DEFF Research Database (Denmark)
Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian
2011-01-01
In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of 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 Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling
Directory of Open Access Journals (Sweden)
Ruochen Liu
2013-01-01
Full Text Available The permutation flow shop scheduling problem (PFSSP is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO based memetic algorithm (MPSOMA is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS and individual improvement scheme (IIS. Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA, on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.
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.
Precision cosmological parameter estimation
Fendt, William Ashton, Jr.
2009-09-01
methods. These techniques will help in the understanding of new physics contained in current and future data sets as well as benefit the research efforts of the cosmology community. Our idea is to shift the computationally intensive pieces of the parameter estimation framework to a parallel training step. We then provide a machine learning code that uses this training set to learn the relationship between the underlying cosmological parameters and the function we wish to compute. This code is very accurate and simple to evaluate. It can provide incredible speed- ups of parameter estimation codes. For some applications this provides the convenience of obtaining results faster, while in other cases this allows the use of codes that would be impossible to apply in the brute force setting. In this thesis we provide several examples where our method allows more accurate computation of functions important for data analysis than is currently possible. As the techniques developed in this work are very general, there are no doubt a wide array of applications both inside and outside of cosmology. We have already seen this interest as other scientists have presented ideas for using our algorithm to improve their computational work, indicating its importance as modern experiments push forward. In fact, our algorithm will play an important role in the parameter analysis of Planck, the next generation CMB space mission.
Spread, estimators and nuisance parameters
Heuvel, Edwin R. van den; Klaassen, Chris A.J.
1997-01-01
A general spread inequality for arbitrary estimators of a one-dimensional parameter is given. This finite-sample inequality yields bounds on the distribution of estimators in the presence of finite- or infinite-dimensional nuisance parameters.
Using PSO-Based Hierarchical Feature Selection Algorithm
Directory of Open Access Journals (Sweden)
Zhiwei Ji
2014-01-01
Full Text Available Hepatocellular carcinoma (HCC is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.
Parameter estimation in food science.
Dolan, Kirk D; Mishra, Dharmendra K
2013-01-01
Modeling includes two distinct parts, the forward problem and the inverse problem. The forward problem-computing y(t) given known parameters-has received much attention, especially with the explosion of commercial simulation software. What is rarely made clear is that the forward results can be no better than the accuracy of the parameters. Therefore, the inverse problem-estimation of parameters given measured y(t)-is at least as important as the forward problem. However, in the food science literature there has been little attention paid to the accuracy of parameters. The purpose of this article is to summarize the state of the art of parameter estimation in food science, to review some of the common food science models used for parameter estimation (for microbial inactivation and growth, thermal properties, and kinetics), and to suggest a generic method to standardize parameter estimation, thereby making research results more useful. Scaled sensitivity coefficients are introduced and shown to be important in parameter identifiability. Sequential estimation and optimal experimental design are also reviewed as powerful parameter estimation methods that are beginning to be used in the food science literature.
Data Handling and Parameter Estimation
DEFF Research Database (Denmark)
Sin, Gürkan; Gernaey, Krist
2016-01-01
). 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...... various kinetic parameters for different microbial groups and their activities in WWTPs by using parameter estimation techniques. Indeed, estimating parameters is an integral part of model development and application (Seber andWild, 1989; Ljung, 1999; Dochain and Vanrolleghem,2001; Omlin and Reichert......, 1999; Brun et al., 2002; Sinet al., 2010) and can be broadly defined as follows: Given a model and a set of data/measurements from the experimental setup in question, estimate all or some of the parameters of the model using an appropriate statistical method. The focus of this chapter is to provide...
Load Estimation from Modal Parameters
DEFF Research Database (Denmark)
Aenlle, Manuel López; Brincker, Rune; Fernández, Pelayo Fernández
2007-01-01
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...... the accuracy in the load estimation. Finally, the results of an experimental program carried out on a simple structure are presented....
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
Mode choice model parameters estimation
Strnad, Irena
2010-01-01
The present work focuses on parameter estimation of two mode choice models: multinomial logit and EVA 2 model, where four different modes and five different trip purposes are taken into account. Mode choice model discusses the behavioral aspect of mode choice making and enables its application to a traffic model. Mode choice model includes mode choice affecting trip factors by using each mode and their relative importance to choice made. When trip factor values are known, it...
Inflation and cosmological parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Hamann, J.
2007-05-15
In this work, we focus on two aspects of cosmological data analysis: inference of parameter values and the search for new effects in the inflationary sector. Constraints on cosmological parameters are commonly derived under the assumption of a minimal model. We point out that this procedure systematically underestimates errors and possibly biases estimates, due to overly restrictive assumptions. In a more conservative approach, we analyse cosmological data using a more general eleven-parameter model. We find that regions of the parameter space that were previously thought ruled out are still compatible with the data; the bounds on individual parameters are relaxed by up to a factor of two, compared to the results for the minimal six-parameter model. Moreover, we analyse a class of inflation models, in which the slow roll conditions are briefly violated, due to a step in the potential. We show that the presence of a step generically leads to an oscillating spectrum and perform a fit to CMB and galaxy clustering data. We do not find conclusive evidence for a step in the potential and derive strong bounds on quantities that parameterise the step. (orig.)
PSO based PI controller design for a solar charger system.
Yau, Her-Terng; Lin, Chih-Jer; Liang, Qin-Cheng
2013-01-01
Due to global energy crisis and severe environmental pollution, the photovoltaic (PV) system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT) and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC) to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV) method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs).
PSO Based PI Controller Design for a Solar Charger System
Directory of Open Access Journals (Sweden)
Her-Terng Yau
2013-01-01
Full Text Available Due to global energy crisis and severe environmental pollution, the photovoltaic (PV system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs.
Estimation of Synchronous Machine Parameters
Directory of Open Access Journals (Sweden)
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.
PSO Based Optimization of Testing and Maintenance Cost in NPPs
Directory of Open Access Journals (Sweden)
Qiang Chou
2014-01-01
Full Text Available Testing and maintenance activities of safety equipment have drawn much attention in Nuclear Power Plant (NPP to risk and cost control. The testing and maintenance activities are often implemented in compliance with the technical specification and maintenance requirements. Technical specification and maintenance-related parameters, that is, allowed outage time (AOT, maintenance period and duration, and so forth, in NPP are associated with controlling risk level and operating cost which need to be minimized. The above problems can be formulated by a constrained multiobjective optimization model, which is widely used in many other engineering problems. Particle swarm optimizations (PSOs have proved their capability to solve these kinds of problems. In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Numerical results have demonstrated the efficiency of our proposed algorithm.
Implementation and comparison of PSO-based algorithms for multi-modal optimization problems
Sriyanyong, Pichet; Lu, Haiyan
2013-10-01
This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration.
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
A PSO-based hybrid metaheuristic for permutation flowshop scheduling problems.
Zhang, Le; Wu, Jinnan
2014-01-01
This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.
Method for estimating solubility parameter
Lawson, D. D.; Ingham, J. D.
1973-01-01
Semiempirical correlations have been developed between solubility parameters and refractive indices for series of model hydrocarbon compounds and organic polymers. Measurement of intermolecular forces is useful for assessment of material compatibility, glass-transition temperature, and transport properties.
Parameter Uncertainty in Exponential Family Tail Estimation
Landsman, Z.; Tsanakas, A.
2012-01-01
Actuaries are often faced with the task of estimating tails of loss distributions from just a few observations. Thus estimates of tail probabilities (reinsurance prices) and percentiles (solvency capital requirements) are typically subject to substantial parameter uncertainty. We study the bias and MSE of estimators of tail probabilities and percentiles, with focus on 1-parameter exponential families. Using asymptotic arguments it is shown that tail estimates are subject to significant positi...
On parameter estimation in deformable models
DEFF Research Database (Denmark)
Fisker, Rune; Carstensen, Jens Michael
1998-01-01
Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian...... formulation of deformable templates. In the supervised estimation the parameters are estimated using a likelihood and a least squares criterion given a training set. For most deformable template models the supervised estimation provides the opportunity for simulation of the prior model. The unsupervised...
Estimation of physical parameters in induction motors
DEFF Research Database (Denmark)
Børsting, H.; Knudsen, Morten; Rasmussen, Henrik
1994-01-01
Parameter estimation in induction motors is a field of great interest, because accurate models are needed for robust dynamic control of induction motors......Parameter estimation in induction motors is a field of great interest, because accurate models are needed for robust dynamic control of induction motors...
Parameter estimation applied to physiological systems
Rideout, V.C.; Beneken, J.E.W.
Parameter estimation techniques are of ever-increasing interest in the fields of medicine and biology, as greater efforts are currently being made to describe physiological systems in explicit quantitative form. Although some of the techniques of parameter estimation as developed for use in other
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
Directory of Open Access Journals (Sweden)
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
Algebraic Parameter Estimation of Damped Exponentials
Neves, Aline; Miranda, Maria; Mboup, M.
2007-01-01
International audience; The parameter estimation of a sum of exponentials or the exponential fitting of data is a well known problem with a rich history. It is a nonlinear problem which presents several difficulties as the ill-conditioning when roots have close values and the order of the estimated parameters, among others. One of the best existing methods is the modified Prony algorithm which suffers in the presence of noise. In this paper we propose an algebraic method for the parameter est...
Estimating parametes for systems with complicated dynamics
Goodwin, J; Goodwin, Justin; Brown, Reggie
1998-01-01
Changes in parameters of a physical device can eventually give way to catastrophic failure. In this paper we present a method for estimating the parameters of a device from time series data. We also examine the robustness of this method to noise in the data. For our examples, the parameter estimates are good to about two decimal places even at 0 dB signal to noise ratio.
Cosmological parameter estimation using Particle Swarm Optimization
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Parameter Estimation of Partial Differential Equation Models.
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab
2013-01-01
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 present 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 LIDAR data.
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.
Vimalarani, C; Subramanian, R; Sivanandam, S N
2016-01-01
Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network
Directory of Open Access Journals (Sweden)
C. Vimalarani
2016-01-01
Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
Application of spreadsheet to estimate infiltration parameters
Directory of Open Access Journals (Sweden)
Mohammad Zakwan
2016-09-01
Full Text Available Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach for estimation of infiltration parameters often fails to estimate the infiltration parameters precisely. The generalised reduced gradient (GRG solver is reported to be a powerful tool for estimating parameters of nonlinear equations and it has, therefore, been implemented to estimate the infiltration parameters in the present paper. Field data of infiltration rate available in literature for sandy loam soils of Umuahia, Nigeria were used to evaluate the performance of GRG solver. A comparative study of graphical method and GRG solver shows that the performance of GRG solver is better than that of conventional graphical method for estimation of infiltration rates. Further, the performance of Kostiakov model has been found to be better than the Horton and Philip's model in most of the cases based on both the approaches of parameter estimation.
Association measures and estimation of copula parameters ...
African Journals Online (AJOL)
We apply the inversion method of estimation, with several combinations of two among the four most popular association measures, to estimate the parameters of copulas in the case of bivariate distributions. We carry out a simulation study with two examples, namely Farlie-Gumbel-Morgenstern and Marshall-Olkin ...
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Estimation of Modal Parameters and their Uncertainties
DEFF Research Database (Denmark)
Andersen, P.; Brincker, Rune
1999-01-01
In this paper it is shown how to estimate the modal parameters as well as their uncertainties using the prediction error method of a dynamic system on the basis of uotput measurements only. The estimation scheme is assessed by means of a simulation study. As a part of the introduction, an example...... is given showing how the uncertainty estimates can be used in applications such as damage detection....
Reionization history and CMB parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Dizgah, Azadeh Moradinezhad; Gnedin, Nickolay Y.; Kinney, William H.
2013-05-01
We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case.
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.
Uncertainty Analysis in the Noise Parameters Estimation
Directory of Open Access Journals (Sweden)
Pawlik P.
2012-07-01
Full Text Available The new approach to the uncertainty estimation in modelling acoustic hazards by means of the interval arithmetic is presented in the paper. In the case of the noise parameters estimation the selection of parameters specifying the acoustic wave propagation in an open space as well as parameters which are required in a form of average values – often constitutes a difficult problem. In such case, it is necessary to determine the variance and then, related strictly to it, the uncertainty of model parameters. The application of the interval arithmetic formalism allows to estimate the input data uncertainties without the necessity of the determination their probability distribution, which is required by other methods of uncertainty assessment. A successive problem in the acoustic hazards estimation is a lack of the exact knowledge of the input parameters. In connection with the above, the analysis of the modelling uncertainty in dependence of inaccuracy of model parameters was performed. To achieve this aim the interval arithmetic formalism – representing the value and its uncertainty in a form of an interval – was applied. The proposed approach was illustrated by the example of the application the Dutch RMR SRM Method, recommended by the European Union Directive 2002/49/WE, in the railway noise modelling.
Interval Estimation of Seismic Hazard Parameters
Orlecka-Sikora, Beata; Lasocki, Stanislaw
2017-03-01
The paper considers Poisson temporal occurrence of earthquakes and presents a way to integrate uncertainties of the estimates of mean activity rate and magnitude cumulative distribution function in the interval estimation of the most widely used seismic hazard functions, such as the exceedance probability and the mean return period. The proposed algorithm can be used either when the Gutenberg-Richter model of magnitude distribution is accepted or when the nonparametric estimation is in use. When the Gutenberg-Richter model of magnitude distribution is used the interval estimation of its parameters is based on the asymptotic normality of the maximum likelihood estimator. When the nonparametric kernel estimation of magnitude distribution is used, we propose the iterated bias corrected and accelerated method for interval estimation based on the smoothed bootstrap and second-order bootstrap samples. The changes resulted from the integrated approach in the interval estimation of the seismic hazard functions with respect to the approach, which neglects the uncertainty of the mean activity rate estimates have been studied using Monte Carlo simulations and two real dataset examples. The results indicate that the uncertainty of mean activity rate affects significantly the interval estimates of hazard functions only when the product of activity rate and the time period, for which the hazard is estimated, is no more than 5.0. When this product becomes greater than 5.0, the impact of the uncertainty of cumulative distribution function of magnitude dominates the impact of the uncertainty of mean activity rate in the aggregated uncertainty of the hazard functions. Following, the interval estimates with and without inclusion of the uncertainty of mean activity rate converge. The presented algorithm is generic and can be applied also to capture the propagation of uncertainty of estimates, which are parameters of a multiparameter function, onto this function.
A PSO based unified power flow controller for damping of power system oscillations
Energy Technology Data Exchange (ETDEWEB)
Shayeghi, H. [Technical Engineering Dept., Univ. of Mohaghegh Ardabili, Daneshgah Street, P.O. Box 179, Ardabil (Iran); Shayanfar, H.A. [Center of Excellence for Power Automation and Operation, Electrical Engineering Dept., Iran Univ. of Science and Technology, Tehran (Iran); Jalilzadeh, S.; Safari, A. [Technical Engineering Dept., Zanjan Univ., Zanjan (Iran)
2009-10-15
On the basis of the linearized Phillips-Herffron model of a single-machine power system, we approach the problem of select the best input control signal of the unified power flow controller (UPFC) and design optimal UPFC based damping controller in order to enhance the damping of the power system low frequency oscillations. The potential of the UPFC supplementary controllers to enhance the dynamic stability is evaluated. This controller is tuned to simultaneously shift the undamped electromechanical modes to a prescribed zone in the s-plane. The problem of robustly UPFC based damping controller is formulated as an optimization problem according to the eigenvalue-based multiobjective function comprising the damping factor, and the damping ratio of the undamped electromechanical modes to be solved using particle swarm optimization technique (PSO) that has a strong ability to find the most optimistic results. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed controller is demonstrated through eigenvalue analysis, nonlinear time-domain simulation and some performance indices studies. The results analysis reveals that the tuned PSO based UPFC controller using the proposed multiobjective function has an excellent capability in damping power system low frequency oscillations and enhance greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions show that the {delta}{sub E} based controller is superior to the m{sub B} based controller. (author)
Postprocessing MPEG based on estimated quantization parameters
DEFF Research Database (Denmark)
Forchhammer, Søren
2009-01-01
Postprocessing of MPEG(-2) video is widely used to attenuate the coding artifacts, especially deblocking but also deringing have been addressed. The focus has been on filters where the decoder has access to the code stream and e.g. utilizes information about the quantization parameter. We consider...... 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......' postprocessing compares favorable to a reference postprocessing filter which has access to the quantization parameters not only for I-frames but also on P and B-frames....
Parameter Estimation of Turbo Code Encoder
Directory of Open Access Journals (Sweden)
Mehdi Teimouri
2014-01-01
Full Text Available The problem of reconstruction of a channel code consists of finding out its design parameters solely based on its output. This paper investigates the problem of reconstruction of parallel turbo codes. Reconstruction of a turbo code has been addressed in the literature assuming that some of the parameters of the turbo encoder, such as the number of input and output bits of the constituent encoders and puncturing pattern, are known. However in practical noncooperative situations, these parameters are unknown and should be estimated before applying reconstruction process. Considering such practical situations, this paper proposes a novel method to estimate the above-mentioned code parameters. The proposed algorithm increases the efficiency of the reconstruction process significantly by judiciously reducing the size of search space based on an analysis of the observed channel code output. Moreover, simulation results show that the proposed algorithm is highly robust against channel errors when it is fed with noisy observations.
Multi-Parameter Estimation for Orthorhombic Media
Masmoudi, Nabil
2015-08-19
Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.
Parameter Estimation for Thurstone Choice Models
Energy Technology Data Exchange (ETDEWEB)
Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-04-24
We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.
Parameter inference with estimated covariance matrices
Sellentin, Elena; Heavens, Alan F.
2016-02-01
When inferring parameters from a Gaussian-distributed data set by computing a likelihood, a covariance matrix is needed that describes the data errors and their correlations. If the covariance matrix is not known a priori, it may be estimated and thereby becomes a random object with some intrinsic uncertainty itself. We show how to infer parameters in the presence of such an estimated covariance matrix, by marginalizing over the true covariance matrix, conditioned on its estimated value. This leads to a likelihood function that is no longer Gaussian, but rather an adapted version of a multivariate t-distribution, which has the same numerical complexity as the multivariate Gaussian. As expected, marginalization over the true covariance matrix improves inference when compared with Hartlap et al.'s method, which uses an unbiased estimate of the inverse covariance matrix but still assumes that the likelihood is Gaussian.
Robot arm geometric link parameter estimation
Hayati, S. A.
1983-01-01
A general method for estimating serial link manipulator geometric parameter errors is proposed in this paper. The positioning accuracy of the end-effector may be increased significantly by updating the nominal link parameters in the control software to represent the physical system more accurately. The proposed method is applicable for serial link manipulators with any combination of revolute or prismatic joints, and is not limited to a specific measurement technique.
Directory of Open Access Journals (Sweden)
Hossein Soleymani
2017-08-01
Full Text Available Interconnection of the power system utilities and grids offers a formidable dispute in front of design engineers. With the interconnections, power system has emerged as a more intricate and nonlinear system. Recent years small signal stability problems have achieved much significance along with the conventional transient constancy problems. Transient stability of the power system can be attained with high gain and fast acting Automatic Voltage Regulators (AVRs. Yet, AVRs establish negative damping in the system. Propagation of small signals is hazardous for system’s health and offers a potential threat to system’s oscillatory stability. These small signals have magnitude of 0.2 to 2 Hz. The professional control tactic to develop system damping is Power System Stabilizer (PSS.This paper presents application of swarm intelligence for PSS parameter estimation issue on standard IEEE 10 Generator 39 Bus power network (New England. Realization of the objective function is done with the help of interpolation investigation using MATLAB. The system performance is compared with the conventional optimization algorithms like Genetic Algorithm (GA and Particle Swarm Optimization (PSO based PSS controller. The strength of proposed controller is tested by examining various operating conditions. An Eigen property analysis is done on this system i.e. before installing PSS, and after the employment of GA and PSO tuned PSSs. A significant comparison is carried out with GA and PSO on the basis of convergence uniqueness and dynamic response of speed deviation curves of various generators.
New approaches to estimation of magnetotelluric parameters
Energy Technology Data Exchange (ETDEWEB)
Egbert, G.D.
1991-01-01
Fully efficient robust data processing procedures were developed and tested for single station and remote reference magnetotelluric (Mr) data. Substantial progress was made on development, testing and comparison of optimal procedures for single station data. A principal finding of this phase of the research was that the simplest robust procedures can be more heavily biased by noise in the (input) magnetic fields, than standard least squares estimates. To deal with this difficulty we developed a robust processing scheme which combined the regression M-estimate with coherence presorting. This hybrid approach greatly improves impedance estimates, particularly in the low signal-to-noise conditions often encountered in the dead band'' (0.1--0.0 hz). The methods, and the results of comparisons of various single station estimators are described in detail. Progress was made on developing methods for estimating static distortion parameters, and for testing hypotheses about the underlying dimensionality of the geological section.
Parameter estimation for an expanding universe
Directory of Open Access Journals (Sweden)
Jieci Wang
2015-03-01
Full Text Available We study the parameter estimation for excitations of Dirac fields in the expanding Robertson–Walker universe. We employ quantum metrology techniques to demonstrate the possibility for high precision estimation for the volume rate of the expanding universe. We show that the optimal precision of the estimation depends sensitively on the dimensionless mass m˜ and dimensionless momentum k˜ of the Dirac particles. The optimal precision for the ratio estimation peaks at some finite dimensionless mass m˜ and momentum k˜. We find that the precision of the estimation can be improved by choosing the probe state as an eigenvector of the hamiltonian. This occurs because the largest quantum Fisher information is obtained by performing projective measurements implemented by the projectors onto the eigenvectors of specific probe states.
Statistical estimation of percolation cluster parameters
Moskalev, P. V.; Grebennikov, K. V.; Shitov, V. V.
2011-01-01
In this paper we study statistical methods of parameters estimation of the site percolation model. Advantages of the proposed method is demonstrated for the computing of the confidence interval of mass fractal dimension of a percolation clusters sampling, formed by the Monte Carlo method.
Bounds of parameter estimation for interference signals.
Li, Chengshuai; Zhu, Yizheng
2017-08-20
Parameter estimation, especially frequency estimation, from noisy observations of interference is essential in optical interferometric sensing and metrology. The Cramer-Rao bound (CRB) of such estimation determines measurement sensitivity limit. Unlike the well-studied complex sinusoids in communication theory, an optical interference signal is distinctly different in its model parameters and noise statistics. The connection between these parameters and their estimation bounds has not been well understood. Here we propose a complete, realistic multiparameter interference model corrupted by a combination of shot noise, dark noise, and readout noise. We derive the Fisher information matrix and the CRBs for all model parameters, including intensity, visibility, optical path length (frequency), and initial phase. We show that the CRBs of frequency and phase are coupled but not affected by the knowledge of intensity and visibility. Knowing the initial phase offers significant sensitivity advantage, which is verified by both theoretical derivations and numerical simulations. In addition to the complete model, a shot noise-limited case is studied, permitting the calculation of the CRBs directly from measured data.
Simultaneous estimation of earthquake source parameters and ...
Indian Academy of Sciences (India)
This paper presents the simultaneous estimation of source parameters and crustal Q values for small to moderate-size aftershocks (Mw 2.1–5.1) of the Mw 7.7 2001 Bhuj earthquake. The horizontal-component. S-waves of 144 well located earthquakes (2001–2010) recorded at 3–10 broadband seismograph sites in.
Simultaneous estimation of earthquake source parameters and ...
Indian Academy of Sciences (India)
This paper presents the simultaneous estimation of source parameters and crustal Q values for small to moderate-size aftershocks ( 2.1–5.1) of the 7.7 2001 Bhuj earthquake. The horizontal-component S-waves of 144 well located earthquakes (2001–2010) recorded at 3–10 broadband seismograph sites in the ...
Angstrom equation parameter estimation by unrestricted method
Energy Technology Data Exchange (ETDEWEB)
Sen, Zeka I. [Istanbul Technical Univ., Hydraulics Div., Istanbul (Turkey)
2001-07-01
Global solar irradiation is directly related to the sunshine duration record through a linear model which was first proposed by Angstrom in 1924. Generally, the parameter estimations of this model are achieved by using the regression technique based on the least squares method. This technique imposes procedural restrictions on the parameter estimations leading to under-estimations for clear sky condition and over-estimations in the case of overcast sky conditions. These restrictions include linearity, normality, means of conditional distribution, homoscedasticity, autocorrelation and lack of measurement error. In this paper, an alternative unrestricted method (UM) is proposed for preserving the means and variances of the global irradiation and the sunshine duration data. In the restrictive regression approach (Angstrom equation) the cross-correlation coefficient represents only linear relationships. By not considering this coefficient in the UM, some nonlinearities in the solar irradiation-sunshine duration relationship are taken into account. Especially when the scatter diagram of solar irradiation versus sunshine duration does not show any distinguishable pattern such as a straight line or a curve, then the use of UM is recommended for parameter estimations. The UM is contrasted with the Angstrom regression method for 27 solar irradiation stations in Turkey. (Author)
Discriminative parameter estimation for random walks segmentation.
Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan
2013-01-01
The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.
Parameter estimation in stochastic differential equations
Bishwal, Jaya P N
2008-01-01
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.
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.
Parameter estimation in channel network flow simulation
Directory of Open Access Journals (Sweden)
Han Longxi
2008-03-01
Full Text Available 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.
Parameter estimation uncertainty: Comparing apples and apples?
Hart, D.; Yoon, H.; McKenna, S. A.
2012-12-01
Given a highly parameterized ground water model in which the conceptual model of the heterogeneity is stochastic, an ensemble of inverse calibrations from multiple starting points (MSP) provides an ensemble of calibrated parameters and follow-on transport predictions. However, the multiple calibrations are computationally expensive. Parameter estimation uncertainty can also be modeled by decomposing the parameterization into a solution space and a null space. From a single calibration (single starting point) a single set of parameters defining the solution space can be extracted. The solution space is held constant while Monte Carlo sampling of the parameter set covering the null space creates an ensemble of the null space parameter set. A recently developed null-space Monte Carlo (NSMC) method combines the calibration solution space parameters with the ensemble of null space parameters, creating sets of calibration-constrained parameters for input to the follow-on transport predictions. Here, we examine the consistency between probabilistic ensembles of parameter estimates and predictions using the MSP calibration and the NSMC approaches. A highly parameterized model of the Culebra dolomite previously developed for the WIPP project in New Mexico is used as the test case. A total of 100 estimated fields are retained from the MSP approach and the ensemble of results defining the model fit to the data, the reproduction of the variogram model and prediction of an advective travel time are compared to the same results obtained using NSMC. We demonstrate that the NSMC fields based on a single calibration model can be significantly constrained by the calibrated solution space and the resulting distribution of advective travel times is biased toward the travel time from the single calibrated field. To overcome this, newly proposed strategies to employ a multiple calibration-constrained NSMC approach (M-NSMC) are evaluated. Comparison of the M-NSMC and MSP methods suggests
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
Sensor Placement for Modal Parameter Subset Estimation
DEFF Research Database (Denmark)
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...... anticipated effectiveness are ranked according to the noted criterion. It is contended that the examined max-min criterion satisfies the objective of optimizing the accuracy of an identification setup more effectively than the more commonly used trace or determinant of the Fisher information matrix (FIM...
Online Dynamic Parameter Estimation of Synchronous Machines
West, Michael R.
Traditionally, synchronous machine parameters are determined through an offline characterization procedure. The IEEE 115 standard suggests a variety of mechanical and electrical tests to capture the fundamental characteristics and behaviors of a given machine. These characteristics and behaviors can be used to develop and understand machine models that accurately reflect the machine's performance. To perform such tests, the machine is required to be removed from service. Characterizing a machine offline can result in economic losses due to down time, labor expenses, etc. Such losses may be mitigated by implementing online characterization procedures. Historically, different approaches have been taken to develop methods of calculating a machine's electrical characteristics, without removing the machine from service. Using a machine's input and response data combined with a numerical algorithm, a machine's characteristics can be determined. This thesis explores such characterization methods and strives to compare the IEEE 115 standard for offline characterization with the least squares approximation iterative approach implemented on a 20 h.p. synchronous machine. This least squares estimation method of online parameter estimation shows encouraging results for steady-state parameters, in comparison with steady-state parameters obtained through the IEEE 115 standard.
Estimation of tracked vehicle suspension parameters
Directory of Open Access Journals (Sweden)
Aldélio Bueno Caldeira
2017-02-01
Full Text Available This work aims to estimate the suspension stiffness and damping coefficient of a tracked vehicle by using an inverse problem technique based on Particle Swarm Optimization (PSO and on Random Restricted Window (R2W. The tracked vehicle has ten road wheels. Each road wheel is linked to a passive and independent suspension. A half car model with seven degrees of freedom describes the bounce and pitch dynamics of the chassis and the vertical dynamics of the wheels. Bounce and pitch accelerations are evaluated when the vehicle traverses a bump terrain. The inverse problem approach minimizes the total quadratic error between estimated and pseudo-experimental data for bounce and pitch accelerations. The viability of a field experiment to estimate the suspension parameters is analyzed, as well as the performance of the employed optimization methods and the effects of the noise on pseudo-experimental data.
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Directory of Open Access Journals (Sweden)
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
Optimal linear estimation of binary star parameters
Burke, Daniel; Devaney, Nicholas; Gladysz, Szymon; Barrett, Harrisson H.; Whitaker, Meredith K.; Caucci, Luca
2008-07-01
We propose a new post-processing technique for the detection of faint companions and the estimation of their parameters from adaptive optics (AO) observations. We apply the optimal linear detector, which is the Hotelling observer, to perform detection, astrometry and photometry on real and simulated data. The real data was obtained from the AO system on the 3m Lick telescope1. The Hotelling detector, which is a prewhitening matched filter, calculates the Hotelling test statistic which is then compared to a threshold. If the test statistic is greater than the threshold the algorithm decides that a companion is present. This decision is the main task performed by the Hotelling observer. After a detection is made the location and intensity of the companion which maximise this test statistic are taken as the estimated values. We compare the Hotelling approach with current detection algorithms widely used in astronomy. We discuss the use of the estimation receiver operating characteristic (EROC) curve in quantifying the performance of the algorithm with no prior estimate of the companion's location or intensity. The robustness of this technique to errors in point spread function (PSF) estimation is also investigated.
Toward unbiased estimations of the statefinder parameters
Aviles, Alejandro; Klapp, Jaime; Luongo, Orlando
2017-09-01
With the use of simulated supernova catalogs, we show that the statefinder parameters turn out to be poorly and biased estimated by standard cosmography. To this end, we compute their standard deviations and several bias statistics on cosmologies near the concordance model, demonstrating that these are very large, making standard cosmography unsuitable for future and wider compilations of data. To overcome this issue, we propose a new method that consists in introducing the series of the Hubble function into the luminosity distance, instead of considering the usual direct Taylor expansions of the luminosity distance. Moreover, in order to speed up the numerical computations, we estimate the coefficients of our expansions in a hierarchical manner, in which the order of the expansion depends on the redshift of every single piece of data. In addition, we propose two hybrids methods that incorporates standard cosmography at low redshifts. The methods presented here perform better than the standard approach of cosmography both in the errors and bias of the estimated statefinders. We further propose a one-parameter diagnostic to reject non-viable methods in cosmography.
Sequential parameter estimation for stochastic systems
Directory of Open Access Journals (Sweden)
G. A. Kivman
2003-01-01
Full Text Available The quality of the prediction of dynamical system evolution is determined by the accuracy to which initial conditions and forcing are known. Availability of future observations permits reducing the effects of errors in assessment the external model parameters by means of a filtering algorithm. Usually, uncertainties in specifying internal model parameters describing the inner system dynamics are neglected. Since they are characterized by strongly non-Gaussian distributions (parameters are positive, as a rule, traditional Kalman filtering schemes are badly suited to reducing the contribution of this type of uncertainties to the forecast errors. An extension of the Sequential Importance Resampling filter (SIR is proposed to this aim. The filter is verified against the Ensemble Kalman filter (EnKF in application to the stochastic Lorenz system. It is shown that the SIR is capable of estimating the system parameters and to predict the evolution of the system with a remarkably better accuracy than the EnKF. This highlights a severe drawback of any Kalman filtering scheme: due to utilizing only first two statistical moments in the analysis step it is unable to deal with probability density functions badly approximated by the normal distribution.
Parameter estimation using B-Trees
DEFF Research Database (Denmark)
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 already present...
Preliminary Estimation of Kappa Parameter in Croatia
Stanko, Davor; Markušić, Snježana; Ivančić, Ines; Mario, Gazdek; Gülerce, Zeynep
2017-12-01
Spectral parameter kappa κ is used to describe spectral amplitude decay “crash syndrome” at high frequencies. The purpose of this research is to estimate spectral parameter kappa for the first time in Croatia based on small and moderate earthquakes. Recordings of local earthquakes with magnitudes higher than 3, epicentre distances less than 150 km, and focal depths less than 30 km from seismological stations in Croatia are used. The value of kappa was estimated from the acceleration amplitude spectrum of shear waves from the slope of the high-frequency part where the spectrum starts to decay rapidly to a noise floor. Kappa models as a function of a site and distance were derived from a standard linear regression of kappa-distance dependence. Site kappa was determined from the extrapolation of the regression line to a zero distance. The preliminary results of site kappa across Croatia are promising. In this research, these results are compared with local site condition parameters for each station, e.g. shear wave velocity in the upper 30 m from geophysical measurements and with existing global shear wave velocity – site kappa values. Spatial distribution of individual kappa’s is compared with the azimuthal distribution of earthquake epicentres. These results are significant for a couple of reasons: to extend the knowledge of the attenuation of near-surface crust layers of the Dinarides and to provide additional information on the local earthquake parameters for updating seismic hazard maps of studied area. Site kappa can be used in the re-creation, and re-calibration of attenuation of peak horizontal and/or vertical acceleration in the Dinarides area since information on the local site conditions were not included in the previous studies.
