. After comparison with previous trials we conclude that the parameter set obtained by the covariation method leads to a better fit between model and observations, with potentially more consistency and robustness.
On Carleman estimates with two large parameters
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.
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 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.
GEODYN- ORBITAL AND GEODETIC PARAMETER ESTIMATION
Putney, B.
1994-01-01
The Orbital and Geodetic Parameter Estimation program, GEODYN, possesses the capability to estimate that set of orbital elements, station positions, measurement biases, and a set of force model parameters such that the orbital tracking data from multiple arcs of multiple satellites best fits the entire set of estimation parameters. The estimation problem can be divided into two parts: the orbit prediction problem, and the parameter estimation problem. GEODYN solves these two problems by employing Cowell's method for integrating the orbit and a Bayesian least squares statistical estimation procedure for parameter estimation. GEODYN has found a wide range of applications including determination of definitive orbits, tracking instrumentation calibration, satellite operational predictions, and geodetic parameter estimation, such as the estimations for global networks of tracking stations. The orbit prediction problem may be briefly described as calculating for some later epoch the new conditions of state for the satellite, given a set of initial conditions of state for some epoch, and the disturbing forces affecting the motion of the satellite. The user is required to supply only the initial conditions of state and GEODYN will provide the forcing function and integrate the equations of motion of the satellite. Additionally, GEODYN performs time and coordinate transformations to insure the continuity of operations. Cowell's method of numerical integration is used to solve the satellite equations of motion and the variational partials for force model parameters which are to be adjusted. This method uses predictor-corrector formulas for the equations of motion and corrector formulas only for the variational partials. The parameter estimation problem is divided into three separate parts: 1) instrument measurement modeling and partial derivative computation, 2) data error correction, and 3) statistical estimation of the parameters. Since all of the measurements modeled by
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.
LISA parameter estimation using numerical merger waveforms
Thorpe, J I; Kelly, B J; Fahey, R P; Arnaud, K; Baker, J G
2008-01-01
Recent advances in numerical relativity provide a detailed description of the waveforms of coalescing massive black hole binaries (MBHBs), expected to be the strongest detectable LISA sources. We present a preliminary study of LISA's sensitivity to MBHB parameters using a hybrid numerical/analytic waveform for equal-mass, non-spinning holes. The Synthetic LISA software package is used to simulate the instrument response and the Fisher information matrix method is used to estimate errors in the parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of one million Solar masses at a redshift of one were found to decrease by a factor of slightly more than two for signals with merger as compared to signals truncated at the Schwarzchild ISCO.
LISA parameter estimation using numerical merger waveforms
Thorpe, J I; McWilliams, S T; Kelly, B J; Fahey, R P; Arnaud, K; Baker, J G, E-mail: James.I.Thorpe@nasa.go [NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771 (United States)
2009-05-07
Recent advances in numerical relativity provide a detailed description of the waveforms of coalescing massive black hole binaries (MBHBs), expected to be the strongest detectable LISA sources. We present a preliminary study of LISA's sensitivity to MBHB parameters using a hybrid numerical/analytic waveform for equal-mass, non-spinning holes. The Synthetic LISA software package is used to simulate the instrument response, and the Fisher information matrix method is used to estimate errors in the parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of 10{sup 6} M{sub o-dot} at a redshift of z approx 1 were found to decrease by a factor of slightly more than two for signals with merger as compared to signals truncated at the Schwarzchild ISCO.
LISA parameter estimation using numerical merger waveforms
Recent advances in numerical relativity provide a detailed description of the waveforms of coalescing massive black hole binaries (MBHBs), expected to be the strongest detectable LISA sources. We present a preliminary study of LISA's sensitivity to MBHB parameters using a hybrid numerical/analytic waveform for equal-mass, non-spinning holes. The Synthetic LISA software package is used to simulate the instrument response, and the Fisher information matrix method is used to estimate errors in the parameters. Initial results indicate that inclusion of the merger signal can significantly improve the precision of some parameter estimates. For example, the median parameter errors for an ensemble of systems with total redshifted mass of 106 Mo-dot at a redshift of z ∼ 1 were found to decrease by a factor of slightly more than two for signals with merger as compared to signals truncated at the Schwarzchild ISCO.
Parameter Estimation of Noise Corrupted Sinusoids
O'Brien, Francis J; Johnnie, Nathan
2011-01-01
Existing algorithms for fitting the parameters of a sinusoid to noisy discrete time observations are not always successful due to initial value sensitivity and other issues. This paper demonstrates the techniques of FIR filtering, Fast Fourier Transform, and nonlinear least squares minimization as useful in the parameter estimation of amplitude, frequency and phase exemplified for a low-frequency time-delayed sinusoid describing simple harmonic motion. Alternative means are described for estimating frequency and phase angle. An autocorrelation function for harmonic motion is also derived.
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
Parameter estimation of the WMTD model
LUO Ji; QIU Hong-bing
2009-01-01
The MTD (mixture transition distribution) model based on Weibull distribution (WMTD model) is proposed in this paper, which is aimed at its parameter estimation. An EM algorithm for estimation is given and shown to work well by some simulations. And bootstrap method is used to obtain confidence regions for the parameters. Finally, the results of a real example--predicting stock prices--show that the WMTD model proposed is able to capture the features of the data from thick-tailed distribution better than GMTD (mixture transition distribution) model.
Hurst Parameter Estimation Using Artificial Neural Networks
S..Ledesma-Orozco
2011-08-01
Full Text Available The Hurst parameter captures the amount of long-range dependence (LRD in a time series. There are severalmethods to estimate the Hurst parameter, being the most popular: the variance-time plot, the R/S plot, theperiodogram, and Whittle’s estimator. The first three are graphical methods, and the estimation accuracy depends onhow the plot is interpreted and calculated. In contrast, Whittle’s estimator is based on a maximum likelihood techniqueand does not depend on a graph reading; however, it is computationally expensive. A new method to estimate theHurst parameter is proposed. This new method is based on an artificial neural network. Experimental results showthat this method outperforms traditional approaches, and can be used on applications where a fast and accurateestimate of the Hurst parameter is required, i.e., computer network traffic control. Additionally, the Hurst parameterwas computed on series of different length using several methods. The simulation results show that the proposedmethod is at least ten times faster than traditional methods.
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.
Performance Analysis of Parameter Estimation Using LASSO
Panahi, Ashkan; Viberg, Mats
2012-01-01
The Least Absolute Shrinkage and Selection Operator (LASSO) has gained attention in a wide class of continuous parametric estimation problems with promising results. It has been a subject of research for more than a decade. Due to the nature of LASSO, the previous analyses have been non-parametric. This ignores useful information and makes it difficult to compare LASSO to traditional estimators. In particular, the role of the regularization parameter and super-resolution properties of LASSO h...
Biosorption Parameter Estimation with Genetic Algorithm
Yung-Tse Hung; Eui Yong Kim; Xiao Feng; Khim Hoong Chu
2011-01-01
In biosorption research, a fairly broad range of mathematical models are used to correlate discrete data points obtained from batch equilibrium, batch kinetic or fixed bed breakthrough experiments. Most of these models are inherently nonlinear in their parameters. Some of the models have enjoyed widespread use, largely because they can be linearized to allow the estimation of parameters by least-squares linear regression. Selecting a model for data correlation appears to be dictated by the ea...
Spin bath narrowing with adaptive parameter estimation
Cappellaro, Paola
2012-01-01
We present a measurement scheme capable of achieving the quantum limit of parameter estimation using an adaptive strategy that minimizes the parameter's variance at each step. The adaptive rule we propose makes the scheme robust against errors, in particular imperfect readouts, a critical requirement to extend adaptive schemes from quantum optics to solid-state sensors. Thanks to recent advances in single-shot readout capabilities for electronic spins in the solid state (such as Nitrogen Vaca...
Parameter Estimation of Noise Corrupted Sinusoids
O'Brien, Jr., W.,; Johnnie, Nathan
2011-01-01
Existing algorithms for fitting the parameters of a sinusoid to noisy discrete time observations are not always successful due to initial value sensitivity and other issues. This paper demonstrates the techniques of FIR filtering, Fast Fourier Transform, circular autocorreltion, and nonlinear least squares minimization as useful in the parameter estimation of amplitude, frequency and phase exemplified for a low-frequency time-delayed sinusoid describing simple harmonic motion. Alternative mea...
Robust estimation of hydrological model parameters
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.
Aquifer parameter estimation from surface resistivity data.
Niwas, Sri; de Lima, Olivar A L
2003-01-01
This paper is devoted to the additional use, other than ground water exploration, of surface geoelectrical sounding data for aquifer hydraulic parameter estimation. In a mesoscopic framework, approximated analytical equations are developed separately for saline and for fresh water saturations. A few existing useful aquifer models, both for clean and shaley sandstones, are discussed in terms of their electrical and hydraulic effects, along with the linkage between the two. These equations are derived for insight and physical understanding of the phenomenon. In a macroscopic scale, a general aquifer model is proposed and analytical relations are derived for meaningful estimation, with a higher level of confidence, of hydraulic parameter from electrical parameters. The physical reasons for two different equations at the macroscopic level are explicitly explained to avoid confusion. Numerical examples from existing literature are reproduced to buttress our viewpoint. PMID:12533080
Blind estimation of compartmental model parameters
Computation of physiologically relevant kinetic parameters from dynamic PET or SPECT imaging requires knowledge of the blood input function. This work is concerned with developing methods to accurately estimate these kinetic parameters blindly; that is, without use of a directly measured blood input function. Instead, only measurements of the output functions - the tissue time-activity curves - are used. The blind estimation method employed here minimizes a set of cross-relation equations, from which the blood term has been factored out, to determine compartmental model parameters. The method was tested with simulated data appropriate for dynamic SPECT cardiac perfusion imaging with 99mTc-teboroxime and for dynamic PET cerebral blood flow imaging with 15O water. The simulations did not model the tomographic process. Noise levels typical of the respective modalities were employed. From three to eight different regions were simulated, each with different time-activity curves. The time-activity curve (24 or 70 time points) for each region was simulated with a compartment model. The simulation used a biexponential blood input function and washin rates between 0.2 and 1.3 min-1 and washout rates between 0.2 and 1.0 min-1. The system of equations was solved numerically and included constraints to bound the range of possible solutions. From the cardiac simulations, washin was determined to within a scale factor of the true washin parameters with less than 6% bias and 12% variability. 99mTc-teboroxime washout results had less than 5% bias, but variability ranged from 14% to 43%. The cerebral blood flow washin parameters were determined with less than 5% bias and 4% variability. The washout parameters were determined with less than 4% bias, but had 15-30% variability. Since washin is often the parameter of most use in clinical studies, the blind estimation approach may eliminate the current necessity of measuring the input function when performing certain dynamic studies
Blind estimation of compartmental model parameters.
Di Bella, E V; Clackdoyle, R; Gullberg, G T
1999-03-01
Computation of physiologically relevant kinetic parameters from dynamic PET or SPECT imaging requires knowledge of the blood input function. This work is concerned with developing methods to accurately estimate these kinetic parameters blindly; that is, without use of a directly measured blood input function. Instead, only measurements of the output functions--the tissue time-activity curves--are used. The blind estimation method employed here minimizes a set of cross-relation equations, from which the blood term has been factored out, to determine compartmental model parameters. The method was tested with simulated data appropriate for dynamic SPECT cardiac perfusion imaging with 99mTc-teboroxime and for dynamic PET cerebral blood flow imaging with 15O water. The simulations did not model the tomographic process. Noise levels typical of the respective modalities were employed. From three to eight different regions were simulated, each with different time-activity curves. The time-activity curve (24 or 70 time points) for each region was simulated with a compartment model. The simulation used a biexponential blood input function and washin rates between 0.2 and 1.3 min(-1) and washout rates between 0.2 and 1.0 min(-1). The system of equations was solved numerically and included constraints to bound the range of possible solutions. From the cardiac simulations, washin was determined to within a scale factor of the true washin parameters with less than 6% bias and 12% variability. 99mTc-teboroxime washout results had less than 5% bias, but variability ranged from 14% to 43%. The cerebral blood flow washin parameters were determined with less than 5% bias and 4% variability. The washout parameters were determined with less than 4% bias, but had 15-30% variability. Since washin is often the parameter of most use in clinical studies, the blind estimation approach may eliminate the current necessity of measuring the input function when performing certain dynamic studies
Parameter estimation in channel network flow simulation
Han Longxi
2008-01-01
Simulations of water flow in channel networks require estimated values of roughness for all the individual channel segments that make up a network. When the number of individual channel segments is large, the parameter calibration workload is substantial and a high level of uncertainty in estimated roughness cannot be avoided. In this study, all the individual channel segments are graded according to the factors determining the value of roughness. It is assumed that channel segments with the same grade have the same value of roughness. Based on observed hydrological data, an optimal model for roughness estimation is built. The procedure of solving the optimal problem using the optimal model is described. In a test of its efficacy, this estimation method was applied successfully in the simulation of tidal water flow in a large complicated channel network in the lower reach of the Yangtze River in China.
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.
Multiple Parameter Estimation With Quantized Channel Output
Mezghani, Amine; Nossek, Josef A
2010-01-01
We present a general problem formulation for optimal parameter estimation based on quantized observations, with application to antenna array communication and processing (channel estimation, time-of-arrival (TOA) and direction-of-arrival (DOA) estimation). The work is of interest in the case when low resolution A/D-converters (ADCs) have to be used to enable higher sampling rate and to simplify the hardware. An Expectation-Maximization (EM) based algorithm is proposed for solving this problem in a general setting. Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of quantization and the optimal choice of the ADC characteristic. Numerical and analytical analysis reveals that reliable estimation may still be possible even when the quantization is very coarse.
Sensor Placement for Modal Parameter Subset Estimation
Ulriksen, Martin Dalgaard; Bernal, Dionisio; Damkilde, Lars
2016-01-01
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......). 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....
On closure parameter estimation in chaotic systems
J. Hakkarainen
2012-02-01
Full Text Available Many dynamical models, such as numerical weather prediction and climate models, contain so called closure parameters. These parameters usually appear in physical parameterizations of sub-grid scale processes, and they act as "tuning handles" of the models. Currently, the values of these parameters are specified mostly manually, but the increasing complexity of the models calls for more algorithmic ways to perform the tuning. Traditionally, parameters of dynamical systems are estimated by directly comparing the model simulations to observed data using, for instance, a least squares approach. However, if the models are chaotic, the classical approach can be ineffective, since small errors in the initial conditions can lead to large, unpredictable deviations from the observations. In this paper, we study numerical methods available for estimating closure parameters in chaotic models. We discuss three techniques: off-line likelihood calculations using filtering methods, the state augmentation method, and the approach that utilizes summary statistics from long model simulations. The properties of the methods are studied using a modified version of the Lorenz 95 system, where the effect of fast variables are described using a simple parameterization.
Cosmological parameter estimation: impact of CMB aberration
The peculiar motion of an observer with respect to the CMB rest frame induces an apparent deflection of the observed CMB photons, i.e. aberration, and a shift in their frequency, i.e. Doppler effect. Both effects distort the temperature multipoles alm's via a mixing matrix at any l. The common lore when performing a CMB based cosmological parameter estimation is to consider that Doppler affects only the l = 1 multipole, and neglect any other corrections. In this paper we reconsider the validity of this assumption, showing that it is actually not robust when sky cuts are included to model CMB foreground contaminations. Assuming a simple fiducial cosmological model with five parameters, we simulated CMB temperature maps of the sky in a WMAP-like and in a Planck-like experiment and added aberration and Doppler effects to the maps. We then analyzed with a MCMC in a Bayesian framework the maps with and without aberration and Doppler effects in order to assess the ability of reconstructing the parameters of the fiducial model. We find that, depending on the specific realization of the simulated data, the parameters can be biased up to one standard deviation for WMAP and almost two standard deviations for Planck. Therefore we conclude that in general it is not a solid assumption to neglect aberration in a CMB based cosmological parameter estimation
CosmoSIS: modular cosmological parameter estimation
Zuntz, Joe; Jennings, Elise; Rudd, Douglas; Manzotti, Alessandro; Dodelson, Scott; Bridle, Sarah; Sehrish, Saba; Kowalkowski, James
2014-01-01
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 CosmoSIS, 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
Bayesian parameter estimation for effective field theories
Wesolowski, S; Furnstahl, R J; Phillips, D R; Thapaliya, A
2015-01-01
We present procedures based on Bayesian statistics for effective field theory (EFT) parameter estimation from data. The extraction of low-energy constants (LECs) is guided by theoretical expectations that supplement such information in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools are developed that analyze the fit and ensure that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems and the extraction of LECs for the nucleon mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
Bayesian parameter estimation for effective field theories
Wesolowski, S.; Klco, N.; Furnstahl, R. J.; Phillips, D. R.; Thapaliya, A.
2016-07-01
We present procedures based on Bayesian statistics for estimating, from data, the parameters of effective field theories (EFTs). The extraction of low-energy constants (LECs) is guided by theoretical expectations in a quantifiable way through the specification of Bayesian priors. A prior for natural-sized LECs reduces the possibility of overfitting, and leads to a consistent accounting of different sources of uncertainty. A set of diagnostic tools is developed that analyzes the fit and ensures that the priors do not bias the EFT parameter estimation. The procedures are illustrated using representative model problems, including the extraction of LECs for the nucleon-mass expansion in SU(2) chiral perturbation theory from synthetic lattice data.
Measurement Data Modeling and Parameter Estimation
Wang, Zhengming; Yao, Jing; Gu, Defeng
2011-01-01
Measurement Data Modeling and Parameter Estimation integrates mathematical theory with engineering practice in the field of measurement data processing. Presenting the first-hand insights and experiences of the authors and their research group, it summarizes cutting-edge research to facilitate the application of mathematical theory in measurement and control engineering, particularly for those interested in aeronautics, astronautics, instrumentation, and economics. Requiring a basic knowledge of linear algebra, computing, and probability and statistics, the book illustrates key lessons with ta
Estimating Earthen Dam-Breach Parameters
Mahdi Moharrampour
2013-12-01
Full Text Available Dam failure leads to release of high volume of water which causes huge waves in downstream. Failure in dam may cause financial loss but life losses depend on the drowned zone, population residing in downstream of the danger zone and warning time. Therefore, it is inevitable to predict dam failure and its resulting dangers to reduce life and financial losses. Flood simulation models simulate flood caused by dam failure (such as DAMBRK and FLDWAV, the resulting outflow in dam failure and its route in downstream of the river. Such models mostly focused on outflow hydrographs. In these models, physical development of failure is not simulated but they describe dam failure process as parametric process which is defined as form of failure, final size and time required for developing it (destruction time. Therefore, dam failure parameters should be estimated for simulating dam failure and applied as input information in simulation model. For this reason, failure of Bidakan earth dam located in Chahar Mahal va Bakhtiari Province has been simulated by estimating parameters of failure, analyzing uncertainty of experimental methods for estimating parameters of failure and applying SMPDBK model.
Taking Variable Correlation into Consideration during Parameter Estimation
T.J. Santos; Pinto, J C.
1998-01-01
Variable correlations are usually neglected during parameter estimation. Very frequently these are gross assumptions and may potentially lead to inadequate interpretation of final estimation results. For this reason, variable correlation and model parameters are sometimes estimated simultaneously in certain parameter estimation procedures. It is shown, however, that usually taking variable correlation into consideration during parameter estimation may be inadequate and unnecessary, unless ind...
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Hadiyanto Hadiyanto
2012-05-01
Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels. Abstrak PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan
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...
Composite likelihood estimation of demographic parameters
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
Toward unbiased estimations of the statefinder parameters
Aviles, Alejandro; Luongo, Orlando
2016-01-01
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 cos...
Parameter estimation using B-Trees
Schmidt, Albrecht; Bøhlen, Michael H.
2004-01-01
This paper presents a method for accelerating algorithms for computing common statistical operations like parameter estimation or sampling on B-Tree indexed data; the work was carried out in the context of visualisation of large scientific data sets. The underlying idea is the following: the shape...... of balanced data structures like B-Trees encodes and reflects data semantics according to the balance criterion. For example, clusters in the index attribute are somewhat likely to be present not only on the data or leaf level of the tree but should propagate up into the interior levels. The paper...... also hints at opportunities and limitations of this approach for visualisation of large data sets. The advantages of the method are manifold. Not only does it enable advanced algorithms through a performance boost for basic operations like density estimation, but it also builds on functionality that is...
Parameter Estimation in Active Plate Structures
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...... through gradient based optimization techniques, while the second is based on a metamodel of the inverse problem, using artificial neural networks. A numerical higher order finite element laminated plate model is used in both methods and results are compared and discussed through a simulated and an...
Fast cosmological parameter estimation using neural networks
Auld, T; Hobson, M P; Gull, S F
2006-01-01
We present a method for accelerating the calculation of CMB power spectra, matter power spectra and likelihood functions for use in cosmological parameter estimation. The algorithm, called CosmoNet, is based on training a multilayer perceptron neural network and shares all the advantages of the recently released Pico algorithm of Fendt & Wandelt, but has several additional benefits in terms of simplicity, computational speed, memory requirements and ease of training. We demonstrate the capabilities of CosmoNet by computing CMB power spectra over a box in the parameter space of flat \\Lambda CDM models containing the 3\\sigma WMAP1 confidence region. We also use CosmoNet to compute the WMAP3 likelihood for flat \\Lambda CDM models and show that marginalised posteriors on parameters derived are very similar to those obtained using CAMB and the WMAP3 code. We find that the average error in the power spectra is typically 2-3% of cosmic variance, and that CosmoNet is \\sim 7 \\times 10^4 faster than CAMB (for flat ...
Multifrequency SAR data for estimating hydrological parameters
The sensitivity of backscattering coefficients to some geophysical parameters which play a significant role in hydrological processes (vegetation biomass, soil moisture and surface roughness) is discussed. Experimental results show that P-band makes it possible the monitoring of forest biomass, L-band appears to be good for wide-leaf crops, and C- and X-bands for small-leaf crops. Moreover, L-band backscattering makes the highest contribution in estimating soil moisture and surface roughness. The sensitivity to spatial distribution of soil moisture and surface roughness is rather low, since both quantities affect the radar signal. However, observing data collected at different dates and averaged over several fields, the correlation to soil moisture is significant, since the effects of spatial roughness variations are smoothed. The retrieval of both soil moisture and surface roughness has been performed by means of a semiempirical model
Translational Motion Compensation for Ballistic Targets Based on Delayed Conjugated Multiplication
He Si-san; Zhao Hui-ning; Zhang Yong-shun
2014-01-01
The micro-motion is combined with the high velocity of translation motion for ballistic targets. The translation motion should be compensated for micro-Doppler information extraction. A new method based on delay conjugate multiplication is proposed to compensate the translation motion of ballistic target. By delay conjugate multiplication of the received signal, the micro-Doppler information are canceled out and the translation motion parameters estimation problem is transformed as an multi-p...
An Inhomogeneous Bayesian Texture Model for Spatially Varying Parameter Estimation
Dharmagunawardhana, Chathurika; Mahmoodi, Sasan; Bennett, Michael; Niranjan, Mahesan
2014-01-01
In statistical model based texture feature extraction, features based on spatially varying parameters achieve higher discriminative performances compared to spatially constant parameters. In this paper we formulate a novel Bayesian framework which achieves texture characterization by spatially varying parameters based on Gaussian Markov random fields. The parameter estimation is carried out by Metropolis-Hastings algorithm. The distributions of estimated spatially varying paramete...
Ocean wave parameters estimation using backpropagation neural networks
Mandal, S.; SubbaRao; Raju, D.H.
In the present study, various ocean wave parameters are estimated from theoretical Pierson-Moskowitz spectra as well as measured ocean wave spectra using back propagation neural networks (BNN). Ocean wave parameters estimation by BNN shows...
System and method for motor parameter estimation
Luhrs, Bin; Yan, Ting
2014-03-18
A system and method for determining unknown values of certain motor parameters includes a motor input device connectable to an electric motor having associated therewith values for known motor parameters and an unknown value of at least one motor parameter. The motor input device includes a processing unit that receives a first input from the electric motor comprising values for the known motor parameters for the electric motor and receive a second input comprising motor data on a plurality of reference motors, including values for motor parameters corresponding to the known motor parameters of the electric motor and values for motor parameters corresponding to the at least one unknown motor parameter value of the electric motor. The processor determines the unknown value of the at least one motor parameter from the first input and the second input and determines a motor management strategy for the electric motor based thereon.
Parameter estimation with Sandage-Loeb test
The Sandage-Loeb (SL) test directly measures the expansion rate of the universe in the redshift range of 2 ∼< z ∼< 5 by detecting redshift drift in the spectra of Lyman-α forest of distant quasars. We discuss the impact of the future SL test data on parameter estimation for the ΛCDM, the wCDM, and the w0waCDM models. To avoid the potential inconsistency with other observational data, we take the best-fitting dark energy model constrained by the current observations as the fiducial model to produce 30 mock SL test data. The SL test data provide an important supplement to the other dark energy probes, since they are extremely helpful in breaking the existing parameter degeneracies. We show that the strong degeneracy between Ωm and H0 in all the three dark energy models is well broken by the SL test. Compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraints on Ωm and H0 by more than 60% for all the three models. But the SL test can only moderately improve the constraint on the equation of state of dark energy. We show that a 30-yr observation of SL test could help improve the constraint on constant w by about 25%, and improve the constraints on w0 and wa by about 20% and 15%, respectively. We also quantify the constraining power of the SL test in the future high-precision joint geometric constraints on dark energy. The mock future supernova and baryon acoustic oscillation data are simulated based on the space-based project JDEM. We find that the 30-yr observation of SL test would help improve the measurement precision of Ωm, H0, and wa by more than 70%, 20%, and 60%, respectively, for the w0waCDM model
Parameter estimation with Sandage-Loeb test
Geng, Jia-Jia; Zhang, Jing-Fei; Zhang, Xin, E-mail: gengjiajia163@163.com, E-mail: jfzhang@mail.neu.edu.cn, E-mail: zhangxin@mail.neu.edu.cn [Department of Physics, College of Sciences, Northeastern University, Shenyang 110004 (China)
2014-12-01
The Sandage-Loeb (SL) test directly measures the expansion rate of the universe in the redshift range of 2 ∼< z ∼< 5 by detecting redshift drift in the spectra of Lyman-α forest of distant quasars. We discuss the impact of the future SL test data on parameter estimation for the ΛCDM, the wCDM, and the w{sub 0}w{sub a}CDM models. To avoid the potential inconsistency with other observational data, we take the best-fitting dark energy model constrained by the current observations as the fiducial model to produce 30 mock SL test data. The SL test data provide an important supplement to the other dark energy probes, since they are extremely helpful in breaking the existing parameter degeneracies. We show that the strong degeneracy between Ω{sub m} and H{sub 0} in all the three dark energy models is well broken by the SL test. Compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraints on Ω{sub m} and H{sub 0} by more than 60% for all the three models. But the SL test can only moderately improve the constraint on the equation of state of dark energy. We show that a 30-yr observation of SL test could help improve the constraint on constant w by about 25%, and improve the constraints on w{sub 0} and w{sub a} by about 20% and 15%, respectively. We also quantify the constraining power of the SL test in the future high-precision joint geometric constraints on dark energy. The mock future supernova and baryon acoustic oscillation data are simulated based on the space-based project JDEM. We find that the 30-yr observation of SL test would help improve the measurement precision of Ω{sub m}, H{sub 0}, and w{sub a} by more than 70%, 20%, and 60%, respectively, for the w{sub 0}w{sub a}CDM model.