Gait parameter and event estimation using smartphones.
Pepa, Lucia; Verdini, Federica; Spalazzi, Luca
2017-09-01
The use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well. This work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland-Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects. High correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count. This study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications. Copyright © 2017 Elsevier B.V. All rights reserved.
Statistical distributions applications and parameter estimates
Thomopoulos, Nick T
2017-01-01
This book gives a description of the group of statistical distributions that have ample application to studies in statistics and probability. Understanding statistical distributions is fundamental for researchers in almost all disciplines. The informed researcher will select the statistical distribution that best fits the data in the study at hand. Some of the distributions are well known to the general researcher and are in use in a wide variety of ways. Other useful distributions are less understood and are not in common use. The book describes when and how to apply each of the distributions in research studies, with a goal to identify the distribution that best applies to the study. The distributions are for continuous, discrete, and bivariate random variables. In most studies, the parameter values are not known a priori, and sample data is needed to estimate parameter values. In other scenarios, no sample data is available, and the researcher seeks some insight that allows the estimate of ...
Multifrequency SAR data for estimating hydrological parameters
Energy Technology Data Exchange (ETDEWEB)
Paloscia, S.; Pampaloni, P.; Macelloni, G. [Consiglio Nazionale delle Ricerche, Istituto di Ricerca sulle Onde Elettromagnetiche, Florence (Italy)
2001-02-01
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.
Parameter estimation in fractional diffusion models
Kubilius, Kęstutis; Ralchenko, Kostiantyn
2017-01-01
This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is “white,” i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides s...
[Statistical estimation of parameters in allometric equations].
Zotin, A A
2000-01-01
An algorithm for estimating allometric coefficients widely used in biological studies is presented. The coefficients can be estimated only when the relationship between logarithms of the approximated data meets the linearity criterion. The proposed algorithm was applied for the brain-body weight relationship in mammals and oxygen consumption rate-body weight relationship in amphibians.
Parameter Estimation for Observation Bias with an Ensemble Kalman Filter
Lorente-Plazas, R.; Hacker, J.
2015-12-01
In this work we evaluate a method to estimate systematic errors of individual in-situ observations. The approach is based on parameter estimation using an augmented state in an ensemble adjustment Kalman filter. Biased observations are assimilated in the highly chaotic Lorenz (2005) model that combines small and large scales. For this purpose, synthetic observations are created by introducing a grid-point dependent bias and random errors to a nature run (truth). The sensitivity of the methodology to model error, the number of ensemble members, the number of parameters, and the parameter variance is evaluated. Results demonstrate that the methodology is able to estimate observation bias for a both a perfect model and an imperfect model if the model error is estimated. The parameter estimation and the rms errors are significantly deteriorated if model error is ignored. Errors are qualitatively independent of model forcing when model error is estimated. By contrast, parameter estimation depends on model error when model error is not estimated, especially if observation biases are estimated. Errors increase with the number of estimated parameters, but they are independent of ensemble size as long as the number of ensembles members is large enough. There is an optimum value of parameter variance that minimizes the errors and improves the parameter estimation, but this value depends on observation bias and model error. Overall, the results suggest more accuracy in observation bias estimates than model error estimates.
Across flock genetic parameter estimation for yearling body weight ...
African Journals Online (AJOL)
Across flock genetic parameter estimation for yearling body weight and fleece. ... Accurate genetic parameter estimates are needed upon which to perform multiple-trait across flock breed analyses. Genetic parameters for ... Keywords: Direct heritability, maternal effects, genotype x environment interaction, correlations
Genetic parameter estimation of 16-month live weight and ...
African Journals Online (AJOL)
Genetic evaluation systems require the accurate estimation of genetic parameters. The genetic, phenotypic and environmental parameters for live weight and objectively measured wool traits were estimated for a South African Merino flock. Records of the Tygerhoek Merino resource flock were used to estimate these ...
Entropy-Based Parameter Estimation for the Four-Parameter Exponential Gamma Distribution
Directory of Open Access Journals (Sweden)
Songbai Song
2017-04-01
Full Text Available Two methods based on the principle of maximum entropy (POME, the ordinary entropy method (ENT and the parameter space expansion method (PSEM, are developed for estimating the parameters of a four-parameter exponential gamma distribution. Using six data sets for annual precipitation at the Weihe River basin in China, the PSEM was applied for estimating parameters for the four-parameter exponential gamma distribution and was compared to the methods of moments (MOM and of maximum likelihood estimation (MLE. It is shown that PSEM enables the four-parameter exponential distribution to fit the data well, and can further improve the estimation.
Simultaneous estimation of experimental and material parameters
CSIR Research Space (South Africa)
Jansen van Rensburg, GJ
2012-07-01
Full Text Available This conference contribution focusses on the invertibility of non-ideal material tests to accurately determine material parameters. This is done by attempting to model non-ideal test cases and comparing strains as well as force history...
ESTIMATION OF SHEAR STRENGTH PARAMETERS OF ...
African Journals Online (AJOL)
This research work seeks to develop models for predicting the shear strength parameters (cohesion and angle of friction) of lateritic soils in central and southern areas of Delta State using artificial neural network modeling technique. The application of these models will help reduce cost and time in acquiring geotechnical ...
A Comparative Study of Distribution System Parameter Estimation Methods
Energy Technology Data Exchange (ETDEWEB)
Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup
2016-07-17
In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.
Neural networks for estimation of ocean wave parameters
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Rao, S.; Raju, D.H.
Ocean wave parameters play a significant role in the design of all coastal and offshore structures. In the present study, neural networks are used to estimate various ocean wave parameters from theoretical Pierson-Moskowitz spectra as well...
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests......, or powder samples of the catalyst, which leads to problems when upscaling is done to the full-scale application. This contribution presents a methodology to sequentially estimate the kinetic parameters in 2 steps using steady-state limited small-scale reactor data, with the goal that the parameters should...
Novel Method for 5G Systems NLOS Channels Parameter Estimation
Directory of Open Access Journals (Sweden)
Vladeta Milenkovic
2017-01-01
Full Text Available For the development of new 5G systems to operate in mm bands, there is a need for accurate radio propagation modelling at these bands. In this paper novel approach for NLOS channels parameter estimation will be presented. Estimation will be performed based on LCR performance measure, which will enable us to estimate propagation parameters in real time and to avoid weaknesses of ML and moment method estimation approaches.
Asymptotic estimation of shift parameter of a quantum state
Holevo, A. S.
2003-01-01
We develop an asymptotic theory of estimation of a shift parameter in a pure quantum state to study the relation between entangled and unentangled covariant estimates in the analytically most transparent way. After recollecting basics of estimation of shift parameter in Sec. 2, we study the structure of the optimal covariant estimate in Sec. 3, showing how entanglement comes into play for several independent trials. In Secs. 4,5 we give the asymptotics of the performance of the optimal covari...
Parameter Estimation for Improving Association Indicators in Binary Logistic Regression
Directory of Open Access Journals (Sweden)
Mahdi Bashiri
2012-02-01
Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.
PSO-based optimization of PI regulator and VA loading of a SRF ...
African Journals Online (AJOL)
DR OKE
results show that the proposed PSO technique causes minimum error and minimum VA loading angle by optimally choosing parameters of PI and minimum VA angle as compared to GA technique. Keywords: Active power filter (APF), synchronous reference frame (SRF), Genetic algorithm (GA), Particle Swarm. Optimization ...
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... was applied.Capture zone modelling was conducted on a synthetic stationary 3-dimensional flow problem involving river, surface and groundwater flow. Simulated capture zones were illustrated as likelihood maps and compared with a deterministic capture zones derived from a reference model. The results showed...
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.
The observer-based synchronization and parameter estimation of a ...
Indian Academy of Sciences (India)
Observer-based synchronization and parameter estimation of chaotic systems has been an interesting and important issue in theory and various fields of application. In this paper first we investigate the observer-based synchronization of a class of chaotic systems, and then discuss its parameter estimation via a single ...
Improving the precision of dynamic forest parameter estimates using Landsat
Evan B. Brooks; John W. Coulston; Randolph H. Wynne; Valerie A. Thomas
2016-01-01
The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is wellestablished.When reducing the variance of post-stratification estimates for forest change parameters such as forestgrowth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is
Robust Parameter and Signal Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.
This thesis deals with theories and methods for robust parameter and signal estimation in induction motors. The project originates in industrial interests concerning sensor-less control of electrical drives. During the work, some general problems concerning estimation of signals and parameters...
Genetic parameter estimates for functional herd life for the South ...
African Journals Online (AJOL)
Genetic parameter estimates for functional herd life for the South African Jersey breed using a multiple trait linear model (Short Communication) ... The secondary objective was to compare estimates of genetic parameters from the linear sire and animal models. Data and pedigree records on purebred Jersey cows that ...
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…
Genetic parameter estimates for tick resistance in Bonsmara cattle ...
African Journals Online (AJOL)
The objectives of the study were to estimate genetic parameters for tick resistance and to evaluate the effect of the level of tick infestation on the estimates of genetic parameters for South African Bonsmara cattle. Field data of repeated tick count records (n = 11 280) on 1 176 animals were collected between 1993 and 2005 ...
A neural network applied to estimate Burr XII distribution parameters
Energy Technology Data Exchange (ETDEWEB)
Abbasi, B., E-mail: b.abbasi@gmail.co [Department of Industrial Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Hosseinifard, S.Z. [Department of Statistics and Operations Research, RMIT University, Melbourne (Australia); Coit, D.W. [Department of Industrial and System Engineering, Rutgers University, Piscataway, NJ (United States)
2010-06-15
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.
Bayesian Parameter Estimation for Heavy-Duty Vehicles
Energy Technology Data Exchange (ETDEWEB)
Miller, Eric; Konan, Arnaud; Duran, Adam
2017-03-28
Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
METHODS FOR ESTIMATING THE PARAMETERS OF THE POWER FUNCTION DISTRIBUTION.
Directory of Open Access Journals (Sweden)
azam zaka
2013-10-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE In this paper, we present some methods for estimating the parameters of the two parameter Power function distribution. We used the least squares method (LSM, relative least squares method (RELS and ridge regression method (RR. Sampling behavior of the estimates is indicated by a Monte Carlo simulation. The objective of identifying the best estimator amongst them we use the Total Deviation (T.D and Mean Square Error (M.S.E as performance index. We determined the best method for estimation using different values for the parameters and different sample sizes.
Estimation of pulsed driven qubit parameters via quantum Fisher information
Metwally, N.; Hassan, S. S.
2017-11-01
We estimate the initial weight and phase parameters (θ, φ) of a single qubit system initially prepared in the coherent state \\renewcommand{\\ket}[1]{\\bigl\\vert #1 \\bigr>} \\ketθ, φ and in interaction with three different shapes of pulses; rectangular, exponential, and \\sin2 -pulses. In general, we show that the estimation degree of the weight parameter depends on the pulse shape and the initial phase angle, (φ) . For the rectangular pulse case, increasing the estimating rate of the weight parameter via the Fisher information function (F_θ) is possible with small values of the atomic detuning parameter and larger values of pulse strength. Fisher information (F_φ) increases suddenly at the resonant case to reach its maximum value if the initial phase φ=π/2 and consequently one may estimate the phase parameter with a high degree of precision. If the initial system is coded with classical information, the upper bounds of Fisher information for resonant and non-resonant cases are much larger and consequently, one may estimate the phase parameter with a high degree of accuracy. Similarly, as the detuning increases the Fisher information decreases and therefore the possibility of estimating the phase parameter decreases. For exponential and \\sin2 -pulses the Fisher information is maximum (Fθ, φ=1 ) and consequently, one can always estimate the weight and the phase parameters (θ, φ) with a high degree of precision.
A PSO-based approach to optimize the triangular membership functions in a fuzzy logic controller
Maniscalco, Vincenzo; Lombardo, Francesco
2017-11-01
In this paper a Particle Swarm Optimization (PSO) algorithm is considered in order to optimize the triangular Membership Functions (MF) in a Fuzzy Logic Controller (FLC). PSO algorithm belongs to the class of Swarm Intelligence (SI) techniques and is considered an efficient heuristic technique for optimization problem in a continuous and multidimen-sional search spaces. Performance of a FLC depends on the fuzzy partition of each input/output space considered and the PSO algorithm can be used to obtain the optimal or near optimal parameters of the triangular membership functions in order to achieve the best results in the defuzzification process. Simulation results obtained by this approach to tune the triangular membership functions of a FLC for an application concerning the optimization of the energy consumption in Industrial Wireless Sensor Networks (IWSN) are reported.
PSO based neuro fuzzy sliding mode control for a robot manipulator
Directory of Open Access Journals (Sweden)
M. Vijay
2017-05-01
Full Text Available This paper presents the control strategy of two degrees of freedom (2DOF rigid robot manipulator based on the coupling of artificial neuro fuzzy inference system (ANFIS with sliding mode control (SMC. Initially SMC with proportional integral derivative (PID sliding surface is adapted to control the robot manipulator. The parameters of the sliding surface are obtained by minimizing a quadratic performance indices using particle swarm optimization (PSO. Variations of SMC i.e. boundary sliding mode control (BSMC and boundary sliding mode control with PID sliding surface (PIDBSMC are developed for optimized performance index. Finally an ANFIS adaptive controller is proposed to generate the adaptive control signal and found to be more robust with regard to disturbances in input torque.
An Adaptive-PSO-Based Self-Organizing RBF Neural Network.
Han, Hong-Gui; Lu, Wei; Hou, Ying; Qiao, Jun-Fei
2018-01-01
In this paper, a self-organizing radial basis function (SORBF) neural network is designed to improve both accuracy and parsimony with the aid of adaptive particle swarm optimization (APSO). In the proposed APSO algorithm, to avoid being trapped into local optimal values, a nonlinear regressive function is developed to adjust the inertia weight. Furthermore, the APSO algorithm can optimize both the network size and the parameters of an RBF neural network simultaneously. As a result, the proposed APSO-SORBF neural network can effectively generate a network model with a compact structure and high accuracy. Moreover, the analysis of convergence is given to guarantee the successful application of the APSO-SORBF neural network. Finally, multiple numerical examples are presented to illustrate the effectiveness of the proposed APSO-SORBF neural network. The results demonstrate that the proposed method is more competitive in solving nonlinear problems than some other existing SORBF neural networks.
Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels
Directory of Open Access Journals (Sweden)
Taiyong Li
2016-12-01
Full Text Available Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD, adaptive particle swarm optimization (APSO, and relevance vector machine (RVM—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE, mean absolute percent error (MAPE, and directional statistic (Dstat, showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.
Estimation of Physical Parameters in Linear and Nonlinear Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten
for certain input in the time or frequency domain, are emphasised. Consequently, some special techniques are required, in particular for input signal design and model validation. The model structure containing physical parameters is constructed from basic physical laws (mathematical modelling). It is possible...... and estimation of physical parameters in particular. 2. To apply the new methods for modelling of specific objects, such as loudspeakers, ac- and dc-motors wind turbines and beat exchangers. A reliable quality measure of an obtained parameter estimate is a prerequisite for any reasonable use of the result......Estimation of physical parameters is an important subclass of system identification. The specific objective is to obtain accurate estimates of the model parameters, while the objective of other aspects of system identification might be to determine a model where other properties, such as responses...
Catchment tomography - An approach for spatial parameter estimation
Baatz, D.; Kurtz, W.; Hendricks Franssen, H. J.; Vereecken, H.; Kollet, S. J.
2017-09-01
The use of distributed-physically based hydrological models is often hampered by the lack of information on key parameters and their spatial distribution and temporal dynamics. Typically, the estimation of parameter values is impeded by the lack of sufficient observations leading to mathematically underdetermined estimation problems and thus non-uniqueness. Catchment tomography (CT) presents a method to estimate spatially distributed model parameters by resolving the integrated signal of stream runoff in response to precipitation. Basically CT exploits the information content generated by a distributed precipitation signal both in time and space. In a moving transmitter-receiver concept, high resolution, radar based precipitation data are applied with a distributed surface runoff model. Synthetic stream water level observations, serving as receivers, are assimilated with an Ensemble Kalman Filter. With a joint state-parameter update the spatially distributed Manning's roughness coefficient, n, is estimated using the coupled Terrestrial Systems Modelling Platform and the Parallel Data Assimilation Framework (TerrSysMP-PDAF). The sequential data assimilation in combination with the distributed precipitation continuously integrates new information into the model, thus, increasingly constraining the parameter space. With this large amount of data included for the parameter estimation, CT reduces the problem of underdetermined model parameters. The initially biased Manning's coefficients spatially distributed in two and four fixed parameter zones are estimated with errors of less than 3% and 17%, respectively, with only 64 model realizations. It is shown that the distributed precipitation is of major importance for this approach.
Development of approaches to estimation of risk parameters
Directory of Open Access Journals (Sweden)
M.A. Yeliseyeva
2017-09-01
To solve the risk estimation problems, issues involved in the estimation of risk parameters have been considered with different options of the HPF state graphical space interpretation. Peculiarities of estimating the risk sensitivity and the risk degree have been described and the evolution of approaches to the estimation of risk in the HPF design and operation has been shown. Big data analysis methods for risk management have been proposed.
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.
Parameter estimation for chaotic systems using improved bird swarm algorithm
Xu, Chuangbiao; Yang, Renhuan
2017-12-01
Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.
Parameter Estimation for Sensorless Controlled Induction Motors using Nonlinear Filters
Hozuki, Takashi; Kawabata, Yoshitaka; Sugimoto, Sueo
In this paper, we consider parameter estimation of the state variables and unknown parameters of Induction Motors (IMs) using nonlinear filters. Simultaneous estimation is the most general method for sensorless controlled IMs, and at present, by the advance of computer processors, nonlinear filters have been applied to various occasions, so we describe the method for applying nonlinear filters to Induction Motors model, and consider its estimate performance by simulations. Simulation results showed that nonlinear filters have more accuracy estimate performance than the adaptive observer, and the excellent noise immunity.
A variational approach to parameter estimation in ordinary differential equations.
Kaschek, Daniel; Timmer, Jens
2012-08-14
Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.
Models for estimating photosynthesis parameters from in situ production profiles
Kovač, Žarko; Platt, Trevor; Sathyendranath, Shubha; Antunović, Suzana
2017-12-01
The rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of
Beef quality parameters estimation using ultrasound and color images
National Research Council Canada - National Science Library
Nunes, Jose; Piquerez, Martín; Pujadas, Leonardo; Armstrong, Eileen; Fernández, Alicia; Lecumberry, Federico
2015-01-01
.... In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat...
Dynamic Mode Decomposition based on Kalman Filter for Parameter Estimation
Shibata, Hisaichi; Nonomura, Taku; Takaki, Ryoji
2017-11-01
With the development of computational fluid dynamics, large-scale data can now be obtained. In order to model physical phenomena from such data, it is required to extract features of flow field. Dynamic mode decomposition (DMD) is a method which meets the request. DMD can compute dominant eigenmodes of flow field by approximating system matrix. From this point of view, DMD can be considered as parameter estimation of system matrix. To estimate such parameters, we propose a novel method based on Kalman filter. Our numerical experiments indicated that the proposed method can estimate the parameters more accurately if it is compared with standard DMD methods. With this method, it is also possible to improve the parameter estimation accuracy if characteristics of noise acting on the system is given.
Estimates of lactation curve parameters for Bonsmara and Nguni ...
African Journals Online (AJOL)
Estimates of lactation curve parameters for Bonsmara and Nguni cattle using the weigh-suckle-weigh technique. A Maiwashe, NB Nengovhela, KA Nephawe, J Sebei, T Netshilema, HD Mashaba, L Nesengani, D Norris ...
Another Look at the EWMA Control Chart with Estimated Parameters
Saleh, N.A.; Mahmoud, M.A.; Jones-Farmer, L.A.; Zwetsloot, I.; Woodall, W.H.
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.
On-Line Estimation of Allan Variance Parameters
National Research Council Canada - National Science Library
Ford, J
1999-01-01
... (Inertial Measurement Unit) gyros and accelerometers. The on-line method proposes a state space model and proposes parameter estimators for quantities previously measured from off-line data techniques such as the Allan variance graph...
Kalman filter estimation of RLC parameters for UMP transmission line
Directory of Open Access Journals (Sweden)
Mohd Amin Siti Nur Aishah
2018-01-01
Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.
Statistical Estimation and Clustering of Group-invariant Orientation Parameters
Chen, Yu-Hui; Newstadt, Gregory; DeGraef, Marc; Simmons, Jeffrey; Hero, Alfred
2015-01-01
We treat the problem of estimation of orientation parameters whose values are invariant to transformations from a spherical symmetry group. Previous work has shown that any such group-invariant distribution must satisfy a restricted finite mixture representation, which allows the orientation parameter to be estimated using an Expectation Maximization (EM) maximum likelihood (ML) estimation algorithm. In this paper, we introduce two parametric models for this spherical symmetry group estimation problem: 1) the hyperbolic Von Mises Fisher (VMF) mixture distribution and 2) the Watson mixture distribution. We also introduce a new EM-ML algorithm for clustering samples that come from mixtures of group-invariant distributions with different parameters. We apply the models to the problem of mean crystal orientation estimation under the spherically symmetric group associated with the crystal form, e.g., cubic or octahedral or hexahedral. Simulations and experiments establish the advantages of the extended EM-VMF and ...
Kalman filter data assimilation: targeting observations and parameter estimation.
Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex
2014-06-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
Directory of Open Access Journals (Sweden)
Daigle Bernie J
2012-05-01
Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods
Windowed Least Square Algorithm Based PMSM Parameters Estimation
Directory of Open Access Journals (Sweden)
Song Wang
2013-01-01
Full Text Available Stator resistance and inductances in d-axis and q-axis of permanent magnet synchronous motors (PMSMs are important parameters. Acquiring these accurate parameters is usually the fundamental part in driving and controlling system design, to guarantee the performance of driver and controller. In this paper, we adopt a novel windowed least algorithm (WLS to estimate the parameters with fixed value or the parameter with time varying characteristic. The simulation results indicate that the WLS algorithm has a better performance in fixed parameters estimation and parameters with time varying characteristic identification than the recursive least square (RLS and extended Kalman filter (EKF. It is suitable for engineering realization in embedded system due to its rapidity, less system resource possession, less computation, and flexibility to adjust the window size according to the practical applications.
Estimation of Parameters of the Beta-Extreme Value Distribution
Directory of Open Access Journals (Sweden)
Zafar Iqbal
2008-09-01
Full Text Available In this research paper The Beta Extreme Value Type (III distribution which is developed by Zafar and Aleem (2007 is considered and parameters are estimated by using moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m’ & ‘n’ are real and moments of the Beta-Extreme Value (Type III Distribution when the parameters ‘m��� & ‘n’ are integers and then a Comparison between rth moments about origin when parameters are ‘m’ & ‘n’ are real and when parameters are ‘m’ & ‘n’ are integers. At the end second method, method of Maximum Likelihood is used to estimate the unknown parameters of the Beta Extreme Value Type (III distribution.
Estimation of transport and degradation parameters for naphthalene ...
African Journals Online (AJOL)
These transport and degradation parameters were compared to those obtained by a transport model using a nonlinear least square curve-fitting program CXTFIT (version 2.0). Results obtained showed that the MOM fits the breakthrough curve with tailing better than the CXTFIT. The initial estimates of these parameters ...
In-Flight Parameter Estimation for Multirotor Aerial Vehicles
Directory of Open Access Journals (Sweden)
de Castro Davi Ferreira
2016-01-01
Full Text Available This paper proposes a method for in-flight parameter estimation for Multirotor Aerial Vehicles (MAV. This task is important because it provides parameters with better accuracy for the actual vehicle operation. In order to simulate a flight it is adopted a simulation environment Software-In-the-Loop (SIL.
Parameter estimation for subsurface flow using ensemble data assimilation
S. Ruchi (Sangeetika); S. Dubinkina (Svetlana)
2017-01-01
textabstractOver the years, different data assimilation methods have been implemented to acquire improved estimations of model parameters by adjusting the uncertain parameter values in such a way that the mathematical model approximates the observed data as closely and consistently as possible.
ASCAL: A Microcomputer Program for Estimating Logistic IRT Item Parameters.
Vale, C. David; Gialluca, Kathleen A.
ASCAL is a microcomputer-based program for calibrating items according to the three-parameter logistic model of item response theory. It uses a modified multivariate Newton-Raphson procedure for estimating item parameters. This study evaluated this procedure using Monte Carlo Simulation Techniques. The current version of ASCAL was then compared to…
parameter extraction and estimation based on the pv panel outdoor ...
African Journals Online (AJOL)
userpc
PV panel under varying weather conditions to estimate the PV parameters. Outdoor ... panel parameters. The majority of the methods are based on measurements of the I-V characteristic of the panel (Jack et al., 2015). The main aspect of PV simulation that requires attention ... incident solar irradiance, the cell temperature,.
State and parameter estimation in biotechnical batch reactors
Keesman, K.J.
2000-01-01
In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually nonlinear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly > 7, in
State and parameter estimation in biotechnical batch reactors
Keesman, K.J.
2002-01-01
In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually non-linear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly >7, in
Pollen parameters estimates of genetic variability among newly ...
African Journals Online (AJOL)
Estimates of some pollen parameters where used to assess the genetic diversity among some newly selected Nigerian Roselle (Hibiscus sabdariffa L.). Standard procedures were used to determine the pollen parameters such as: percentage pollen fertility, percentage pollen sterility, pollen diameters as well as anther ...
Estimation of the petrophysical parameters of sediments from Chad ...
African Journals Online (AJOL)
Three petrophysical parameters; shaliness, porosity and thermal conductivity were estimated from well logs obtained from the Faltu-1 well, the Chad Basin. ... Estimates of average effective porosities of formations favorably compared with measurements, while analysis of the plots of various functions of porosity with depth ...
Estimation Of The Parameters Of The Bilinear Seasonal Arima Time ...
African Journals Online (AJOL)
The parameters of the seasonal bilinear time series model were estimated analytically. This was followed by some numerical illustrations. The closeness of the estimated values to the true values attested to the adequacy of the least square method of minimizing error and the Newton-Raphson iterative procedure. Keywords: ...
On robust parameter estimation in brain–computer interfacing
Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert
2017-12-01
Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain–computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
2002-01-01
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of non-linear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Estimation of genetic parameters for body weights of Kurdish sheep ...
African Journals Online (AJOL)
Genetic parameters and (co)variance components were estimated by restricted maximum likelihood (REML) procedure, using animal models of kind 1, 2, 3, 4, 5 and 6, for body weight in birth, three, six, nine and 12 months of age in a Kurdish sheep flock. Direct and maternal breeding values were estimated using the best ...
Analyzing and constraining signaling networks: parameter estimation for the user.
Geier, Florian; Fengos, Georgios; Felizzi, Federico; Iber, Dagmar
2012-01-01
The behavior of most dynamical models not only depends on the wiring but also on the kind and strength of interactions which are reflected in the parameter values of the model. The predictive value of mathematical models therefore critically hinges on the quality of the parameter estimates. Constraining a dynamical model by an appropriate parameterization follows a 3-step process. In an initial step, it is important to evaluate the sensitivity of the parameters of the model with respect to the model output of interest. This analysis points at the identifiability of model parameters and can guide the design of experiments. In the second step, the actual fitting needs to be carried out. This step requires special care as, on the one hand, noisy as well as partial observations can corrupt the identification of system parameters. On the other hand, the solution of the dynamical system usually depends in a highly nonlinear fashion on its parameters and, as a consequence, parameter estimation procedures get easily trapped in local optima. Therefore any useful parameter estimation procedure has to be robust and efficient with respect to both challenges. In the final step, it is important to access the validity of the optimized model. A number of reviews have been published on the subject. A good, nontechnical overview is provided by Jaqaman and Danuser (Nat Rev Mol Cell Biol 7(11):813-819, 2006) and a classical introduction, focussing on the algorithmic side, is given in Press (Numerical recipes: The art of scientific computing, Cambridge University Press, 3rd edn., 2007, Chapters 10 and 15). We will focus on the practical issues related to parameter estimation and use a model of the TGFβ-signaling pathway as an educative example. Corresponding parameter estimation software and models based on MATLAB code can be downloaded from the authors's web page ( http://www.bsse.ethz.ch/cobi ).
Algorithms of optimum location of sensors for solidification parameters estimation
Directory of Open Access Journals (Sweden)
J. Mendakiewicz
2010-10-01
Full Text Available The algorithms of optimal sensor location for estimation of solidification parameters are discussed. These algorithms base on the Fisher Information Matrix and A-optimality or D-optimality criterion. Numerical examples of planning algorithms are presented and next foroptimal position of sensors the inverse problems connected with the identification of unknown parameters are solved. The examplespresented concern the simultaneous estimation of mould thermophysical parameters (volumetric specific heat and thermal conductivityand also the components of volumetric latent heat of cast iron.
Estimating the macroseismic parameters of earthquakes in eastern Iran
Amini, H.; Gasperini, P.; Zare, M.; Vannucci, G.
2017-10-01
Macroseismic intensity values allow assessing the macroseismic parameters of earthquakes such as location, magnitude, and fault orientation. This information is particularly useful for historical earthquakes whose parameters were estimated with low accuracy. Eastern Iran (56°-62°E, 29.5°-35.5°N), which is characterized by several active faults, was selected for this study. Among all earthquakes occurred in this region, only 29 have some macroseismic information. Their intensity values were reported in various intensity scales. After collecting the descriptions, their intensity values were re-estimated in a uniform intensity scale. Thereafter, Boxer method was applied to estimate their corresponding macroseismic parameters. Boxer estimates of macroseismic parameters for instrumental earthquakes (after 1964) were found to be consistent with those published by Global Centroid Moment Tensor Catalog (GCMT). Therefore, this method was applied to estimate location, magnitude, source dimension, and orientation of these earthquakes with macroseismic description in the period 1066-2012. Macroseismic parameters seem to be more reliable than instrumental ones not only for historical earthquakes but also for instrumental earthquakes especially for the ones occurred before 1960. Therefore, as final results of this study we propose to use the macroseismically determined parameters in preparing a catalog for earthquakes before 1960.
Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA. The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX model. The performance of the model is validated using mean squares error (MSE between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square. Based on the results obtained, MSE produce from Bat Algorithm (BA is outperformed the Least Square (LS method.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Zhang, Guanqun; Hahn, Jin-Oh; Mukkamala, Ramakrishna
2011-01-01
A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel) while being defined by only a few parameters (unlike comprehensive distributed-parameter models). As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications. PMID:22053157
Method for Estimating the Parameters of LFM Radar Signal
Directory of Open Access Journals (Sweden)
Tan Chuan-Zhang
2017-01-01
Full Text Available In order to obtain reliable estimate of parameters, it is very important to protect the integrality of linear frequency modulation (LFM signal. Therefore, in the practical LFM radar signal processing, the length of data frame is often greater than the pulse width (PW of signal. In this condition, estimating the parameters by fractional Fourier transform (FrFT will cause the signal to noise ratio (SNR decrease. Aiming at this problem, we multiply the data frame by a Gaussian window to improve the SNR. Besides, for a further improvement of parameters estimation precision, a novel algorithm is derived via Lagrange interpolation polynomial, and we enhance the algorithm by a logarithmic transformation. Simulation results demonstrate that the derived algorithm significantly reduces the estimation errors of chirp-rate and initial frequency.
Bayesian auxiliary particle filters for estimating neural tuning parameters.
Mountney, John; Sobel, Marc; Obeid, Iyad
2009-01-01
A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled here as a Poisson process, and the biological driving signal. The Bayesian auxiliary particle filter was evaluated by simultaneously tracking the three parameters of a hippocampal place cell and compared to a stochastic state point process filter. It is shown that Bayesian auxiliary particle filters are substantially more accurate and robust than alternative methods of state parameter estimation. The effects of time-averaging on parameter estimation are also evaluated.
A software for parameter estimation in dynamic models
Directory of Open Access Journals (Sweden)
M. Yuceer
2008-12-01
Full Text Available A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.