Estimation of high altitude Martian dust parameters
Pabari, Jayesh; Bhalodi, Pinali
2016-07-01
Dust devils are known to occur near the Martian surface mostly during the mid of Southern hemisphere summer and they play vital role in deciding background dust opacity in the atmosphere. The second source of high altitude Martian dust could be due to the secondary ejecta caused by impacts on Martian Moons, Phobos and Deimos. Also, the surfaces of the Moons are charged positively due to ultraviolet rays from the Sun and negatively due to space plasma currents. Such surface charging may cause fine grains to be levitated, which can easily escape the Moons. It is expected that the escaping dust form dust rings within the orbits of the Moons and therefore also around the Mars. One more possible source of high altitude Martian dust is interplanetary in nature. Due to continuous supply of the dust from various sources and also due to a kind of feedback mechanism existing between the ring or tori and the sources, the dust rings or tori can sustain over a period of time. Recently, very high altitude dust at about 1000 km has been found by MAVEN mission and it is expected that the dust may be concentrated at about 150 to 500 km. However, it is mystery how dust has reached to such high altitudes. Estimation of dust parameters before-hand is necessary to design an instrument for the detection of high altitude Martian dust from a future orbiter. In this work, we have studied the dust supply rate responsible primarily for the formation of dust ring or tori, the life time of dust particles around the Mars, the dust number density as well as the effect of solar radiation pressure and Martian oblateness on dust dynamics. The results presented in this paper may be useful to space scientists for understanding the scenario and designing an orbiter based instrument to measure the dust surrounding the Mars for solving the mystery. The further work is underway.
Estimates of Armington parameters for a landlocked economy
Nganou, Jean-Pascal
2005-01-01
One of the most debated issues in the Computable General Equilibrium (CGE) literature concerns the validity of the key behavioral parameters used in the calibration process. CGE modelers seldom estimate those parameters, preferring to borrow from the handful of estimates available in the literature. The lack of data is often cited as a reason for this type of modus operandi (technique). Estimating key parameters is very crucial since CGE results are quite sensitive to parameter specification....
Parameter estimation for estimation of bottom hole pressure during drilling.
Vea, Hans Kristian
2009-01-01
In this thesis we examine four bottom hole pressure estimators based on adaptive estimation of the friction pressure for the drill string and the annulus. Knowledge about the bottom hole pressure is crucial to achieve security and commercial objectives. Bottom hole pressure measurements transmitted by mud pulse telemetry have limited bandwidth and it is common to use additional models to estimate the bottom hole pressure when measurements are unavailable. The motivation for an adaptive approa...
On drift parameter estimation in models with fractional Brownian motion
Kozachenko, Yuriy; Mishura, Yuliya
2011-01-01
We consider a stochastic differential equation involving standard and fractional Brownian motion with unknown drift parameter to be estimated. We investigate the standard maximum likelihood estimate of the drift parameter, two non-standard estimates and three estimates for the sequential estimation. Model strong consistency and some other properties are proved. The linear model and Ornstein-Uhlenbeck model are studied in detail. As an auxiliary result, an asymptotic behavior of the fractional derivative of the fractional Brownian motion is established.
Parameter estimation in dynamic Casimir force measurements with known periodicity
Cui, Song, E-mail: cuis@imre.a-star.edu.sg [Institute of Materials Research and Engineering, 3 Research Link, Singapore 117602 (Singapore); Soh, Yeng Chai, E-mail: eycsoh@ntu.edu.sg [School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798 (Singapore)
2011-12-05
It is important to have an accurate estimate of the unknown parameters such as the separation distance between interacting materials in Casimir force measurements. Current methods tend to produce large estimation errors. In this Letter, we present a novel method based on an adaptive control approach to estimate the unknown parameters using large amplitude dynamic Casimir measurements at separation distances of below 1 μm where both electrostatic force and Casimir force are significant. The estimate is proved to be accurate and the effectiveness of our method is demonstrated via a numerical example. -- Highlights: ► Unknown parameters like separation gap are nonlinearly parameterized in Casimir force measurements ► A two-stage parameter estimation method is proposed to estimate unknown parameters accurately. ► Our method is proved to be effective by theoretical derivation and simulations. ► Our method can be applied to a broad range of nonlinear parameter estimation problems.
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
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, or p...
Fackler, M L
1986-12-01
Wound profiles made under controlled conditions in the wound ballistics laboratory at the Letterman Army Institute of Research showed the location along their tissue path at which projectiles cause tissue disruption and the type of disruption (crush from direct contact with the projectile or stretch from temporary cavitation). Comparison of wound profiles showed the fallacy in attempting to judge wound severity using velocity alone, and laid to rest the common belief that in treating a wound caused by a high-velocity missile, one needs to excise tissue far in excess of that which appears damaged. All penetrating projectile wounds, whether civilian or military, therefore should be treated the same regardless of projectile velocity. Diagnosis of the approximate amount and location of tissue disruption is made by physical examination and appropriate radiographic studies. These wounds are contaminated, and coverage with a penicillin-type antibiotic should be provided. PMID:3777618
METHOD ON ESTIMATION OF DRUG'S PENETRATED PARAMETERS
刘宇红; 曾衍钧; 许景锋; 张梅
2004-01-01
Transdermal drug delivery system (TDDS) is a new method for drug delivery. The analysis of plenty of experiments in vitro can lead to a suitable mathematical model for the description of the process of the drug's penetration through the skin, together with the important parameters that are related to the characters of the drugs.After the research work of the experiments data,a suitable nonlinear regression model was selected. Using this model, the most important parameter-penetrated coefficient of 20 drugs was computed.In the result one can find, this work supports the theory that the skin can be regarded as singular membrane.
Estimation of motility parameters from trajectory data
Vestergaard, Christian L.; Pedersen, Jonas Nyvold; Mortensen, Kim I.;
2015-01-01
Given a theoretical model for a self-propelled particle or micro-organism, how does one optimally determine the parameters of the model from experimental data in the form of a time-lapse recorded trajectory? For very long trajectories, one has very good statistics, and optimality may matter little...... which similar results may be obtained also for self-propelled particles....
M-Testing Using Finite and Infinite Dimensional Parameter Estimators
White, Halbert; Hong, Yongmiao
1999-01-01
The m-testing approach provides a general and convenient framework in which to view and construct specification tests for econometric models. Previous m-testing frameworks only consider test statistics that involve finite dimensional parameter estimators and infinite dimensional parameter estimators affecting the limit distribution of the m-test statistics. In this paper we propose a new m-testing framework using both finite and infinite dimensional parameter estimators, where the latter may ...
On-line parameter estimation of a magnetic bearing
Delpoux, Romain; Floquet, Thierry
2011-01-01
This article presents a parameter estimation algorithm for a magnetic bearing. Such process have strongly nonlinear dynamics andare inherently unstable systems. A simplified model of the magnetic bearing is developed in order to be able to estimate certain parameters. These parameters are difficult to measure, and may slightly vary over time. The expression of the estimates is written as a function of integrals of the inputs and outputs of the system. The experiments show a fast and robust on...
Parameter estimation using compensatory neural networks
M Sinha; P K Kalra; K Kumar
2000-04-01
Proposed here is a new neuron model, a basis for Compensatory Neural Network Architecture (CNNA), which not only reduces the total number of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron model as well as the higher neuron model (multiplicative aggregation function). It can adapt to standard neuron and higher order neuron, as well as a combination of the two. This approach is found to estimate the orbit with accuracy significantly better than Kalman Filter (KF) and Feedforward Multilayer Neural Network (FMNN) (also simply referred to as Artificial Neural Network, ANN) with lambda-gamma learning. The typical simulation runs also bring out the superiority of the proposed scheme over Kalman filter from the standpoint of computation time and the amount of data needed for the desired degree of estimated accuracy for the specific problem of orbit determination.
Muscle parameters estimation based on biplanar radiography.
Dubois, G; Rouch, P; Bonneau, D; Gennisson, J L; Skalli, W
2016-11-01
The evaluation of muscle and joint forces in vivo is still a challenge. Musculo-Skeletal (musculo-skeletal) models are used to compute forces based on movement analysis. Most of them are built from a scaled-generic model based on cadaver measurements, which provides a low level of personalization, or from Magnetic Resonance Images, which provide a personalized model in lying position. This study proposed an original two steps method to access a subject-specific musculo-skeletal model in 30 min, which is based solely on biplanar X-Rays. First, the subject-specific 3D geometry of bones and skin envelopes were reconstructed from biplanar X-Rays radiography. Then, 2200 corresponding control points were identified between a reference model and the subject-specific X-Rays model. Finally, the shape of 21 lower limb muscles was estimated using a non-linear transformation between the control points in order to fit the muscle shape of the reference model to the X-Rays model. Twelfth musculo-skeletal models were reconstructed and compared to their reference. The muscle volume was not accurately estimated with a standard deviation (SD) ranging from 10 to 68%. However, this method provided an accurate estimation the muscle line of action with a SD of the length difference lower than 2% and a positioning error lower than 20 mm. The moment arm was also well estimated with SD lower than 15% for most muscle, which was significantly better than scaled-generic model for most muscle. This method open the way to a quick modeling method for gait analysis based on biplanar radiography. PMID:27082150
Incremental parameter estimation of kinetic metabolic network models
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.
Parameter and Uncertainty Estimation in Groundwater Modelling
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...
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.
A Combined Algorithm for Ballistic Parameters of Bomb Trajectory%炸弹弹道落点参数拟合算法
李飞飞; 吕颖; 南英
2013-01-01
A combined algorithm is presented for ballistic parameters fitting of bomb trajectory with very large range of the initial release conditions .This data fitting algorithm is the combination of bivariate interpolation and neutral network algorithm .The relationship between ballistic trajectory parameters ( terminal time and gliding flight distance ) and bomb releasing initial conditions can be described with threshold and weight of neural network,and the errors created due to large range of initial release conditions,are corrected by bivariate interpolation .The combined algorithm was used for online calculation test of the releasing point of a certain type aerial bomb,and the result showed that the algorithm can implement online calculation of the releasing point in real time .A great deal of numerical simulation results show that the combined algorithm is simple and reliable,and has fine real-time performance and high fitting accuracy .%针对很宽大范围抛射条件下炸弹的标准弹道系数库，提出了炸弹弹道落点参数的一种组合拟合算法。该组合拟合算法由二元插值法与神经网络法组成。首先，采用BP神经网络对炸弹弹道落点进行离线拟合，由于抛射条件范围很宽大造成了一定的拟合误差；然后，利用二元插值实时在线修正神经网络拟合所产生的计算误差。采用此组合算法对某种型号航空炸弹的弹道落点进行实时在线运算测试，通过测试实例说明，应用此组合算法进行弹道落点在线实时运算不但实时在线特性良好、拟合精度高，而且具有运算简单、结果可靠的特性。
FUZZY SUPERNOVA TEMPLATES. II. PARAMETER ESTIMATION
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 rmsz = 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.
Parameter Estimation of the T-Book
This paper summarizes the statistical assumptions and methods that have been used in the work on the T-book, a reliability data handbook which is used in safety analyses of nuclear power plants in Sweden and in the Swedish design plants in Finland. The author discusses the conceptual framework for the description and handling of uncertainty. He briefly outlines the two-stage 'Bayes empirical Bayes' method. To express the inherent tail-uncertainty in the distribution of failure rate, a class of contaminated distributions with three (hyper) parameters is proposed. Attention is focused on the properties of this T-book approach with regard to how it can be used to describe the parametric uncertainties, how uncertainty distributions can be used for predictive purposes, and how distributions can be updated
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…
Bender, D. F.
1978-01-01
The only ballistic trajectory mode feasible for a close solar probe or for an orbit inclined approximately 90 degrees to the ecliptic is the Jupiter gravity assisted mode. A comparison of the trajectories of the Solar Polar and the Solar Probe Mission for 1983 launches is shown. The geometry of the solar encounter phase is practically the same for the 4.3 year orbit achieved by a Jupiter gravity assist and for a one year orbit. Data describing the geometry of an orbit with perihelion at 4 solar radii and aphelion at Jupiter are listed. The range of apparent directions of the solar wind if it is flowing radially outward from the Sun with a speed of either 150 or 300 km/sec is shown. The minimum sun-earth-probe angle during the solar encounter as a function of the earth-node angle and the orbital inclination is also shown. If the inclination is 60 degrees or more, the minimum SEP angle is not greatly different from the 90 degree value.
Parameter Estimates in Differential Equation Models for Chemical Kinetics
Winkel, Brian
2011-01-01
We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…
A neural network applied to estimate Burr XII distribution parameters
The Burr XII distribution can closely approximate many other well-known probability density functions such as the normal, gamma, lognormal, exponential distributions as well as Pearson type I, II, V, VII, IX, X, XII families of distributions. Considering a wide range of shape and scale parameters of the Burr XII distribution, it can have an important role in reliability modeling, risk analysis and process capability estimation. However, estimating parameters of the Burr XII distribution can be a complicated task and the use of conventional methods such as maximum likelihood estimation (MLE) and moment method (MM) is not straightforward. Some tables to estimate Burr XII parameters have been provided by Burr (1942) but they are not adequate for many purposes or data sets. Burr tables contain specific values of skewness and kurtosis and their corresponding Burr XII parameters. Using interpolation or extrapolation to estimate other values may provide inappropriate estimations. In this paper, we present a neural network to estimate Burr XII parameters for different values of skewness and kurtosis as inputs. A trained network is presented, and one can use it without previous knowledge about neural networks to estimate Burr XII distribution parameters. Accurate estimation of the Burr parameters is an extension of simulation studies.
Parameter Estimation for a Computable General Equilibrium Model
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...... to estimating a CGE model of Mozambique....... Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied...
Parameter and State Estimator for State Space Models
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.
Estimating parameters of chaotic systems under noise-induced synchronization
Kim et al. introduced in 2002 [Kim CM, Rim S, Kye WH. Sequential synchronization of chaotic systems with an application to communication. Phys Rev Lett 2002;88:014103] a hierarchically structured communication scheme based on sequential synchronization, a modification of noise-induced synchronization (NIS). We propose in this paper an approach that can estimate the parameters of chaotic systems under NIS. In this approach, a dimensionally-expanded parameter estimating system is first constructed according to the original chaotic system. By feeding chaotic transmitted signal and external driving signal, the parameter estimating system can be synchronized with the original chaotic system. Consequently, parameters would be estimated. Numerical simulation shows that this approach can estimate all the parameters of chaotic systems under two feeding modes, which implies the potential weakness of the chaotic communication scheme under NIS or sequential synchronization.
Estimating parameters of chaotic systems under noise-induced synchronization
Wu Xiaogang [Institute of PR and AI, Huazhong University of Science and Technology, Wuhan 430074 (China)], E-mail: seanwoo@mail.hust.edu.cn; Wang Zuxi [Institute of PR and AI, Huazhong University of Science and Technology, Wuhan 430074 (China)
2009-01-30
Kim et al. introduced in 2002 [Kim CM, Rim S, Kye WH. Sequential synchronization of chaotic systems with an application to communication. Phys Rev Lett 2002;88:014103] a hierarchically structured communication scheme based on sequential synchronization, a modification of noise-induced synchronization (NIS). We propose in this paper an approach that can estimate the parameters of chaotic systems under NIS. In this approach, a dimensionally-expanded parameter estimating system is first constructed according to the original chaotic system. By feeding chaotic transmitted signal and external driving signal, the parameter estimating system can be synchronized with the original chaotic system. Consequently, parameters would be estimated. Numerical simulation shows that this approach can estimate all the parameters of chaotic systems under two feeding modes, which implies the potential weakness of the chaotic communication scheme under NIS or sequential synchronization.
Accurate Parameter Estimation for Unbalanced Three-Phase System
Yuan Chen; Hing Cheung So
2014-01-01
Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via the α β-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newt...
Efficient Estimation of Nonlinear Finite Population Parameters Using Nonparametrics
Goga, Camelia; Ruiz-Gazen, Anne
2012-01-01
Currently, the high-precision estimation of nonlinear parameters such as Gini indices, low-income proportions or other measures of inequality is particularly crucial. In the present paper, we propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a nonparametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any nonlinea...
Quantum estimation of coupled parameters and the role of entanglement
Kok, Pieter; Dunningham, Jacob; Ralph, Jason F.
2015-01-01
The quantum Cramer-Rao bound places a limit on the mean square error of a parameter estimation procedure, and its numerical value is determined by the quantum Fisher information. For single parameters, this leads to the well- known Heisenberg limit that surpasses the classical shot-noise limit. When estimating multiple parameters, the situation is more complicated and the quantum Cramer-Rao bound is generally not attainable. In such cases, the use of entanglement typically still offers an enh...
Parameter Estimation of Photovoltaic Models via Cuckoo Search
Jieming Ma; Ting, T. O.; Ka Lok Man; Nan Zhang; Sheng-Uei Guan; Wong, Prudence W. H.
2013-01-01
Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. S...
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
Janches, D.; Mathews, J. D.; Meisel, D. D.; Zhou, Q.-H.
2000-05-01
We present a sample of radar meteors detected during the November 1997 Leonids shower period using the narrow-beam, high-power Arecibo Observatory 430-MHz radar. During this period ˜7700 events were detected over 73 h of observations that included six mornings. Near apex-crossing, 6-10 events per minute were observed in the ˜300-m diameter beam. From these events a total of 390 meteors are characterized by a clear linear deceleration as derived from the radial Doppler speed determined from the meteor-echo leading-edge (head-echo). We interpret our results in terms of the meteor ballistic parameter—the ratio of the meteoroid mass to cross-sectional area—yielding a physical characterization of these particles prior to any assumptions regarding meteoroid shape and mass density. In addition, we compare these measurements with the results of a numerical solution of the meteor deceleration equation and find them in good agreement. The size and dynamical mass of the meteoroids are estimated considering these particles to be spheres with densities of 3 g/cm 3. We also discuss atmospheric energy-loss mechanisms of these meteroids. We believe these are the first radar meteor decelerations detected since those ones reported by J. V. Evans (1966, J. Geophys. Res. 71, 171-188) and F. Verniani (1966, J. Geophys. Res. 71, 2749-2761; 1973, J. Geophys. Res. 78, 8429-8462) and the first ones for meteors of this size.
Parameter Estimation for Generalized Brownian Motion with Autoregressive Increments
Fendick, Kerry
2011-01-01
This paper develops methods for estimating parameters for a generalization of Brownian motion with autoregressive increments called a Brownian ray with drift. We show that a superposition of Brownian rays with drift depends on three types of parameters - a drift coefficient, autoregressive coefficients, and volatility matrix elements, and we introduce methods for estimating each of these types of parameters using multidimensional times series data. We also cover parameter estimation in the contexts of two applications of Brownian rays in the financial sphere: queuing analysis and option valuation. For queuing analysis, we show how samples of queue lengths can be used to estimate the conditional expectation functions for the length of the queue and for increments in its net input and lost potential output. For option valuation, we show how the Black-Scholes-Merton formula depends on the price of the security on which the option is written through estimates not only of its volatility, but also of a coefficient ...
Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters
Shi, L.
2015-12-01
This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.
Parameter Estimation in Epidemiology: from Simple to Complex Dynamics
Aguiar, Maíra; Ballesteros, Sebastién; Boto, João Pedro; Kooi, Bob W.; Mateus, Luís; Stollenwerk, Nico
2011-09-01
We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models like multi-strain dynamics to describe the virus-host interaction in dengue fever, even most recently developed parameter estimation techniques, like maximum likelihood iterated filtering, come to their computational limits. However, the first results of parameter estimation with data on dengue fever from Thailand indicate a subtle interplay between stochasticity and deterministic skeleton. The deterministic system on its own already displays complex dynamics up to deterministic chaos and coexistence of multiple attractors.
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.
Response-Based Estimation of Sea State Parameters
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 on the...
Parameter estimation during a transient - application to BWR stability
Tambouratzis, T. [Institute of Nuclear Technology - Radiation Protection, NCSR ' Demokritos' , Aghia Paraskevi, Athens 153 10 (Greece)]. E-mail: tatiana@ipta.demokritos.gr; Antonopoulos-Domis, M. [Institute of Nuclear Technology - Radiation Protection, NCSR ' Demokritos' , Aghia Paraskevi, Athens 153 10 (Greece)
2004-12-01
The estimation of system parameters is of obvious practical interest. During transient operation, these parameters are expected to change, whereby the system is rendered time-varying and classical signal processing techniques are not applicable. A novel methodology is proposed here, which combines wavelet multi-resolution analysis and selective wavelet coefficient removal with classical signal processing techniques in order to provide short-term estimates of the system parameters of interest. The use of highly overlapping time-windows further monitors the gradual changes in system parameter values. The potential of the proposed methodology is demonstrated with numerical experiments for the problem of stability evaluation of boiling water reactors during a transient.
Parameter estimation during a transient - application to BWR stability
The estimation of system parameters is of obvious practical interest. During transient operation, these parameters are expected to change, whereby the system is rendered time-varying and classical signal processing techniques are not applicable. A novel methodology is proposed here, which combines wavelet multi-resolution analysis and selective wavelet coefficient removal with classical signal processing techniques in order to provide short-term estimates of the system parameters of interest. The use of highly overlapping time-windows further monitors the gradual changes in system parameter values. The potential of the proposed methodology is demonstrated with numerical experiments for the problem of stability evaluation of boiling water reactors during a transient
Another Look at the EWMA Control Chart with Estimated Parameters
N.A. Saleh; M.A. Mahmoud; L.A. Jones-Farmer; I. Zwetsloot; W.H. Woodall
2015-01-01
The authors assess the in-control performance of the exponentially weighted moving average (EWMA) control chart in terms of the SDARL and percentiles of the ARL distribution when the process parameters are estimated.
Kalman filter data assimilation: Targeting observations and parameter estimation
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
Robust Parameter and Signal Estimation in Induction Motors
Børsting, H.
been used as a third input. - the rotor speed and the driving torque of the induction motor have successfully been estimated based on measurements of the terminal quantities only. The following methods have been applied: a recursive prediction error method and a method based on a steady state model of...... 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......: - identifiability has been treated in theory and practice in connection with parameter and signal estimation in induction motors. - a non recursive prediction error method has successfully been used to estimate physical related parameters in a continuous-time model of the induction motor. The speed of the rotor has...
Kalman filter application for distributed parameter estimation in reactor systems
An application of the Kalman filter has been developed for the real-time identification of a distributed parameter in a nuclear power plant. This technique can be used to improve numerical method-based best-estimate simulation of complex systems such as nuclear power plants. The application to a reactor system involves a unique modal model that approximates physical components, such as the reactor, as a coupled oscillator, i.e., a modal model with coupled modes. In this model both states and parameters are described by an orthogonal expansion. The Kalman filter with the sequential least-squares parameter estimation algorithm was used to estimate the modal coefficients of all states and one parameter. Results show that this state feedback algorithm is an effective way to parametrically identify a distributed parameter system in the presence of uncertainties
Dynamic noise, chaos and parameter estimation in population biology
Stollenwerk, N.; Aguiar, M; Ballesteros, S.; Boto, J.; Kooi, B. W.; Mateus, L.
2012-01-01
We revisit the parameter estimation framework for population biological dynamical systems, and apply it to calibrate various models in epidemiology with empirical time series, namely influenza and dengue fever. When it comes to more complex models such as multi-strain dynamics to describe the virus–host interaction in dengue fever, even the most recently developed parameter estimation techniques, such as maximum likelihood iterated filtering, reach their computational limits. However, the fir...
Estimation of Clarinet Reed Parameters by Inverse Modelling
CHATZIIOANNOU, Vasileios; van Walstijn, Maarten
2012-01-01
Analysis of the acoustical functioning of musical instruments invariably involves the estimation of model parameters. The broad aim of this paper is to develop methods for estimation of clarinet reed parameters that are representative of actual playing conditions. This presents various challenges because of the di?culties of measuring the directly relevant variables without interfering with the control of the instrument. An inverse modelling approach is therefore proposed, in which the equati...
Xie, Minggang; Zhu, Meng-Hua
2016-04-01
A clear understanding of thickness distributions of primary ejecta and local material is critical to interpreting the process of ballistic sedimentation, provenances of lunar samples, the evolution of the lunar surface, and the origin of multi-ring basins. The youngest lunar multi-ring basin, Orientale, provides the best preserved structure for determining the thicknesses of primary ejecta and local material. In general, the primary ejecta thickness was often estimated using crater morphometry. However, previous methods ignored either crater erosion, the crater interior geometry, or both. In addition, ejecta deposits were taken as mostly primary ejecta. And, as far as we know, the local material thickness had not been determined for the Orientale. In this work, we proposed a model based on matching measurements of partially filled pre-Orientale craters (PFPOCs) with the simulations of crater erosion to determine their thicknesses. We provided estimates of primary ejecta thickness distribution with the thickness of 0.85 km at Cordillera ring and a decay power law exponent of b = 2.8, the transient crater radius of 200 km, excavation volume of 2.3 ×106 km3, primary ejecta volume of 2.8 ×106 km3. These results suggest that previous works (e.g., Fassett et al., 2011; Moore et al., 1974) might overestimate the primary ejecta thicknesses of Orientale, and the primary ejecta thickness model of Pike (1974a) for multi-ring basins may give better estimates than the widely cited model of McGetchin et al. (1973) and the scaling law for impacts into Ottawa Sand (Housen et al., 1983). Structural uplift decays slower than previously thought, and rim relief is mostly rim uplift for Orientale. The main reason for rim uplift may be the fracturing and squeezing upward of the surrounding rocks. The proportion of local material to ejecta deposits increases with increasing radial distance from basin center, and the thickness of local material is larger than that of primary ejecta at
Simultaneous optimal experimental design for in vitro binding parameter estimation.
Ernest, C Steven; Karlsson, Mats O; Hooker, Andrew C
2013-10-01
Simultaneous optimization of in vitro ligand binding studies using an optimal design software package that can incorporate multiple design variables through non-linear mixed effect models and provide a general optimized design regardless of the binding site capacity and relative binding rates for a two binding system. Experimental design optimization was employed with D- and ED-optimality using PopED 2.8 including commonly encountered factors during experimentation (residual error, between experiment variability and non-specific binding) for in vitro ligand binding experiments: association, dissociation, equilibrium and non-specific binding experiments. Moreover, a method for optimizing several design parameters (ligand concentrations, measurement times and total number of samples) was examined. With changes in relative binding site density and relative binding rates, different measurement times and ligand concentrations were needed to provide precise estimation of binding parameters. However, using optimized design variables, significant reductions in number of samples provided as good or better precision of the parameter estimates compared to the original extensive sampling design. Employing ED-optimality led to a general experimental design regardless of the relative binding site density and relative binding rates. Precision of the parameter estimates were as good as the extensive sampling design for most parameters and better for the poorly estimated parameters. Optimized designs for in vitro ligand binding studies provided robust parameter estimation while allowing more efficient and cost effective experimentation by reducing the measurement times and separate ligand concentrations required and in some cases, the total number of samples. PMID:23943088
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
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
Simultaneous estimation of parameters in the bivariate Emax model.