Improving the realism of hydrologic model through multivariate parameter estimation
Rakovec, Oldrich; Kumar, Rohini; Attinger, Sabine; Samaniego, Luis
2017-04-01
Increased availability and quality of near real-time observations should improve understanding of predictive skills of hydrological models. Recent studies have shown the limited capability of river discharge data alone to adequately constrain different components of distributed model parameterizations. In this study, the GRACE satellite-based total water storage (TWS) anomaly is used to complement the discharge data with an aim to improve the fidelity of mesoscale hydrologic model (mHM) through multivariate parameter estimation. The study is conducted in 83 European basins covering a wide range of hydro-climatic regimes. The model parameterization complemented with the TWS anomalies leads to statistically significant improvements in (1) discharge simulations during low-flow period, and (2) evapotranspiration estimates which are evaluated against independent (FLUXNET) data. Overall, there is no significant deterioration in model performance for the discharge simulations when complemented by information from the TWS anomalies. However, considerable changes in the partitioning of precipitation into runoff components are noticed by in-/exclusion of TWS during the parameter estimation. A cross-validation test carried out to assess the transferability and robustness of the calibrated parameters to other locations further confirms the benefit of complementary TWS data. In particular, the evapotranspiration estimates show more robust performance when TWS data are incorporated during the parameter estimation, in comparison with the benchmark model constrained against discharge only. This study highlights the value for incorporating multiple data sources during parameter estimation to improve the overall realism of hydrologic model and its applications over large domains. Rakovec, O., Kumar, R., Attinger, S. and Samaniego, L. (2016): Improving the realism of hydrologic model functioning through multivariate parameter estimation. Water Resour. Res., 52, http://dx.doi.org/10
Traveltime approximations and parameter estimation for orthorhombic media
Masmoudi, Nabil
2016-05-30
Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters if we relate them analytically to traveltimes. Using perturbation theory, we have developed traveltime approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2, and Δχ in inhomogeneous background media. The parameter Δχ is related to Tsvankin-Thomsen notation and ensures easier computation of traveltimes in the background model. Specifically, our expansion assumes an inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. We have used the Shanks transform to enhance the accuracy of the formulas. A homogeneous medium simplification of the traveltime expansion provided a nonhyperbolic moveout description of the traveltime that was more accurate than other derived approximations. Moreover, the formulation provides a computationally efficient tool to solve the eikonal equation of an orthorhombic medium, without any constraints on the background model complexity. Although, the expansion is based on the factorized representation of the perturbation parameters, smooth variations of these parameters (represented as effective values) provides reasonable results. Thus, this formulation provides a mechanism to estimate the three effective parameters η1, η2, and Δχ. We have derived Dix-type formulas for orthorhombic medium to convert the effective parameters to their interval values.
Parameter estimation and forecasting for multiplicative log-normal cascades.
Leövey, Andrés E; Lux, Thomas
2012-04-01
We study the well-known multiplicative log-normal cascade process in which the multiplication of Gaussian and log normally distributed random variables yields time series with intermittent bursts of activity. Due to the nonstationarity of this process and the combinatorial nature of such a formalism, its parameters have been estimated mostly by fitting the numerical approximation of the associated non-Gaussian probability density function to empirical data, cf. Castaing et al. [Physica D 46, 177 (1990)]. More recently, alternative estimators based upon various moments have been proposed by Beck [Physica D 193, 195 (2004)] and Kiyono et al. [Phys. Rev. E 76, 041113 (2007)]. In this paper, we pursue this moment-based approach further and develop a more rigorous generalized method of moments (GMM) estimation procedure to cope with the documented difficulties of previous methodologies. We show that even under uncertainty about the actual number of cascade steps, our methodology yields very reliable results for the estimated intermittency parameter. Employing the Levinson-Durbin algorithm for best linear forecasts, we also show that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results from the GMM and Kiyono et al.'s procedure via Monte Carlo simulations. We finally test the applicability of our approach by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and foreign exchange markets.
Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.
Kim, Seock-Ho; Cohen, Allan S.
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Modeling extreme events: Sample fraction adaptive choice in parameter estimation
Neves, Manuela; Gomes, Ivette; Figueiredo, Fernanda; Gomes, Dora Prata
2012-09-01
When modeling extreme events there are a few primordial parameters, among which we refer the extreme value index and the extremal index. The extreme value index measures the right tail-weight of the underlying distribution and the extremal index characterizes the degree of local dependence in the extremes of a stationary sequence. Most of the semi-parametric estimators of these parameters show the same type of behaviour: nice asymptotic properties, but a high variance for small values of k, the number of upper order statistics to be used in the estimation, and a high bias for large values of k. This shows a real need for the choice of k. Choosing some well-known estimators of those parameters we revisit the application of a heuristic algorithm for the adaptive choice of k. The procedure is applied to some simulated samples as well as to some real data sets.
Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS and differential evolution (DE algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE. Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.
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...
Parameter estimation and model selection in computational biology.
Lillacci, Gabriele; Khammash, Mustafa
2010-03-05
A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
CTER—Rapid estimation of CTF parameters with error assessment
Energy Technology Data Exchange (ETDEWEB)
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.
MPEG2 video parameter and no reference PSNR estimation
DEFF Research Database (Denmark)
Li, Huiying; Forchhammer, Søren
2009-01-01
MPEG coded video may be processed for quality assessment or postprocessed to reduce coding artifacts or transcoded. Utilizing information about the MPEG stream may be useful for these tasks. This paper deals with estimating MPEG parameter information from the decoded video stream without access...... to the MPEG stream. This may be used in systems and applications where the coded stream is not accessible. Detection of MPEG I-frames and DCT (discrete cosine transform) block size is presented. For the I-frames, the quantization parameters are estimated. Combining these with statistics of the reconstructed...... DCT coefficients, the PSNR is estimated from the decoded video without reference images. Tests on decoded fixed rate MPEG2 sequences demonstrate perfect detection rates and good performance of the PSNR estimation....
AMT-200S Motor Glider Parameter and Performance Estimation
Taylor, Brian R.
2011-01-01
Parameter and performance estimation of an instrumented motor glider was conducted at the National Aeronautics and Space Administration Dryden Flight Research Center in order to provide the necessary information to create a simulation of the aircraft. An output-error technique was employed to generate estimates from doublet maneuvers, and performance estimates were compared with results from a well-known flight-test evaluation of the aircraft in order to provide a complete set of data. Aircraft specifications are given along with information concerning instrumentation, flight-test maneuvers flown, and the output-error technique. Discussion of Cramer-Rao bounds based on both white noise and colored noise assumptions is given. Results include aerodynamic parameter and performance estimates for a range of angles of attack.
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Distributed Systems Integration Dept.
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
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).
Parameter Estimation in Stochastic Differential Equations; An Overview
DEFF Research Database (Denmark)
Nielsen, Jan Nygaard; Madsen, Henrik; Young, P. C.
2000-01-01
This paper presents an overview of the progress of research on parameter estimation methods for stochastic differential equations (mostly in the sense of Ito calculus) over the period 1981-1999. These are considered both without measurement noise and with measurement noise, where the discretely...... observed stochastic differential equations are embedded in a continuous-discrete time state space model. Every attempts has been made to include results from other scientific disciplines. Maximum likelihood estimation of parameters in nonlinear stochastic differential equations is in general not possible...
Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation
Directory of Open Access Journals (Sweden)
Maja Marasović
2017-01-01
Full Text Available Accurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, is very important in modern biology. To this date, linearization of kinetic equations is still widely established practice for determining these parameters in chemical and enzyme catalysis. Although simplicity of linear optimization is alluring, these methods have certain pitfalls due to which they more often then not result in misleading estimation of enzyme parameters. In order to obtain more accurate predictions of parameter values, the use of nonlinear least-squares fitting techniques is recommended. However, when there are outliers present in the data, these techniques become unreliable. This paper proposes the use of a robust nonlinear regression estimator based on modified Tukey’s biweight function that can provide more resilient results in the presence of outliers and/or influential observations. Real and synthetic kinetic data have been used to test our approach. Monte Carlo simulations are performed to illustrate the efficacy and the robustness of the biweight estimator in comparison with the standard linearization methods and the ordinary least-squares nonlinear regression. We then apply this method to experimental data for the tyrosinase enzyme (EC 1.14.18.1 extracted from Solanum tuberosum, Agaricus bisporus, and Pleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values of Km and Vmax.
Adjustment of Sensor Locations During Thermal Property Parameter Estimation
Milos, Frank S.; Marschall, Jochen; Rasky, Daniel J. (Technical Monitor)
1996-01-01
The temperature dependent thermal properties of a material may be evaluated from transient temperature histories using nonlinear parameter estimation techniques. The usual approach is to minimize the sum of the squared errors between measured and calculated temperatures at specific locations in the body. Temperature measurements are usually made with thermocouples and it is customary to take thermocouple locations as known and fixed during parameter estimation computations. In fact, thermocouple locations are never known exactly. Location errors on the order of the thermocouple wire diameter are intrinsic to most common instrumentation procedures (e.g., inserting a thermocouple into a drilled hole) and additional errors can be expected for delicate materials, difficult installations, large thermocouple beads, etc.. Thermocouple location errors are especially significant when estimating thermal properties of low diffusively materials which can sustain large temperature gradients during testing. In the present work, a parameter estimation formulation is presented which allows for the direct inclusion of thermocouple positions into the primary parameter estimation procedure. It is straightforward to set bounds on thermocouple locations which exclude non-physical locations and are consistent with installation tolerances. Furthermore, bounds may be tightened to an extent consistent with any independent verification of thermocouple location, such as x-raying, and so the procedure is entirely consonant with experimental information. A mathematical outline of the procedure is given and its implementation is illustrated through numerical examples characteristic of light-weight, high-temperature ceramic insulation during transient heating. The efficacy and the errors associated with the procedure are discussed.
Revisiting Boltzmann learning: parameter estimation in Markov random fields
DEFF Research Database (Denmark)
Hansen, Lars Kai; Andersen, Lars Nonboe; Kjems, Ulrik
1996-01-01
This article presents a generalization of the Boltzmann machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization...... and generalization in the context of Boltzmann machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov field. The regularized adaptation produces a parameter set that closely resembles the “teacher” parameters, hence, will produce segmentations that closely reproduce...
Enhancing parameter estimation precision by non-Hermitian operator process
Guo, You-neng; Fang, Mao-fa; Wang, Guo-you; Hang, Jiang; Zeng, Ke
2017-12-01
Recently, Zhong et al. (Phys Rev A 87:022337, 2013) investigated the dynamics of quantum Fisher information (QFI) in the presence of decoherence. We here reform their results and propose two schemes to enhance and preserve the QFIs for a qubit system subjected to a decoherence noisy environment by applying {non {-}Hermitian} operator process either before or after the amplitude damping noise. Resorting to the Bloch sphere representation, we derive the exact analytical expressions of the QFIs with respect to the amplitude parameter θ and the phase parameter φ , and in detail investigate the influence of {non {-}Hermitian} operator parameters on the QFIs. Compared with pure decoherence process (without non-Hermitian operator process), we find that the {post non {-}Hermitian} operator process can potentially enhance and preserve the QFIs by choosing appropriate {non {-}Hermitian} operator parameters, while with the help of the {prior non {-}Hermitian} operator process one could completely eliminate the effect of decoherence to improve the parameters estimation. Finally, a generalized non-Hermitian operator parameters effect on the parameters estimation is also discussed.
Estimation of Saxophone Control Parameters by Convex Optimization.
Wang, Cheng-I; Smyth, Tamara; Lipton, Zachary C
2014-12-01
In this work, an approach to jointly estimating the tone hole configuration (fingering) and reed model parameters of a saxophone is presented. The problem isn't one of merely estimating pitch as one applied fingering can be used to produce several different pitches by bugling or overblowing. Nor can a fingering be estimated solely by the spectral envelope of the produced sound (as it might for estimation of vocal tract shape in speech) since one fingering can produce markedly different spectral envelopes depending on the player's embouchure and control of the reed. The problem is therefore addressed by jointly estimating both the reed (source) parameters and the fingering (filter) of a saxophone model using convex optimization and 1) a bank of filter frequency responses derived from measurement of the saxophone configured with all possible fingerings and 2) sample recordings of notes produced using all possible fingerings, played with different overblowing, dynamics and timbre. The saxophone model couples one of several possible frequency response pairs (corresponding to the applied fingering), and a quasi-static reed model generating input pressure at the mouthpiece, with control parameters being blowing pressure and reed stiffness. Applied fingering and reed parameters are estimated for a given recording by formalizing a minimization problem, where the cost function is the error between the recording and the synthesized sound produced by the model having incremental parameter values for blowing pressure and reed stiffness. The minimization problem is nonlinear and not differentiable and is made solvable using convex optimization. The performance of the fingering identification is evaluated with better accuracy than previous reported value.
Zhu, Zhiliang; Meng, Zhiqiang; Cao, Tingting; Zhang, Zhengjiang; Dai, Yuxing
2017-06-01
State and parameter estimation (SPE) plays an important role in process monitoring, online optimization, and process control. The estimation of states and parameters is generally solved simultaneously in the SPE problem, where the parameters to be estimated are specified as augmented states. When state and/or measurement equations are highly nonlinear and the posterior probability of the state is non-Gaussian, particle filter (PF) is commonly used for SPE. However, when the parameters switch with the operating conditions, the change of parameters cannot be detected and tracked by the conventional SPE method. This paper proposes a PF-based robust SPE method for a nonlinear process system with variable parameters. The measurement test criterion based on observation error is introduced to indirectly identify whether the parameters are changed. Based on the result of identification, the variances of the particles are modified adaptively for the tracking of the changed parameters. Finally, reliable SPE can be derived through iterative particles. The proposed PF-based robust SPE method is applied to two nonlinear process systems. The results demonstrate the effectiveness and robustness of the proposed method.
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... distribution. Methods from structural reliability analysis are used to model the uncertainties and to assess the reliability for fatigue failure. Maximum Likelihood and Least Square estimation techniques are used to estimate fatigue life distribution parameters....... for degradation modeling and failure criteria determination. The time dependent accumulated damage is assumed linearly proportional to the time dependent degradation level. It is observed that the deterministic accumulated damage at the level of unity closely estimates the characteristic fatigue life of Weibull...
Parameter Estimation for a Class of Lifetime Models
Directory of Open Access Journals (Sweden)
Xinyang Ji
2014-01-01
Full Text Available Our purpose in this paper is to present a better method of parametric estimation for a bivariate nonlinear regression model, which takes the performance indicator of rubber aging as the dependent variable and time and temperature as the independent variables. We point out that the commonly used two-step method (TSM, which splits the model and estimate parameters separately, has limitation. Instead, we apply the Marquardt’s method (MM to implement parametric estimation directly for the model and compare these two methods of parametric estimation by random simulation. Our results show that MM has better effect of data fitting, more reasonable parametric estimates, and smaller prediction error compared with TSM.
Low Complexity Parameter Estimation For Off-the-Grid Targets
Jardak, Seifallah
2015-10-05
In multiple-input multiple-output radar, to estimate the reflection coefficient, spatial location, and Doppler shift of a target, a derived cost function is usually evaluated and optimized over a grid of points. The performance of such algorithms is directly affected by the size of the grid: increasing the number of points will enhance the resolution of the algorithm but exponentially increase its complexity. In this work, to estimate the parameters of a target, a reduced complexity super resolution algorithm is proposed. For off-the-grid targets, it uses a low order two dimensional fast Fourier transform to determine a suboptimal solution and then an iterative algorithm to jointly estimate the spatial location and Doppler shift. Simulation results show that the mean square estimation error of the proposed estimators achieve the Cram\\'er-Rao lower bound. © 2015 IEEE.
Aircraft parameter estimation ± A tool for development of ...
Indian Academy of Sciences (India)
ing, (iv) a relatively new approach called multiple-shooting based on efficient techniques for the solution of two-point boundary value problems, and (v) parameter estimation by filtering approach using extended Kalman filters, are possible (Hamel & Jategaonkar. 1996). These approaches, although providing solutions in ...
Estimation of genetic and phenotypic parameters for sow ...
African Journals Online (AJOL)
Beke
been identified as a key factor affecting the efficiency and economic viability of the pig ... Estimates of genetic parameters for sow productivity traits reported in .... mean of zero and variance covariance structure as shown below: e ..... Sow productivity traits are generally of low heritability and repeatability in this population.
Genetic parameter estimates for type traits in the South African ...
African Journals Online (AJOL)
Unknown
influencing type traits and to estimate the applicable genetic parameters for .... b1 A = linear regression of the deviation from the mean on age at first calving, ... X, Z = incidence matrix which relates an observation to the fixed and random effects ...
Estimation of stature from facial parameters in adult Abakaliki people ...
African Journals Online (AJOL)
This study is carried out in order to estimate the height of adult Igbo people of Abakaliki ethnic group in South-Eastern Nigeria from their facial Morphology. The parameters studied include Facial Length, Bizygomatic Diameter, Bigonial Diameter, Nasal Length, and Nasal Breadth. A total of 1000 subjects comprising 669 ...
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,…
Procedures for parameter estimates of computational models for localized failure
Iacono, C.
2007-01-01
In the last years, many computational models have been developed for tensile fracture in concrete. However, their reliability is related to the correct estimate of the model parameters, not all directly measurable during laboratory tests. Hence, the development of inverse procedures is needed, that
Parameter estimation of an aeroelastic aircraft using neural networks
Indian Academy of Sciences (India)
Application of neural networks to the problem of aerodynamic modelling and parameter estimation for aeroelastic aircraft is addressed. A neural model capable of predicting generalized force and moment coefficients using measured motion and control variables only, without any need for conventional normal elastic ...
Estimation of genetic and phenotypic parameters for sow ...
African Journals Online (AJOL)
The objective of the study was to estimate genetic and phenotypic parameters for sow productivity traits of South African Large White pigs, using data from the Integrated Registration and Genetic Information Systems. The analyses were done on 29 719 records for 7 983 sows from 29 herds, which farrowed between 1990 ...
REML estimates of genetic parameters of sexual dimorphism for ...
Indian Academy of Sciences (India)
Administrator
2 CNRS, Laboratoire Populations, Génétique et Evolution, 91198 Gif-sur-Yvette Cedex, France. 3 CNRS, Laboratoire Biométrie, Biologie Evolutive, Université de Lyon I, 69622 Villeurbanne Cedex, France. Abstract. Restricted maximum likelihood was used to estimate genetic parameters of male and female wing and thorax ...
Parameter estimation of linear and quadratic chirps by employing ...
Indian Academy of Sciences (India)
ship between IF and time. Various methods are discussed in literature for detection and parameter estimation of the chirp ... Time frequency representation of an LFM and QFM sinusoidal signal. Section 2 introduces .... representation of the signal corresponding to a counter-clockwise rotation of the time axis by π/2. It is also ...
Estimates of genetic parameters and genetic gains for growth traits ...
African Journals Online (AJOL)
The aims of this study were, firstly, to determine the magnitude of genotype × environment interaction of E. urophylla in Zululand; secondly, to estimate genetic parameters and correlations for diameter at breast height (DBH), height and volume; and thirdly, to identify selections to advance the current breeding population as ...
Estimation of genetic parameters for carcass traits in Japanese quail ...
African Journals Online (AJOL)
The aim of this study was to estimate genetic parameters of some carcass characteristics in the Japanese quail. For this aim, carcass weight (Cw), breast weight (Bw), leg weight (Lw), abdominal fat weight (AFw), carcass yield (CP), breast percentage (BP), leg percentage (LP) and abdominal fat percentage (AFP) were ...
Infinite Parameter Estimates in Logistic Regression: Opportunities, not Problems.
Rindskopf, David
2002-01-01
Asserts that, in principle, an analyst should be satisfied with infinite estimates slope in logistic regression because it indicates that a predictor is perfect. Using simple approaches, hypothesis tests may be performed and confidence intervals calculated even when a slope is infinite. Some functions of parameters that are infinite are still…
A novel parameter estimation method for metal oxide surge arrester ...
Indian Academy of Sciences (India)
In this paper, a new technique, which is the combination of Adaptive Particle Swarm Optimization (APSO) and Ant Colony Optimization (ACO) algorithms and linking the MATLAB and EMTP, is proposed to estimate the parameters of MO surge arrester models. The proposed algorithm is named Modiﬁed Adaptive Particle ...
The effect of selection on genetic parameter estimates
African Journals Online (AJOL)
Unknown
South African Society of Animal Science. The South African Journal of Animal Science is available online at http://www.sasas.co.za/Sajas.html. 107. The effect of selection on genetic parameter estimates. R. van Dyk. 1. , F.W.C. Neser. 1# and F.H. Kanfer. 2. 1 Department of Animal Science, University of the Free State, P.O. ...
Simultaneous estimation of QTL parameters for mapping multiple traits
Indian Academy of Sciences (India)
Z
proposed a strategy of simultaneously estimating all QTL parameters in the multiple-trait multiple. -interval mapping. ... (A201207), the Science and Technology Innovation Team in Higher Education Institutions of. Heilongjiang Province (No. 2014TD005), and the Innovation Project for University Students of. Heilongjiang ...
Estimation of light transport parameters in biological media using ...
Indian Academy of Sciences (India)
Apart from these, the use of the coherent backscattered peak from tissues to estimate the transport parameters ... length (РЧ = 1 ( ×)) defined as the reciprocal of the average of the product of the single particle scattering .... К is the beam radius) of about 75 rad is present, yielding a minimum measurable « of about 0.1 cm-½.
Parameter extraction and estimation based on the PV panel outdoor ...
African Journals Online (AJOL)
This work presents a novel approach to predict the voltage-current (V-I) characteristics of a PV panel under varying weather conditions to estimate the PV parameters. Outdoor performance of the PV module (AP-PM-15) was carried out for several times. The experimental data obtained are validated and compared with the ...
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.
Estimation of light transport parameters in biological media using ...
Indian Academy of Sciences (India)
The suitability of using the angular peak shape of the coherent backscattered light for estimating the light transport parameters of biological media has been investigated. Milk and methylene blue doped milk were used as tissue phantoms for the measurements carried out with a He–Ne laser (632.8 nm). Results indicate that ...
Estimation of genetic parameters for growth traits in Brangus cattle
African Journals Online (AJOL)
p2492989
Abstract. A combination of multiple trait and repeatability models were used to estimate genetic parameters for birth weight (BW), weaning weight (WW), yearling weight (YW), eighteen month weight (FW) and three measurements of mature weight (MW) using 23 768 records obtained from the South African Brangus.
Estimation of Genetic Parameters for body weights of Kurdish Sheep ...
African Journals Online (AJOL)
karimi
2012-01-26
Jan 26, 2012 ... Genetic parameters and (co)variance components were estimated by restricted maximum likelihood. (REML) procedure, using animal models of kind 1, 2, 3, 4, 5 and 6, for body weight in birth, three, six, nine and 12 months of age in a Kurdish sheep flock. Direct and maternal breeding values were.
Genetic parameter estimates for weaning weight of Boran cattle ...
African Journals Online (AJOL)
Chrislukovian Wasike
Abstract. Genetic parameters were estimated for weaning weight (WW) in Kenya Boran cattle using animal models that assumed non-zero direct-maternal genetic covariance. In addition to the direct and maternal genetic effects, maternal permanent environmental and sire by herd-year interaction effects were tested. Two.
A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri
2014-01-01
This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...
On Modal Parameter Estimates from Ambient Vibration Tests
DEFF Research Database (Denmark)
Agneni, A.; Brincker, Rune; Coppotelli, B.
2004-01-01
Modal parameter estimates from ambient vibration testing are turning into the preferred technique when one is interested in systems under actual loadings and operational conditions. Moreover, with this approach, expensive devices to excite the structure are not needed, since it can be adequately...
Parameter estimation of an aeroelastic aircraft using neural networks
Indian Academy of Sciences (India)
e-mail: scr@iitk.ac.in. Abstract. Application of neural networks to the problem of aerodynamic modelling and parameter estimation for aeroelastic aircraft is addressed. A neural model capable of ... of the network in terms of the number of neurons in the hidden layer, the learning rate, the momentum rate etc. is not an exact ...
Estimation of genetic parameters for body measurements and their ...
African Journals Online (AJOL)
Me
2014-06-05
Jun 5, 2014 ... The main objective of this study was to estimate the genetic parameters for body measurement and yearling live bodyweight ... a basis for calculating selection indices for body measurements, as well as revealing their association with yearling bodyweight ..... Performance of three tropical hair sheep breeds.
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.
Estimation of parameter sensitivities for stochastic reaction networks
Gupta, Ankit
2016-01-07
Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.
Dual ant colony operational modal analysis parameter estimation method
Sitarz, Piotr; Powałka, Bartosz
2018-01-01
Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.
ORAN- ORBITAL AND GEODETIC PARAMETER ESTIMATION ERROR ANALYSIS
Putney, B.
1994-01-01
The Orbital and Geodetic Parameter Estimation Error Analysis program, ORAN, was developed as a Bayesian least squares simulation program for orbital trajectories. ORAN does not process data, but is intended to compute the accuracy of the results of a data reduction, if measurements of a given accuracy are available and are processed by a minimum variance data reduction program. Actual data may be used to provide the time when a given measurement was available and the estimated noise on that measurement. ORAN is designed to consider a data reduction process in which a number of satellite data periods are reduced simultaneously. If there is more than one satellite in a data period, satellite-to-satellite tracking may be analyzed. The least squares estimator in most orbital determination programs assumes that measurements can be modeled by a nonlinear regression equation containing a function of parameters to be estimated and parameters which are assumed to be constant. The partitioning of parameters into those to be estimated (adjusted) and those assumed to be known (unadjusted) is somewhat arbitrary. For any particular problem, the data will be insufficient to adjust all parameters subject to uncertainty, and some reasonable subset of these parameters is selected for estimation. The final errors in the adjusted parameters may be decomposed into a component due to measurement noise and a component due to errors in the assumed values of the unadjusted parameters. Error statistics associated with the first component are generally evaluated in an orbital determination program. ORAN is used to simulate the orbital determination processing and to compute error statistics associated with the second component. Satellite observations may be simulated with desired noise levels given in many forms including range and range rate, altimeter height, right ascension and declination, direction cosines, X and Y angles, azimuth and elevation, and satellite-to-satellite range and
PhyloPars: estimation of missing parameter values using phylogeny.
Bruggeman, Jorn; Heringa, Jaap; Brandt, Bernd W
2009-07-01
A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible. The PhyloPars web server provides a statistically consistent method that combines an incomplete set of empirical observations with the species phylogeny to produce a complete set of parameter estimates for all species. It builds upon a state-of-the-art evolutionary model, extended with the ability to handle missing data. The resulting approach makes optimal use of all available information to produce estimates that can be an order of magnitude more accurate than ad-hoc alternatives. Uploading a phylogeny and incomplete feature matrix suffices to obtain estimates of all missing values, along with a measure of certainty. Real-time cross-validation provides further insight in the accuracy and bias expected for estimated values. The server allows for easy, efficient estimation of metabolic parameters, which can benefit a wide range of fields including systems biology and ecology. PhyloPars is available at: http://www.ibi.vu.nl/programs/phylopars/.
Directory of Open Access Journals (Sweden)
Jonathan R Karr
2015-05-01
Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.
Estimation of Soft Tissue Mechanical Parameters from Robotic Manipulation Data.
Boonvisut, Pasu; Jackson, Russell; Cavuşoğlu, M Cenk
2012-12-31
Robotic motion planning algorithms used for task automation in robotic surgical systems rely on availability of accurate models of target soft tissue's deformation. Relying on generic tissue parameters in constructing the tissue deformation models is problematic; because, biological tissues are known to have very large (inter- and intra-subject) variability. A priori mechanical characterization (e.g., uniaxial bench test) of the target tissues before a surgical procedure is also not usually practical. In this paper, a method for estimating mechanical parameters of soft tissue from sensory data collected during robotic surgical manipulation is presented. The method uses force data collected from a multiaxial force sensor mounted on the robotic manipulator, and tissue deformation data collected from a stereo camera system. The tissue parameters are then estimated using an inverse finite element method. The effects of measurement and modeling uncertainties on the proposed method are analyzed in simulation. The results of experimental evaluation of the method are also presented.
ESTIMATION OF SIGNAL PARAMETERS FOR MULTIPLE TARGET LOCALIZATION
Directory of Open Access Journals (Sweden)
X. Susan Christina
2014-12-01
Full Text Available Target detection and localization is an active research area due to its importance in a wide range of application such as biomedical and military applications. In this paper, a novel method for the detection and estimation of signal parameters such as range and direction of arrival for multiple far- field target using wideband echo chirp signals is proposed. Sonar and radar are the active detection systems transmit well defined signals into a region of interest. A model preprocessing procedure is designed for the echo signal. The parameters estimation method for multiple targets is developed based on the Linear Canonical transform and the Fast Root MUSIC algorithm which is a high resolution DOA estimation method originally proposed for any arbitrary arrays to reduce the computational complexity in existing systems. The proposed method provides high accuracy of detection and resolution even in very low SNR values.
Optimal estimation of parameters of an entangled quantum state
Virzì, S.; Avella, A.; Piacentini, F.; Gramegna, M.; Brida, G.; Degiovanni, I. P.; Genovese, M.
2017-05-01
Two-photon entangled quantum states are a fundamental tool for quantum information and quantum cryptography. A complete description of a generic quantum state is provided by its density matrix: the technique allowing experimental reconstruction of the density matrix is called quantum state tomography. Entangled states density matrix reconstruction requires a large number of measurements on many identical copies of the quantum state. An alternative way of certifying the amount of entanglement in two-photon states is represented by the estimation of specific parameters, e.g., negativity and concurrence. If we have a priori partial knowledge of our state, it’s possible to develop several estimators for these parameters that require lower amount of measurements with respect to full density matrix reconstruction. The aim of this work is to introduce and test different estimators for negativity and concurrence for a specific class of two-photon states.
Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
Directory of Open Access Journals (Sweden)
Siyu Liu
2017-10-01
Full Text Available This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models.
Global parameter estimation for thermodynamic models of transcriptional regulation.
Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N
2013-07-15
Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. Copyright © 2013 Elsevier Inc. All rights reserved.
Stable Parameter Estimation for Autoregressive Equations with Random Coefficients
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2014-01-01
Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross
Directory of Open Access Journals (Sweden)
A. Elsonbaty
2014-10-01
Full Text Available In this article, the adaptive chaos synchronization technique is implemented by an electronic circuit and applied to the hyperchaotic system proposed by Chen et al. We consider the more realistic and practical case where all the parameters of the master system are unknowns. We propose and implement an electronic circuit that performs the estimation of the unknown parameters and the updating of the parameters of the slave system automatically, and hence it achieves the synchronization. To the best of our knowledge, this is the first attempt to implement a circuit that estimates the values of the unknown parameters of chaotic system and achieves synchronization. The proposed circuit has a variety of suitable real applications related to chaos encryption and cryptography. The outputs of the implemented circuits and numerical simulation results are shown to view the performance of the synchronized system and the proposed circuit.
Consistent Parameter and Transfer Function Estimation using Context Free Grammars
Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten
2017-04-01
This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a
METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS
Directory of Open Access Journals (Sweden)
V. Panteleev Andrei
2017-01-01
Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and
Optimal sensor placement for parameter estimation of bridges
Eskew, Edward; Jang, Shinae
2017-04-01
Gathering measurements from a structure can be extremely valuable for tasks such as verifying a numerical model, or structural health monitoring (SHM) to identify changes in the natural frequencies and mode shapes which can be attributed to changes in the system. In most monitoring applications, the number of potential degrees-of-freedom (DOF) for monitoring greatly outnumbers the available sensors. Optimal sensor placement (OSP) is a field of research into different methods for locating the available sensors to gather the optimal measurements. Three common methods of OSP are the effective independence (EI), effective independence driving point residue (EI-DPR), and modal kinetic energy (MKE) methods. However, comparisons of the different OSP methods for SHM applications are limited. In this paper, a comparison of the performance of the three described OSP methods for parameter estimation is performed. Parameter estimation is implemented using modified parameter localization with direct model updating, and added mass quantification utilizing a genetic algorithm (GA). The quantification of the mass addition, using simulated measurements from the sensor networks developed by each OSP method, is compared to provide an evaluation of each OSP methods capability for parameter estimation applications.
Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome
Singh, V. P.; Guo, H.; Yu, F. X.
1993-09-01
The principle of maximum entropy (POME) was employed to derive a new method of parameter estimation for the 3-parameter log-logistic distribution (LLD3). Monte Carlo simulated data were used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). Simulation results showed that POME's performance was superior in predicting quantiles of large recurrence intervals when population skew was greater than or equal to 2.0. In all other cases, POME's performance was comparable to other methods.
Anisotropic parameter estimation using velocity variation with offset analysis
Energy Technology Data Exchange (ETDEWEB)
Herawati, I.; Saladin, M.; Pranowo, W.; Winardhie, S.; Priyono, A. [Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Jalan Ganesa 10, Bandung, 40132 (Indonesia)
2013-09-09
Seismic anisotropy is defined as velocity dependent upon angle or offset. Knowledge about anisotropy effect on seismic data is important in amplitude analysis, stacking process and time to depth conversion. Due to this anisotropic effect, reflector can not be flattened using single velocity based on hyperbolic moveout equation. Therefore, after normal moveout correction, there will still be residual moveout that relates to velocity information. This research aims to obtain anisotropic parameters, ε and δ, using two proposed methods. The first method is called velocity variation with offset (VVO) which is based on simplification of weak anisotropy equation. In VVO method, velocity at each offset is calculated and plotted to obtain vertical velocity and parameter δ. The second method is inversion method using linear approach where vertical velocity, δ, and ε is estimated simultaneously. Both methods are tested on synthetic models using ray-tracing forward modelling. Results show that δ value can be estimated appropriately using both methods. Meanwhile, inversion based method give better estimation for obtaining ε value. This study shows that estimation on anisotropic parameters rely on the accuracy of normal moveout velocity, residual moveout and offset to angle transformation.