Magnusdottir, Bergrun T; Nyquist, Hans
2015-12-10
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation. PMID:26190048
Parameter estimation of hidden periodic model in random fields
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
A Fully Conditional Estimation Procedure for Rasch Model Parameters.
Choppin, Bruce
A strategy for overcoming problems with the Rasch model's inability to handle missing data involves a pairwise algorithm which manipulates the data matrix to separate out the information needed for the estimation of item difficulty parameters in a test. The method of estimation compares two or three items at a time, separating out the ability…
Parameter estimation in stochastic rainfall-runoff models
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
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...
Variance gamma process simulation and it's parameters estimation
Kuzmina, A. V.
2010-01-01
Variance gamma process is a three parameter process. Variance gamma process is simulated as a gamma time-change Brownian motion and as a difference of two independent gamma processes. Estimations of simulated variance gamma process parameters are presented in this paper.
Computational methods for estimation of parameters in hyperbolic systems
Banks, H. T.; Ito, K.; Murphy, K. A.
1983-01-01
Approximation techniques for estimating spatially varying coefficients and unknown boundary parameters in second order hyperbolic systems are discussed. Methods for state approximation (cubic splines, tau-Legendre) and approximation of function space parameters (interpolatory splines) are outlined and numerical findings for use of the resulting schemes in model "one dimensional seismic inversion' problems are summarized.
Estimation of Parameters of the Beta-Extreme Value Distribution
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.
This review describes the ballistic quality assurance for stereotactic intracranial irradiation treatments delivered with Gamma KnifeR 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)
Adaptive Unified Biased Estimators of Parameters in Linear Model
Hu Yang; Li-xing Zhu
2004-01-01
To tackle multi collinearity or ill-conditioned design matrices in linear models,adaptive biased estimators such as the time-honored Stein estimator,the ridge and the principal component estimators have been studied intensively.To study when a biased estimator uniformly outperforms the least squares estimator,some suficient conditions are proposed in the literature.In this paper,we propose a unified framework to formulate a class of adaptive biased estimators.This class includes all existing biased estimators and some new ones.A suficient condition for outperforming the least squares estimator is proposed.In terms of selecting parameters in the condition,we can obtain all double-type conditions in the literature.
Bayesian estimation of one-parameter qubit gates
Teklu, Berihu; Olivares, Stefano; Paris, Matteo G. A.
2008-01-01
We address estimation of one-parameter unitary gates for qubit systems and seek for optimal probes and measurements. Single- and two-qubit probes are analyzed in details focusing on precision and stability of the estimation procedure. Bayesian inference is employed and compared with the ultimate quantum limits to precision, taking into account the biased nature of Bayes estimator in the non asymptotic regime. Besides, through the evaluation of the asymptotic a posteriori distribution for the ...
Parameter Estimation and Experimental Design in Groundwater Modeling
SUN Ne-zheng
2004-01-01
This paper reviews the latest developments on parameter estimation and experimental design in the field of groundwater modeling. Special considerations are given when the structure of the identified parameter is complex and unknown. A new methodology for constructing useful groundwater models is described, which is based on the quantitative relationships among the complexity of model structure, the identifiability of parameter, the sufficiency of data, and the reliability of model application.
Global parameter estimation methods for stochastic biochemical systems
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
Maximum Likelihood Estimation of the Identification Parameters and Its Correction
无
2002-01-01
By taking the subsequence out of the input-output sequence of a system polluted by white noise, anindependent observation sequence and its probability density are obtained and then a maximum likelihood estimation of theidentification parameters is given. In order to decrease the asymptotic error, a corrector of maximum likelihood (CML)estimation with its recursive algorithm is given. It has been proved that the corrector has smaller asymptotic error thanthe least square methods. A simulation example shows that the corrector of maximum likelihood estimation is of higherapproximating precision to the true parameters than the least square methods.
Biases on cosmological parameter estimators from galaxy cluster number counts
Penna-Lima, M; Wuensche, C A
2013-01-01
The abundance of galaxy clusters is becoming a standard cosmological probe. In particular, Sunyaev-Zel'dovich (SZ) surveys are promising probes of the Dark Energy (DE) equation of state (eqos), given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to hundreds of objects. In this case, it is not guaranteed that maximum likelihood (ML) estimators of cosmological parameters are unbiased. In this work we study estimators from cluster abundance for some cosmological parameters. We derive an unbinned likelihood for cluster abundance, showing that it is equivalent to the one commonly used in the literature. We use the Monte Carlo (MC) approach to determine the presence of bias using this likelihood and its behavior with both area and depth of the survey, and the number of cosmological parameters fitted simultaneously. Assuming perfect knowledge on mass and redshift, we obtain that some estimators have non negligible biases. For example, the bias ...
Image-driven parameter estimation for low grade gliomas
Gholami, Amir; Biros, George
2014-01-01
We present a numerical scheme for solving a parameter estimation problem for a model of low-grade glioma growth. Our goal is to estimate tumor infiltration into the brain parenchyma for a reaction-diffusion tumor growth model. We use a constrained optimization formulation that results in a system of nonlinear partial differential equations (PDEs). In our formulation, we estimate the parameters using the data from segmented images at two different time instances, along with white matter fiber directions derived from diffusion tensor imaging (DTI). The parameters we seek to estimate are the spatial tumor concentration and the extent of anisotropic tumor diffusion. The optimization problem is solved with a Gauss-Newton reduced space algorithm. We present the formulation, outline the numerical algorithms and conclude with numerical experiments on synthetic datasets. Our results show the feasibility of the proposed methodology.
The Robustness Optimization of Parameter Estimation in Chaotic Control Systems
Zhen Xu
2014-10-01
Full Text Available Standard particle swarm optimization algorithm has problems of bad adaption and weak robustness in the parameter estimation model of chaotic control systems. In light of this situation, this paper puts forward a new estimation model based on improved particle swarm optimization algorithm. It firstly constrains the search space of the population with Tent and Logistic double mapping to regulate the initialized population size, optimizes the fitness value by evolutionary state identification strategy so as to avoid its premature convergence, optimizes the inertia weight by the nonlinear decrease strategy to reach better global and local optimal solution, and then optimizes the iteration of particle swarm optimization algorithm with the hybridization concept from genetic algorithm. Finally, this paper applies it into the parameter estimation of chaotic systems control. Simulation results show that the proposed parameter estimation model shows higher accuracy, anti-noise ability and robustness compared with the model based on standard particle swarm optimization algorithm.
Iterative methods for distributed parameter estimation in parabolic PDE
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
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.
Li Wen XU; Song Gui WANG
2007-01-01
In this paper, the authors address the problem of the minimax estimator of linear com-binations of stochastic regression coefficients and parameters in the general normal linear model with random effects. Under a quadratic loss function, the minimax property of linear estimators is inves- tigated. In the class of all estimators, the minimax estimator of estimable functions, which is unique with probability 1, is obtained under a multivariate normal distribution.
Evaluating parasite densities and estimation of parameters in transmission systems
Heinzmann D.
2008-09-01
Full Text Available Mathematical modelling of parasite transmission systems can provide useful information about host parasite interactions and biology and parasite population dynamics. In addition good predictive models may assist in designing control programmes to reduce the burden of human and animal disease. Model building is only the first part of the process. These models then need to be confronted with data to obtain parameter estimates and the accuracy of these estimates has to be evaluated. Estimation of parasite densities is central to this. Parasite density estimates can include the proportion of hosts infected with parasites (prevalence or estimates of the parasite biomass within the host population (abundance or intensity estimates. Parasite density estimation is often complicated by highly aggregated distributions of parasites within the hosts. This causes additional challenges when calculating transmission parameters. Using Echinococcus spp. as a model organism, this manuscript gives a brief overview of the types of descriptors of parasite densities, how to estimate them and on the use of these estimates in a transmission model.
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.
A symptotic Bias for GMM and GEL Estimators with Estimated Nuisance Parameter
Newey, Whitney K.; Joaquim J. S. Ramalho; Smith, Richard J.
2003-01-01
This papers studies and compares the asymptotic bias of GMM and generalized empirical likelihood (GEL) estimators in the presence of estimated nuisance parameters. We consider cases in which the nuisance parameter is estimated from independent and identical samples. A simulation experiment is conducted for covariance structure models. Empirical likelihood offers much reduced mean and median bias, root mean squared error and mean absolute error, as compared with two-step GMM and other GEL meth...
Parameter estimation for chaotic systems by particle swarm optimization
Parameter estimation for chaotic systems is an important issue in nonlinear science and has attracted increasing interests from various research fields, which could be essentially formulated as a multi-dimensional optimization problem. As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications, owing to its simple concept, easy implementation and quick convergence. However, to the best of our knowledge, there is no published work on PSO for estimating parameters of chaotic systems. In this paper, a PSO approach is applied to estimate the parameters of Lorenz system. Numerical simulation and the comparisons demonstrate the effectiveness and robustness of PSO. Moreover, the effect of population size on the optimization performances is investigated as well
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.
CTER—Rapid estimation of CTF parameters with error assessment
Penczek, Pawel A., E-mail: Pawel.A.Penczek@uth.tmc.edu [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Fang, Jia [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Li, Xueming; Cheng, Yifan [The Keck Advanced Microscopy Laboratory, Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158 (United States); Loerke, Justus; Spahn, Christian M.T. [Institut für Medizinische Physik und Biophysik, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin (Germany)
2014-05-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300 kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03 Å without, and 3.85 Å with, inclusion of astigmatism parameters. - Highlights: • We describe methodology for estimation of CTF parameters with error assessment. • Error estimates provide means for automated elimination of inferior micrographs. • High computational efficiency allows real-time monitoring of EM data quality. • Accurate CTF estimation yields structure of the 80S human ribosome at 3.85 Å.
CTER—Rapid estimation of CTF parameters with error assessment
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300 kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03 Å without, and 3.85 Å with, inclusion of astigmatism parameters. - Highlights: • We describe methodology for estimation of CTF parameters with error assessment. • Error estimates provide means for automated elimination of inferior micrographs. • High computational efficiency allows real-time monitoring of EM data quality. • Accurate CTF estimation yields structure of the 80S human ribosome at 3.85 Å
Estimation of shape model parameters for 3D surfaces
Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen; Ourselin, Sébastien; Ersbøll, Bjarne Kjær
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 is......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...... 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...
Accelerated gravitational-wave parameter estimation with reduced order modeling
Canizares, Priscilla; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2014-01-01
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current parameter estimation approaches for such scenarios can lead to computationally intractable problems in practice. Therefore there is a pressing need for new, fast and accurate Bayesian inference techniques. In this letter we demonstrate that a reduced order modeling approach enables rapid parameter estimation studies. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of non-spinning binary neutron star inspirals can be sped up by a factor of 30 for the early advanced detectors' configurations. This speed-up will increase to about $150$ as the detectors improve their low-frequency limit to 10Hz, reducing to hours analyses which would otherwise take months to complete. Although thes...
Estimation of distances to stars with stellar parameters from LAMOST
Carlin, Jeffrey L; Newberg, Heidi Jo; Beers, Timothy C; Chen, Li; Deng, Licai; Guhathakurta, Puragra; Hou, Jinliang; Hou, Yonghui; Lepine, Sebastien; Li, Guangwei; Luo, A-Li; Smith, Martin C; Wu, Yue; Yang, Ming; Yanny, Brian; Zhang, Haotong; Zheng, Zheng
2015-01-01
We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star's absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ~5-degree diameter "plate" that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show ...
Parameter estimation of an aeroelastic aircraft using neural networks
S C Raisinghani; A K Ghosh
2000-04-01
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 variables ortheirtime derivatives, is proposed. Furthermore, it is shown that such a neural model can be used to extract equivalent stability and control derivatives of a flexible aircraft. Results are presented for aircraft with different levels of flexibility to demonstrate the utility ofthe neural approach for both modelling and estimation of parameters.
Estimates of Genetic Parameters for Racing Times of Thoroughbred Horses
EKİZ, Bülent; KOÇAK, Ömür
2007-01-01
The aim of this study was to estimate the genetic parameters for racing times, which are needed for a selection program of Thoroughbred horses in Turkey. The racing records used in the study were obtained from the Turkish Jockey Club. The trait used in the study was racing time for racing distances of 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, and 2400 m. The data from each racing distance were analyzed separately. Genetic parameters were estimated by REML procedure usi...
On Modal Parameter Estimates from Ambient Vibration Tests
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...... excited by human activities, wind, gust, etc. In this paper, the comparison between two differeiit vibration testing techniques is presented. The first approach takes advantage of the Frequency Domain Decomposition, FDD, of the response cross power spectral densities to estimate both the natural...... parameters of two simple structures (a beam and a plate), excited by an acoustical random signal....
Estimation of the elastic Earth parameters from the SLR technique
Rutkowska, Milena
ABSTRACT. The global elastic parameters (Love and Shida numbers) associated with the tide variations for satellite and stations are estimated from the Satellite Laser Ranging (SLR) data. The study is based on satellite observations taken by the global network of the ground stations during the period from January 1, 2005 until January 1, 2007 for monthly orbital arcs of Lageos 1 satellite. The observation equations contain unknown for orbital arcs, some constants and elastic Earth parameters which describe tide variations. The adjusted values are discussed and compared with geophysical estimations of Love numbers. All computations were performed employing the NASA software GEODYN II (eddy et al. 1990).
Usefulness of single nucleotide polymorphism data for estimating population parameters.
Kuhner, M K; Beerli, P; Yamato, J; Felsenstein, J
2000-01-01
Single nucleotide polymorphism (SNP) data can be used for parameter estimation via maximum likelihood methods as long as the way in which the SNPs were determined is known, so that an appropriate likelihood formula can be constructed. We present such likelihoods for several sampling methods. As a test of these approaches, we consider use of SNPs to estimate the parameter Theta = 4N(e)micro (the scaled product of effective population size and per-site mutation rate), which is related to the br...