Multivariate phase type distributions - Applications and parameter estimation
DEFF Research Database (Denmark)
Meisch, David
, allowing for different estimation methods for the whole class or subclasses of phase type distributions. These attributes make this class of distributions an interesting alternative to the normal distribution. When facing multivariate problems, the only general distribution that allows for estimation...... and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate...... and reducing model uncertainties. Research has shown that the errors on cost estimates for infrastructure projects clearly do not follow a normal distribution but is skewed towards cost overruns. This skewness can be described using phase type distributions. Cost benefit analysis assesses potential future...
Observable Priors: Limiting Biases in Estimated Parameters for Incomplete Orbits
Kosmo, Kelly; Martinez, Gregory; Hees, Aurelien; Witzel, Gunther; Ghez, Andrea M.; Do, Tuan; Sitarski, Breann; Chu, Devin; Dehghanfar, Arezu
2017-01-01
Over twenty years of monitoring stellar orbits at the Galactic center has provided an unprecedented opportunity to study the physics and astrophysics of the supermassive black hole (SMBH) at the center of the Milky Way Galaxy. In order to constrain the mass of and distance to the black hole, and to evaluate its gravitational influence on orbiting bodies, we use Bayesian statistics to infer black hole and stellar orbital parameters from astrometric and radial velocity measurements of stars orbiting the central SMBH. Unfortunately, most of the short period stars in the Galactic center have periods much longer than our twenty year time baseline of observations, resulting in incomplete orbital phase coverage--potentially biasing fitted parameters. Using the Bayesian statistical framework, we evaluate biases in the black hole and orbital parameters of stars with varying phase coverage, using various prior models to fit the data. We present evidence that incomplete phase coverage of an orbit causes prior assumptions to bias statistical quantities, and propose a solution to reduce these biases for orbits with low phase coverage. The explored solution assumes uniformity in the observables rather than in the inferred model parameters, as is the current standard method of orbit fitting. Of the cases tested, priors that assume uniform astrometric and radial velocity observables reduce the biases in the estimated parameters. The proposed method will not only improve orbital estimates of stars orbiting the central SMBH, but can also be extended to other orbiting bodies with low phase coverage such as visual binaries and exoplanets.
Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine
Directory of Open Access Journals (Sweden)
Jeremy T. Howard
2018-02-01
Full Text Available In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198 that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope. The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite was 0.15 (0.18 and 0.31 (0.40, respectively. For the parent drug (metabolite, the mean heritability across time was 0.27 (0.60 and 0.14 (0.44 for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug
J-A Hysteresis Model Parameters Estimation using GA
Directory of Open Access Journals (Sweden)
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.
Bayesian Parameter Estimation via Filtering and Functional Approximations
Matthies, Hermann G.
2016-11-25
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik
1995-01-01
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...
Classification, parameter estimation and state estimation: an engineering approach using matlab
van der Heijden, Ferdinand; Duin, R.P.W.; de Ridder, D.; Tax, D.M.J.
Classification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MatLab. "Prtools" is a powerful MatLab toolbox for pattern recognition and is
Estimation of a four-parameter item response theory model.
Loken, Eric; Rulison, Kelly L
2010-11-01
We explore the justification and formulation of a four-parameter item response theory model (4PM) and employ a Bayesian approach to recover successfully parameter estimates for items and respondents. For data generated using a 4PM item response model, overall fit is improved when using the 4PM rather than the 3PM or the 2PM. Furthermore, although estimated trait scores under the various models correlate almost perfectly, inferences at the high and low ends of the trait continuum are compromised, with poorer coverage of the confidence intervals when the wrong model is used. We also show in an empirical example that the 4PM can yield new insights into the properties of a widely used delinquency scale. We discuss the implications for building appropriate measurement models in education and psychology to model more accurately the underlying response process.
Probabilistic estimation of the constitutive parameters of polymers
Directory of Open Access Journals (Sweden)
Siviour C.R.
2012-08-01
Full Text Available The Mulliken-Boyce constitutive model predicts the dynamic response of crystalline polymers as a function of strain rate and temperature. This paper describes the Mulliken-Boyce model-based estimation of the constitutive parameters in a Bayesian probabilistic framework. Experimental data from dynamic mechanical analysis and dynamic compression of PVC samples over a wide range of strain rates are analyzed. Both experimental uncertainty and natural variations in the material properties are simultaneously considered as independent and joint distributions; the posterior probability distributions are shown and compared with prior estimates of the material constitutive parameters. Additionally, particular statistical distributions are shown to be effective at capturing the rate and temperature dependence of internal phase transitions in DMA data.
Distribution Line Parameter Estimation Under Consideration of Measurement Tolerances
DEFF Research Database (Denmark)
Prostejovsky, Alexander; Gehrke, Oliver; Kosek, Anna Magdalena
2016-01-01
State estimation and control approaches in electric distribution grids rely on precise electric models that may be inaccurate. This work presents a novel method of estimating distribution line parameters using only root mean square voltage and power measurements under consideration of measurement...... tolerances, noise, and asynchronous timestamps. A measurement tolerance compensation model and an alternative representation of the power flow equations without voltage phase angles are introduced. The line parameters are obtained using numeric methods. The simulation demonstrates in case of the series...... conductance that the absolute compensated error is −1.05% and −1.07% for both representations, as opposed to the expected uncompensated error of −79.68%. Identification of a laboratory distribution line using real measurement data grid yields a deviation of 6.75% and 4.00%, respectively, from a calculation...
Estimating parameters from rotating ring disc electrode measurements
Energy Technology Data Exchange (ETDEWEB)
Santhanagopalan, Shriram; White, Ralph E.
2017-10-01
Rotating ring disc electrode (RRDE) experiments are a classic tool for investigating kinetics of electrochemical reactions. Several standardized methods exist for extracting transport parameters and reaction rate constants using RRDE measurements. In this work, we compare some approximate solutions to the convective diffusion used popularly in the literature to a rigorous numerical solution of the Nernst-Planck equations coupled to the three dimensional flow problem. In light of these computational advancements, we explore design aspects of the RRDE that will help improve sensitivity of our parameter estimation procedure to experimental data. We use the oxygen reduction in acidic media involving three charge transfer reactions and a chemical reaction as an example, and identify ways to isolate reaction currents for the individual processes in order to accurately estimate the exchange current densities.
Basic Earth's Parameters as estimated from VLBI observations
Directory of Open Access Journals (Sweden)
Ping Zhu
2017-11-01
Full Text Available The global Very Long Baseline Interferometry observation for measuring the Earth rotation's parameters was launched around 1970s. Since then the precision of the measurements is continuously improving by taking into account various instrumental and environmental effects. The MHB2000 nutation model was introduced in 2002, which is constructed based on a revised nutation series derived from 20 years VLBI observations (1980–1999. In this work, we firstly estimated the amplitudes of all nutation terms from the IERS-EOP-C04 VLBI global solutions w.r.t. IAU1980, then we further inferred the BEPs (Basic Earth's Parameters by fitting the major nutation terms. Meanwhile, the BEPs were obtained from the same nutation time series using a BI (Bayesian Inversion. The corrections to the precession rate and the estimated BEPs are in an agreement, independent of which methods have been applied.
Propagation channel characterization, parameter estimation, and modeling for wireless communications
Yin, Xuefeng
2016-01-01
Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are ...
CosmoSIS: A System for MC Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Zuntz, Joe [Manchester U.; Paterno, Marc [Fermilab; Jennings, Elise [Chicago U., EFI; Rudd, Douglas [U. Chicago; Manzotti, Alessandro [Chicago U., Astron. Astrophys. Ctr.; Dodelson, Scott [Chicago U., Astron. Astrophys. Ctr.; Bridle, Sarah [Manchester U.; Sehrish, Saba [Fermilab; Kowalkowski, James [Fermilab
2015-01-01
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. We present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in Cosmo- SIS, including camb, Planck, cosmic shear calculations, and a suite of samplers. We illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis.
Identification of vehicle parameters and estimation of vertical forces
Imine, H.; Fridman, L.; Madani, T.
2015-12-01
The aim of the present work is to estimate the vertical forces and to identify the unknown dynamic parameters of a vehicle using the sliding mode observers approach. The estimation of vertical forces needs a good knowledge of dynamic parameters such as damping coefficient, spring stiffness and unsprung masses, etc. In this paper, suspension stiffness and unsprung masses have been identified by the Least Square Method. Real-time tests have been carried out on an instrumented static vehicle, excited vertically by hydraulic jacks. The vehicle is equipped with different sensors in order to measure its dynamics. The measurements coming from these sensors have been considered as unknown inputs of the system. However, only the roll angle and the suspension deflection measurements have been used in order to perform the observer. Experimental results are presented and discussed to show the quality of the proposed approach.
Analysis of neutron scattering data: Visualization and parameter estimation
Energy Technology Data Exchange (ETDEWEB)
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
Factorized Estimation of Partially Shared Parameters in Diffusion Networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2017-01-01
Roč. 65, č. 19 (2017), s. 5153-5163 ISSN 1053-587X R&D Projects: GA ČR(CZ) GP14-06678P; GA ČR(CZ) GA16-09848S Institutional support: RVO:67985556 Keywords : Diffusion network * Diffusion estimation * Heterogeneous parameters * Multitask networks Subject RIV: BD - Theory of Information Impact factor: 4.300, year: 2016 http:// library .utia.cas.cz/separaty/2017/AS/dedecius-0477044.pdf
White matter lesion segmentation using robust parameter estimation algorithms
Yang, Faguo; Zhu, Litao; Jiang, Tianzi
2003-05-01
White matter lesions are common brain abnormalities. In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from brain MRI images. The intensities of each tissue is assumed to be Gaussian distributed, whose parameters (mean vector and covariance matrix) are estimated using a tissue distribution model. And then a measure is defined to indicate in how much content a voxel belongs to the lesions. Experimental results demonstrate that our algorithm works well.
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.
Directory of Open Access Journals (Sweden)
Akatsuki eKimura
2015-03-01
Full Text Available Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE in a prediction or to maximize likelihood. A (local maximum of likelihood or (local minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.
Hydraulic parameters estimation from well logging resistivity and geoelectrical measurements
Perdomo, S.; Ainchil, J. E.; Kruse, E.
2014-06-01
In this paper, a methodology is suggested for deriving hydraulic parameters, such as hydraulic conductivity or transmissivity combining classical hydrogeological data with geophysical measurements. Estimates values of transmissivity and conductivity, with this approach, can reduce uncertainties in numerical model calibration and improve data coverage, reducing time and cost of a hydrogeological investigation at a regional scale. The conventional estimation of hydrogeological parameters needs to be done by analyzing wells data or laboratory measurements. Furthermore, to make a regional survey many wells should be considered, and the location of each one plays an important role in the interpretation stage. For this reason, the use of geoelectrical methods arises as an effective complementary technique, especially in developing countries where it is necessary to optimize resources. By combining hydraulic parameters from pumping tests and electrical resistivity from well logging profiles, it was possible to adjust three empirical laws in a semi-confined alluvial aquifer in the northeast of the province of Buenos Aires (Argentina). These relations were also tested to be used with surficial geoelectrical data. The hydraulic conductivity and transmissivity estimated in porous material were according to expected values for the region (20 m/day; 457 m2/day), and are very consistent with previous results from other authors (25 m/day and 500 m2/day). The methodology described could be used with similar data sets and applied to other areas with similar hydrogeological conditions.
Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.
Sobhani-Tehrani, E; Talebi, H A; Khorasani, K
2014-02-01
This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.
Likelihood transform: making optimization and parameter estimation easier
Wang, Yan
2014-01-01
Parameterized optimization and parameter estimation is of great importance in almost every branch of modern science, technology and engineering. A practical issue in the problem is that when the parameter space is large and the available data is noisy, the geometry of the likelihood surface in the parameter space will be complicated. This makes searching and optimization algorithms computationally expensive, sometimes even beyond reach. In this paper, we define a likelihood transform which can make the structure of the likelihood surface much simpler, hence reducing the intrinsic complexity and easing optimization significantly. We demonstrate the properties of likelihood transform by apply it to a simplified gravitational wave chirp signal search. For the signal with an signal-to-noise ratio 20, likelihood transform has made a deterministic template-based search possible for the first time, which turns out to be 1000 times more efficient than an exhaustive grid- based search. The method in principle can be a...
Orientational order parameter estimated from molecular polarizabilities - an optical study
Lalitha Kumari, J.; Datta Prasad, P. V.; Madhavi Latha, D.; Pisipati, V. G. K. M.
2012-01-01
An optical study of N-(p-n-alkyloxybenzylidene)-p-n-butyloxyanilines, nO.O4 compounds with the alkoxy chain number n = 1, 3, 6, 7, and 10 has been carried out by measuring the refractive indices using modified spectrometer and direct measurement of birefringence employing the Newton's rings method. Further, the molecular polarizability anisotropies are evaluated using Lippincott δ-function model, the molecular vibration method, Haller's extrapolation method, and scaling factor method. The molecular polarizabilities α e and α 0 are calculated using Vuk's isotropic and Neugebauer anisotropic local field models. The order parameter S is estimated by employing the molecular polarizability values determined from experimental refractive indices and density data and the polarizability anisotropy values. Further, the order parameter S is also obtained directly from the birefringence data. A comparison has been carried out among the order parameter obtained from different ways and the results are compared with the body of the data available in the literature.
Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.
Fröhlich, Fabian; Kaltenbacher, Barbara; Theis, Fabian J; Hasenauer, Jan
2017-01-01
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.
Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.
Directory of Open Access Journals (Sweden)
Fabian Fröhlich
2017-01-01
Full Text Available Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.
Genetic Algorithm-based Affine Parameter Estimation for Shape Recognition
Directory of Open Access Journals (Sweden)
Yuxing Mao
2014-06-01
Full Text Available Shape recognition is a classically difficult problem because of the affine transformation between two shapes. The current study proposes an affine parameter estimation method for shape recognition based on a genetic algorithm (GA. The contributions of this study are focused on the extraction of affine-invariant features, the individual encoding scheme, and the fitness function construction policy for a GA. First, the affine-invariant characteristics of the centroid distance ratios (CDRs of any two opposite contour points to the barycentre are analysed. Using different intervals along the azimuth angle, the different numbers of CDRs of two candidate shapes are computed as representations of the shapes, respectively. Then, the CDRs are selected based on predesigned affine parameters to construct the fitness function. After that, a GA is used to search for the affine parameters with optimal matching between candidate shapes, which serve as actual descriptions of the affine transformation between the shapes. Finally, the CDRs are resampled based on the estimated parameters to evaluate the similarity of the shapes for classification. The experimental results demonstrate the robust performance of the proposed method in shape recognition with translation, scaling, rotation and distortion.
Automatic estimation of elasticity parameters in breast tissue
Skerl, Katrin; Cochran, Sandy; Evans, Andrew
2014-03-01
Shear wave elastography (SWE), a novel ultrasound imaging technique, can provide unique information about cancerous tissue. To estimate elasticity parameters, a region of interest (ROI) is manually positioned over the stiffest part of the shear wave image (SWI). The aim of this work is to estimate the elasticity parameters i.e. mean elasticity, maximal elasticity and standard deviation, fully automatically. Ultrasonic SWI of a breast elastography phantom and breast tissue in vivo were acquired using the Aixplorer system (SuperSonic Imagine, Aix-en-Provence, France). First, the SWI within the ultrasonic B-mode image was detected using MATLAB then the elasticity values were extracted. The ROI was automatically positioned over the stiffest part of the SWI and the elasticity parameters were calculated. Finally all values were saved in a spreadsheet which also contains the patient's study ID. This spreadsheet is easily available for physicians and clinical staff for further evaluation and so increase efficiency. Therewith the efficiency is increased. This algorithm simplifies the handling, especially for the performance and evaluation of clinical trials. The SWE processing method allows physicians easy access to the elasticity parameters of the examinations from their own and other institutions. This reduces clinical time and effort and simplifies evaluation of data in clinical trials. Furthermore, reproducibility will be improved.
Basic MR sequence parameters systematically bias automated brain volume estimation
Energy Technology Data Exchange (ETDEWEB)
Haller, Sven [University of Geneva, Faculty of Medicine, Geneva (Switzerland); Affidea Centre de Diagnostique Radiologique de Carouge CDRC, Geneva (Switzerland); Falkovskiy, Pavel; Roche, Alexis; Marechal, Benedicte [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Meuli, Reto [University Hospital (CHUV), Department of Radiology, Lausanne (Switzerland); Thiran, Jean-Philippe [LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland); Krueger, Gunnar [Siemens Medical Solutions USA, Inc., Boston, MA (United States); Lovblad, Karl-Olof [University of Geneva, Faculty of Medicine, Geneva (Switzerland); University Hospitals of Geneva, Geneva (Switzerland); Kober, Tobias [Siemens Healthcare HC CEMEA SUI DI BM PI, Advanced Clinical Imaging Technology, Lausanne (Switzerland); LTS5, Ecole Polytechnique Federale de Lausanne, Lausanne (Switzerland)
2016-11-15
Automated brain MRI morphometry, including hippocampal volumetry for Alzheimer disease, is increasingly recognized as a biomarker. Consequently, a rapidly increasing number of software tools have become available. We tested whether modifications of simple MR protocol parameters typically used in clinical routine systematically bias automated brain MRI segmentation results. The study was approved by the local ethical committee and included 20 consecutive patients (13 females, mean age 75.8 ± 13.8 years) undergoing clinical brain MRI at 1.5 T for workup of cognitive decline. We compared three 3D T1 magnetization prepared rapid gradient echo (MPRAGE) sequences with the following parameter settings: ADNI-2 1.2 mm iso-voxel, no image filtering, LOCAL- 1.0 mm iso-voxel no image filtering, LOCAL+ 1.0 mm iso-voxel with image edge enhancement. Brain segmentation was performed by two different and established analysis tools, FreeSurfer and MorphoBox, using standard parameters. Spatial resolution (1.0 versus 1.2 mm iso-voxel) and modification in contrast resulted in relative estimated volume difference of up to 4.28 % (p < 0.001) in cortical gray matter and 4.16 % (p < 0.01) in hippocampus. Image data filtering resulted in estimated volume difference of up to 5.48 % (p < 0.05) in cortical gray matter. A simple change of MR parameters, notably spatial resolution, contrast, and filtering, may systematically bias results of automated brain MRI morphometry of up to 4-5 %. This is in the same range as early disease-related brain volume alterations, for example, in Alzheimer disease. Automated brain segmentation software packages should therefore require strict MR parameter selection or include compensatory algorithms to avoid MR parameter-related bias of brain morphometry results. (orig.)
Chloramine demand estimation using surrogate chemical and microbiological parameters.
Moradi, Sina; Liu, Sanly; Chow, Christopher W K; van Leeuwen, John; Cook, David; Drikas, Mary; Amal, Rose
2017-07-01
A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (Fm) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection. Copyright © 2017. Published by Elsevier B.V.
Estimation Of Signal Parameters Via Rotational Invariance Techniques - ESPRIT
Roy, R.; Paulraj, A.; Kailath, T.
1986-04-01
A novel approach to the general problem of signal parameter estimation is described. Though the technique (ESPRIT) is discussed in the context of direction-of-arrival estimation, it can be applied to a wide variety of problems including spectral estimation. ESPRIT exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure (e.g., pairwise matched and co-directional antenna element doublets). The new approach has several significant advantages over earlier techniques such as MUSIC including improved performance, reduced computational load, freedom from array characterization/calibration, and reduced sensitivity to array perturbations. Results of computer simulations carried out to evaluate the new algorithm are presented.
Estimation of multiexponential fluorescence decay parameters using compressive sensing
Yang, Sejung; Lee, Joohyun; Lee, Youmin; Lee, Minyung; Lee, Byung-Uk
2015-09-01
Fluorescence lifetime imaging microscopy (FLIM) is a microscopic imaging technique to present an image of fluorophore lifetimes. It circumvents the problems of typical imaging methods such as intensity attenuation from depth since a lifetime is independent of the excitation intensity or fluorophore concentration. The lifetime is estimated from the time sequence of photon counts observed with signal-dependent noise, which has a Poisson distribution. Conventional methods usually estimate single or biexponential decay parameters. However, a lifetime component has a distribution or width, because the lifetime depends on macromolecular conformation or inhomogeneity. We present a novel algorithm based on a sparse representation which can estimate the distribution of lifetime. We verify the enhanced performance through simulations and experiments.
Estimates of genetic parameters for fat yield in Murrah buffaloes
Directory of Open Access Journals (Sweden)
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.
Biases on cosmological parameter estimators from galaxy cluster number counts
Penna-Lima, M.; Makler, M.; Wuensche, C. A.
2014-05-01
Sunyaev-Zel'dovich (SZ) surveys are promising probes of cosmology — in particular for Dark Energy (DE) —, given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to hundreds of objects and maximum likelihood estimators may present biases for such sample sizes. In this work we study estimators from cluster abundance for some cosmological parameters, in particular the DE equation of state parameter w0, the amplitude of density fluctuations σ8, and the Dark Matter density parameter Ωc. We begin by deriving an unbinned likelihood for cluster number counts, showing that it is equivalent to the one commonly used in the literature. We use the Monte Carlo approach to determine the presence of bias using this likelihood and study its behavior with both the area and depth of the survey, and the number of cosmological parameters fitted. Our fiducial models are based on the South Pole Telescope (SPT) SZ survey. Assuming perfect knowledge of mass and redshift some estimators have non-negligible biases. For example, the bias of σ8 corresponds to about 40% of its statistical error bar when fitted together with Ωc and w0. Including a SZ mass-observable relation decreases the relevance of the bias, for the typical sizes of current SZ surveys. Considering a joint likelihood for cluster abundance and the so-called ``distance priors'', we obtain that the biases are negligible compared to the statistical errors. However, we show that the biases from SZ estimators do not go away with increasing sample sizes and they may become the dominant source of error for an all sky survey at the SPT sensitivity. Finally, we compute the confidence regions for the cosmological parameters using Fisher matrix and profile likelihood approaches, showing that they are compatible with the Monte Carlo ones. The results of this work validate the use of the current maximum likelihood methods for present SZ surveys, but highlight the
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.
Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R
2006-11-02
We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.
Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems
Directory of Open Access Journals (Sweden)
Banga Julio R
2006-11-01
Full Text Available Abstract Background We consider the problem of parameter estimation (model calibration in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector. In order to surmount these difficulties, global optimization (GO methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown structure (i.e. black-box models. In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously
Estimation of Eruption Source Parameters from Plume Growth Rate
Pouget, Solene; Bursik, Marcus; Webley, Peter; Dehn, Jon; Pavalonis, Michael; Singh, Tarunraj; Singla, Puneet; Patra, Abani; Pitman, Bruce; Stefanescu, Ramona; Madankan, Reza; Morton, Donald; Jones, Matthew
2013-04-01
The eruption of Eyjafjallajokull, Iceland in April and May, 2010, brought to light the hazards of airborne volcanic ash and the importance of Volcanic Ash Transport and Dispersion models (VATD) to estimate the concentration of ash with time. These models require Eruption Source Parameters (ESP) as input, which typically include information about the plume height, the mass eruption rate, the duration of the eruption and the particle size distribution. However much of the time these ESP are unknown or poorly known a priori. We show that the mass eruption rate can be estimated from the downwind plume or umbrella cloud growth rate. A simple version of the continuity equation can be applied to the growth of either an umbrella cloud or the downwind plume. The continuity equation coupled with the momentum equation using only inertial and gravitational terms provides another model. Numerical modeling or scaling relationships can be used, as necessary, to provide values for unknown or unavailable parameters. Use of these models applied to data on plume geometry provided by satellite imagery allows for direct estimation of plume volumetric and mass growth with time. To test our methodology, we compared our results with five well-studied and well-characterized historical eruptions: Mount St. Helens, 1980; Pinatubo, 1991, Redoubt, 1990; Hekla, 2000 and Eyjafjallajokull, 2010. These tests show that the methodologies yield results comparable to or better than currently accepted methodologies of ESP estimation. We then applied the methodology to umbrella clouds produced by the eruptions of Okmok, 12 July 2008, and Sarychev Peak, 12 June 2009, and to the downwind plume produced by the eruptions of Hekla, 2000; Kliuchevsko'i, 1 October 1994; Kasatochi 7-8 August 2008 and Bezymianny, 1 September 2012. The new methods allow a fast, remote assessment of the mass eruption rate, even for remote volcanoes. They thus provide an additional path to estimation of the ESP and the forecasting
Temporal Parameters Estimation for Wheelchair Propulsion Using Wearable Sensors
Directory of Open Access Journals (Sweden)
Manoela Ojeda
2014-01-01
Full Text Available Due to lower limb paralysis, individuals with spinal cord injury (SCI rely on their upper limbs for mobility. The prevalence of upper extremity pain and injury is high among this population. We evaluated the performance of three triaxis accelerometers placed on the upper arm, wrist, and under the wheelchair, to estimate temporal parameters of wheelchair propulsion. Twenty-six participants with SCI were asked to push their wheelchair equipped with a SMARTWheel. The estimated stroke number was compared with the criterion from video observations and the estimated push frequency was compared with the criterion from the SMARTWheel. Mean absolute errors (MAE and mean absolute percentage of error (MAPE were calculated. Intraclass correlation coefficients and Bland-Altman plots were used to assess the agreement. Results showed reasonable accuracies especially using the accelerometer placed on the upper arm where the MAPE was 8.0% for stroke number and 12.9% for push frequency. The ICC was 0.994 for stroke number and 0.916 for push frequency. The wrist and seat accelerometer showed lower accuracy with a MAPE for the stroke number of 10.8% and 13.4% and ICC of 0.990 and 0.984, respectively. Results suggested that accelerometers could be an option for monitoring temporal parameters of wheelchair propulsion.
PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
S. Kalaivani
2012-07-01
Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.
Pedotransfer functions estimating soil hydraulic properties using different soil parameters
DEFF Research Database (Denmark)
Børgesen, Christen Duus; Iversen, Bo Vangsø; Jacobsen, Ole Hørbye
2008-01-01
Estimates of soil hydraulic properties using pedotransfer functions (PTF) are useful in many studies such as hydrochemical modelling and soil mapping. The objective of this study was to calibrate and test parametric PTFs that predict soil water retention and unsaturated hydraulic conductivity...... conductivity parameters. A larger data set (1618 horizons) with a broader textural range was used in the development of PTFs to predict the van Genuchten parameters. The PTFs using either three or seven textural classes combined with soil organic mater and bulk density gave the most reliable predictions...... of the hydraulic properties of the studied soils. We found that introducing measured water content as a predictor generally gave lower errors for water retention predictions and higher errors for conductivity predictions. The best of the developed PTFs for predicting hydraulic conductivity was tested against PTFs...
Transport parameter estimation from lymph measurements and the Patlak equation.
Watson, P D; Wolf, M B
1992-01-01
Two methods of estimating protein transport parameters for plasma-to-lymph transport data are presented. Both use IBM-compatible computers to obtain least-squares parameters for the solvent drag reflection coefficient and the permeability-surface area product using the Patlak equation. A matrix search approach is described, and the speed and convenience of this are compared with a commercially available gradient method. The results from both of these methods were different from those of a method reported by Reed, Townsley, and Taylor [Am. J. Physiol. 257 (Heart Circ. Physiol. 26): H1037-H1041, 1989]. It is shown that the Reed et al. method contains a systematic error. It is also shown that diffusion always plays an important role for transmembrane transport at the exit end of a membrane channel under all conditions of lymph flow rate and that the statement that diffusion becomes zero at high lymph flow rate depends on a mathematical definition of diffusion.
Estimation of the empirical model parameters of unsaturated soils
Directory of Open Access Journals (Sweden)
Bouchemella Salima
2016-01-01
Full Text Available For each flow modelling in the unsaturated soils, it is necessary to determine the retention curve and the hydraulic conductivity curve of studied soils. Some empirical models use the same parameters to describe these two hydraulic properties. For this reason, the estimation of these parameters is achieved by adjusting the experimental points to the retention curve only, which is more easily measured as compared with the hydraulic conductivity curve. In this work, we show that the adjustment of the retention curve θ (h is not generally sufficient to describe the hydraulic conductivity curve K (θ and the spatio-temporal variation of the moisture in the soil θ (z. The models used in this study are van Genuchten- Mualem model (1980-1976 and Brooks and Corey model (1964, for two different soils; Gault clay and Givors silt.
Synchronization and parameter estimations of an uncertain Rikitake system
Energy Technology Data Exchange (ETDEWEB)
Aguilar-Ibanez, Carlos, E-mail: caguilar@cic.ipn.m [CIC-IPN, Av. Juan de Dios Batiz s/n, Esq. Manuel Othon de M., Unidad Profesional Adolfo Lopez Mateos, Col. Nueva Industrial Vallejo, Del. Gustavo A. Madero, C.P. 07738, Mexico D.F. (Mexico); Martinez-Guerra, Rafael, E-mail: rguerra@ctrl.cinvestav.m [CINVESTAV-IPN, Departamento de Control Automatico, Av. Instituto Politecnico Nacional 2508, Col. San Pedro Zacatenco, Mexico, D. F., 07360 (Mexico); Aguilar-Lopez, Ricardo [CINVESTAV-IPN, Departamento de Biotecnologia y Bioingenieria (Mexico); Mata-Machuca, Juan L. [CINVESTAV-IPN, Departamento de Control Automatico, Av. Instituto Politecnico Nacional 2508, Col. San Pedro Zacatenco, Mexico, D. F., 07360 (Mexico)
2010-08-02
In this Letter we address the synchronization and parameter estimation of the uncertain Rikitake system, under the assumption the state is partially known. To this end we use the master/slave scheme in conjunction with the adaptive control technique. Our control approach consists of proposing a slave system which has to follow asymptotically the uncertain Rikitake system, refereed as the master system. The gains of the slave system are adjusted continually according to a convenient adaptation control law, until the measurable output errors converge to zero. The convergence analysis is carried out by using the Barbalat's Lemma. Under this context, uncertainty means that although the system structure is known, only a partial knowledge of the corresponding parameter values is available.
Estimation of Modelling Parameters for H.263-Quantized Video Traces
Directory of Open Access Journals (Sweden)
A. Drigas
2013-01-01
Full Text Available We propose methods for selecting the modelling parameters of H.263-quantized video traffic under two different encoding scenarios. For videos encoded with a constant quantization step (unconstrained, we conclude that a two-parameter power relation holds between the exhibited video bit rate and the quantizer value and that the autocorrelation decay rate remains constant for all cases. On the basis of these results, we propose a generic method for estimating the modelling parameters of unconstrained traffic by means of measuring the statistics of the single “raw” video trace. For rate-controlled video (constrained, we propose an approximate method based on the adjustment of the “shape” parameter of the counterpart—with respect to rate—unconstrained video trace. The convergence of the constructed models is assessed via q-q plots and queuing simulations. On the assumption that the popular MPEG-4 encoders like XVID, DIVX usually employ identical H.263 quantization and rate control schemes, it is expected that the results of this paper also hold for the MPEG-4 part 2 family.
Energy parameter estimation in solar powered wireless sensor networks
Mousa, Mustafa
2014-02-24
The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.
Observer based parallel IM speed and parameter estimation
Directory of Open Access Journals (Sweden)
Skoko Saša
2014-01-01
Full Text Available The detailed presentation of modern algorithm for the rotor speed estimation of an induction motor (IM is shown. The algorithm includes parallel speed and resistance parameter estimation and allows a robust shaft-sensorless operation in diverse conditions, including full load and low speed operation with a large thermal drift. The direct connection between the injected electric signal in the d-axis and the component of injected rotor flux were pointed at. The algorithm that has been applied in the paper uses the extracted component of the injected rotor flux in the d-axis from the observer state vector and filtrated measured electricity of one motor phase. By applying the mentioned algorithm, the system converges towards the given reference. [Projekat Ministarstva nauke Republike Srbije, br. III 42004
Parameter Estimation for Grouped and Truncated Data or Truncated Data
Komori, Yoshio; Hirose, Hideo
2001-01-01
In the present article we deal with parameter estimation about truncateddata which are of a variety of truncated time a truncated data areof a truncated time b a truncated data are of a truncated time b and so on We show some conditions to get the maximum likelihoodestimations in the two cases in one of which the truncated data aregiven as data values and in the other of which the truncated data aregiven as grouped data that is each expresses the number of the datafall into a subinter...
Parameter Estimation Analysis for Hybrid Adaptive Fault Tolerant Control
Eshak, Peter B.
Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller. This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored. In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to
Genetic Parameter Estimation in Seedstock Swine Population for Growth Performances
Directory of Open Access Journals (Sweden)
Jae Gwan Choi
2013-04-01
Full Text Available The objective of this study was to estimate genetic parameters that are to be used for across-herd genetic evaluations of seed stock pigs at GGP level. Performance data with pedigree information collected from swine breeder farms in Korea were provided by Korea Animal Improvement Association (AIAK. Performance data were composed of final body weights at test days and ultrasound measures of back fat thickness (BF, rib eye area (EMA and retail cut percentage (RCP. Breeds of swine tested were Landrace, Yorkshire and Duroc. Days to 90 kg body weight (DAYS90 were estimated with linear function of age and ADG calculated from body weights at test days. Ultrasound measures were taken with A-mode ultrasound scanners by trained technicians. Number of performance records after censoring outliers and keeping records pigs only born from year 2000 were of 78,068 Duroc pigs, 101,821 Landrace pigs and 281,421 Yorkshire pigs. Models included contemporary groups defined by the same herd and the same seasons of births of the same year, which was regarded as fixed along with the effect of sex for all traits and body weight at test day as a linear covariate for ultrasound measures. REML estimation was processed with REMLF90 program. Heritability estimates were 0.40, 0.32, 0.21 0.39 for DAYS90, ADG, BF, EMA, RCP, respectively for Duroc population. Respective heritability estimates for Landrace population were 0.43, 0.41, 0.22, and 0.43 and for Yorkshire population were 0.36, 0.38, 0.22, and 0.42. Genetic correlation coefficients of DAYS90 with BF, EMA, or RCP were estimated to be 0.00 to 0.09, −0.15 to −0.25, 0.22 to 0.28, respectively for three breeds populations. Genetic correlation coefficients estimated between BF and EMA was −0.33 to −0.39. Genetic correlation coefficient estimated between BF and RCP was high and negative (−0.78 to −0.85 but the environmental correlation coefficients between these two traits was medium and negative (near −0
Optimal Joint Target Detection and Parameter Estimation By MIMO Radar
Tajer, Ali; Wang, Xiaodong; Moustakides, George V
2009-01-01
We consider multiple-input multiple-output (MIMO) radar systems with widely-spaced antennas. Such antenna configuration facilitates capturing the inherent diversity gain due to independent signal dispersion by the target scatterers. We consider a new MIMO radar framework for detecting a target that lies in an unknown location. This is in contrast with conventional MIMO radars which break the space into small cells and aim at detecting the presence of a target in a specified cell. We treat this problem through offering a novel composite hypothesis testing framework for target detection when (i) one or more parameters of the target are unknown and we are interested in estimating them, and (ii) only a finite number of observations are available. The test offered optimizes a metric which accounts for both detection and estimation accuracies. In this paper as the parameter of interest we focus on the vector of time-delays that the waveforms undergo from being emitted by the transmit antennas until being observed b...
Variational Bayesian Parameter Estimation Techniques for the General Linear Model
Directory of Open Access Journals (Sweden)
Ludger Starke
2017-09-01
Full Text Available Variational Bayes (VB, variational maximum likelihood (VML, restricted maximum likelihood (ReML, and maximum likelihood (ML are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation.
A robust methodology for modal parameters estimation applied to SHM
Cardoso, Rharã; Cury, Alexandre; Barbosa, Flávio
2017-10-01
The subject of structural health monitoring is drawing more and more attention over the last years. Many vibration-based techniques aiming at detecting small structural changes or even damage have been developed or enhanced through successive researches. Lately, several studies have focused on the use of raw dynamic data to assess information about structural condition. Despite this trend and much skepticism, many methods still rely on the use of modal parameters as fundamental data for damage detection. Therefore, it is of utmost importance that modal identification procedures are performed with a sufficient level of precision and automation. To fulfill these requirements, this paper presents a novel automated time-domain methodology to identify modal parameters based on a two-step clustering analysis. The first step consists in clustering modes estimates from parametric models of different orders, usually presented in stabilization diagrams. In an automated manner, the first clustering analysis indicates which estimates correspond to physical modes. To circumvent the detection of spurious modes or the loss of physical ones, a second clustering step is then performed. The second step consists in the data mining of information gathered from the first step. To attest the robustness and efficiency of the proposed methodology, numerically generated signals as well as experimental data obtained from a simply supported beam tested in laboratory and from a railway bridge are utilized. The results appeared to be more robust and accurate comparing to those obtained from methods based on one-step clustering analysis.
Periodic orbits of hybrid systems and parameter estimation via AD.
Energy Technology Data Exchange (ETDEWEB)
Guckenheimer, John. (Cornell University); Phipps, Eric Todd; Casey, Richard (INRIA Sophia-Antipolis)
2004-07-01
Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high fidelity as periodic orbits of dynamical systems is challenging because: (1) (most) nonlinear differential equations can only be solved numerically; (2) accurate computation requires solving boundary value problems; (3) many problems and solutions are only piecewise smooth; (4) many problems require solving differential-algebraic equations; (5) sensitivity information for parameter dependence of solutions requires solving variational equations; and (6) truncation errors in numerical integration degrade performance of optimization methods for parameter estimation. In addition, mathematical models of biological processes frequently contain many poorly-known parameters, and the problems associated with this impedes the construction of detailed, high-fidelity models. Modelers are often faced with the difficult problem of using simulations of a nonlinear model, with complex dynamics and many parameters, to match experimental data. Improved computational tools for exploring parameter space and fitting models to data are clearly needed. This paper describes techniques for computing periodic orbits in systems of hybrid differential-algebraic equations and parameter estimation methods for fitting these orbits to data. These techniques make extensive use of automatic differentiation to accurately and efficiently evaluate derivatives for time integration, parameter sensitivities, root finding and optimization. The boundary value problem representing a periodic orbit in a hybrid system of differential algebraic equations is discretized via multiple-shooting using a high-degree Taylor series integration method [GM00, Phi03]. Numerical solutions to the shooting equations are then estimated by a Newton process yielding an approximate periodic orbit. A metric is defined for computing the distance
Estimating Mass Parameters of Doubly Synchronous Binary Asteroids
Davis, Alex; Scheeres, Daniel J.
2017-10-01
The non-spherical mass distributions of binary asteroid systems lead to coupled mutual gravitational forces and torques. Observations of the coupled attitude and orbital dynamics can be leveraged to provide information about the mass parameters of the binary system. The full 3-dimensional motion has 9 degrees of freedom, and coupled dynamics require the use of numerical investigation only. In the current study we simplify the system to a planar ellipsoid-ellipsoid binary system in a doubly synchronous orbit. Three modes are identified for the system, which has 4 degrees of freedom, with one degree of freedom corresponding to an ignorable coordinate. The three modes correspond to the three major librational modes of the system when it is in a doubly synchronous orbit. The linearized periods of each mode are a function of the mass parameters of the two asteroids, enabling measurement of these parameters based on observations of the librational motion. Here we implement estimation techniques to evaluate the capabilities of this mass measurement method. We apply this methodology to the Trojan binary asteroid system 617 Patroclus and Menoetius (1906 VY), the final flyby target of the recently announced LUCY Discovery mission. This system is of interest because a stellar occultation campaign of the Patroclus and Menoetius system has suggested that the asteroids are similarly sized oblate ellipsoids moving in a doubly-synchronous orbit, making the system an ideal test for this investigation. A number of missed observations during the campaign also suggested the possibility of a crater on the southern limb of Menoetius, the presence of which could be evaluated by our mass estimation method. This presentation will review the methodology and potential accuracy of our approach in addition to evaluating how the dynamical coupling can be used to help understand light curve and stellar occultation observations for librating binary systems.
Extended models for nosocomial infection: parameter estimation and model selection.
Thomas, Alun; Khader, Karim; Redd, Andrew; Leecaster, Molly; Zhang, Yue; Jones, Makoto; Greene, Tom; Samore, Matthew
2017-10-12
We consider extensions to previous models for patient level nosocomial infection in several ways, provide a specification of the likelihoods for these new models, specify new update steps required for stochastic integration, and provide programs that implement these methods to obtain parameter estimates and model choice statistics. Previous susceptible-infected models are extended to allow for a latent period between initial exposure to the pathogen and the patient becoming themselves infectious, and the possibility of decolonization. We allow for multiple facilities, such as acute care hospitals or long-term care facilities and nursing homes, and for multiple units or wards within a facility. Patient transfers between units and facilities are tracked and accounted for in the models so that direct importation of a colonized individual from one facility or unit to another might be inferred. We allow for constant transmission rates, rates that depend on the number of colonized individuals in a unit or facility, or rates that depend on the proportion of colonized individuals. Statistical analysis is done in a Bayesian framework using Markov chain Monte Carlo methods to obtain a sample of parameter values from their joint posterior distribution. Cross validation, deviance information criterion and widely applicable information criterion approaches to model choice fit very naturally into this framework and we have implemented all three. We illustrate our methods by considering model selection issues and parameter estimation for data on methicilin-resistant Staphylococcus aureus surveillance tests over 1 year at a Veterans Administration hospital comprising seven wards. © The authors 2017. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.
Thermodynamic criteria for estimating the kinetic parameters of catalytic reactions
Mitrichev, I. I.; Zhensa, A. V.; Kol'tsova, E. M.
2017-01-01
Kinetic parameters are estimated using two criteria in addition to the traditional criterion that considers the consistency between experimental and modeled conversion data: thermodynamic consistency and the consistency with entropy production (i.e., the absolute rate of the change in entropy due to exchange with the environment is consistent with the rate of entropy production in the steady state). A special procedure is developed and executed on a computer to achieve the thermodynamic consistency of a set of kinetic parameters with respect to both the standard entropy of a reaction and the standard enthalpy of a reaction. A problem of multi-criterion optimization, reduced to a single-criterion problem by summing weighted values of the three criteria listed above, is solved. Using the reaction of NO reduction with CO on a platinum catalyst as an example, it is shown that the set of parameters proposed by D.B. Mantri and P. Aghalayam gives much worse agreement with experimental values than the set obtained on the basis of three criteria: the sum of the squares of deviations for conversion, the thermodynamic consistency, and the consistency with entropy production.
Automated modal parameter estimation using correlation analysis and bootstrap sampling
Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.
2018-02-01
The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to
On-line estimation of concentration parameters in fermentation processes.
Xiong, Zhi-hua; Huang, Guo-hong; Shao, Hui-he
2005-06-01
It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effective use of those measurements that are already available, which enable improvement in fermentation process control. The proposed method is based on mixtures of Gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models. The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge would be most relevant for local models' specific operating points of the process and then combine them into a global one. Demonstrated by on-line estimate of yeast concentration in fermentation industry as an example, it is shown that soft sensor based state estimation is a powerful technique for both enhancing automatic control performance of biological systems and implementing on-line monitoring and optimization.
US-Based Drug Cost Parameter Estimation for Economic Evaluations.
Levy, Joseph F; Meek, Patrick D; Rosenberg, Marjorie A
2015-07-01
In the United States, more than 10% of national health expenditures are for prescription drugs. Assessing drug costs in US economic evaluation studies is not consistent, as the true acquisition cost of a drug is not known by decision modelers. Current US practice focuses on identifying one reasonable drug cost and imposing some distributional assumption to assess uncertainty. We propose a set of Rules based on current pharmacy practice that account for the heterogeneity of drug product costs. The set of products derived from our Rules, and their associated costs, form an empirical distribution that can be used for more realistic sensitivity analyses and create transparency in drug cost parameter computation. The Rules specify an algorithmic process to select clinically equivalent drug products that reduce pill burden, use an appropriate package size, and assume uniform weighting of substitutable products. Three diverse examples show derived empirical distributions and are compared with previously reported cost estimates. The shapes of the empirical distributions among the 3 drugs differ dramatically, including multiple modes and different variation. Previously published estimates differed from the means of the empirical distributions. Published ranges for sensitivity analyses did not cover the ranges of the empirical distributions. In one example using lisinopril, the empirical mean cost of substitutable products was $444 (range = $23-$953) as compared with a published estimate of $305 (range = $51-$523). Our Rules create a simple and transparent approach to creating cost estimates of drug products and assessing their variability. The approach is easily modified to include a subset of, or different weighting for, substitutable products. The derived empirical distribution is easily incorporated into 1-way or probabilistic sensitivity analyses. © The Author(s) 2014.
Estimation of the Alpha Factor Parameters Using the ICDE Database
Energy Technology Data Exchange (ETDEWEB)
Kang, Dae Il; Hwang, M. J.; Han, S. H
2007-04-15
Detailed common cause failure (CCF) analysis generally need for the data for CCF events of other nuclear power plants because the CCF events rarely occur. KAERI has been participated at the international common cause failure data exchange (ICDE) project to get the data for the CCF events. The operation office of the ICDE project sent the CCF event data for EDG to the KAERI at December 2006. As a pilot study, we performed the detailed CCF analysis of EDGs for Yonggwang Units 3 and 4 and Ulchin Units 3 and 4 using the ICDE database. There are two onsite EDGs for each NPP. When an offsite power and the two onsite EDGs are not available, one alternate AC (AAC) diesel generator (hereafter AAC) is provided. Two onsite EDGs and the AAC are manufactured by the same company, but they are designed differently. We estimated the Alpha Factor and the CCF probability for the cases where three EDGs were assumed to be identically designed, and for those were assumed to be not identically designed. For the cases where three EDGs were assumed to be identically designed, double CCF probabilities of Yonggwang Units 3/4 and Ulchin Units 3/4 for 'fails to start' were estimated as 2.20E-4 and 2.10E-4, respectively. Triple CCF probabilities of those were estimated as 2.39E-4 and 2.42E-4, respectively. As each NPP has no experience for 'fails to run', Yonggwang Units 3/4 and Ulchin Units 3/4 have the same CCF probability. The estimated double and triple CCF probabilities for 'fails to run' are 4.21E-4 and 4.61E-4, respectively. Quantification results show that the system unavailability for the cases where the three EDGs are identical is higher than that where the three EDGs are different. The estimated system unavailability of the former case was increased by 3.4% comparing with that of the latter. As a future study, a computerization work for the estimations of the CCF parameters will be performed.
Colocated MIMO Radar: Beamforming, Waveform design, and Target Parameter Estimation
Jardak, Seifallah
2014-04-01
Thanks to its improved capabilities, the Multiple Input Multiple Output (MIMO) radar is attracting the attention of researchers and practitioners alike. Because it transmits orthogonal or partially correlated waveforms, this emerging technology outperformed the phased array radar by providing better parametric identifiability, achieving higher spatial resolution, and designing complex beampatterns. To avoid jamming and enhance the signal to noise ratio, it is often interesting to maximize the transmitted power in a given region of interest and minimize it elsewhere. This problem is known as the transmit beampattern design and is usually tackled as a two-step process: a transmit covariance matrix is firstly designed by minimizing a convex optimization problem, which is then used to generate practical waveforms. In this work, we propose simple novel methods to generate correlated waveforms using finite alphabet constant and non-constant-envelope symbols. To generate finite alphabet waveforms, the proposed method maps easily generated Gaussian random variables onto the phase-shift-keying, pulse-amplitude, and quadrature-amplitude modulation schemes. For such mapping, the probability density function of Gaussian random variables is divided into M regions, where M is the number of alphabets in the corresponding modulation scheme. By exploiting the mapping function, the relationship between the cross-correlation of Gaussian and finite alphabet symbols is derived. The second part of this thesis covers the topic of target parameter estimation. To determine the reflection coefficient, spatial location, and Doppler shift of a target, maximum likelihood estimation yields the best performance. However, it requires a two dimensional search problem. Therefore, its computational complexity is prohibitively high. So, we proposed a reduced complexity and optimum performance algorithm which allows the two dimensional fast Fourier transform to jointly estimate the spatial location
Estimation of ICBM (intercontinental ballistic missile) performance parameters
Crumpton, Kenneth S.
1986-12-01
The estimation of launch vehicle performance parameters was explored through the use of a Bayes Filter. The missile dynamics were modeled using a seven state system consisting of the vehicle position and velocity vectors, and the acceleration due to thrust. An assumed solution was used in the model obviating the need for a numerical integration subroutine. The filter was run with two different missile flight profiles. The program produced good approximations of the missile thrust acceleration even through staging events. The results of the Bayes filter were improved with the use of fading memory. The fading memory values were adjusted until the filter closely approximated the missile acceleration values. The filter was able to handle the second missile flight profile without any adjustment in the fading memory and with no degradation of results.
Dynamic systems models new methods of parameter and state estimation
2016-01-01
This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics. Dynamic Systems Models demonstrates the use of algorithms based on polynomial approximation which have weaker requirements than already-popular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow non-differentiable elements in the vector functions being approximated. The text covers all the points necessary for the understanding and use of polynomial approximation from the mathematical fundamentals, through algorithm development to the application of the method in, for instance, aeroplane flight dynamic...
MANOVA, LDA, and FA criteria in clusters parameter estimation
Directory of Open Access Journals (Sweden)
Stan Lipovetsky
2015-12-01
Full Text Available Multivariate analysis of variance (MANOVA and linear discriminant analysis (LDA apply such well-known criteria as the Wilks’ lambda, Lawley–Hotelling trace, and Pillai’s trace test for checking quality of the solutions. The current paper suggests using these criteria for building objectives for finding clusters parameters because optimizing such objectives corresponds to the best distinguishing between the clusters. Relation to Joreskog’s classification for factor analysis (FA techniques is also considered. The problem can be reduced to the multinomial parameterization, and solution can be found in a nonlinear optimization procedure which yields the estimates for the cluster centers and sizes. This approach for clustering works with data compressed into covariance matrix so can be especially useful for big data.
Robustness of Modal Parameter Estimation Methods Applied to Lightweight Structures
DEFF Research Database (Denmark)
Dickow, Kristoffer Ahrens; Kirkegaard, Poul Henning; Andersen, Lars Vabbersgaard
2013-01-01
. The ability to handle closely spaced modes and broad frequency ranges is investigated for a numerical model of a lightweight junction under dierent signal-to-noise ratios. The selection of both excitation points and response points are discussed. It is found that both the Rational Fraction Polynomial-Z method...... of nominally identical test subjects. However, the literature on modal testing of timber structures is rather limited and the applicability and robustness of dierent curve tting methods for modal analysis of such structures is not described in detail. The aim of this paper is to investigate the robustness...... of two parameter estimation methods built into the commercial modal testing software B&K Pulse Re ex Advanced Modal Analysis. The investigations are done by means of frequency response functions generated from a nite-element model and subjected to articial noise before being analyzed with Pulse Re ex...
Estimation of genetic parameters for reproductive traits in Shall sheep.
Amou Posht-e-Masari, Hesam; Shadparvar, Abdol Ahad; Ghavi Hossein-Zadeh, Navid; Hadi Tavatori, Mohammad Hossein
2013-06-01
The objective of this study was to estimate genetic parameters for reproductive traits in Shall sheep. Data included 1,316 records on reproductive performances of 395 Shall ewes from 41 sires and 136 dams which were collected from 2001 to 2007 in Shall breeding station in Qazvin province at the Northwest of Iran. Studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW). Test of significance to include fixed effects in the statistical model was performed using the general linear model procedure of SAS. The effects of lambing year and ewe age at lambing were significant (Psheep.
Application of Genetic Algorithms for Parameter Estimation in Liquid Chromatography
Directory of Open Access Journals (Sweden)
Orestes Llanes Santiago
2011-11-01
Full Text Available Normal 0 21 false false false ES X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} 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 fracture parameters using elastic full-waveform inversion
Zhang, Zhendong
2017-08-17
Current methodologies to characterize fractures at the reservoir scale have serious limitations in spatial resolution and suffer from uncertainties in the inverted parameters. Here, we propose to estimate the spatial distribution and physical properties of fractures using full-waveform inversion (FWI) of multicomponent surface seismic data. An effective orthorhombic medium with five clusters of vertical fractures distributed in a checkboard fashion is used to test the algorithm. A shape regularization term is added to the objective function to improve the estimation of the fracture azimuth, which is otherwise poorly constrained. The cracks are assumed to be penny-shaped to reduce the nonuniqueness in the inverted fracture weaknesses and achieve a faster convergence. To better understand the inversion results, we analyze the radiation patterns induced by the perturbations in the fracture weaknesses and orientation. Due to the high-resolution potential of elastic FWI, the developed algorithm can recover the spatial fracture distribution and identify localized “sweet spots” of intense fracturing. However, the fracture azimuth can be resolved only using long-offset data.
Genetic parameter estimates and identification of superior white maize populations
Directory of Open Access Journals (Sweden)
Sara Regina Silvestrin Rovaris
2017-04-01
Full Text Available In Brazil, there is a shortage of white maize cultivars and genetic studies for special maize breeding programs. This study aimed to identify populations and promising hybrid white maize for main agronomic traits and grits processing and to estimate the genetic parameters of parents and heterosis. In the 2012/13 growing season, fifteen hybrids were obtained by complete diallel crosses, and six parental and commercial check varieties were evaluated for: female flowering (FF, ear height (EH, grain yield (GY, ear length (EL, volumetric mass (VM and grits processing (GP in two locations in São Paulo State, Campinas and Mococa, using a randomized block design. Analyses of variance were carried out, and diallel crosses were performed using the Gardner and Eberhart model. The populations P3 and P6 stood out because of the estimated effects of the parents and of heterosis; the studied characters are promising for obtaining new lines and forming composites. For GP, the treatments showed no differences, implying the need to introduce new sources of germplasm.
Genetic parameter estimation of reproductive traits of Litopenaeus vannamei
Tan, Jian; Kong, Jie; Cao, Baoxiang; Luo, Kun; Liu, Ning; Meng, Xianhong; Xu, Shengyu; Guo, Zhaojia; Chen, Guoliang; Luan, Sheng
2017-02-01
In this study, the heritability, repeatability, phenotypic correlation, and genetic correlation of the reproductive and growth traits of L. vannamei were investigated and estimated. Eight traits of 385 shrimps from forty-two families, including the number of eggs (EN), number of nauplii (NN), egg diameter (ED), spawning frequency (SF), spawning success (SS), female body weight (BW) and body length (BL) at insemination, and condition factor (K), were measured,. A total of 519 spawning records including multiple spawning and 91 no spawning records were collected. The genetic parameters were estimated using an animal model, a multinomial logit model (for SF), and a sire-dam and probit model (for SS). Because there were repeated records, permanent environmental effects were included in the models. The heritability estimates for BW, BL, EN, NN, ED, SF, SS, and K were 0.49 ± 0.14, 0.51 ± 0.14, 0.12 ± 0.08, 0, 0.01 ± 0.04, 0.06 ± 0.06, 0.18 ± 0.07, and 0.10 ± 0.06, respectively. The genetic correlation was 0.99 ± 0.01 between BW and BL, 0.90 ± 0.19 between BW and EN, 0.22 ± 0.97 between BW and ED, -0.77 ± 1.14 between EN and ED, and -0.27 ± 0.36 between BW and K. The heritability of EN estimated without a covariate was 0.12 ± 0.08, and the genetic correlation was 0.90 ± 0.19 between BW and EN, indicating that improving BW may be used in selection programs to genetically improve the reproductive output of L. vannamei during the breeding. For EN, the data were also analyzed using body weight as a covariate (EN-2). The heritability of EN-2 was 0.03 ± 0.05, indicating that it is difficult to improve the reproductive output by genetic improvement. Furthermore, excessive pursuit of this selection is often at the expense of growth speed. Therefore, the selection of high-performance spawners using BW and SS may be an important strategy to improve nauplii production.
Weak-Values Metrological Techniques for Parameter Estimation
Martinez-Rincon, Julian Rodrigo
Precision measurements are bounded by the Standard Quantum Limit, and preparing non-classical states is often used to circumvent such a limit. In all cases, it is common to improve the precision in a parameter estimation procedure by averaging measurements of a large ensemble of identically prepared systems. However, such a task cannot be performed indefinitely due to sources of technical noise setting an experimental bound. Weak-Value Amplification (WVA) allows one to overcome some of these issues by amplifying a signal of interest above the technical-noise floor. This built-in robustness to external sources of noise relies on a weak coupling to a meter and postselection. In this document we evaluate, theoretically and experimentally, under what circumstances the technique is superior to non-postselected standard techniques. We also present a novel protocol where a WVA-like response is induced in an optical homodyne-type detection technique. We dub this technique Almost-Balanced Weak Values (ABWV) and present three experimental measurements of different physical velocities to evaluate the practical advantages over the well-known technique of WVA. In addition, we point out the existence of a third postselected-weak-measurements technique for metrology, Inverse Weak Value (IWV), that has been ignored by the scientific community. We use this protocol to measure ultra small tilts of a mirror in a Sagnac interferometer. We report all three techniques as complementary to each other, and show their robustness for low-frequency signals.
Yu, Xiaolin; Zhang, Shaoqing; Lin, Xiaopei; Li, Mingkui
2017-03-01
The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slow-varying ocean, this study examines the sensitivity of state-parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying atmosphere with a chaotic nature is the major source of the inaccuracy of estimated state-parameter covariance. Thus, enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.
Automated Modal Parameter Estimation of Civil Engineering Structures
DEFF Research Database (Denmark)
Andersen, Palle; Brincker, Rune; Goursat, Maurice
In this paper the problems of doing automatic modal parameter extraction of ambient excited civil engineering structures is considered. Two different approaches for obtaining the modal parameters automatically are presented: The Frequency Domain Decomposition (FDD) technique and a correlation...
Estimation of uranium migration parameters in sandstone aquifers.
Malov, A I
2016-03-01
The chemical composition and isotopes of carbon and uranium were investigated in groundwater samples that were collected from 16 wells and 2 sources in the Northern Dvina Basin, Northwest Russia. Across the dataset, the temperatures in the groundwater ranged from 3.6 to 6.9 °C, the pH ranged from 7.6 to 9.0, the Eh ranged from -137 to +128 mV, the total dissolved solids (TDS) ranged from 209 to 22,000 mg L(-1), and the dissolved oxygen (DO) ranged from 0 to 9.9 ppm. The (14)C activity ranged from 0 to 69.96 ± 0.69 percent modern carbon (pmC). The uranium content in the groundwater ranged from 0.006 to 16 ppb, and the (234)U:(238)U activity ratio ranged from 1.35 ± 0.21 to 8.61 ± 1.35. The uranium concentration and (234)U:(238)U activity ratio increased from the recharge area to the redox barrier; behind the barrier, the uranium content is minimal. The results were systematized by creating a conceptual model of the Northern Dvina Basin's hydrogeological system. The use of uranium isotope dating in conjunction with radiocarbon dating allowed the determination of important water-rock interaction parameters, such as the dissolution rate:recoil loss factor ratio Rd:p (a(-1)) and the uranium retardation factor:recoil loss factor ratio R:p in the aquifer. The (14)C age of the water was estimated to be between modern and >35,000 years. The (234)U-(238)U age of the water was estimated to be between 260 and 582,000 years. The Rd:p ratio decreases with increasing groundwater residence time in the aquifer from n × 10(-5) to n × 10(-7) a(-1). This finding is observed because the TDS increases in that direction from 0.2 to 9 g L(-1), and accordingly, the mineral saturation indices increase. Relatively high values of R:p (200-1000) characterize aquifers in sandy-clayey sediments from the Late Pleistocene and the deepest parts of the Vendian strata. In samples from the sandstones of the upper part of the Vendian strata, the R:p value is ∼ 24, i.e., sorption processes are
Lord, Frederic M.
This paper is primarily concerned with determining the statistical bias in the maximum likelihood estimate of the examinee ability parameter in item response theory, and of certain functions of such parameters. Given known item parameters, unbiased estimators are derived for (1) an examinee's ability parameter and proportion-correct true score;…
Single-Channel Blind Estimation of Reverberation Parameters
DEFF Research Database (Denmark)
Doire, C.S.J.; Brookes, M. D.; Naylor, P. A.
2015-01-01
The reverberation of an acoustic channel can be characterised by two frequency-dependent parameters: the reverberation time and the direct-to-reverberant energy ratio. This paper presents an algorithm for blindly determining these parameters from a single-channel speech signal. The algorithm uses...
A Note on Parameter Estimation For Lazarsfeld's Latent Class Analysis
Formann, Anton K.
1978-01-01
As the literature indicates, no method is presently available which takes explicitly into account that the parameters of Lazarsfeld's latent class analysis are defined as probabilities and are therefore restricted to the interval (0,1). An appropriate transform on the parameters is presented in order to satisfy this constraint. (Author/JKS)
Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator
Pan, Xueping; Ju, Ping; Wu, Feng; Jin, Yuqing
2017-09-01
A new hierarchical parameter estimation method for doubly fed induction generator (DFIG) and drive train system in a wind turbine generator (WTG) is proposed in this paper. Firstly, the parameters of the DFIG and the drive train are estimated locally under different types of disturbances. Secondly, a coordination estimation method is further applied to identify the parameters of the DFIG and the drive train simultaneously with the purpose of attaining the global optimal estimation results. The main benefit of the proposed scheme is the improved estimation accuracy. Estimation results confirm the applicability of the proposed estimation technique.
Asymptotic Parameter Estimation for a Class of Linear Stochastic Systems Using Kalman-Bucy Filtering
Directory of Open Access Journals (Sweden)
Xiu Kan
2012-01-01
Full Text Available The asymptotic parameter estimation is investigated for a class of linear stochastic systems with unknown parameter θ:dXt=(θα(t+β(tXtdt+σ(tdWt. Continuous-time Kalman-Bucy linear filtering theory is first used to estimate the unknown parameter θ based on Bayesian analysis. Then, some sufficient conditions on coefficients are given to analyze the asymptotic convergence of the estimator. Finally, the strong consistent property of the estimator is discussed by comparison theorem.
da Silveira, Christian L; Mazutti, Marcio A; Salau, Nina P G
2016-07-08
Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid-state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905-917, 2016. © 2016 American Institute of Chemical Engineers.
Minimum Variance Estimation of Yield Parameters of Rubber Tree ...
African Journals Online (AJOL)
Although growth and yield data are available in rubber plantations in Nigeria for aggregate rubber production planning, existing models poorly estimate the yield per rubber tree for the incoming year. Kalman lter, a exible statistical estimator, is used to combine the inexact prediction of the rubber production with an equally ...
Estimates of genetic parameters and effect of inbreeding on milk ...
African Journals Online (AJOL)
The effect of inbreeding on the 305-d yields of milk, fat and protein, and the percentages of fat and protein in the first three lactations was estimated using records on the South African Jersey cows that participated in the National Dairy Animal Improvement Scheme. Inbreeding coefficients were estimated using the entire ...
Robust Speed and Parameter Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.; Vadstrup, P.
1995-01-01
This paper presents a Model Reference Adaptive System (MRAS) for the estimation of the induction motor speed, based on measured terminal voltages and currents.......This paper presents a Model Reference Adaptive System (MRAS) for the estimation of the induction motor speed, based on measured terminal voltages and currents....
Improved Parameter Estimation for First-Order Markov Process
Directory of Open Access Journals (Sweden)
Deepak Batra
2009-01-01
Full Text Available This correspondence presents a linear transformation, which is used to estimate correlation coefficient of first-order Markov process. It outperforms zero-forcing (ZF, minimum mean-squared error (MMSE, and whitened least-squares (WTLSs estimators by controlling output noise variance at the cost of increased computational complexity.
Genetic and phenotypic parameter estimates of production traits of ...
African Journals Online (AJOL)
Henderson's Method 3 and REML on a sire model produced almost identical estimates. Phenotypic, environmental ... Single-trait models require heritability estimates of the specific trait in the base population. ..... of these (co)variances can be done jointly with predic- tion of breeding values when solving the mixed model.
Energy Technology Data Exchange (ETDEWEB)
Meliopoulos, Sakis [Georgia Inst. of Technology, Atlanta, GA (United States); Cokkinides, George [Georgia Inst. of Technology, Atlanta, GA (United States); Fardanesh, Bruce [New York Power Authority, NY (United States); Hedrington, Clinton [U.S. Virgin Islands Water and Power Authority (WAPA), St. Croix (U.S. Virgin Islands)
2013-12-31
This is the final report for this project that was performed in the period: October1, 2009 to June 30, 2013. In this project, a fully distributed high-fidelity dynamic state estimator (DSE) that continuously tracks the real time dynamic model of a wide area system with update rates better than 60 times per second is achieved. The proposed technology is based on GPS-synchronized measurements but also utilizes data from all available Intelligent Electronic Devices in the system (numerical relays, digital fault recorders, digital meters, etc.). The distributed state estimator provides the real time model of the system not only the voltage phasors. The proposed system provides the infrastructure for a variety of applications and two very important applications (a) a high fidelity generating unit parameters estimation and (b) an energy function based transient stability monitoring of a wide area electric power system with predictive capability. Also the dynamic distributed state estimation results are stored (the storage scheme includes data and coincidental model) enabling an automatic reconstruction and “play back” of a system wide disturbance. This approach enables complete play back capability with fidelity equal to that of real time with the advantage of “playing back” at a user selected speed. The proposed technologies were developed and tested in the lab during the first 18 months of the project and then demonstrated on two actual systems, the USVI Water and Power Administration system and the New York Power Authority’s Blenheim-Gilboa pumped hydro plant in the last 18 months of the project. The four main thrusts of this project, mentioned above, are extremely important to the industry. The DSE with the achieved update rates (more than 60 times per second) provides a superior solution to the “grid visibility” question. The generator parameter identification method fills an important and practical need of the industry. The “energy function” based
A Consistent Methodology Based Parameter Estimation for a Lactic Acid Bacteria Fermentation Model
DEFF Research Database (Denmark)
Spann, Robert; Roca, Christophe; Kold, David
2017-01-01
mechanisms in a lactic acid bacteria fermentation. We present here a consistent approach for a methodology based parameter estimation for a lactic acid fermentation. In the beginning, just an initial knowledge based guess of parameters was available and an initial parameter estimation of the complete set...... parameter estimation including an evaluation of the correlation and confidence intervals of those parameters to double-check identifiability issues. Such a consistent approach supports process modelling and understanding as i.e., one avoids questionable interpretations caused by estimates of actually...