Parameter Estimation in Stochastic Differential Equations; An Overview
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
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
Estimation of effective hydrogeological parameters in heterogeneous and anisotropic aquifers
Lin, Hsien-Tsung; Tan, Yih-Chi; Chen, Chu-Hui; Yu, Hwa-Lung; Wu, Shih-Ching; Ke, Kai-Yuan
2010-07-01
SummaryObtaining reasonable hydrological input parameters is a key challenge in groundwater modeling. Analysis of temporal evolution during pump-induced drawdown is one common approach used to estimate the effective transmissivity and storage coefficients in a heterogeneous aquifer. In this study, we propose a Modified Tabu search Method (MTM), an improvement drawn from an alliance between the Tabu Search (TS) and the Adjoint State Method (ASM) developed by Tan et al. (2008). The latter is employed to estimate effective parameters for anisotropic, heterogeneous aquifers. MTM is validated by several numerical pumping tests. Comparisons are made to other well-known techniques, such as the type-curve method (TCM) and the straight-line method (SLM), to provide insight into the challenge of determining the most effective parameter for an anisotropic, heterogeneous aquifer. The results reveal that MTM can efficiently obtain the best representative and effective aquifer parameters in terms of the least mean square errors of the drawdown estimations. The use of MTM may involve less artificial errors than occur with TCM and SLM, and lead to better solutions. Therefore, effective transmissivity is more likely to be comprised of the geometric mean of all transmissivities within the cone of depression based on a precise estimation of MTM. Further investigation into the applicability of MTM shows that a higher level of heterogeneity in an aquifer can induce an uncertainty in estimations, while the changes in correlation length will affect the accuracy of MTM only once the degree of heterogeneity has also risen.
Parameter Estimation for Single Diode Models of Photovoltaic Modules
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.
Estimation of diffusion parameters for discretely observed diffusion processes
Sørensen, Helle
2002-01-01
We study the estimation of diffusion parameters for one-dimensional, ergodic diffusion processes that are discretely observed. We discuss a method based on a functional relationship between the drift function, the diffusion function and the invariant density and use empirical process theory to show that the estimator is $\\sqrt{n}$-consistent and in certain cases weakly convergent. The Chan-Karolyi-Longstaff-Sanders (CKLS) model is used as an example and a numerical example i...
Estimation of dynamic stability parameters from drop model flight tests
Chambers, J. R.; Iliff, K. W.
1981-01-01
The overall remotely piloted drop model operation, descriptions, instrumentation, launch and recovery operations, piloting concept, and parameter identification methods are discussed. Static and dynamic stability derivatives were obtained for an angle attack range from -20 deg to 53 deg. It is indicated that the variations of the estimates with angle of attack are consistent for most of the static derivatives, and the effects of configuration modifications to the model were apparent in the static derivative estimates.
Multi-criteria parameter estimation for the Unified Land Model
B. Livneh; Lettenmaier, D. P.
2012-01-01
We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operational Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water...
Multi-criteria parameter estimation for the unified land model
B. Livneh; Lettenmaier, D. P.
2012-01-01
We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the Continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operation Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial wa...
Comparison of Jump-Diffusion Parameters Using Passage Times Estimation
K. Khaldi
2014-01-01
Full Text Available The main purposes of this paper are two contributions: (1 it presents a new method, which is the first passage time generalized for all passage times (PT method, in order to estimate the parameters of stochastic jump-diffusion process. (2 It compares in a time series model, share price of gold, the empirical results of the estimation and forecasts obtained with the PT method and those obtained by the moments method applied to the MJD model.
Human ECG signal parameters estimation during controlled physical activity
Maciejewski, Marcin; Surtel, Wojciech; Dzida, Grzegorz
2015-09-01
ECG signal parameters are commonly used indicators of human health condition. In most cases the patient should remain stationary during the examination to decrease the influence of muscle artifacts. During physical activity, the noise level increases significantly. The ECG signals were acquired during controlled physical activity on a stationary bicycle and during rest. Afterwards, the signals were processed using a method based on Pan-Tompkins algorithms to estimate their parameters and to test the method.
Parameter estimation in systems biology models using spline approximation
Yeung Lam F; Zhan Choujun
2011-01-01
Abstract Background Mathematical models for revealing the dynamics and interactions properties of biological systems play an important role in computational systems biology. The inference of model parameter values from time-course data can be considered as a "reverse engineering" process and is still one of the most challenging tasks. Many parameter estimation methods have been developed but none of these methods is effective for all cases and can overwhelm all other approaches. Instead, vari...
Parameter Estimation of Superimposed Damped Sinusoids Using Exponential Windows
Al-Radhawi, Muhammad Ali; Abed-Meraim, Karim
2014-01-01
This paper presents a preprocessing technique based on exponential windowing (EW) for parameter estimation of superimposed exponentially damped sinusoids. It is shown that the EW technique signiﬁcantly improves the robustness to noise over two other commonly used preprocessing techniques: subspace decomposition and higher order statistics. An ad-hoc but eﬃcient approach for the EW parameter selection is provided and shown to provide close to CRB performance.
Bayesian estimation of parameters in a regional hydrological model
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
Optimal measurement locations for parameter estimation of non linear distributed parameter systems
J. E. Alaña
2010-12-01
Full Text Available A sensor placement approach for the purpose of accurately estimating unknown parameters of a distributed parameter system is discussed. The idea is to convert the sensor location problem to a classical experimental design. The technique consists of analysing the extrema values of the sensitivity coefficients derived from the system and their corresponding spatial positions. This information is used to formulate an efficient computational optimum experiment design on discrete domains. The scheme studied is verified by a numerical example regarding the chemical reaction in a tubular reactor for two possible scenarios; stable and unstable operation conditions. The resulting approach is easy to implement and good estimates for the parameters of the system are obtained. This study shows that the measurement location plays an essential role in the parameter estimation procedure.
Parameter Estimation for a Class of Lifetime Models
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.
Online vegetation parameter estimation using passive microwave remote sensing observations
In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...
Estimation of Parameters of Gravitational Waves from Pulsars
Krolak, A
1997-01-01
The problem of search for nearly periodic gravitational wave sources in the data from laser interferometric detectors is discussed using a simple model of the signal. Accuracies of estimation of the parameters and computational requirements to do the search are assessed.
Stability of Parameter Estimates in the Split Population Exponential Distribution.
Miley, Alan D.
1978-01-01
The split-population exponential design suggested by Maltz and McCleary to predict parolee recidivism (TM 502 998) was applied to discharged psychiatric inpatients. Parameter estimates changed systematically as greater and greater observation time was allowed in the computation, thus limiting extrapolability. (Author/GDC)
A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation
Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri; Rao, Bhaskar D.
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...
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.
Parameter Estimates in Differential Equation Models for Population Growth
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
A parameter estimation framework for patient-specific hemodynamic computations
Itu, Lucian; Sharma, Puneet; Passerini, Tiziano; Kamen, Ali; Suciu, Constantin; Comaniciu, Dorin
2015-01-01
We propose a fully automated parameter estimation framework for performing patient-specific hemodynamic computations in arterial models. To determine the personalized values of the windkessel models, which are used as part of the geometrical multiscale circulation model, a parameter estimation problem is formulated. Clinical measurements of pressure and/or flow-rate are imposed as constraints to formulate a nonlinear system of equations, whose fixed point solution is sought. A key feature of the proposed method is a warm-start to the optimization procedure, with better initial solution for the nonlinear system of equations, to reduce the number of iterations needed for the calibration of the geometrical multiscale models. To achieve these goals, the initial solution, computed with a lumped parameter model, is adapted before solving the parameter estimation problem for the geometrical multiscale circulation model: the resistance and the compliance of the circulation model are estimated and compensated. The proposed framework is evaluated on a patient-specific aortic model, a full body arterial model, and multiple idealized anatomical models representing different arterial segments. For each case it leads to the best performance in terms of number of iterations required for the computational model to be in close agreement with the clinical measurements.
Parameter estimation of an air-bearing suspended test table
Fu, Zhenxian; Lin, Yurong; Liu, Yang; Chen, Xinglin; Chen, Fang
2015-02-01
A parameter estimation approach is proposed for parameter determination of a 3-axis air-bearing suspended test table. The table is to provide a balanced and frictionless environment for spacecraft ground test. To balance the suspension, the mechanical parameters of the table, including its angular inertias and centroid deviation from its rotating center, have to be determined first. Then sliding masses on the table can be adjusted by stepper motors to relocate the centroid of the table to its rotating center. Using the angular momentum theorem and the coriolis theorem, dynamic equations are derived describing the rotation of the table under the influence of gravity imbalance torque and activating torques. To generate the actuating torques, use of momentum wheels is proposed, whose virtue is that no active control is required to the momentum wheels, which merely have to spin at constant rates, thus avoiding the singularity problem and the difficulty of precisely adjusting the output torques, issues associated with control moment gyros. The gyroscopic torques generated by the momentum wheels, as they are forced by the table to precess, are sufficient to activate the table for parameter estimation. Then least-square estimation is be employed to calculate the desired parameters. The effectiveness of the method is validated by simulation.
A parameter identifiability and estimation study in Yesilirmak River.
Berber, R; Yuceer, M; Karadurmus, E
2009-01-01
Water quality models have relatively large number of parameters, which need to be estimated against observed data through a non-trivial task that is associated with substantial difficulties. This work involves a systematic model calibration and validation study for river water quality. The model considered was composed of dynamic mass balances for eleven pollution constituents, stemming from QUAL2E water quality model by considering a river segment as a series of continuous stirred-tank reactors (CSTRs). Parameter identifiability was analyzed from the perspective of sensitivity measure and collinearity index, which indicated that 8 parameters would fall within the identifiability range. The model parameters were then estimated by an integration based optimization algorithm coupled with sequential quadratic programming. Dynamic field data consisting of major pollutant concentrations were collected from sampling stations along Yesilirmak River around the city of Amasya in Turkey, and compared with model predictions. The calibrated model responses were in good agreement with the observed river water quality data, and this indicated that the suggested procedure provided an effective means for reliable estimation of model parameters and dynamic simulation for river streams. PMID:19214006
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.
PARAMETER ESTIMATION METHODOLOGY FOR NONLINEAR SYSTEMS: APPLICATION TO INDUCTION MOTOR
G.KENNE; F.FLORET; H.NKWAWO; F.LAMNABHI-LAGARRIGUE
2005-01-01
This paper deals with on-line state and parameter estimation of a reasonably large class of nonlinear continuous-time systems using a step-by-step sliding mode observer approach. The method proposed can also be used for adaptation to parameters that vary with time. The other interesting feature of the method is that it is easily implementable in real-time. The efficiency of this technique is demonstrated via the on-line estimation of the electrical parameters and rotor flux of an induction motor. This application is based on the standard model of the induction motor expressed in rotor coordinates with the stator current and voltage as well as the rotor speed assumed to be measurable.Real-time implementation results are then reported and the ability of the algorithm to rapidly estimate the motor parameters is demonstrated. These results show the robustness of this approach with respect to measurement noise, discretization effects, parameter uncertainties and modeling inaccuracies.Comparisons between the results obtained and those of the classical recursive least square algorithm are also presented. The real-time implementation results show that the proposed algorithm gives better performance than the recursive least square method in terms of the convergence rate and the robustness with respect to measurement noise.
Estimation of rice biophysical parameters using multitemporal RADARSAT-2 images
Li, S.; Ni, P.; Cui, G.; He, P.; Liu, H.; Li, L.; Liang, Z.
2016-04-01
Compared with optical sensors, synthetic aperture radar (SAR) has the capability of acquiring images in all-weather conditions. Thus, SAR images are suitable for using in rice growth regions that are characterized by frequent cloud cover and rain. The objective of this paper was to evaluate the probability of rice biophysical parameters estimation using multitemporal RADARSAT-2 images, and to develop the estimation models. Three RADARSTA-2 images were acquired during the rice critical growth stages in 2014 near Meishan, Sichuan province, Southwest China. Leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), height, biomass and canopy water content (WC) were observed at 30 experimental plots over 5 periods. The relationship between RADARSAT-2 backscattering coefficients (σ 0) or their ratios and rice biophysical parameters were analysed. These biophysical parameters were significantly and consistently correlated with the VV and VH σ 0 ratio (σ 0 VV/ σ 0 VH) throughout all growth stages. The regression model were developed between biophysical parameters and σ 0 VV/ σ 0 VH. The results suggest that the RADARSAT-2 data has great potential capability for the rice biophysical parameters estimation and the timely rice growth monitoring.
Modal parameters estimation using ant colony optimisation algorithm
Sitarz, Piotr; Powałka, Bartosz
2016-08-01
The paper puts forward a new estimation method of modal parameters for dynamical systems. The problem of parameter estimation has been simplified to optimisation which is carried out using the ant colony system algorithm. The proposed method significantly constrains the solution space, determined on the basis of frequency plots of the receptance FRFs (frequency response functions) for objects presented in the frequency domain. The constantly growing computing power of readily accessible PCs makes this novel approach a viable solution. The combination of deterministic constraints of the solution space with modified ant colony system algorithms produced excellent results for systems in which mode shapes are defined by distinctly different natural frequencies and for those in which natural frequencies are similar. The proposed method is fully autonomous and the user does not need to select a model order. The last section of the paper gives estimation results for two sample frequency plots, conducted with the proposed method and the PolyMAX algorithm.
Estimating Arrhenius parameters using temperature programmed molecular dynamics
Imandi, Venkataramana; Chatterjee, Abhijit
2016-07-01
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.