Efficient estimates of cochlear hearing loss parameters in individual listeners
DEFF Research Database (Denmark)
Fereczkowski, Michal; Jepsen, Morten Løve; Dau, Torsten
2013-01-01
) are presented and used to estimate the knee-point level and the compression ratio of the I/O function. A time-efficient paradigm based on the single-interval-up-down method (SIUD; Lecluyse and Meddis (2009)) was used. In contrast with previous studies, the present study used only on-frequency TMCs to derive...... to Jepsen and Dau (2011) IHL + OHL = HLT [dB], where HLT stands for total hearing loss. Hence having estimates of the total hearing loss and OHC loss, one can estimate the IHL. In the present study, results from forward masking experiments based on temporal masking curves (TMC; Nelson et al., 2001...
ESTIMATIONS OF THE PARAMETERS OF THE WEIBULL DISTRIBUTION WITH PROGRESSIVELY CENSORED DATA
Shuo-Jye, Wu; Department of Statistics, Tamkang University
2002-01-01
We obtained estimation results concerning a progressively type-II censored sample from a two-parameter Weibull distribution. The maximum likelihood method is used to derive the point estimators of the parameters. An exact confidence interval and an exact joint confidence region for the parameters are constructed. A numerical example is presented to illustrate the methods proposed here.
Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly
Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G.
2013-01-01
Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values,…
Estimation of transport and degradation parameters for naphthalene ...
African Journals Online (AJOL)
-dispersive solute transport involving linear equilibrium sorption and first order degradation for time pulse sources has been applied to analyze experimental data from a soil microcosm reactor. Estimation of the pore-water velocity V for a ...
Achieving a Confidence Interval for Parameters Estimated by Simulation
Adam, Nabil R.
1983-01-01
This paper presents a procedure for determining the number of simulation observations required to achieve a preassigned confidence interval for means estimated by simulation. This procedure, which is simple to implement and efficient to use, is compared with two other methods for determining the required sample size in a simulation run. The empirical results show that this procedure gives good results in the precision of estimated means and in sample size requirement.
A Simple Method for Estimation of Parameters in First order Systems
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Miklos, Robert
2014-01-01
A simple method for estimation of parameters in first order systems with time delays is presented in this paper. The parameter estimation approach is based on a step response for the open loop system. It is shown that the estimation method does not require a complete step response, only a part of...
Empirical estimation of school siting parameter towards improving children's safety
Aziz, I. S.; Yusoff, Z. M.; Rasam, A. R. A.; Rahman, A. N. N. A.; Omar, D.
2014-02-01
Distance from school to home is a key determination in ensuring the safety of hildren. School siting parameters are made to make sure that a particular school is located in a safe environment. School siting parameters are made by Department of Town and Country Planning Malaysia (DTCP) and latest review was on June 2012. These school siting parameters are crucially important as they can affect the safety, school reputation, and not to mention the perception of the pupil and parents of the school. There have been many studies to review school siting parameters since these change in conjunction with this ever-changing world. In this study, the focus is the impact of school siting parameter on people with low income that live in the urban area, specifically in Johor Bahru, Malaysia. In achieving that, this study will use two methods which are on site and off site. The on site method is to give questionnaires to people and off site is to use Geographic Information System (GIS) and Statistical Product and Service Solutions (SPSS), to analyse the results obtained from the questionnaire. The output is a maps of suitable safe distance from school to house. The results of this study will be useful to people with low income as their children tend to walk to school rather than use transportation.
An Introduction to Goodness of Fit for PMU Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Riepnieks, Artis; Kirkham, Harold
2017-10-01
New results of measurements of phasor-like signals are presented based on our previous work on the topic. In this document an improved estimation method is described. The algorithm (which is realized in MATLAB software) is discussed. We examine the effect of noisy and distorted signals on the Goodness of Fit metric. The estimation method is shown to be performing very well with clean data and with a measurement window as short as a half a cycle and as few as 5 samples per cycle. The Goodness of Fit decreases predictably with added phase noise, and seems to be acceptable even with visible distortion in the signal. While the exact results we obtain are specific to our method of estimation, the Goodness of Fit method could be implemented in any phasor measurement unit.
Sugarcane maturity estimation through edaphic-climatic parameters
Directory of Open Access Journals (Sweden)
Scarpari Maximiliano Salles
2004-01-01
Full Text Available Sugarcane (Saccharum officinarum L. grows under different weather conditions directly affecting crop maturation. Raw material quality predicting models are important tools in sugarcane crop management; the goal of these models is to provide productivity estimates during harvesting, increasing the efficiency of strategical and administrative decisions. The objective of this work was developing a model to predict Total Recoverable Sugars (TRS during harvesting, using data related to production factors such as soil water storage and negative degree-days. The database of a sugar mill for the crop seasons 1999/2000, 2000/2001 and 2001/2002 was analyzed, and statistical models were tested to estimate raw material. The maturity model for a one-year old sugarcane proved to be significant, with a coefficient of determination (R² of 0.7049*. No differences were detected between measured and estimated data in the simulation (P < 0.05.
Response-Based Estimation of Sea State Parameters
DEFF Research Database (Denmark)
Nielsen, Ulrik Dam
2007-01-01
of measured ship responses. It is therefore interesting to investigate how the filtering aspect, introduced by FRF, affects the final outcome of the estimation procedures. The paper contains a study based on numerical generated time series, and the study shows that filtering has an influence...... calculated by a 3-D time domain code and by closed-form (analytical) expressions, respectively. Based on comparisons with wave radar measurements and satellite measurements it is seen that the wave estimations based on closedform expressions exhibit a reasonable energy content, but the distribution of energy...
Estimation of source parameters of Chamoli Earthquake, India
Indian Academy of Sciences (India)
R. Narasimhan, Krishtel eMaging Solutions
earthquake in south Iceland. In the present study the source parameters for the 1999 Chamoli earth- quake are calculated at three different sites using strong motion data recorded on Delhi Strong Motion. Accelerograph (DSMA) network maintained by the. CBRI. 2. Data and methodology. Sixteen Digital Triaxial Strong ...
Measurement Error Estimation for Capacitive Voltage Transformer by Insulation Parameters
Directory of Open Access Journals (Sweden)
Bin Chen
2017-03-01
Full Text Available Measurement errors of a capacitive voltage transformer (CVT are relevant to its equivalent parameters for which its capacitive divider contributes the most. In daily operation, dielectric aging, moisture, dielectric breakdown, etc., it will exert mixing effects on a capacitive divider’s insulation characteristics, leading to fluctuation in equivalent parameters which result in the measurement error. This paper proposes an equivalent circuit model to represent a CVT which incorporates insulation characteristics of a capacitive divider. After software simulation and laboratory experiments, the relationship between measurement errors and insulation parameters is obtained. It indicates that variation of insulation parameters in a CVT will cause a reasonable measurement error. From field tests and calculation, equivalent capacitance mainly affects magnitude error, while dielectric loss mainly affects phase error. As capacitance changes 0.2%, magnitude error can reach −0.2%. As dielectric loss factor changes 0.2%, phase error can reach 5′. An increase of equivalent capacitance and dielectric loss factor in the high-voltage capacitor will cause a positive real power measurement error. An increase of equivalent capacitance and dielectric loss factor in the low-voltage capacitor will cause a negative real power measurement error.
Genetic parameter estimates for functional herd life for the South ...
African Journals Online (AJOL)
Longevity reflects the ability of a cow to avoid being culled for low production, low fertility or illness. Longevity can be used in breeding programmes if genetic parameters are known. Various measures are used for longevity. In this study survival in each of the first three lactations was analysed. Survival was denoted by a 1 if ...
A general method of estimating stellar astrophysical parameters from photometry
Belikov, A. N.; Roeser, S.
2008-01-01
Context. Applying photometric catalogs to the study of the population of the Galaxy is obscured by the impossibility to map directly photometric colors into astrophysical parameters. Most of all-sky catalogs like ASCC or 2MASS are based upon broad-band photometric systems, and the use of broad
The observer-based synchronization and parameter estimation of a ...
Indian Academy of Sciences (India)
Haipeng Su
2017-10-31
Oct 31, 2017 ... and important issue in theory and various fields of application. In this paper first we ... nonlinear systems, such as communication system, ... ity theory. Banerjee and Chowdhury [16] developed a new method of synchronization between two nonlinear systems based on Lyapunov function and parameter esti-.
PhyloPars: estimation of missing parameter values using phylogeny.
Bruggeman, J.; Heringa, J.; Brandt, B.W.
2009-01-01
A wealth of information on metabolic parameters of a species can be inferred from observations on species that are phylogenetically related. Phylogeny-based information can complement direct empirical evidence, and is particularly valuable if experiments on the species of interest are not feasible.
Estimation of genetic and genotypic parameters for growth and ...
African Journals Online (AJOL)
Bekezela
2013-12-19
Dec 19, 2013 ... tomography (McEvoy et al., 2009) have been used to determine carcass fat content in live animals. In South Africa, despite a fairly sophisticated commercial pig industry, there is little evaluation of the genetic potential and performance of the pig population. Genetic parameters for growth and carcass traits ...
Genetic parameter estimates for tick resistance in Bonsmara cattle ...
African Journals Online (AJOL)
The distribution of tick count records were normalized using a Box-Cox transformation. Data were divided into seven sub-data sets based on the mean tick count per contemporary group, to facilitate the investigation of the effect of level of tick infestation on the derived genetic parameters. A repeatability animal model ...
Estimation of earthquake source parameters in the Kachchh seismic ...
Indian Academy of Sciences (India)
Durgada Nagamani
2017-07-25
Jul 25, 2017 ... Earthquake source parameters and crustal Q0 values for the 138 selected local events of (Mw:2.5−4.4) the 2001 Bhuj earthquake sequence have been computed through inversion modelling of S-waves from three-component broadband seismometer data. SEISAN software has been used to locate the ...
The estimation of modified non-specific solubility parameter of ...
African Journals Online (AJOL)
For apolar liquids, the modified non-specific solubility parameter ' has been correlated with a form of the Lorentz-Lorenz refractive index function and the molar energy of vaporization per unit volume, and two expressions have been developed. Using one form of these expressions, and by introducing the contribution of ...
Estimation of source parameters of Chamoli Earthquake, India
Indian Academy of Sciences (India)
The devastating earthquake (mb = 6.6) at Chamoli, Garhwal Himalaya, which occurred in the morning hours on 29th March 1999, was recorded on Delhi Strong Motion Accelerograph (DSMA) Network operated by the Central Building Research Institute, Roorkee. In this paper the source parameters of this event calculated ...
Genetic parameter estimates for the South African Mutton Merino ...
African Journals Online (AJOL)
Unknown
which vary from 0.22 – 0.43 (Dzakuma et al., 1978; Atkins, 1986; Boujenane & Kerfal, 1990). The genetic correlation estimates between animal effects (direct and maternal) were generally highly negative. They also, as expected, decline with age. This corresponds with results obtained by Tosh & Kemp (1994) in Polled.
Parameter estimation of electricity spot models from futures prices
Aihara, ShinIchi; Bagchi, Arunabha; Imreizeeq, E.S.N.; Walter, E.
We consider a slight perturbation of the Schwartz-Smith model for the electricity futures prices and the resulting modified spot model. Using the martingale property of the modified price under the risk neutral measure, we derive the arbitrage free model for the spot and futures prices. We estimate
Estimates of crossbreeding parameters in a multibreed beef cattle ...
African Journals Online (AJOL)
Unknown
crossbreeding effects, using the CBE3 package of Wolf (1996) and fitting Kinghorn's Model 7 (Kinghorn 1987;. Wolf et al., 1995), for BW and WW, respectively. This model provides a large variety of options to choose from in estimating crossbreeding effects. The following individual genetic crossbreeding effects were ...
Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2010-01-01
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficult...
Estimates of roughness parameters for arrays of obstacles
DEFF Research Database (Denmark)
Duijm, N.J.
1999-01-01
Some methods are evaluated and extended to estimate roughness length and zero plane displacement height for atmospheric flow over arrays of obstacles, typically buildings. It appears that the method proposed by Bottema, with an extension to account for low density obstacle arrays, performs best. ...
Genetic parameter estimates in the South African Jersey breed
African Journals Online (AJOL)
1997-11-01
Nov 1, 1997 ... Discarding records that do not meet a minimum lactation length results in biased genetic evaluations (Norman et al., 1985), especially when the animal model is used in estimating breeding values for both male and female animals. Standard practice has been to project/extend lactations of different length to ...
Revised models and genetic parameter estimates for production and ...
African Journals Online (AJOL)
The corresponding phenotypic, environmental and ewe permanent environmental correlations were all medium to high and estimated with a fair deal of accuracy according to low standard errors. The genetic relationship between weaning weight of the ewe and her lifetime reproduction (accumulated over four lambing ...
Estimation of preferred water flow parameters for four species of ...
African Journals Online (AJOL)
Mean water column velocities at each sampled stone were measured using a mini current meter, while flow velocities closer to the boundary layer where blackfly larvae occurred were estimated using indirect techniques (standard hemispheres and aerating tablets). Standard hemispheres were also used to calculate more ...
estimation of the petrophysical parameters of sediments from chad ...
African Journals Online (AJOL)
DJFLEX
2009-12-22
Dec 22, 2009 ... Reiter M.A. and Tovar, R.J.C., 1982. Estimates of terrestrial heat flow in Northern Chihuahua,. Mexico, Canadian Journal of Earth Sciences,. 22: 1512 – 7. Reyment, R.A., 1980. Biostratigraphy of the Saharan. Cretaceous and Paleocene epicontinental transgression, Cretaceous Reseach,1: 299 –. 327.
Online Parameter Estimation for a Centrifugal Decanter System
DEFF Research Database (Denmark)
Larsen, Jesper Abildgaard; Alstrøm, Preben
2014-01-01
In many processing plants decanter systems are used for separation of heterogenious mixtures, and even though they account for a large fraction of the energy consumption, most decanters just runs at a fixed setpoint. Here, multi model estimation is applied to a waste water treatment plant...
minimum variance estimation of yield parameters of rubber tree
African Journals Online (AJOL)
2013-03-01
Mar 1, 2013 ... raw material supply chain and hence marketing plan. The result matches the wet and dry spell of southern. Nigeria and its effect on rubber tree latex production. The seasonal value by quarter is estimated for each clone to be used for appropriate quarterly adjustment in the aggregate rubber production ...
Fish growth parameters are commonly estimated from the counts of ...
African Journals Online (AJOL)
spamer
fishery scientists, which have concentrated on selected species or specific areas. There is potential to use these data .... 472 × 0.252 (1 – –––) for male galjoen, (7) dt. 472. 10. South African Journal of Marine Science 22. 2000 ..... growth rates of sandbar sharks Carcharhinus plumbeus estimated from tag-recovery data were.
Kurugol, Sila; Freiman, Moti; Afacan, Onur; Domachevsky, Liran; Perez-Rossello, Jeannette M; Callahan, Michael J; Warfield, Simon K
2017-07-01
Quantitative body DW-MRI can detect abdominal abnormalities as well as monitor response-to-therapy for applications including cancer and inflammatory bowel disease with increased accuracy. Parameter estimates are obtained by fitting a forward model of DW-MRI signal decay to the observed data acquired with several b-values. The DW-MRI signal decay models typically used do not account for respiratory, cardiac and peristaltic motion, however, which may deteriorate the accuracy and robustness of parameter estimates. In this work, we introduce a new model of DW-MRI signal decay that explicitly accounts for motion. Specifically, we estimated motion-compensated model parameters by simultaneously solving image registration and model estimation (SIR-ME) problems utilizing the interdependence of acquired volumes along the diffusion-weighting dimension. To accomplish this, we applied the SIR-ME model to the in-vivo DW-MRI data sets of 26 Crohn's disease (CD) patients and achieved improved precision of the estimated parameters by reducing the coefficient of variation by 8%, 24% and 8% for slow diffusion (D), fast diffusion (D*) and fast diffusion fraction (f) parameters respectively, compared to parameters estimated with independent registration in normal-appearing bowel regions. Moreover, the parameters estimated with the SIR-ME model reduced the error rate in classifying normal and abnormal bowel loops to 12% for D and 10% for f parameter with a reduction in error rate by 13% and 11% for D and f parameters, respectively, compared to the error rate in classifying parameter estimates obtained with independent registration. The experiments in DW-MRI of liver in 20 subjects also showed that the SIR-ME model improved the precision of parameter estimation by reducing the coefficient of variation to 7% for D, 23% for D*, and 8% for the f parameter. Using the SIR-ME model, the coefficient of variation was reduced by 4%, 14% and 6% for D, D* and f parameters, respectively, compared
Models to estimate genetic parameters in crossbred dairy cattle populations under selection
Werf, van der J.H.J.
1990-01-01
Estimates of genetic parameters needed to control breeding programs, have to be regularly updated, due to changing environments and ongoing selection and crossing of populations. Restricted maximum likelihood methods optimally provide these estimates, assuming that the
Parameter Estimation in Electrical Power Systems Using Prony's Method
Ortbandt, Claudius; Dzienis, Cezary; Matussek, Robert; Schulte, Horst
2015-11-01
This paper presents the basic principle of Prony's method, which is used for determining electrical power system parameters such as phasors, angular speeds as well as damping factors in a large frequency range. For fixed sampling frequencies, the approach to finding an optimal model order and to extracting transient parameters for further identification of different system effects is shown. Furthermore, it is shown that the method can be applied to the diverse plausibility checks for a confirmation of the result obtained using a conventional Fourier based approach. It will be presented that the significant advantage of the Prony algorithm in comparison to Fourier based methods is the possibility of computing an accurate frequency and in determining a damping factor.
Quantum parameter estimation with the Landau-Zener transition
Yang, Jing; Pang, Shengshi; Jordan, Andrew N.
2017-08-01
We investigate the fundamental limits in precision allowed by quantum mechanics from Landau-Zener transitions, concerning Hamiltonian parameters. While the Landau-Zener transition probabilities depend sensitively on the system parameters, much more precision may be obtained using the acquired phase, quantified by the quantum Fisher information. This information scales with a power of the elapsed time for the quantum case, whereas it is time independent if the transition probabilities alone are used. We add coherent control to the system and increase the permitted maximum precision in this time-dependent quantum system. The case of multiple passes before measurement, Landau-Zener-Stueckelberg interferometry, is considered, and we demonstrate that proper quantum control can cause the quantum Fisher information about the oscillation frequency to scale as T4, where T is the elapsed time. These results are foundational for frequency standards and quantum clocks.
An Iterated Local Search Algorithm for Estimating the Parameters of the Gamma/Gompertz Distribution
Directory of Open Access Journals (Sweden)
Behrouz Afshar-Nadjafi
2014-01-01
Full Text Available Extensive research has been devoted to the estimation of the parameters of frequently used distributions. However, little attention has been paid to estimation of parameters of Gamma/Gompertz distribution, which is often encountered in customer lifetime and mortality risks distribution literature. This distribution has three parameters. In this paper, we proposed an algorithm for estimating the parameters of Gamma/Gompertz distribution based on maximum likelihood estimation method. Iterated local search (ILS is proposed to maximize likelihood function. Finally, the proposed approach is computationally tested using some numerical examples and results are analyzed.
Parameter Estimates for a Polymer Electrolyte Membrane Fuel Cell Cathode
Guo, Qingzhi; Sethuraman, Vijay A.; White, Ralph E.
2013-01-01
Five parameters of a model of a polymer electrolyte membrane fuel cell cathode (the porosity of the gas diffusion layer, the porosity of the catalyst layer, the exchange current density of the oxygen reduction reaction, the effective ionic conductivity of the electrolyte, and the ratio of the effective diffusion coefficient of oxygen in a flooded spherical agglomerate particle to the squared particle radius) were determined by the least square fitting of experimental polarization curves. The ...
Estimation of groundwater recharge parameters by time series analysis.
Naff, R.L.; Gutjahr, A.L.
1983-01-01
A model is proposed that relates water level fluctuations in a Dupuit aquifer to effective precipitation at the top of the unsaturated zone. Effective precipitation, defined herein as that portion of precipitation which becomes recharge, is related to precipitation measured in a nearby gage by a two-parameter function. A second-order stationary assumption is used to connect the spectra of effective precipitation and water level fluctuations.-from Authors
Parameter estimation of gravitational wave echoes from exotic compact objects
Maselli, Andrea; Völkel, Sebastian H.; Kokkotas, Kostas D.
2017-09-01
Relativistic ultracompact objects without an event horizon may be able to form in nature and merge as binary systems, mimicking the coalescence of ordinary black holes. The postmerger phase of such processes presents characteristic signatures, which appear as repeated pulses within the emitted gravitational waveform, i.e., echoes with variable amplitudes and frequencies. Future detections of these signals can shed new light on the existence of horizonless geometries and provide new information on the nature of gravity in a genuine strong-field regime. In this work we analyze phenomenological templates used to characterize echolike structures produced by exotic compact objects, and we investigate for the first time the ability of current and future interferometers to constrain their parameters. Using different models with an increasing level of accuracy, we determine the features that can be measured with the largest precision, and we span the parameter space to find the most favorable configurations to be detected. Our analysis shows that current detectors may already be able to extract all the parameters of the echoes with good accuracy, and that multiple interferometers can measure frequencies and damping factors of the signals at the level of percent.
Parameter estimation and its application using nonstatistical theory
Xia, Xintao; Chen, Xiaoyang; Wang, Zhongyu; Zhang, Yongzhen
2006-11-01
In many social science and natural science research, researchers are often faced with the problems such as: the system have no enough system distinctive features, the probability distribution unknown and the number of data very small. It is hard to use statistical theory in these researches. A novel method called non-statistical method is proposed in this paper to obtain more characteristics and further information in the system. The method permits the probability distribution unknown and the number of data very small, because of some deficiency of classical statistics in system and information science. In virtue of the special strategy of data processing, more characteristics and further information in the system could be obtained using this method. Some basic concepts, characteristics and elements of non-statistical method including point estimation, interval estimation, optimum level, practicable interval and systemic characteristic mapping etc, are also recommended. The validity of this method is examined by some measurement cases and practical engineering examples.
Kappa (κ): estimates, origins, and correlation to site characterisation parameters
Ktenidou, O. J.; Cotton, F.; Drouet, S.; Theodoulidis, N.; Chaljub, E. O.
2012-12-01
Knowledge of the acceleration spectral shape is important for various applications in engineering seismology. At high frequencies spectral amplitude drops rapidly. Anderson and Hough (1984) modelled this drop with the spectral decay factor κ, observing that, above a certain frequency, the acceleration spectrum decreases linearly in lin-log space. Thirty years later, and though the debate as to its source, path and site components is still on, κ constitutes a basic input parameter for the generation of stochastic ground motion and the calibration and adjustment of GMPEs. We study κ in the EUROSEISTEST site (http://euroseis.civil.auth.gr): a geologically complex site in Northern Greece, with a permanent strong motion array including surface and downhole stations. Site effects are of great importance here, and records are available from a variety of conditions ranging from soft soil to hard rock. We derive the site-related component of κ (κ0) at 16 stations following two approaches: 1. directly, measuring κ on individual S-wave spectra and regressing to zero distance as per Anderson and Hough (1984), following the procedure proposed by Ktenidou et al. (2012); 2. indirectly, deriving station-specific κ0 values from the high-frequency part of the station transfer functions, which are derived from a source-path-site inversion procedure proposed by Drouet et al. (2008). The agreement in κ0 is good. This supports the notion that κ0 is primarily a site effect, since in the second approach source and path effects are accounted for separately. The two approaches also yield similar results for anelastic attenuation within the frequency range studied: both show low regional Q, comparable to the results of crustal Q studies in Greece. We focus on κ0 values, which range from 0.02 s to 0.08 s depending on site type. As expected, κ0 increases for soft sites, but so does the scatter. Because κ0 is considered a site effect proxy, we examine its correlation with local site
Influence of parameter estimation uncertainty in Kriging: Part 1 - Theoretical Development
Todini, E.
2001-01-01
This paper deals with a theoretical approach to assessing the effects of parameter estimation uncertainty both on Kriging estimates and on their estimated error variance. Although a comprehensive treatment of parameter estimation uncertainty is covered by full Bayesian Kriging at the cost of extensive numerical integration, the proposed approach has a wide field of application, given its relative simplicity. The approach is based upon a truncated Taylor expansion approximation and, within the...
Estimation of the Randomized Complete Block Design Parameters with Fuzzy Goal Programming
Kula, Kamile; Apaydin, Ayşen
2011-01-01
Since goal programming was introduced by Charnes, Cooper and Ferguson (1955), goal programming has been widely studied and applied in various areas. Parameter estimation is quite important in many areas. Recently, many researches have been studied in fuzzy estimation. In this study, fuzzy goal programming was proposed by Hannan (1981) adapted to estimation of randomized complete block design parameters. Suggested fuzzy goal programming is used for estimation of randomized complete block desig...
Parameter Estimation in Multivariate Logit models with Many Binary Choices
Bel, Koen; Fok, Dennis; Paap, Richard
2014-01-01
markdownabstract__Abstract__ The multivariate choice problem with correlated binary choices is investigated. The Multivariate Logit [MVL] model is a convenient model to describe such choices as it provides a closed-form likelihood function. The disadvantage of the MVL model is that the computation time required for the calculation of choice probabilities increases exponentially with the number of binary choices under consideration. This makes maximum likelihood-based estimation infeasible in ...
Comparison of Estimation Techniques for the Four Parameter Beta Distribution.
1981-12-01
that Fisher, the initial developer of the maximum likelihood method, " mathematically proved that the inherent variance of an NM estimator [of the...International Mathematical Statistics Library (IMSL) were widely used. The reader should refer to the IMSL manual (Ref 12) if specific information about these...of Stat- istical Comutation and Simulation, 2: 253-258 (1978). 3. Conover, W. T. Practical NoAnaIAmetric Sttistics (Second Edition). New York: John
Comparing Three Estimation Methods for the Three-Parameter Logistic IRT Model
Lamsal, Sunil
2015-01-01
Different estimation procedures have been developed for the unidimensional three-parameter item response theory (IRT) model. These techniques include the marginal maximum likelihood estimation, the fully Bayesian estimation using Markov chain Monte Carlo simulation techniques, and the Metropolis-Hastings Robbin-Monro estimation. With each…
[Adult Stature Estimation by Multiple Parameters of Body Torso Segment].
Wu, R Q; Wang, T; Shi, Q; Xiao, B; Ma, K J; Chen, X
2017-06-01
To promote the further research on body stature estimation and the innovative applications based on the distances between the anatomical landmarks on body torso surface. A specification for the collection of distances between the anatomical landmarks on body torso surface was established. The data of 933 cases of adult population in Yangtze River Delta region were collected. Multiple linear regression method was used to statistical analyse and establish the regression equation of stature estimation. A regression equation about 5 variables including gender （ x ₁）, cervical vertebrae-coccyx line （ x ₂）, sterna-pubis line （ x ₃）, distance between acromion and iliospinale anterius （ x ₄） and shoulder breadth （ x ₅）, and stature （ y ） was established, y =105.406+5.414 x ₁+0.436 x ₂+0.286 x ₃+0.225 x ₄+ 0.193 x ₅. The method is suitable for the rapid, simple and accurate estimation of stature for the forensic experts.
Modified cross-validation as a method for estimating parameter
Shi, Chye Rou; Adnan, Robiah
2014-12-01
Best subsets regression is an effective approach to distinguish models that can attain objectives with as few predictors as would be prudent. Subset models might really estimate the regression coefficients and predict future responses with smaller variance than the full model using all predictors. The inquiry of how to pick subset size λ depends on the bias and variance. There are various method to pick subset size λ. Regularly pick the smallest model that minimizes an estimate of the expected prediction error. Since data are regularly small, so Repeated K-fold cross-validation method is the most broadly utilized method to estimate prediction error and select model. The data is reshuffled and re-stratified before each round. However, the "one-standard-error" rule of Repeated K-fold cross-validation method always picks the most stingy model. The objective of this research is to modify the existing cross-validation method to avoid overfitting and underfitting model, a modified cross-validation method is proposed. This paper compares existing cross-validation and modified cross-validation. Our results reasoned that the modified cross-validation method is better at submodel selection and evaluation than other methods.
Marker-based estimation of genetic parameters in genomics.
Directory of Open Access Journals (Sweden)
Zhiqiu Hu
Full Text Available Linear mixed model (LMM analysis has been recently used extensively for estimating additive genetic variances and narrow-sense heritability in many genomic studies. While the LMM analysis is computationally less intensive than the Bayesian algorithms, it remains infeasible for large-scale genomic data sets. In this paper, we advocate the use of a statistical procedure known as symmetric differences squared (SDS as it may serve as a viable alternative when the LMM methods have difficulty or fail to work with large datasets. The SDS procedure is a general and computationally simple method based only on the least squares regression analysis. We carry out computer simulations and empirical analyses to compare the SDS procedure with two commonly used LMM-based procedures. Our results show that the SDS method is not as good as the LMM methods for small data sets, but it becomes progressively better and can match well with the precision of estimation by the LMM methods for data sets with large sample sizes. Its major advantage is that with larger and larger samples, it continues to work with the increasing precision of estimation while the commonly used LMM methods are no longer able to work under our current typical computing capacity. Thus, these results suggest that the SDS method can serve as a viable alternative particularly when analyzing 'big' genomic data sets.
Parameter estimation for the fractional Schrödinger equation using Bayesian method
Zhang, Hui; Jiang, Xiaoyun; Fan, Wenping
2016-08-01
In this paper, the fractional Schrödinger equation is studied. The Bayesian method is put forward to estimate some relevant parameters of the equation. Results show that the estimated values can fit well with the exact solution. The varying initial values and maximum iterations have little effect on the estimated results. It indicates that the Bayesian method is efficient for the multi-parameter estimation for the fractional Schrödinger equation. This method can also be used to estimate parameters for the fractional Schrödinger equation in other potential field.
Visco-piezo-elastic parameter estimation in laminated plate structures
DEFF Research Database (Denmark)
Araujo, A. L.; Mota Soares, C. M.; Herskovits, J.
2009-01-01
of 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...... determining the material parameters for the best fit. The solution of the inverse problem is obtained by gradient-based optimization techniques, through constrained minimization of an error functional, which expresses the deviation of the numerical model's response with respect to the experimentally measured...
Estimation of the Processing Parameters in Electron Beam Thermal Treatments
Directory of Open Access Journals (Sweden)
DULAU Mircea
2014-05-01
Full Text Available Electron beam have many special properties which make them particularly well suited for use in materials handling through melting, welding, surface treatment, etc., taking into account that this manufacturing is performed in vacuum. The use of electron beam for surface limited heat treatment of workpiece has brought about a noticeable extension of the beam technologies. Some theoretical aspects and simulation results are presented in this paper, considering a high power electron beam processing system and Matlab facilities. This paper can be used in power engineering and electro-technologies fields as a guideline, in order to simulate and analyse the process parameters.
Parameter estimation via conditional expectation: a Bayesian inversion
Matthies, Hermann G.
2016-08-11
When a mathematical or computational model is used to analyse some system, it is usual that some parameters resp. functions or fields in the model are not known, and hence uncertain. These parametric quantities are then identified by actual observations of the response of the real system. In a probabilistic setting, Bayes’s theory is the proper mathematical background for this identification process. The possibility of being able to compute a conditional expectation turns out to be crucial for this purpose. We show how this theoretical background can be used in an actual numerical procedure, and shortly discuss various numerical approximations.
Estimation of atomic interaction parameters by photon counting
DEFF Research Database (Denmark)
Kiilerich, Alexander Holm; Mølmer, Klaus
2014-01-01
Detection of radiation signals is at the heart of precision metrology and sensing. In this article we show how the fluctuations in photon counting signals can be exploited to optimally extract information about the physical parameters that govern the dynamics of the emitter. For a simple two......-level emitter subject to photon counting, we show that the Fisher information and the Cram\\'er- Rao sensitivity bound based on the full detection record can be evaluated from the waiting time distribution in the fluorescence signal which can, in turn, be calculated for both perfect and imperfect detectors...
Estimation of Parameters in Latent Class Models with Constraints on the Parameters.
1986-06-01
the item parameters. Let us briefly review the elements of latent class models. The reader desiring a thorough introduction can consult Lazarsfeld and...parameters, including most of the models which have been proposed to date. The latent distance model of Lazarsfeld and Henry (1968) and the quasi...Psychometrika, 1964, 29, 115-129. Lazarsfeld , P.F., and Henry, N.W. Latent structure analysis. Boston: Houghton-Mifflin, 1968. L6. - 29 References continued
Directory of Open Access Journals (Sweden)
Chuii Khim Chong
2012-06-01
Full Text Available This paper introduces an improved Differential Evolution algorithm (IDE which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE in this paper is a hybrid of a Differential Evolution algorithm (DE and a Kalman Filter (KF. The outcome of IDE is proven to be superior than Genetic Algorithm (GA and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms
Parameters influencing deposit estimation when using water sensitive papers
Directory of Open Access Journals (Sweden)
Emanuele Cerruto
2013-10-01
Full Text Available The aim of the study was to assess the possibility of using water sensitive papers (WSP to estimate the amount of deposit on the target when varying the spray characteristics. To identify the main quantities influencing the deposit, some simplifying hypotheses were applied to simulate WSP behaviour: log-normal distribution of the diameters of the drops and circular stains randomly placed on the images. A very large number (4704 of images of WSPs were produced by means of simulation. The images were obtained by simulating drops of different arithmetic mean diameter (40-300 μm, different coefficient of variation (0.1-1.5, and different percentage of covered surface (2-100%, not considering overlaps. These images were considered to be effective WSP images and then analysed using image processing software in order to measure the percentage of covered surface, the number of particles, and the area of each particle; the deposit was then calculated. These data were correlated with those used to produce the images, varying the spray characteristics. As far as the drop populations are concerned, a classification based on the volume median diameter only should be avoided, especially in case of high variability. This, in fact, results in classifying sprays with very low arithmetic mean diameter as extremely or ultra coarse. The WSP image analysis shows that the relation between simulated and computed percentage of covered surface is independent of the type of spray, whereas impact density and unitary deposit can be estimated from the computed percentage of covered surface only if the spray characteristics (arithmetic mean and coefficient of variation of the drop diameters are known. These data can be estimated by analysing the particles on the WSP images. The results of a validation test show good agreement between simulated and computed deposits, testified by a high (0.93 coefficient of determination.