Power Network Parameter Estimation Method Based on Data Mining Technology
ZHANG Qi-ping; WANG Cheng-min; HOU Zhi-fian
2008-01-01
The parameter values which actually change with the circumstances, weather and load level etc.produce great effect to the result of state estimation. A new parameter estimation method based on data mining technology was proposed. The clustering method was used to classify the historical data in supervisory control and data acquisition (SCADA) database as several types. The data processing technology was impliedto treat the isolated point, missing data and yawp data in samples for classified groups. The measurement data which belong to each classification were introduced to the linear regression equation in order to gain the regression coefficient and actual parameters by the least square method. A practical system demonstrates the high correctness, reliability and strong practicability of the proposed method.
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.
Mizukami, Naoki; Clark, Martyn; Newman, Andrew; Wood, Andy
2016-04-01
Estimation of spatially distributed parameters is one of the biggest challenges in hydrologic modeling over a large spatial domain. This problem arises from methodological challenges such as the transfer of calibrated parameters to ungauged locations. Consequently, many current large scale hydrologic assessments rely on spatially inconsistent parameter fields showing patchwork patterns resulting from individual basin calibration or spatially constant parameters resulting from the adoption of default or a-priori estimates. In this study we apply the Multi-scale Parameter Regionalization (MPR) framework (Samaniego et al., 2010) to generate spatially continuous and optimized parameter fields for the Variable Infiltration Capacity (VIC) model over the contiguous United States(CONUS). The MPR method uses transfer functions that relate geophysical attributes (e.g., soil) to model parameters (e.g., parameters that describe the storage and transmission of water) at the native resolution of the geophysical attribute data and then scale to the model spatial resolution with several scaling functions, e.g., arithmetic mean, harmonic mean, and geometric mean. Model parameter adjustments are made by calibrating the parameters of the transfer function rather than the model parameters themselves. In this presentation, we first discuss conceptual challenges in a "model agnostic" continental-domain application of the MPR approach. We describe development of transfer functions for the soil parameters, and discuss challenges associated with extending MPR for VIC to multiple models. Next, we discuss the "computational shortcut" of headwater basin calibration where we estimate the parameters for only 500 headwater basins rather than conducting simulations for every grid box across the entire domain. We first performed individual basin calibration to obtain a benchmark of the maximum achievable performance in each basin, and examined their transferability to the other basins. We then
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik; Arvin, Erik
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...... and the growth of the biomass are described by the Monod model consisting of two nonlinear coupled first-order differential equations. The objective of this study was to estimate the kinetic parameters in the Monod model and to test whether the parameters from the three identical experiments have the...... same values. Estimation of the parameters was obtained using an iterative maximum likelihood method and the test used was an approximative likelihood ratio test. The test showed that the three sets of parameters were identical only on a 4% alpha level....
Concurrent learning for parameter estimation using dynamic state-derivative estimators
Kamalapurkar, Rushikesh; Reish, Ben; Chowdhary, Girish; Dixon, Warren E.
2015-01-01
A concurrent learning (CL)-based parameter estimator is developed to identify the unknown parameters in a linearly parameterized uncertain control-affine nonlinear system. Unlike state-of-the-art CL techniques that assume knowledge of the state-derivative or rely on numerical smoothing, CL is implemented using a dynamic state-derivative estimator. A novel purging algorithm is introduced to discard possibly erroneous data recorded during the transient phase for concurrent learning. Since purgi...
Seafloor elastic parameters estimation based on AVO inversion
Liu, Yangting; Liu, Xuewei
2015-12-01
Seafloor elastic parameters play an important role in many fields as diverse as marine construction, seabed resources exploration and seafloor acoustics. In order to estimate seafloor elastic parameters, we perform AVO inversion with seafloor reflected seismic data. As a particular reflection interface, the seafloor reflector does not support S-waves and the elastic parameters change dramatically across it. Conventional approximations to the Zoeppritz equations are not applicable for the seafloor situation. In this paper, we perform AVO inversion with the exact Zoeppritz equations through an unconstrained optimization method. Our synthetic study proves that the inversion method does not show strong dependence on the initial model for both unconsolidated and semi-consolidated seabed situations. The inversion uncertainty of the elastic parameters increases with the noise level, and decreases with the incidence angle range. Finally, we perform inversion of data from the South China Sea, and obtain satisfactory results, which are in good agreement with previous research.
Cosmological parameter estimation using Particle Swarm Optimization (PSO)
Prasad, Jayanti
2011-01-01
Obtaining the set of cosmological parameters consistent with observational data is an important exercise in current cosmological research. It involves finding the global maximum of the likelihood function in the multi-dimensional parameter space. Currently sampling based methods, which are in general stochastic in nature, like Markov-Chain Monte Carlo(MCMC), are being commonly used for parameter estimation. The beauty of stochastic methods is that the computational cost grows, at the most, linearly in place of exponentially (as in grid based approaches) with the dimensionality of the search space. MCMC methods sample the full joint probability distribution (posterior) from which one and two dimensional probability distributions, best fit (average) values of parameters and then error bars can be computed. In the present work we demonstrate the application of another stochastic method, named Particle Swarm Optimization (PSO), that is widely used in the field of engineering and artificial intelligence, for cosmo...
Parameter estimation in nonlinear models for pesticide degradation
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.)
Informed spectral analysis: audio signal parameter estimation using side information
Fourer, Dominique; Marchand, Sylvain
2013-12-01
Parametric models are of great interest for representing and manipulating sounds. However, the quality of the resulting signals depends on the precision of the parameters. When the signals are available, these parameters can be estimated, but the presence of noise decreases the resulting precision of the estimation. Furthermore, the Cramér-Rao bound shows the minimal error reachable with the best estimator, which can be insufficient for demanding applications. These limitations can be overcome by using the coding approach which consists in directly transmitting the parameters with the best precision using the minimal bitrate. However, this approach does not take advantage of the information provided by the estimation from the signal and may require a larger bitrate and a loss of compatibility with existing file formats. The purpose of this article is to propose a compromised approach, called the 'informed approach,' which combines analysis with (coded) side information in order to increase the precision of parameter estimation using a lower bitrate than pure coding approaches, the audio signal being known. Thus, the analysis problem is presented in a coder/decoder configuration where the side information is computed and inaudibly embedded into the mixture signal at the coder. At the decoder, the extra information is extracted and is used to assist the analysis process. This study proposes applying this approach to audio spectral analysis using sinusoidal modeling which is a well-known model with practical applications and where theoretical bounds have been calculated. This work aims at uncovering new approaches for audio quality-based applications. It provides a solution for challenging problems like active listening of music, source separation, and realistic sound transformations.
Estimation of atmospheric parameters from time-lapse imagery
McCrae, Jack E.; Basu, Santasri; Fiorino, Steven T.
2016-05-01
A time-lapse imaging experiment was conducted to estimate various atmospheric parameters for the imaging path. Atmospheric turbulence caused frame-to-frame shifts of the entire image as well as parts of the image. The statistics of these shifts encode information about the turbulence strength (as characterized by Cn2, the refractive index structure function constant) along the optical path. The shift variance observed is simply proportional to the variance of the tilt of the optical field averaged over the area being tracked. By presuming this turbulence follows the Kolmogorov spectrum, weighting functions can be derived which relate the turbulence strength along the path to the shifts measured. These weighting functions peak at the camera and fall to zero at the object. The larger the area observed, the more quickly the weighting function decays. One parameter we would like to estimate is r0 (the Fried parameter, or atmospheric coherence diameter.) The weighting functions derived for pixel sized or larger parts of the image all fall faster than the weighting function appropriate for estimating the spherical wave r0. If we presume Cn2 is constant along the path, then an estimate for r0 can be obtained for each area tracked, but since the weighting function for r0 differs substantially from that for every realizable tracked area, it can be expected this approach would yield a poor estimator. Instead, the weighting functions for a number of different patch sizes can be combined through the Moore-Penrose pseudo-inverse to create a new weighting function which yields the least-squares optimal linear combination of measurements for estimation of r0. This approach is carried out, and it is observed that this approach is somewhat noisy because the pseudo-inverse assigns weights much greater than one to many of the observations.
Visco-piezo-elastic parameter estimation in laminated plate structures
Araujo, A. L.; Mota Soares, C. M.; Herskovits, J.;
2009-01-01
measured natural frequencies of free vibration and corresponding modal loss factors. An equivalent single layer higher order numerical model is used for the free vibration analysis of active laminated plate structures and the response of the model is adjusted in order to match the experimental data, hence......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 of...... data. Results are presented for the estimation of elastic, piezoelectric and viscoelastic properties in laminated plates....
J-A Hysteresis Model Parameters Estimation using GA
Bogomir Zidaric
2005-01-01
Full Text Available This paper presents the Jiles and Atherton (J-A hysteresis model parameter estimation for soft magnetic composite (SMC material. The calculation of Jiles and Atherton hysteresis model parameters is based on experimental data and genetic algorithms (GA. Genetic algorithms operate in a given area of possible solutions. Finding the best solution of a problem in wide area of possible solutions is uncertain. A new approach in use of genetic algorithms is proposed to overcome this uncertainty. The basis of this approach is in genetic algorithm built in another genetic algorithm.
Ushirobira, Rosane; Korporal, Anja; PERRUQUETTI, Wilfrid
2014-01-01
International audience — In this communication, we discuss two estimation problems dealing with partial derivatives systems. Namely, estimating partial derivatives of a multivariate noisy signal and identifying parameters of partial differential equations. The multivariate noisy signal is expressed as a truncated Taylor expression in a small time interval. An algebraic method can be then used to estimate its partial derivatives in the opera-tional domain. The same approach applies for the ...
Analysis of neutron scattering data: Visualization and parameter estimation
Beauchamp, J.J.; Fedorov, V.; Hamilton, W.A.; Yethiraj, M.
1998-09-01
Traditionally, small-angle neutron and x-ray scattering (SANS and SAXS) data analysis requires measurements of the signal and corrections due to the empty sample container, detector efficiency and time-dependent background. These corrections are then made on a pixel-by-pixel basis and estimates of relevant parameters (e.g., the radius of gyration) are made using the corrected data. This study was carried out in order to determine whether treatment of the detector efficiency and empty sample cell in a more statistically sound way would significantly reduce the uncertainties in the parameter estimators. Elements of experiment design are shortly discussed in this paper. For instance, we studied the way the time for a measurement should be optimally divided between the counting for signal, background and detector efficiency. In Section 2 we introduce the commonly accepted models for small-angle neutron and x-scattering and confine ourselves to the Guinier and Rayleigh models and their minor generalizations. The traditional approaches of data analysis are discussed only to the extent necessary to allow their comparison with the proposed techniques. Section 3 describes the main stages of the proposed method: visual data exploration, fitting the detector sensitivity function, and fitting a compound model. This model includes three additive terms describing scattering by the sampler, scattering with an empty container and a background noise. We compare a few alternatives for the first term by applying various scatter plots and computing sums of standardized squared residuals. Possible corrections due to smearing effects and randomness of estimated parameters are also shortly discussed. In Section 4 the robustness of the estimators with respect to low and upper bounds imposed on the momentum value is discussed. We show that for the available data set the most accurate and stable estimates are generated by models containing double terms either of Guinier's or Rayleigh
CosmoSIS: A System for MC Parameter Estimation
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.
PARAMETER ESTIMATION OF THE HYBRID CENSORED LOMAX DISTRIBUTION
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.
Estimating parameters of chaotic systems synchronized by external driving signal
Wu Xiaogang [Institute of PR and AI, Huazhong University of Science and Technology, Wuhan 430074 (China)]. E-mail: seanwoo@mail.hust.edu.cn; Wang Zuxi [Institute of PR and AI, Huazhong University of Science and Technology, Wuhan 430074 (China)
2007-07-15
Noise-induced synchronization (NIS) has evoked great research interests recently. Two uncoupled identical chaotic systems can achieve complete synchronization (CS) by feeding a common noise with appropriate intensity. Actually, NIS belongs to the category of external feedback control (EFC). The significance of applying EFC in secure communication lies in fact that the trajectory of chaotic systems is disturbed so strongly by external driving signal that phase space reconstruction attack fails. In this paper, however, we propose an approach that can accurately estimate the parameters of the chaotic systems synchronized by external driving signal through chaotic transmitted signal, driving signal and their derivatives. Numerical simulation indicates that this approach can estimate system parameters and external coupling strength under two driving modes in a very rapid manner, which implies that EFC is not superior to other methods in secure communication.
Estimating parameters of chaotic systems synchronized by external driving signal
Noise-induced synchronization (NIS) has evoked great research interests recently. Two uncoupled identical chaotic systems can achieve complete synchronization (CS) by feeding a common noise with appropriate intensity. Actually, NIS belongs to the category of external feedback control (EFC). The significance of applying EFC in secure communication lies in fact that the trajectory of chaotic systems is disturbed so strongly by external driving signal that phase space reconstruction attack fails. In this paper, however, we propose an approach that can accurately estimate the parameters of the chaotic systems synchronized by external driving signal through chaotic transmitted signal, driving signal and their derivatives. Numerical simulation indicates that this approach can estimate system parameters and external coupling strength under two driving modes in a very rapid manner, which implies that EFC is not superior to other methods in secure communication
A Bayesian framework for parameter estimation in dynamical models.
Flávio Codeço Coelho
Full Text Available Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.
On optimal detection and estimation of the FCN parameters
Yatskiv, Y.
2009-09-01
Statistical approach for detection and estimation of parameters of short-term quasi- periodic processes was used in order to investigate the Free Core Nutation (FCN) signal in the Celestial Pole Offset (CPO). The results show that this signal is very unstable and that it disappeared in year 2000. The amplitude of oscillation with period of about 435 days is larger for dX as compared with that for dY .
Estimation of Secondary Meteorological Parameters Using Mining Data Techniques
Rosabel Zerquera Díaz; Ayleen Morales Montejo; Gil Cruz Lemus; Alejandro Rosete Suárez
2010-01-01
This work develops a process of Knowledge Discovery in Databases (KDD) at the Higher Polytechnic Institute José Antonio Echeverría for the group of Environmental Research in collaboration with the Center of Information Management and Energy Development (CUBAENERGÍA) in order to obtain a data model to estimate the behavior of secondary weather parameters from surface data. It describes some aspects of Data Mining and its application in the meteorological environment, also selects and describes...
Bayesian estimation of parameters in a regional hydrological model
Engeland, K.; Gottschalk, L.
2002-01-01
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 funct...
Bayesian estimation of parameters in a regional hydrological model
Engeland, K.; Gottschalk, L.
2002-01-01
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 ...
Kinetic parameter estimation from TGA: Optimal design of TGA experiments
Dirion, Jean-Louis; Reverte, Cédric; Cabassud, Michel
2008-01-01
This work presents a general methodology to determine kinetic models of solid thermal decomposition with thermogravimetric analysis (TGA) instruments. The goal is to determine a simple and robust kinetic model for a given solid with the minimum of TGA experiments. From this last point of view, this work can be seen as an attempt to find the optimal design of TGA experiments for kinetic modelling. Two computation tools were developed. The first is a nonlinear parameter estimation procedure for...
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.
Parameter estimation for fractional birth and fractional death processes
Cahoy, Dexter O.; Polito, Federico
2013-01-01
The fractional birth and the fractional death processes are more desirable in practice than their classical counterparts as they naturally provide greater flexibility in modeling growing and decreasing systems. In this paper, we propose formal parameter estimation procedures for the fractional Yule, the fractional linear death, and the fractional sublinear death processes. The methods use all available data possible, are computationally simple and asymptotically unbiased. The procedures explo...
Estimation of water diffusivity parameters on grape dynamic drying
Ramos, Inês N.; Miranda, João M.R.; Brandão, Teresa R. S.; Cristina L.M. Silva
2010-01-01
A computer program was developed, aiming at estimating water diffusivity parameters in a dynamic drying process with grapes, assessing the predictability of corresponding non-isothermal drying curves. It numerically solves Fick’s second law for a sphere, by explicit finite differences, in a shrinking system, with anisotropic properties and changing boundary conditions. Experiments were performed in a pilot convective dryer, with simulated air conditions observed in a solar dryer, for modellin...
Iterative importance sampling algorithms for parameter estimation problems
Morzfeld, Matthias; Day, Marcus S.; Grout, Ray W.; Pau, George Shu Heng; Finsterle, Stefan A.; Bell, John B.
2016-01-01
In parameter estimation problems one approximates a posterior distribution over uncertain param- eters defined jointly by a prior distribution, a numerical model, and noisy data. Typically, Markov Chain Monte Carlo (MCMC) is used for the numerical solution of such problems. An alternative to MCMC is importance sampling, where one draws samples from a proposal distribution, and attaches weights to each sample to account for the fact that the proposal distribution is not the posterior distribut...
Estimation of Parameters in Mean-Reverting Stochastic Systems
2014-01-01
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...
Marginalized Particle Filters for Bayesian Estimation of Gaussian Noise Parameters
Saha, S.; Okzan, E.; Gustafsson, F.; Šmídl, Václav
Edinburgh : IET, 2010, s. 1-8. ISBN 978-0-9824438-1-1. [13th International Conference on Information Fusion. Edinburgh (GB), 26.07.2010-29.07.2010] Institutional research plan: CEZ:AV0Z10750506 Keywords : marginalized particle filter * unknown noise statistics * bayesian conjugate prior Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2010/AS/smidl-marginalized particle filters for bayesian estimation of gaussian noise parameters.pdf
Multi-criteria parameter estimation for the unified land model
B. Livneh
2012-04-01
Full Text Available We describe a parameter estimation framework for the Unified Land Model (ULM that utilizes multiple independent data sets over the Continental United States. These include a satellite-based evapotranspiration (ET product based on MODerate resolution Imaging Spectroradiometer (MODIS and Geostationary Operation Environmental Satellites (GOES imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR atmospheric fields, terrestrial water storage content (TWSC data from the Gravity Recovery and Climate Experiment (GRACE, and streamflow (Q primarily from the United States Geological Survey (USGS stream gauges. The study domain includes 10 large-scale (≥10^{5} km^{2} river basins and 250 smaller-scale (<10^{4} km^{2} tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting model, is the basis for these experiments. Calibrations were made using each of the criteria individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large-scales calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under estimation of low (high flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.
Multi-criteria parameter estimation for the Unified Land Model
B. Livneh
2012-08-01
Full Text Available We describe a parameter estimation framework for the Unified Land Model (ULM that utilizes multiple independent data sets over the continental United States. These include a satellite-based evapotranspiration (ET product based on MODerate resolution Imaging Spectroradiometer (MODIS and Geostationary Operational Environmental Satellites (GOES imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR atmospheric fields, terrestrial water storage content (TWSC data from the Gravity Recovery and Climate Experiment (GRACE, and streamflow (Q primarily from the United States Geological Survey (USGS stream gauges. The study domain includes 10 large-scale (≥10^{5} km^{2} river basins and 250 smaller-scale (<10^{4} km^{2} tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting Model, is the basis for these experiments. Calibrations were made using each of the data sets individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large scales, calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under estimation of low (high flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.
Model parameters estimation and sensitivity by genetic algorithms
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
Estimation of stellar atmospheric parameters from SDSS/SEGUE spectra
Re Fiorentin, P.; Bailer-Jones, C. A. L.; Lee, Y. S.; Beers, T. C.; Sivarani, T.; Wilhelm, R.; Allende Prieto, C.; Norris, J. E.
2007-06-01
We present techniques for the estimation of stellar atmospheric parameters (T_eff, log~g, [Fe/H]) for stars from the SDSS/SEGUE survey. The atmospheric parameters are derived from the observed medium-resolution (R = 2000) stellar spectra using non-linear regression models trained either on (1) pre-classified observed data or (2) synthetic stellar spectra. In the first case we use our models to automate and generalize parametrization produced by a preliminary version of the SDSS/SEGUE Spectroscopic Parameter Pipeline (SSPP). In the second case we directly model the mapping between synthetic spectra (derived from Kurucz model atmospheres) and the atmospheric parameters, independently of any intermediate estimates. After training, we apply our models to various samples of SDSS spectra to derive atmospheric parameters, and compare our results with those obtained previously by the SSPP for the same samples. We obtain consistency between the two approaches, with RMS deviations on the order of 150 K in T_eff, 0.35 dex in log~g, and 0.22 dex in [Fe/H]. The models are applied to pre-processed spectra, either via Principal Component Analysis (PCA) or a Wavelength Range Selection (WRS) method, which employs a subset of the full 3850-9000Å spectral range. This is both for computational reasons (robustness and speed), and because it delivers higher accuracy (better generalization of what the models have learned). Broadly speaking, the PCA is demonstrated to deliver more accurate atmospheric parameters when the training data are the actual SDSS spectra with previously estimated parameters, whereas WRS appears superior for the estimation of log~g via synthetic templates, especially for lower signal-to-noise spectra. From a subsample of some 19 000 stars with previous determinations of the atmospheric parameters, the accuracies of our predictions (mean absolute errors) for each parameter are T_eff to 170/170 K, log~g to 0.36/0.45 dex, and [Fe/H] to 0.19/0.26 dex, for methods (1
Estimating hydraulic parameters when poroelastic effects are significant.
Berg, Steven J; Hsieh, Paul A; Illman, Walter A
2011-01-01
For almost 80 years, deformation-induced head changes caused by poroelastic effects have been observed during pumping tests in multilayered aquifer-aquitard systems. As water in the aquifer is released from compressive storage during pumping, the aquifer is deformed both in the horizontal and vertical directions. This deformation in the pumped aquifer causes deformation in the adjacent layers, resulting in changes in pore pressure that may produce drawdown curves that differ significantly from those predicted by traditional groundwater theory. Although these deformation-induced head changes have been analyzed in several studies by poroelasticity theory, there are at present no practical guidelines for the interpretation of pumping test data influenced by these effects. To investigate the impact that poroelastic effects during pumping tests have on the estimation of hydraulic parameters, we generate synthetic data for three different aquifer-aquitard settings using a poroelasticity model, and then analyze the synthetic data using type curves and parameter estimation techniques, both of which are based on traditional groundwater theory and do not account for poroelastic effects. Results show that even when poroelastic effects result in significant deformation-induced head changes, it is possible to obtain reasonable estimates of hydraulic parameters using methods based on traditional groundwater theory, as long as pumping is sufficiently long so that deformation-induced effects have largely dissipated. PMID:21204832
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.
Learn-As-You-Go Acceleration of Cosmological Parameter Estimates
Aslanyan, Grigor; 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 $\\Lambda$CDM posterior probabilities. The computation is significantly accelerated wit...
Maximum-likelihood fits to histograms for improved parameter estimation
Fowler, Joseph W
2013-01-01
Straightforward methods for adapting the familiar chi^2 statistic to histograms of discrete events and other Poisson distributed data generally yield biased estimates of the parameters of a model. The bias can be important even when the total number of events is large. For the case of estimating a microcalorimeter's energy resolution at 6 keV from the observed shape of the Mn K-alpha fluorescence spectrum, a poor choice of chi^2 can lead to biases of at least 10% in the estimated resolution when up to thousands of photons are observed. The best remedy is a Poisson maximum-likelihood fit, through a simple modification of the standard Levenberg-Marquardt algorithm for chi^2 minimization. Where the modification is not possible, another approach allows iterative approximation of the maximum-likelihood fit.
Estimating demographic parameters using hidden process dynamic models.
Gimenez, Olivier; Lebreton, Jean-Dominique; Gaillard, Jean-Michel; Choquet, Rémi; Pradel, Roger
2012-12-01
Structured population models are widely used in plant and animal demographic studies to assess population dynamics. In matrix population models, populations are described with discrete classes of individuals (age, life history stage or size). To calibrate these models, longitudinal data are collected at the individual level to estimate demographic parameters. However, several sources of uncertainty can complicate parameter estimation, such as imperfect detection of individuals inherent to monitoring in the wild and uncertainty in assigning a state to an individual. Here, we show how recent statistical models can help overcome these issues. We focus on hidden process models that run two time series in parallel, one capturing the dynamics of the true states and the other consisting of observations arising from these underlying possibly unknown states. In a first case study, we illustrate hidden Markov models with an example of how to accommodate state uncertainty using Frequentist theory and maximum likelihood estimation. In a second case study, we illustrate state-space models with an example of how to estimate lifetime reproductive success despite imperfect detection, using a Bayesian framework and Markov Chain Monte Carlo simulation. Hidden process models are a promising tool as they allow population biologists to cope with process variation while simultaneously accounting for observation error. PMID:22373775
The Truth About Ballistic Coefficients
Courtney, Michael
2007-01-01
The ballistic coefficient of a bullet describes how it slows in flight due to air resistance. This article presents experimental determinations of ballistic coefficients showing that the majority of bullets tested have their previously published ballistic coefficients exaggerated from 5-25% by the bullet manufacturers. These exaggerated ballistic coefficients lead to inaccurate predictions of long range bullet drop, retained energy and wind drift.
Genetic Algorithm-based Affine Parameter Estimation for Shape Recognition
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.
Estimation of Physical Parameters in Linear and Nonlinear Dynamic Systems
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 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...... sensitivity and the relative parameter variance and confidence ellipsoid is demonstrated. The relation is based on a new theorem on maxima of an ellipsoid. The procedure for input signal design and physical parameter estimation is tested on a number of examples, linear as well as nonlinear and simulated as...
Parameter estimation in a spatial unit root autoregressive model
Baran, Sándor
2011-01-01
Spatial autoregressive model $X_{k,\\ell}=\\alpha X_{k-1,\\ell}+\\beta X_{k,\\ell-1}+\\gamma X_{k-1,\\ell-1}+\\epsilon_{k,\\ell}$ is investigated in the unit root case, that is when the parameters are on the boundary of the domain of stability that forms a tetrahedron with vertices $(1,1,-1), \\ (1,-1,1),\\ (-1,1,1)$ and $(-1,-1,-1)$. It is shown that the limiting distribution of the least squares estimator of the parameters is normal and the rate of convergence is $n$ when the parameters are in the faces or on the edges of the tetrahedron, while on the vertices the rate is $n^{3/2}$.
Estimates of genetic parameters for fat yield in Murrah buffaloes
Manoj Kumar
2016-03-01
Full Text Available Aim: The present study was performed to investigate the effect of genetic and non-genetic factors affecting milk fat yield and to estimate genetic parameters of monthly test day fat yields (MTDFY and lactation 305-day fat yield (L305FY in Murrah buffaloes. Materials and Methods: The data on total of 10381 MTDFY records comprising the first four lactations of 470 Murrah buffaloes calved from 1993 to 2014 were assessed. These buffaloes were sired by 75 bulls maintained in an organized farm at ICAR-National Dairy Research Institute, Karnal. Least squares maximum likelihood program was used to estimate genetic and non-genetic parameters. Heritability estimates were obtained using paternal half-sib correlation method. Genetic and phenotypic correlations among MTDFY, and 305-day fat yield were calculated from the analysis of variance and covariance matrix among sire groups. Results: The overall least squares mean of L305FY was found to be 175.74±4.12 kg. The least squares mean of overall MTDFY ranged from 3.33±0.14 kg (TD-11 to 7.06±0.17 kg (TD-3. The h2 estimate of L305FY was found to be 0.33±0.16 in this study. The estimates of phenotypic and genetic correlations between 305-day fat yield and different MTDFY ranged from 0.32 to 0.48 and 0.51 to 0.99, respectively. Conclusions: In this study, all the genetic and non-genetic factors except age at the first calving group, significantly affected the traits under study. The estimates of phenotypic and genetic correlations of MTDFY with 305-day fat yield was generally higher in the MTDFY-5 of lactation suggesting that this TD yields could be used as the selection criteria for early evaluation and selection of Murrah buffaloes.