Application of Parameter Estimation for Diffusions and Mixture Models
DEFF Research Database (Denmark)
Nolsøe, Kim
The first part of this thesis proposes a method to determine the preferred number of structures, their proportions and the corresponding geometrical shapes of an m-membered ring molecule. This is obtained by formulating a statistical model for the data and constructing an algorithm which samples...... error models. This is obtained by constructing an estimating function through projections of some chosen function of Yti+1 onto functions of previous observations Yti ; : : : ; Yt0 . The process of interest Xti+1 is partially observed through a measurement equation Yti+1 = h(Xti+1)+ noice, where h...
Estimation of atomic interaction parameters by quantum measurements
DEFF Research Database (Denmark)
Kiilerich, Alexander Holm; Mølmer, Klaus
Quantum systems, ranging from atomic systems to field modes and mechanical devices are useful precision probes for a variety of physical properties and phenomena. Measurements by which we extract information about the evolution of single quantum systems yield random results and cause a back actio...... strategies, we address the Fisher information and the Cramér-Rao sensitivity bound. We investigate monitoring by photon counting, homodyne detection and frequent projective measurements respectively, and exemplify by Rabi frequency estimation in a driven two-level system....
Estimating canopy fuel parameters for Atlantic Coastal Plain forest types.
Energy Technology Data Exchange (ETDEWEB)
Parresol, Bernard, R.
2007-01-15
Abstract It is necessary to quantify forest canopy characteristics to assess crown fire hazard, prioritize treatment areas, and design treatments to reduce crown fire potential. A number of fire behavior models such as FARSITE, FIRETEC, and NEXUS require as input four particular canopy fuel parameters: 1) canopy cover, 2) stand height, 3) crown base height, and 4) canopy bulk density. These canopy characteristics must be mapped across the landscape at high spatial resolution to accurately simulate crown fire. Currently no models exist to forecast these four canopy parameters for forests of the Atlantic Coastal Plain, a region that supports millions of acres of loblolly, longleaf, and slash pine forests as well as pine-broadleaf forests and mixed species broadleaf forests. Many forest cover types are recognized, too many to efficiently model. For expediency, forests of the Savannah River Site are categorized as belonging to 1 of 7 broad forest type groups, based on composition: 1) loblolly pine, 2) longleaf pine, 3) slash pine, 4) pine-hardwood, 5) hardwood-pine, 6) hardwoods, and 7) cypress-tupelo. These 7 broad forest types typify forests of the Atlantic Coastal Plain region, from Maryland to Florida.
Uncertainties associated with parameter estimation in atmospheric infrasound arrays.
Szuberla, Curt A L; Olson, John V
2004-01-01
This study describes a method for determining the statistical confidence in estimates of direction-of-arrival and trace velocity stemming from signals present in atmospheric infrasound data. It is assumed that the signal source is far enough removed from the infrasound sensor array that a plane-wave approximation holds, and that multipath and multiple source effects are not present. Propagation path and medium inhomogeneities are assumed not to be known at the time of signal detection, but the ensemble of time delays of signal arrivals between array sensor pairs is estimable and corrupted by uncorrelated Gaussian noise. The method results in a set of practical uncertainties that lend themselves to a geometric interpretation. Although quite general, this method is intended for use by analysts interpreting data from atmospheric acoustic arrays, or those interested in designing and deploying them. The method is applied to infrasound arrays typical of those deployed as a part of the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organization.
Laboratory experiments for estimating chemical osmotic parameters of mudstones
Miyoshi, S.; Tokunaga, T.; Mogi, K.; Ito, K.; Takeda, M.
2010-12-01
Recent studies have quantitatively shown that mudstone can act as semi-permeable membrane and can generate abnormally high pore pressure in sedimentary basins. Reflection coefficient is one of the important properties that affect the chemical osmotic behavior of mudstones. However, not many quantitative studies on the reflection coefficient of mudstones have been done. We have developed a laboratory apparatus to observe chemical osmotic behavior, and a numerical simulation technique to estimate the reflection coefficient and other relating properties of mudstones. A core sample of siliceous mudstone obtained from the drilled core at Horonobe, Japan, was set into the apparatus and was saturated by 0.1mol/L sodium chloride solution. Then, the up-side reservoir was replaced with 0.05mol/L sodium chloride solution, and temporal changes of both pressure and concentration of the solution in both up-side and bottom-side reservoirs were measured. Using the data obtained from the experiment, we estimated the reflection coefficient, effective diffusion coefficient, hydraulic conductivity, and specific storage of the sample by fitting the numerical simulation results with the observed ones. A preliminary numerical simulation of groundwater flow and solute migration was conducted in the area where the core sample was obtained, using the reflection coefficient and other properties obtained from this study. The result suggested that the abnormal pore pressure observed in the region can be explained by the chemical osmosis.
Comparison of parameter estimation algorithms in hydrological modelling
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2006-01-01
for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss......Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well......-Marquardt-Levenberg algorithm (implemented in the PEST software), when applied to a steady-state and a transient groundwater model. The results show that PEST can have severe problems in locating the global optimum and in being trapped in local regions of attractions. The global SCE procedure is, in general, more effective...
Improving Distribution Resiliency with Microgrids and State and Parameter Estimation
Energy Technology Data Exchange (ETDEWEB)
Tuffner, Francis K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Williams, Tess L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Schneider, Kevin P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Elizondo, Marcelo A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sun, Yannan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Liu, Chen-Ching [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Xu, Yin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Gourisetti, Sri Nikhil Gup [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2015-09-30
Modern society relies on low-cost reliable electrical power, both to maintain industry, as well as provide basic social services to the populace. When major disturbances occur, such as Hurricane Katrina or Hurricane Sandy, the nation’s electrical infrastructure can experience significant outages. To help prevent the spread of these outages, as well as facilitating faster restoration after an outage, various aspects of improving the resiliency of the power system are needed. Two such approaches are breaking the system into smaller microgrid sections, and to have improved insight into the operations to detect failures or mis-operations before they become critical. Breaking the system into smaller sections of microgrid islands, power can be maintained in smaller areas where distribution generation and energy storage resources are still available, but bulk power generation is no longer connected. Additionally, microgrid systems can maintain service to local pockets of customers when there has been extensive damage to the local distribution system. However, microgrids are grid connected a majority of the time and implementing and operating a microgrid is much different than when islanded. This report discusses work conducted by the Pacific Northwest National Laboratory that developed improvements for simulation tools to capture the characteristics of microgrids and how they can be used to develop new operational strategies. These operational strategies reduce the cost of microgrid operation and increase the reliability and resilience of the nation’s electricity infrastructure. In addition to the ability to break the system into microgrids, improved observability into the state of the distribution grid can make the power system more resilient. State estimation on the transmission system already provides great insight into grid operations and detecting abnormal conditions by leveraging existing measurements. These transmission-level approaches are expanded to using
Viviroli, Daniel; Mittelbach, Heidi; Gurtz, Joachim; Weingartner, Rolf
2009-10-01
SummaryFlood estimations for ungauged mesoscale catchments are as important as they are difficult. So far, empirical and stochastic methods have mainly been used for this purpose. Experience shows, however, that these procedures entail major errors. In order to make further progress in flood estimation, a continuous precipitation-runoff-modelling approach has been developed for practical application in Switzerland using the process-oriented hydrological modelling system PREVAH (Precipitation-Runoff-EVApotranspiration-HRU related model). The main goal of this approach is to achieve discharge hydrographs for any Swiss mesoscale catchment without measurement of discharge. Subsequently, the relevant flood estimations are to be derived from these hydrographs. On the basis of 140 calibrated catchments ( Viviroli et al., 2009b), a parameter regionalisation scheme has been developed to estimate PREVAH's tuneable parameters where calibration is not possible. The scheme is based on three individual parameter estimation approaches, namely Nearest Neighbours (parameter transfer from catchments similar in attribute space), Kriging (parameter interpolation in physical space) and Regression (parameter estimation from relations to catchment attributes). The most favourable results were achieved when the simulations using these three individual regionalisations were combined by computing their median. It will be demonstrated that the framework introduced here yields plausible flood estimations for ungauged Swiss catchments. Comparing a flood with a return period of 100 years to the reference value derived from the observed record, the median error from 49 representative catchments is only -7%, while the error for half of these catchments ranges between -30% and +8%. Additionally, our estimate lies within the statistical 90% confidence interval of the reference value in more than half of these catchments. The average quality of these flood estimations compares well with present
ESTIMATION OF HUMAN BODY SHAPE PARAMETERS USING MICROSOFT KINECTSENCOR
Directory of Open Access Journals (Sweden)
D. M. Vasilkov
2017-01-01
Full Text Available In the paper a human body shape estimation technology based on scan data acquired from sensor controller Microsoft Kinect is described. This device includes an RGB camera and a depth sensor that provides, for each pixel of the image,a distance from the camera focus to the object. A scan session produces a triangulated high-density surface noised with oscillations, isolated fragments and holes. When scanning a human, additional noise comes from garment folds and wrinkles. An algorithm of creating a sparse and regular 3D human body model (avatar free of these defects, which approximates shape, posture and basic metrics of the scanned body is proposed. This solution finds application in individual clothing industry and computer games, as well.
An EM Approach to Parameter Estimation for the Zinnes and Griggs Paired Comparison IRT Model.
Stark, Stephen; Drasgow, Fritz
2002-01-01
Describes item response and information functions for the Zinnes and Griggs paired comparison item response theory (IRT) model (1974) and presents procedures for estimating stimulus and person parameters. Monte Carlo simulations show that at least 400 ratings are required to obtain reasonably accurate estimates of the stimulus parameters and their…
Bridging the gaps between non-invasive genetic sampling and population parameter estimation
Francesca Marucco; Luigi Boitani; Daniel H. Pletscher; Michael K. Schwartz
2011-01-01
Reliable estimates of population parameters are necessary for effective management and conservation actions. The use of genetic data for captureÂrecapture (CR) analyses has become an important tool to estimate population parameters for elusive species. Strong emphasis has been placed on the genetic analysis of non-invasive samples, or on the CR analysis; however,...
Estimation of CE–CVM energy parameters from miscibility gap data
Indian Academy of Sciences (India)
Home; Journals; Bulletin of Materials Science; Volume 28; Issue 2. Estimation of CE–CVM energy parameters from miscibility gap data. G Srinivasa Gupta G ... As a starting point, a method has been devised to estimate the values of energy parameters from consolute point (miscibility gap maximum) data. Empirical relations ...
A practical approach to parameter estimation applied to model predicting heart rate regulation
DEFF Research Database (Denmark)
Olufsen, Mette; Ottesen, Johnny T.
2013-01-01
. Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate...
Influence of parameter estimation uncertainty in Kriging: Part 1 - Theoretical Development
Directory of Open Access Journals (Sweden)
E. Todini
2001-01-01
Full Text Available This paper deals with a theoretical approach to assessing the effects of parameter estimation uncertainty both on Kriging estimates and on their estimated error variance. Although a comprehensive treatment of parameter estimation uncertainty is covered by full Bayesian Kriging at the cost of extensive numerical integration, the proposed approach has a wide field of application, given its relative simplicity. The approach is based upon a truncated Taylor expansion approximation and, within the limits of the proposed approximation, the conventional Kriging estimates are shown to be biased for all variograms, the bias depending upon the second order derivatives with respect to the parameters times the variance-covariance matrix of the parameter estimates. A new Maximum Likelihood (ML estimator for semi-variogram parameters in ordinary Kriging, based upon the assumption of a multi-normal distribution of the Kriging cross-validation errors, is introduced as a mean for the estimation of the parameter variance-covariance matrix. Keywords: Kriging, maximum likelihood, parameter estimation, uncertainty
Directory of Open Access Journals (Sweden)
Shaolong Chen
2016-01-01
Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.
Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation
DEFF Research Database (Denmark)
Fyhn, Karsten; Duarte, Marco F.; Jensen, Søren Holdt
2015-01-01
We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non......-negative amplitude parameters to arbitrary complex ones, and (ii) we allow for mismatch between the manifold described by the parameters and its polar approximation. To quantify the improvements afforded by the proposed extensions, we evaluate six algorithms for estimation of parameters in sparse translation......-resolution algorithm. The algorithms studied here provide various tradeoffs between computational complexity, estimation precision, and necessary sampling rate. The work shows that compressive sensing for the class of sparse translation-invariant signals allows for a decrease in sampling rate and that the use of polar...
Bowong, Samuel; Kurths, Jurgen
2010-10-01
We propose a method based on synchronization to identify the parameters and to estimate the underlying variables for an epidemic model from real data. We suggest an adaptive synchronization method based on observer approach with an effective guidance parameter to update rule design only from real data. In order, to validate the identifiability and estimation results, numerical simulations of a tuberculosis (TB) model using real data of the region of Center in Cameroon are performed to estimate the parameters and variables. This study shows that some tools of synchronization of nonlinear systems can help to deal with the parameter and state estimation problem in the field of epidemiology. We exploit the close link between mathematical modelling, structural identifiability analysis, synchronization, and parameter estimation to obtain biological insights into the system modelled.
Energy Technology Data Exchange (ETDEWEB)
Bowong, Samuel, E-mail: sbowong@gmail.co [Laboratory of Applied Mathematics, Department of Mathematics and Computer Science, Faculty of Science, University of Douala, P.O. Box 24157 Douala (Cameroon); Postdam Institute for Climate Impact Research (PIK), Telegraphenberg A 31, 14412 Potsdam (Germany); Kurths, Jurgen [Postdam Institute for Climate Impact Research (PIK), Telegraphenberg A 31, 14412 Potsdam (Germany); Department of Physics, Humboldt Universitat zu Berlin, 12489 Berlin (Germany)
2010-10-04
We propose a method based on synchronization to identify the parameters and to estimate the underlying variables for an epidemic model from real data. We suggest an adaptive synchronization method based on observer approach with an effective guidance parameter to update rule design only from real data. In order, to validate the identifiability and estimation results, numerical simulations of a tuberculosis (TB) model using real data of the region of Center in Cameroon are performed to estimate the parameters and variables. This study shows that some tools of synchronization of nonlinear systems can help to deal with the parameter and state estimation problem in the field of epidemiology. We exploit the close link between mathematical modelling, structural identifiability analysis, synchronization, and parameter estimation to obtain biological insights into the system modelled.
Zayane, Chadia
2014-06-01
In this paper, we address a special case of state and parameter estimation, where the system can be put on a cascade form allowing to estimate the state components and the set of unknown parameters separately. Inspired by the nonlinear Balloon hemodynamic model for functional Magnetic Resonance Imaging problem, we propose a hierarchical approach. The system is divided into two subsystems in cascade. The state and input are first estimated from a noisy measured signal using an adaptive observer. The obtained input is then used to estimate the parameters of a linear system using the modulating functions method. Some numerical results are presented to illustrate the efficiency of the proposed method.
A Monte Carlo Study of Marginal Maximum Likelihood Parameter Estimates for the Graded Model.
Ankenmann, Robert D.; Stone, Clement A.
Effects of test length, sample size, and assumed ability distribution were investigated in a multiple replication Monte Carlo study under the 1-parameter (1P) and 2-parameter (2P) logistic graded model with five score levels. Accuracy and variability of item parameter and ability estimates were examined. Monte Carlo methods were used to evaluate…
Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum
Moghaddam Mohsen Ebrahimi; Jamzad Mansour
2007-01-01
Motion blur is one of the most common causes of image degradation. Restoration of such images is highly dependent on accurate estimation of motion blur parameters. To estimate these parameters, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present a novel algorithm to estimate direction and length of motion blur, using Radon transform and fuzzy set concepts. The most...
Use of timesat to estimate phenological parameters in Northwestern Patagonia
Oddi, Facundo; Minotti, Priscilla; Ghermandi, Luciana; Lasaponara, Rosa
2015-04-01
Under a global change context, ecosystems are receiving high pressure and the ecology science play a key role for monitoring and assessment of natural resources. To achieve an effective resources management to develop an ecosystem functioning knowledge based on spatio-temporal perspective is useful. Satellite imagery periodically capture the spectral response of the earth and remote sensing have been widely utilized as classification and change detection tool making possible evaluate the intra and inter-annual plant dynamics. Vegetation spectral indices (e.g., NDVI) are particularly suitable to study spatio-temporal processes related to plant phenology and remote sensing specific software, such as TIMESAT, has been developed to carry out time series analysis of spectral indexes. We used TIMESAT software applied to series of 25 years of NDVI bi-monthly composites (240 images covering the period 1982-2006) from the NOAA-AVHRR sensor (8 x 8 km) to assessment plant pheonology over 900000 ha of shrubby-grasslands in the Northwestern of Patagonia, Argentina. The study area corresponds to a Mediterranean environment and is part of a gradient defined by a sharp drop west-east in the precipitation regime (600 mm to 280 mm). We fitted the temporal series of NDVI data to double logistic functions by least-squares methods evaluating three seasonality parameters: a) start of growing season, b) growing season length, c) NDVI seasonal integral. According to fitted models by TIMESAT, start average of growing season was the second half of September (± 10 days) with beginnings latest in the east (dryer areas). The average growing season length was 180 days (± 15 days) without a clear spatial trend. The NDVI seasonal integral showed a clear trend of decrease in west-east direction following the precipitation gradient. The temporal and spatial information allows revealing important patterns of ecological interest, which can be of great importance to environmental monitoring. In this
Parameter estimation for fm signals in two stages: non uniform fast ...
African Journals Online (AJOL)
This study deals with estimating the parameters of the received signal of the poly phase in three orders. The process of estimation occurs in two stages: in the first stage, the high orders of the signal are estimated highly accurately using the Non Uniform FFT. The received signal passes through the componential match filter ...
Directory of Open Access Journals (Sweden)
N. K. Sajeevkumar
2014-09-01
Full Text Available In this article, we derived the Best Linear Unbiased Estimator (BLUE of the location parameter of certain distributions with known coefficient of variation by record values. Efficiency comparisons are also made on the proposed estimator with some of the usual estimators. Finally we give a real life data to explain the utility of results developed in this article.
The Impact of Fallible Item Parameter Estimates on Latent Trait Recovery
Cheng, Ying; Yuan, Ke-Hai
2010-01-01
In this paper we propose an upward correction to the standard error (SE) estimation of theta[subscript ML], the maximum likelihood (ML) estimate of the latent trait in item response theory (IRT). More specifically, the upward correction is provided for the SE of theta[subscript ML] when item parameter estimates obtained from an independent pretest…
DEFF Research Database (Denmark)
Ditlevsen, Susanne; Yip, Kay-Pong; Holstein-Rathlou, N.-H.
2005-01-01
by a variety of influences, which change over time (blood pressure, hormone levels, etc.). To estimate the key parameters of the model we have developed a new estimation method based on the oscillatory behavior of the data. The dynamics is characterized by the spectral density, which has been estimated...
Implementing Force-Feedback in a Telesurgery Environment, Using Parameter Estimation
DEFF Research Database (Denmark)
Hansen, Thomas; Henningsen, Claus; Nielsen, Jens
2012-01-01
, the forces in the system have been estimated on the basis of the existing actuators, using parameter estimation techniques. The inevitable time-delays in the network, which imposes challenges in control design, are also estimated and compensated for within the control design. During tests, it has been shown...
Inoue, Yukinori; Yamada, Koji; Morimoto, Shigeo; Sanada, Masayuki
This paper proposes a position sensorless drive system combined with on-line parameter identification for an interior permanent magnet synchronous motor. The accuracy of the position estimation can be improved by the proposed system, in which the motor parameters used for the position estimation are identified according to the operating conditions. First, the influence of the parameter error on the estimation position error is examined from the simulation and experimental results. Next, the characteristics of the sensorless drive system and the performance of parameter identification are shown. The experimental results show that the proposed system can achieve more accurate position estimation than the drive system without the parameter identification for all operating conditions.
Synchronization-based parameter estimation of fractional-order neural networks
Gu, Yajuan; Yu, Yongguang; Wang, Hu
2017-10-01
This paper focuses on the parameter estimation problem of fractional-order neural network. By combining the adaptive control and parameter update law, we generalize the synchronization-based identification method that has been reported in several literatures on identifying unknown parameters of integer-order systems. With this method, parameter identification and synchronization can be achieved simultaneously. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.
Identification and Estimation of Erroneous Transmission Line Parameters Using PMU Measurements
Markos Asprou; Elias Kyriakides
2017-01-01
The accurate knowledge of the transmission line parameters can be beneficial for several monitoring and control applications accommodated in the power system control center. Actually transmission line parameters are stored in the control center databases (assuming that they are time invariant); however, the databases might be obsolete and contain erroneous parameters. This paper proposes a methodology for identifying and estimating the erroneous transmission line parameters using measurements...
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
Energy Technology Data Exchange (ETDEWEB)
Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-11
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Parameter Estimation for an Electric Arc Furnace Model Using Maximum Likelihood
Directory of Open Access Journals (Sweden)
Jesser J. Marulanda-Durango
2012-12-01
Full Text Available In this paper, we present a methodology for estimating the parameters of a model for an electrical arc furnace, by using maximum likelihood estimation. Maximum likelihood estimation is one of the most employed methods for parameter estimation in practical settings. The model for the electrical arc furnace that we consider, takes into account the non-periodic and non-linear variations in the voltage-current characteristic. We use NETLAB, an open source MATLAB® toolbox, for solving a set of non-linear algebraic equations that relate all the parameters to be estimated. Results obtained through simulation of the model in PSCADTM, are contrasted against real measurements taken during the furnance's most critical operating point. We show how the model for the electrical arc furnace, with appropriate parameter tuning, captures with great detail the real voltage and current waveforms generated by the system. Results obtained show a maximum error of 5% for the current's root mean square error.
A robust methodology for kinetic model parameter estimation for biocatalytic reactions
DEFF Research Database (Denmark)
Al-Haque, Naweed; Andrade Santacoloma, Paloma de Gracia; Lima Afonso Neto, Watson
2012-01-01
Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme-catalyzed reactions generally include several...... lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches....... The parameter estimation problem is decomposed into five hierarchical steps, where the solution of each of the steps becomes the input for the subsequent step to achieve the final model with the corresponding regressed parameters. The model is further used for validating its performance and determining...
About accuracy of the discrimination parameter estimation for the dual high-energy method
Osipov, S. P.; Chakhlov, S. V.; Osipov, O. S.; Shtein, A. M.; Strugovtsev, D. V.
2015-04-01
A set of the mathematical formulas to estimate the accuracy of discrimination parameters for two implementations of the dual high energy method - by the effective atomic number and by the level lines is given. The hardware parameters which influenced on the accuracy of the discrimination parameters are stated. The recommendations to form the structure of the high energy X-ray radiation impulses are formulated. To prove the applicability of the proposed procedure there were calculated the statistical errors of the discrimination parameters for the cargo inspection system of the Tomsk polytechnic university on base of the portable betatron MIB-9. The comparison of the experimental estimations and the theoretical ones of the discrimination parameter errors was carried out. It proved the practical applicability of the algorithm to estimate the discrimination parameter errors for the dual high energy method.
Nam, Kanghyun
2015-11-11
This article presents methods for estimating lateral vehicle velocity and tire cornering stiffness, which are key parameters in vehicle dynamics control, using lateral tire force measurements. Lateral tire forces acting on each tire are directly measured by load-sensing hub bearings that were invented and further developed by NSK Ltd. For estimating the lateral vehicle velocity, tire force models considering lateral load transfer effects are used, and a recursive least square algorithm is adapted to identify the lateral vehicle velocity as an unknown parameter. Using the estimated lateral vehicle velocity, tire cornering stiffness, which is an important tire parameter dominating the vehicle's cornering responses, is estimated. For the practical implementation, the cornering stiffness estimation algorithm based on a simple bicycle model is developed and discussed. Finally, proposed estimation algorithms were evaluated using experimental test data.
On Drift Parameter Estimation in Models with Fractional Brownian Motion by Discrete Observations
Directory of Open Access Journals (Sweden)
Yuliya Mishura
2014-06-01
Full Text Available We study a problem of an unknown drift parameter estimation in a stochastic differen- tial equation driven by fractional Brownian motion. We represent the likelihood ratio as a function of the observable process. The form of this representation is in general rather complicated. However, in the simplest case it can be simplified and we can discretize it to establish the a. s. convergence of the discretized version of maximum likelihood estimator to the true value of parameter. We also investigate a non-standard estimator of the drift parameter showing further its strong consistency.
Online Estimation Method for Respiratory Parameters Based on a Pneumatic Model.
Shi, Yan; Niu, Jinglong; Cao, Zhixin; Cai, Maolin; Zhu, Jian; Xu, Weiqing
2016-01-01
Mechanical ventilation is an important method to help people breathe. Respiratory parameters of ventilated patients are usually tracked for pulmonary diagnostics and respiratory treatment assessment. In this paper, to improve the estimation accuracy of respiratory parameters, a pneumatic model for mechanical ventilation was proposed. Furthermore, based on the mathematical model, a recursive least-squares algorithm was adopted to estimate the respiratory parameters. Finally, through experimental and numerical study, it was demonstrated that the proposed estimation method was effective and the method can be used in pulmonary diagnostics and treatment.
A Bayesian approach to parameter and reliability estimation in the Poisson distribution.
Canavos, G. C.
1972-01-01
For life testing procedures, a Bayesian analysis is developed with respect to a random intensity parameter in the Poisson distribution. Bayes estimators are derived for the Poisson parameter and the reliability function based on uniform and gamma prior distributions of that parameter. A Monte Carlo procedure is implemented to make possible an empirical mean-squared error comparison between Bayes and existing minimum variance unbiased, as well as maximum likelihood, estimators. As expected, the Bayes estimators have mean-squared errors that are appreciably smaller than those of the other two.
Directory of Open Access Journals (Sweden)
S. Nie
2011-08-01
Full Text Available The performance of the ensemble Kalman filter (EnKF in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD scenarios, the narrow IPD (NIPD scenario and the wide IPD (WIPD scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method
Estimation of metallurgical parameters of flotation process from froth visual features
Directory of Open Access Journals (Sweden)
Mohammad Massinaei
2015-06-01
Full Text Available The estimation of metallurgical parameters of flotation process from froth visual features is the ultimate goal of a machine vision based control system. In this study, a batch flotation system was operated under different process conditions and metallurgical parameters and froth image data were determined simultaneously. Algorithms have been developed for measuring textural and physical froth features from the captured images. The correlation between the froth features and metallurgical parameters was successfully modeled, using artificial neural networks. It has been shown that the performance parameters of flotation process can be accurately estimated from the extracted image features, which is of great importance for developing automatic control systems.
Energy Technology Data Exchange (ETDEWEB)
Marino, Ines P. [Nonlinear Dynamics and Chaos Group, Departamento de Matematicas y Fisica Aplicadas y Ciencias de la Naturaleza, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid (Spain)]. E-mail: ines.perez@urjc.es; Miguez, Joaquin [Departamento de Teoria de la Senal y Comunicaciones, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganes, Madrid (Spain)]. E-mail: jmiguez@ieee.org
2006-03-06
We address the problem of estimating the unknown parameters of a primary chaotic system that produces an observed time series. These observations are used to drive a secondary system in a way that ensures synchronization when the two systems have identical parameters. We propose a new method to adaptively adjust the parameters in the secondary system until synchronization is achieved. It is based on the gradient-descent optimization of a suitably defined cost function and can be systematically applied to arbitrary systems. We illustrate its application by estimating the complete parameter vector of a Lorenz system.
Estimation of the Shape Parameter of Ged Distribution for a Small Sample Size
Directory of Open Access Journals (Sweden)
Purczyński Jan
2014-06-01
Full Text Available In this paper a new method of estimating the shape parameter of generalized error distribution (GED, called ‘approximated moment method’, was proposed. The following estimators were considered: the one obtained through the maximum likelihood method (MLM, approximated fast estimator (AFE, and approximated moment method (AMM. The quality of estimator was evaluated on the basis of the value of the relative mean square error. Computer simulations were conducted using random number generators for the following shape parameters: s = 0.5, s = 1.0 (Laplace distribution s = 2.0 (Gaussian distribution and s = 3.0.
Loaiciga, Hugo A.; MariñO, Miguel A.
1987-06-01
This work deals with a theoretical analysis of parameter uncertainty in groundwater management models. The importance of adopting classical, Bayesian, or deterministic distribution assumptions on parameters is examined from a mathematical standpoint. In the classical case, the parameters (e.g., hydraulic conductivities or storativities) are assumed fixed (i.e., nonrandom) but unknown. The Bayesian assumption considers the parameters as random entities with some probability distribution. The deterministic case, also called certainty equivalence, assumes that the parameters are fixed and known. Previous work on the inverse problem has emphasized the numerical solution for parameter estimates with the subsequent aim to use them in the simulation of field variables. In this paper, the role of parameter uncertainty (measured by their statistical variability) in groundwater management decisions is investigated. It is shown that the classical, Bayesian, and deterministic assumptions lead to analytically different management solutions. Numerically, the difference between such solutions depends upon the covariance of the parameter estimates. The theoretical analyses of this work show the importance of specifying the proper distributional assumption on groundwater parameters, as well as the need for using efficient and statistically consistent methods to solve the inverse problem. The distributional assumptions on groundwater parameters and the covariance of their sample estimators are shown to be the dominant parameter uncertainty factors affecting groundwater management solutions. An example illustrates the conceptual findings of this work.
Robust parameter estimation for dynamical systems from outlier-corrupted data.
Maier, Corinna; Loos, Carolin; Hasenauer, Jan
2017-03-01
Dynamics of cellular processes are often studied using mechanistic mathematical models. These models possess unknown parameters which are generally estimated from experimental data assuming normally distributed measurement noise. Outlier corruption of datasets often cannot be avoided. These outliers may distort the parameter estimates, resulting in incorrect model predictions. Robust parameter estimation methods are required which provide reliable parameter estimates in the presence of outliers. In this manuscript, we propose and evaluate methods for estimating the parameters of ordinary differential equation models from outlier-corrupted data. As alternatives to the normal distribution as noise distribution, we consider the Laplace, the Huber, the Cauchy and the Student's t distribution. We assess accuracy, robustness and computational efficiency of estimators using these different distribution assumptions. To this end, we consider artificial data of a conversion process, as well as published experimental data for Epo-induced JAK/STAT signaling. We study how well the methods can compensate and discover artificially introduced outliers. Our evaluation reveals that using alternative distributions improves the robustness of parameter estimates. The MATLAB implementation of the likelihood functions using the distribution assumptions is available at Bioinformatics online. jan.hasenauer@helmholtz-muenchen.de. Supplementary material are available at Bioinformatics online.
Directory of Open Access Journals (Sweden)
A.N. Safiullina
2016-06-01
Full Text Available The problem of estimating the parameters m and p of the binomial distribution for a sample having the fixed volume n with the help of the method of moments is considered in this paper. Using the delta method, the joint asymptotic normality of the estimates is established and the parameters of the limit distribution are calculated. The moment estimates of the parameters m and p do not have averages and variance. An explanation is offered for the asymptotic normality parameters in terms of characteristics of the accuracy properties of the estimates. On the basis of the data of statistical modelling, the accuracy properties of the estimates by the delta-method and their modifications which do not have initial defects of the estimates (the values of the estimates of p are below zero and those of m are smaller than the greatest value in the sample are explored. An example of estimating the parameters m and p according to the observations of the number of responses in the experiment with nervous synapse (m is the number of vesicles with acetylcholine in the vicinity of the synapse, p is the probability of acetylcholine release by each vesicle is provided.
“In-Process Type” Dynamic Muskingum Model Parameter Estimation Method
Directory of Open Access Journals (Sweden)
Gang Zhang
2017-11-01
Full Text Available This paper discusses the Muskingum model as a novel parameter estimation method. Sixty representative floods over the past four decades serve as research objects; a linear Muskingum model and Pigeon-inspired optimization (PIO algorithm are used to obtain the parameters of each flood. The proposed “in-process type” dynamic parameter estimation (IP-DPE method is used to establish the characteristic attributes set of 50 floods. The characteristic attributes set refers to a set of parameters that could describe the shape, magnitude, and duration of the flood before flood peak; they are the input, whereas parameters K and x of each flood are the output to establish a Neural Network model. Then we input flood characteristic attributes to obtain flood parameters when estimating flood parameters practically. Ten floods were used to test the parameter estimation and flood routing efficacy. The results show that the IP-DPE method can quickly identify parameters and facilitate accurate river flood forecasting.
Estimation of Data Uncertainty Adjustment Parameters for Multivariate Earth Rotation Series
Sung, Li-yu; Steppe, J. Alan
1994-01-01
We have developed a maximum likelihood method to estimate a set of data uncertainty adjustment parameters, iccluding scaling factors and additive variances and covariances, for multivariate Earth rotation series.
Digital Repository Service at National Institute of Oceanography (India)
Chakraborty, B.; Schenke, H.W.; Kodagali, V.N.; Hagen, R.
parameters employing Helmholtz-Kirchhoff theory. Beyond 20 degrees incidence angles, an application of Rayleigh-Rice theory is made by using a necessary splicing technique (combining both of the theories at 20 degrees incidence angle).The estimated interface...
Methodology to estimate parameters of an excitation system based on experimental conditions
Energy Technology Data Exchange (ETDEWEB)
Saavedra-Montes, A.J. [Carrera 80 No 65-223, Bloque M8 oficina 113, Escuela de Mecatronica, Universidad Nacional de Colombia, Medellin (Colombia); Calle 13 No 100-00, Escuela de Ingenieria Electrica y Electronica, Universidad del Valle, Cali, Valle (Colombia); Ramirez-Scarpetta, J.M. [Calle 13 No 100-00, Escuela de Ingenieria Electrica y Electronica, Universidad del Valle, Cali, Valle (Colombia); Malik, O.P. [2500 University Drive N.W., Electrical and Computer Engineering Department, University of Calgary, Calgary, Alberta (Canada)
2011-01-15
A methodology to estimate the parameters of a potential-source controlled rectifier excitation system model is presented in this paper. The proposed parameter estimation methodology is based on the characteristics of the excitation system. A comparison of two pseudo random binary signals, two sampling periods for each one, and three estimation algorithms is also presented. Simulation results from an excitation control system model and experimental results from an excitation system of a power laboratory setup are obtained. To apply the proposed methodology, the excitation system parameters are identified at two different levels of the generator saturation curve. The results show that it is possible to estimate the parameters of the standard model of an excitation system, recording two signals and the system operating in closed loop with the generator. The normalized sum of squared error obtained with experimental data is below 10%, and with simulation data is below 5%. (author)
A model-based initial guess for estimating parameters in systems of ordinary differential equations.
Dattner, Itai
2015-12-01
The inverse problem of parameter estimation from noisy observations is a major challenge in statistical inference for dynamical systems. Parameter estimation is usually carried out by optimizing some criterion function over the parameter space. Unless the optimization process starts with a good initial guess, the estimation may take an unreasonable amount of time, and may converge to local solutions, if at all. In this article, we introduce a novel technique for generating good initial guesses that can be used by any estimation method. We focus on the fairly general and often applied class of systems linear in the parameters. The new methodology bypasses numerical integration and can handle partially observed systems. We illustrate the performance of the method using simulations and apply it to real data. © 2015, The International Biometric Society.
PEIXOTO, F. C.; Medeiros,J. L.
1999-01-01
Fragmentation kinetics is employed to model a continuous reactive mixture. An explicit solution is found and experimental data on the catalytic cracking of a mixture of alkanes are used for deactivation and kinetic parameter estimation.
Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine
National Research Council Canada - National Science Library
Xiaodong Chang; Jinquan Huang; Feng Lu
2017-01-01
In this paper the problem of health parameter estimation in an aero-engine is investigated by using an unknown input observer-based methodology, implemented by a second-order sliding mode observer (SOSMO...
Online Estimation of Model Parameters of Lithium-Ion Battery Using the Cubature Kalman Filter
Tian, Yong; Yan, Rusheng; Tian, Jindong; Zhou, Shijie; Hu, Chao
2017-11-01
Online estimation of state variables, including state-of-charge (SOC), state-of-energy (SOE) and state-of-health (SOH) is greatly crucial for the operation safety of lithium-ion battery. In order to improve estimation accuracy of these state variables, a precise battery model needs to be established. As the lithium-ion battery is a nonlinear time-varying system, the model parameters significantly vary with many factors, such as ambient temperature, discharge rate and depth of discharge, etc. This paper presents an online estimation method of model parameters for lithium-ion battery based on the cubature Kalman filter. The commonly used first-order resistor-capacitor equivalent circuit model is selected as the battery model, based on which the model parameters are estimated online. Experimental results show that the presented method can accurately track the parameters variation at different scenarios.
A Proposed General Method for Parameter Estimation of Noise Corrupted Oscillator Systems
OBrien Jr, Francis J.; Johnnie, Nathan; Maloney, Susan; Ross, Aimee
2012-01-01
This paper provides a proposed means to estimate parameters of noise corrupted oscillator systems. An application for a submarine combat control systems (CCS) rack is described as exemplary of the method.
A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence
Grauer, Jared A.; Morelli, Eugene A.
2015-01-01
A new formulation of the filter-error method for estimating aerodynamic parameters in nonlinear aircraft dynamic models during turbulence was developed and demonstrated. The approach uses an estimate of the measurement noise covariance to identify the model parameters, their uncertainties, and the process noise covariance, in a relaxation method analogous to the output-error method. Prior information on the model parameters and uncertainties can be supplied, and a post-estimation correction to the uncertainty was included to account for colored residuals not considered in the theory. No tuning parameters, needing adjustment by the analyst, are used in the estimation. The method was demonstrated in simulation using the NASA Generic Transport Model, then applied to the subscale T-2 jet-engine transport aircraft flight. Modeling results in different levels of turbulence were compared with results from time-domain output error and frequency- domain equation error methods to demonstrate the effectiveness of the approach.
Directory of Open Access Journals (Sweden)
PEIXOTO F. C.
1999-01-01
Full Text Available Fragmentation kinetics is employed to model a continuous reactive mixture. An explicit solution is found and experimental data on the catalytic cracking of a mixture of alkanes are used for deactivation and kinetic parameter estimation.
A method for estimating both the solubility parameters and molar volumes of liquids
Fedors, R. F.
1974-01-01
Development of an indirect method of estimating the solubility parameter of high molecular weight polymers. The proposed method of estimating the solubility parameter, like Small's method, is based on group additive constants, but is believed to be superior to Small's method for two reasons: (1) the contribution of a much larger number of functional groups have been evaluated, and (2) the method requires only a knowledge of structural formula of the compound.
Directory of Open Access Journals (Sweden)
Mohamed Mahmoud Mohamed
2016-09-01
Full Text Available In this paper we develop approximate Bayes estimators of the parameters,reliability, and hazard rate functions of the Logistic distribution by using Lindley’sapproximation, based on progressively type-II censoring samples. Noninformativeprior distributions are used for the parameters. Quadratic, linexand general Entropy loss functions are used. The statistical performances of theBayes estimates relative to quadratic, linex and general entropy loss functionsare compared to those of the maximum likelihood based on simulation study.
Optimal experiment selection for parameter estimation in biological differential equation models
Directory of Open Access Journals (Sweden)
Transtrum Mark K
2012-07-01
Full Text Available Abstract Background Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore the question to what extent parameters can be efficiently estimated by appropriate experimental selection. Results A minimization formulation is used to find the parameter values that best fit the experiment data. When the data is insufficient, the minimization problem often has many local minima that fit the data reasonably well. We show that selecting a new experiment based on the local Fisher Information of one local minimum generates additional data that allows one to successfully discriminate among the many local minima. The parameters can be estimated to high accuracy by iteratively performing minimization and experiment selection. We show that the experiment choices are roughly independent of which local minima is used to calculate the local Fisher Information. Conclusions We show that by an appropriate choice of experiments, one can, in principle, efficiently and accurately estimate all the parameters of gene regulatory network. In addition, we demonstrate that appropriate experiment selection can also allow one to restrict model predictions without constraining the parameters using many fewer experiments. We suggest that predicting model behaviors and inferring parameters represent two different approaches to model calibration with different requirements on data and experimental cost.
The Chameleonic Behavior of Ionic Liquids and its Impact on the Solubility Parameters Estimation
DEFF Research Database (Denmark)
Batista, Marta; Neves, Catarina S; Carvalho, Pedro Jorge
2011-01-01
The possibility to develop a solubility parameter scale, with the purpose of predicting the performance and help the selection of ILs, is here evaluated. For the estimation of solubility parameters infinite dilution activity coefficient data is used. The results allowed the identification of a cu...
Bayesian estimates of equation system parameters, An application of integration by Monte Carlo
T. Kloek (Teun); H.K. van Dijk (Herman)
1978-01-01
textabstractMonte Carlo (MC) is used to draw parameter values from a distribution defined on the structural parameter space of an equation system. Making use of the prior density, the likelihood, and Bayes' Theorem it is possible to estimate posterior moments of both structural and reduced form
Book review: Estimation of parameters for animal populations: A primer for the rest of us
Post van der Burg, Max
2016-01-01
No abstract available.Estimation of Parameters for Animal Populations: A Primer for the Rest of Us. Larkin A. Powell and George A. Gale. 2015. Caught Napping Publications, Lincoln, Nebraska, USA. 239 pages. (http://larkinpowell.wixsite.com/larkinpowell/estimationof-parameters-for-animal-pop). ISBN: 978-329-06151-4.
Dynamics of a scrapie outbreak in a flock of Romanov sheep-estimation of transmission parameters
Hagenaars, T.H.J.; Donelly, C.A.; Ferguson, N.M.; Anderson, R.M.
2003-01-01
Knowledge of epidemiological mechanisms and parameters underlying scrapie transmission in sheep flocks remains very limited at present. Here we introduce a method for fitting stochastic transmission models to outbreak data to estimate bounds on key transmission parameters. We apply this method to
Directory of Open Access Journals (Sweden)
Y. H. Lee
2006-12-01
Full Text Available In this study, optimal parameter estimations are performed for both physical and computational parameters in a mesoscale meteorological model, and their impacts on the quantitative precipitation forecasting (QPF are assessed for a heavy rainfall case occurred at the Korean Peninsula in June 2005. Experiments are carried out using the PSU/NCAR MM5 model and the genetic algorithm (GA for two parameters: the reduction rate of the convective available potential energy in the Kain-Fritsch (KF scheme for cumulus parameterization, and the Asselin filter parameter for numerical stability. The fitness function is defined based on a QPF skill score. It turns out that each optimized parameter significantly improves the QPF skill. Such improvement is maximized when the two optimized parameters are used simultaneously. Our results indicate that optimizations of computational parameters as well as physical parameters and their adequate applications are essential in improving model performance.
Wu, Yi-Fang
2015-01-01
Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and…
DEFF Research Database (Denmark)
Ottosen, Thor Bjørn; Ketzel, Matthias; Skov, Henrik
2016-01-01
Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street...... applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified. © 2016 Elsevier Ltd...
A novel model of photovoltaic modules for parameter estimation and thermodynamic assessment
Pınar Mert Cuce; Erdem Cuce
2011-01-01
This paper presents a novel model to evaluate the electrical performance of a silicon photovoltaic (PV) module with respect to changes in main environmental parameters such as temperature and illumination intensity. A simple one-diode model is proposed to estimate the electrical parameters of PV module considering the series resistance and shunt conductance. Effects of PV module parameters on current–voltage characteristic curve are investigated. The proposed model also makes a thermodynamic ...
About accuracy of the discrimination parameter estimation for the dual high-energy method
Osipov, Sergey Pavlovich; Chakhlov, Sergey Vladimirovich; Осипов, Олег Сергеевич; Stein, Aleksandr Mikhailovich; Strugovtsev, D. V.
2015-01-01
A set of the mathematical formulas to estimate the accuracy of discrimination parameters for two implementations of the dual high energy method - by the effective atomic number and by the level lines is given. The hardware parameters which influenced on the accuracy of the discrimination parameters are stated. The recommendations to form the structure of the high energy X-ray radiation impulses are formulated. To prove the applicability of the proposed procedure there were calculated the stat...
Casabianca, Jodi M.; Lewis, Charles
2015-01-01
Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation…
Michalak, Anna M.; Hirsch, Adam; Bruhwiler, Lori; Gurney, Kevin R.; Peters, Wouter; Tans, Pieter P.
2005-01-01
This paper introduces a Maximum Likelihood (ML) approach for estimating the statistical parameters required for the covariance matrices used in the solution of Bayesian inverse problems aimed at estimating surface fluxes of atmospheric trace gases. The method offers an objective methodology for
Evaluation of Structural Equation Mixture Models: Parameter Estimates and Correct Class Assignment
Tueller, Stephen; Lubke, Gitta
2010-01-01
Structural equation mixture models (SEMMs) are latent class models that permit the estimation of a structural equation model within each class. Fitting SEMMs is illustrated using data from 1 wave of the Notre Dame Longitudinal Study of Aging. Based on the model used in the illustration, SEMM parameter estimation and correct class assignment are…
A Note on Parameter Estimation for Lazarsfeld's Latent Class Model Using the EM Algorithm.
Everitt, B. S.
1984-01-01
Latent class analysis is formulated as a problem of estimating parameters in a finite mixture distribution. The EM algorithm is used to find the maximum likelihood estimates, and the case of categorical variables with more than two categories is considered. (Author)
Recursive Estimation of π-Line Parameters for Electric Power Distribution Grids
DEFF Research Database (Denmark)
Prostejovsky, Alexander; Gehrke, Oliver; Kosek, Anna Magdalena
2016-01-01
Electrical models of power distribution grids are used in applications such as state estimation and Optimal Power Flow (OPF), the reliability of which depends on the accuracy of the model. This work presents an approach for estimating distribution line parameters from Remote Terminal Unit (RTU...
Chen, Chunhang; 陳, 春航
1995-01-01
The smoothing parameters in the Holt-Winters seasonal forecasting method are often estimated by minimizing the mean square error of one-step forecasts using the sample. In this note we show that such an estimator holds asymptotic normality for some stochastic processes.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate......In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...
A comparison of parameter estimations of the Poisson-generalised Lindley distribution
Denthet, Sunthree
2017-11-01
In this paper, the Poisson-generalised Lindley distribution is presented. It is obtained by mixing the Poisson distribution with a generalised Lindley distribution. This distribution is an alternative distribution for count data with over-distribution. We apply two methods of parameter estimation, maximum likelihood estimation and method of moment, to estimate the parameters. The Monte Carlo simulation study is conducted for efficiency comparison between two methods of estimation based on root of mean squared error. The study exposes that method of moment is highly efficient with maximum likelihood estimation when the model is decreasing or bimodal model. Finally, the proposed distribution is applied to real data sets,but the result based on p-value of the discrete Anderson-Daring test show that maximum likelihood estimation can be hight efficiency for fitting data set.
Covariance estimation in Terms of Stokes Parameters iwth Application to Vector Sensor Imaging
2016-12-15
vector sensor imaging problem: estimating the magnitude, polarization, and direction of plane wave sources from a sample covariance matrix of vector mea...Covariance estimation in terms of Stokes parameters with application to vector sensor imaging Ryan Volz∗, Mary Knapp†, Frank D. Lind∗, Frank C. Robey...Lincoln Laboratory, Lexington, MA Abstract— Vector sensor imaging presents a challeng- ing problem in covariance estimation when allowing arbitrarily
The performance of simulated annealing in parameter estimation for vapor-liquid equilibrium modeling
Directory of Open Access Journals (Sweden)
A. Bonilla-Petriciolet
2007-03-01
Full Text Available In this paper we report the application and evaluation of the simulated annealing (SA optimization method in parameter estimation for vapor-liquid equilibrium (VLE modeling. We tested this optimization method using the classical least squares and error-in-variable approaches. The reliability and efficiency of the data-fitting procedure are also considered using different values for algorithm parameters of the SA method. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. However, in difficult problems it still can converge to local optimums of the objective function.
Nonlinear Adaptive Descriptor Observer for the Joint States and Parameters Estimation
2016-08-29
In this note, the joint state and parameters estimation problem for nonlinear multi-input multi-output descriptor systems is considered. Asymptotic convergence of the adaptive descriptor observer is established by a sufficient set of linear matrix inequalities for the noise-free systems. The noise corrupted systems are also considered and it is shown that the state and parameters estimation errors are bounded for bounded noises. In addition, if the noises are bounded and have zero mean, then the estimation errors asymptotically converge to zero in the mean. The performance of the proposed adaptive observer is illustrated by a numerical example.
A framework for scalable parameter estimation of gene circuit models using structural information.
Kuwahara, Hiroyuki; Fan, Ming; Wang, Suojin; Gao, Xin
2013-07-01
Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. http://sfb.kaust.edu.sa/Pages/Software.aspx. Supplementary data are available at Bioinformatics online.
O'Shaughnessy, Richard; Blackman, Jonathan; Field, Scott E.
2017-07-01
The recent direct observation of gravitational waves has further emphasized the desire for fast, low-cost, and accurate methods to infer the parameters of gravitational wave sources. Due to expense in waveform generation and data handling, the cost of evaluating the likelihood function limits the computational performance of these calculations. Building on recently developed surrogate models and a novel parameter estimation pipeline, we show how to quickly generate the likelihood function as an analytic, closed-form expression. Using a straightforward variant of a production-scale parameter estimation code, we demonstrate our method using surrogate models of effective-one-body and numerical relativity waveforms. Our study is the first time these models have been used for parameter estimation and one of the first ever parameter estimation calculations with multi-modal numerical relativity waveforms, which include all \\ell ≤slant 4 modes. Our grid-free method enables rapid parameter estimation for any waveform with a suitable reduced-order model. The methods described in this paper may also find use in other data analysis studies, such as vetting coincident events or the computation of the coalescing-compact-binary detection statistic.
Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics
Kukreja, Sunil L.; Boyle, Richard D.
2014-01-01
Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.
Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.
Rumschinski, Philipp; Borchers, Steffen; Bosio, Sandro; Weismantel, Robert; Findeisen, Rolf
2010-05-25
Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.
A framework for scalable parameter estimation of gene circuit models using structural information
Kuwahara, Hiroyuki
2013-06-21
Motivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation. Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems. The Author 2013.
Energy Technology Data Exchange (ETDEWEB)
Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA
2016-10-01
The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.
Analysis of the earthquake data and estimation of source parameters in the Kyungsang basin
Energy Technology Data Exchange (ETDEWEB)
Seo, Jeong-Moon; Lee, Jun-Hee [Korea Atomic Energy Research Institute, Taejeon (Korea)
2000-04-01
The purpose of the present study is to determine the response spectrum for the Korean Peninsula and estimate the seismic source parameters and analyze and simulate the ground motion adequately from the seismic characteristics of Korean Peninsula and compare this with the real data. The estimated seismic source parameters such as apparent seismic stress drop is somewhat unstable because the data are insufficient. When the instrumental earthquake data were continuously accumulated in the future, the modification of these parameters may be developed. Although equations presented in this report are derived from the limited data, they can be utilized both in seismology and earthquake engineering. Finally, predictive equations may be given in terms of magnitude and hypocentral distances using these parameters. The estimation of the predictive equation constructed from the simulation is the object of further study. 34 refs., 27 figs., 10 tabs. (Author)
Dynamic State Estimation and Parameter Calibration of DFIG based on Ensemble Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Fan, Rui; Huang, Zhenyu; Wang, Shaobu; Diao, Ruisheng; Meng, Da
2015-07-30
With the growing interest in the application of wind energy, doubly fed induction generator (DFIG) plays an essential role in the industry nowadays. To deal with the increasing stochastic variations introduced by intermittent wind resource and responsive loads, dynamic state estimation (DSE) are introduced in any power system associated with DFIGs. However, sometimes this dynamic analysis canould not work because the parameters of DFIGs are not accurate enough. To solve the problem, an ensemble Kalman filter (EnKF) method is proposed for the state estimation and parameter calibration tasks. In this paper, a DFIG is modeled and implemented with the EnKF method. Sensitivity analysis is demonstrated regarding the measurement noise, initial state errors and parameter errors. The results indicate this EnKF method has a robust performance on the state estimation and parameter calibration of DFIGs.
Closed-Form Formulas for Estimation of Kinetic Parameters in One- and Two-Compartment Models
Energy Technology Data Exchange (ETDEWEB)
Zeng, Gengsheng [Univ. of Utah, Salt Lake City, UT (United States); Kadrmas, Dan [Univ. of Utah, Salt Lake City, UT (United States); Gullberg, Grant [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2011-05-24
.Nowadays the most reliable approach to estimate the kinetic parameters is to minimize an objective function which is essentially the distance between the measured data and the model generated pseudo data. Due to the highly non-linear nature of the model, common optimization algorithms usually fail to find the true minimum. A brute-force search method sometimes must be used to find the true minimum. This paper attempts to use the Laplace transform and Z-transform methods to derive closed-form formulas for kinetic parameter estimation problems, which assume one- or two-compartment models. The proposed method is computationally efficient and its solution is unique. When data sampling interval is small, the proposed method is able to accurately estimate the kinetic parameter; when the interval is larger, the proposed method fails to give a meaningful estimate. The one-compartment method is more robust than the two-compartment method.
Monopulse joint parameter estimation of multiple unresolved targets within the radar beam
Yuan, Hui; Wang, Chunyang; An, Lei; Li, Xin
2017-06-01
Aiming at the problem of the parameter estimation of multiple unresolved targets within the radar beam, using the joint bin processing model, a method of jointly estimating the number and the position of the targets is proposed based on reversible jump Markov Chain Monte Carlo (RJ-MCMC). Reasonable assumptions of the prior distributions and Bayesian theory are adopted to obtain the posterior probability density function of the estimated parameters from the conditional likelihood function of the observation, and then the acceptance ratios of the birth, death and update moves are given. During the update move, a hybrid Metropolis-Hastings (MH) sampling algorithm is used to make a better exploration of the parameter space. The simulation results show that this new method outperforms the method of ML-MLD [11] proposed by X.Zhang for similar estimation accuracy is achieved while fewer sub-pulses are needed.
Directory of Open Access Journals (Sweden)
Marcus Henrique Victor Júnior
Full Text Available Abstract Introduction: This work concerns the assessment of a novel system for mechanical ventilation and a parameter estimation method in a bench test. The tested system was based on a commercial mechanical ventilator and a personal computer. A computational routine was developed do drive the mechanical ventilator and a parameter estimation method was utilized to estimate positive end-expiratory pressure, resistance and compliance of the artificial respiratory system. Methods The computational routine was responsible for establishing connections between devices and controlling them. Parameters such as tidal volume, respiratory rate and others can be set for standard and noisy ventilation regimes. Ventilation tests were performed directly varying parameters in the system. Readings from a calibrated measuring device were the basis for analysis. Adopting a first-order linear model, the parameters could be estimated and the outcomes statistically analysed. Results Data acquisition was effective in terms of sample frequency and low noise content. After filtering, cycle detection and estimation took place. Statistics of median, mean and standard deviation were calculated, showing consistent matching with adjusted values. Changes in positive end-expiratory pressure statistically imply changes in compliance, but not the opposite. Conclusion The developed system was satisfactory in terms of clinical parameters. Statistics exhibited consistent relations between adjusted and estimated values, besides precision of the measurements. The system is expected to be used in animals, with a view to better understand the benefits of noisy ventilation, by evaluating the estimated parameters and performing cross relations among blood gas, ultrasonography and electrical impedance tomography.
Parameter estimation and determinability analysis applied to Drosophila gap gene circuits
Directory of Open Access Journals (Sweden)
Jaeger Johannes
2008-09-01
Full Text Available Abstract Background Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. Results In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. Conclusion Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous.
Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2017-01-01
Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the
Parameter estimation and sensitivity analysis for a mathematical model with time delays of leukemia
Cândea, Doina; Halanay, Andrei; Rǎdulescu, Rodica; Tǎlmaci, Rodica
2017-01-01
We consider a system of nonlinear delay differential equations that describes the interaction between three competing cell populations: healthy, leukemic and anti-leukemia T cells involved in Chronic Myeloid Leukemia (CML) under treatment with Imatinib. The aim of this work is to establish which model parameters are the most important in the success or failure of leukemia remission under treatment using a sensitivity analysis of the model parameters. For the most significant parameters of the model which affect the evolution of CML disease during Imatinib treatment we try to estimate the realistic values using some experimental data. For these parameters, steady states are calculated and their stability is analyzed and biologically interpreted.
Applying fuzzy logic to estimate the parameters of the length-weight relationship.
Bitar, S D; Campos, C P; Freitas, C E C
2016-05-03
We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system.
Schoups, Gerrit; Vrugt, Jasper A.
2010-05-01
Estimation of parameter and predictive uncertainty of hydrologic models usually relies on the assumption of additive residual errors that are independent and identically distributed according to a normal distribution with a mean of zero and a constant variance. Here, we investigate to what extent estimates of parameter and predictive uncertainty are affected when these assumptions are relaxed. Parameter and predictive uncertainty are estimated by Monte Carlo Markov Chain sampling from a generalized likelihood function that accounts for correlation, heteroscedasticity, and non-normality of residual errors. Application to rainfall-runoff modeling using daily data from a humid basin reveals that: (i) residual errors are much better described by a heteroscedastic, first-order auto-correlated error model with a Laplacian density characterized by heavier tails than a Gaussian density, and (ii) proper representation of the statistical distribution of residual errors yields tighter predictive uncertainty bands and more physically realistic parameter estimates that are less sensitive to the particular time period used for inference. The latter is especially useful for regionalization and extrapolation of parameter values to ungauged basins. Application to daily rainfall-runoff data from a semi-arid basin shows that allowing skew in the error distribution yields improved estimates of predictive uncertainty when flows are close to zero.
Bao, Xingxian; Cao, Aixia; Zhang, Jing
2016-07-01
Modal parameters estimation plays an important role for structural health monitoring. Accurately estimating the modal parameters of structures is more challenging as the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of solving the partially described inverse singular value problem (PDISVP) combined with the complex exponential (CE) method to estimate the modal parameters. The PDISVP solving method is to reconstruct an L2-norm optimized (filtered) data matrix from the measured (noisy) data matrix, when the prescribed data constraints are one or several sets of singular triplets of the matrix. The measured data matrix is Hankel structured, which is constructed based on the measured impulse response function (IRF). The reconstructed matrix must maintain the Hankel structure, and be lowered in rank as well. Once the filtered IRF is obtained, the CE method can be applied to extract the modal parameters. Two physical experiments, including a steel cantilever beam with 10 accelerometers mounted, and a steel plate with 30 accelerometers mounted, excited by an impulsive load, respectively, are investigated to test the applicability of the proposed scheme. In addition, the consistency diagram is proposed to exam the agreement among the modal parameters estimated from those different accelerometers. Results indicate that the PDISVP-CE method can significantly remove noise from measured signals and accurately estimate the modal frequencies and damping ratios.
Ait-El-Fquih, Boujemaa
2016-08-12
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.
DYNAMIC PARAMETERS ESTIMATION OF INTERFEROMETRIC SIGNALS BASED ON SEQUENTIAL MONTE CARLO METHOD
Directory of Open Access Journals (Sweden)
M. A. Volynsky
2014-05-01
Full Text Available The paper deals with sequential Monte Carlo method applied to problem of interferometric signals parameters estimation. The method is based on the statistical approximation of the posterior probability density distribution of parameters. Detailed description of the algorithm is given. The possibility of using the residual minimum between prediction and observation as a criterion for the selection of multitude elements generated at each algorithm step is shown. Analysis of input parameters influence on performance of the algorithm has been conducted. It was found that the standard deviation of the amplitude estimation error for typical signals is about 10% of the maximum amplitude value. The phase estimation error was shown to have a normal distribution. Analysis of the algorithm characteristics depending on input parameters is done. In particular, the influence analysis for a number of selected vectors of parameters on evaluation results is carried out. On the basis of simulation results for the considered class of signals, it is recommended to select 30% of the generated vectors number. The increase of the generated vectors number over 150 does not give significant improvement of the obtained estimates quality. The sequential Monte Carlo method is recommended for usage in dynamic processing of interferometric signals for the cases when high immunity is required to non-linear changes of signal parameters and influence of random noise.
Energy Technology Data Exchange (ETDEWEB)
Donskoi, E.; McElwain, D.L.S.; Wibberley, L.J. [Queensland University of Technology, Brisbane, Qld. (Australia). School of Mathematical Science
2003-04-01
The published approaches to the mathematical modeling of rates of reduction, of coal gasification, and of devolatilization of coal in iron/ore coal composites are reviewed and critically analyzed. The effect of different parameters on the overall process is discussed. The concepts of a local rate of reduction and gasification and an integrated rate of reduction are introduced, and the rate-controlling steps in each are reviewed. Current approaches to modeling coal pyrolysis are also described. This review, with the estimates, data, and analysis related to modeling rates of reduction, gasification, and pyrolysis, should prove useful to researchers developing models of coal-based iron ore reduction and related processes.
Estimating Parameters in Physical Models through Bayesian Inversion: A Complete Example
Allmaras, Moritz
2013-02-07
All mathematical models of real-world phenomena contain parameters that need to be estimated from measurements, either for realistic predictions or simply to understand the characteristics of the model. Bayesian statistics provides a framework for parameter estimation in which uncertainties about models and measurements are translated into uncertainties in estimates of parameters. This paper provides a simple, step-by-step example-starting from a physical experiment and going through all of the mathematics-to explain the use of Bayesian techniques for estimating the coefficients of gravity and air friction in the equations describing a falling body. In the experiment we dropped an object from a known height and recorded the free fall using a video camera. The video recording was analyzed frame by frame to obtain the distance the body had fallen as a function of time, including measures of uncertainty in our data that we describe as probability densities. We explain the decisions behind the various choices of probability distributions and relate them to observed phenomena. Our measured data are then combined with a mathematical model of a falling body to obtain probability densities on the space of parameters we seek to estimate. We interpret these results and discuss sources of errors in our estimation procedure. © 2013 Society for Industrial and Applied Mathematics.
Nonlinear systems time-varying parameter estimation: Application to induction motors
Energy Technology Data Exchange (ETDEWEB)
Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, IUT FOTSO Victor, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Ahmed-Ali, Tarek [Ecole Nationale Superieure des Ingenieurs des Etudes et Techniques d' Armement (ENSIETA), 2 Rue Francois Verny, 29806 Brest Cedex 9 (France); Lamnabhi-Lagarrigue, F. [Laboratoire des Signaux et Systemes (L2S), C.N.R.S-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)
2008-11-15
In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines. (author)
A Novel Parameter Estimation Method for Muskingum Model Using New Newton-Type Trust Region Algorithm
Directory of Open Access Journals (Sweden)
Zhou Sheng
2014-01-01
Full Text Available Parameters estimation of Muskingum model is very significative in both exploitation and utilization of water resources and hydrological forecasting. The optimal results of parameters directly affect the accuracy of flood forecasting. This paper considers the parameters estimation problem of Muskingum model from the following two aspects. Firstly, based on the general trapezoid formulas, a class of new discretization methods including a parameter θ to approximate Muskingum model is presented. The accuracy of these methods is second-order, when θ≠1/3. Particularly, if we choose θ=1/3, the accuracy of the presented method can be improved to third-order. Secondly, according to the Newton-type trust region algorithm, a new Newton-type trust region algorithm is given to obtain the parameters of Muskingum model. This method can avoid high dependence on the initial parameters. The average absolute errors (AAE and the average relative errors (ARE of the proposed algorithm of parameters estimation for Muskingum model are 8.208122 and 2.462438%, respectively, where θ=1/3. It is shown from these results that the presented algorithm has higher forecasting accuracy and wider practicability than other methods.
Fienen, M.; Hunt, R.; Krabbenhoft, D.; Clemo, T.
2009-01-01
Flow path delineation is a valuable tool for interpreting the subsurface hydrogeochemical environment. Different types of data, such as groundwater flow and transport, inform different aspects of hydrogeologie parameter values (hydraulic conductivity in this case) which, in turn, determine flow paths. This work combines flow and transport information to estimate a unified set of hydrogeologic parameters using the Bayesian geostatistical inverse approach. Parameter flexibility is allowed by using a highly parameterized approach with the level of complexity informed by the data. Despite the effort to adhere to the ideal of minimal a priori structure imposed on the problem, extreme contrasts in parameters can result in the need to censor correlation across hydrostratigraphic bounding surfaces. These partitions segregate parameters into faci??s associations. With an iterative approach in which partitions are based on inspection of initial estimates, flow path interpretation is progressively refined through the inclusion of more types of data. Head observations, stable oxygen isotopes (18O/16O) ratios), and tritium are all used to progressively refine flow path delineation on an isthmus between two lakes in the Trout Lake watershed, northern Wisconsin, United States. Despite allowing significant parameter freedom by estimating many distributed parameter values, a smooth field is obtained. Copyright 2009 by the American Geophysical Union.
An improved method to estimate the equivalent circuit parameters in DSSCs
Energy Technology Data Exchange (ETDEWEB)
Hanmin, Tian; Xiaobo, Zhang; Shikui, Yuan; Xiangyan, Wang; Zhipeng, Tian; Bin, Liu [Department of Materials Science and Engineering, Nanjing University, Nanjing 210093 (China); Ying, Wang [Eco-Materials and Renewable Energy Research Center (ERERC), Department of Physics, Nanjing University, 22 Hankou Road, Nanjing 210093 (China); Tao, Yu; Zhigang, Zou [National Laboratory of Solid State Microstructures, Nanjing 210093 (China)
2009-05-15
In this report, an improved method to estimate the equivalent circuit parameters in the dye-sensitized solar cells (DSSCs) is introduced. It is founded that, several different groups of values of equivalent circuit parameters can fit well to the same experiment-measured I-V curve. Furthermore, the gap between some parameter values in those different groups is so large that it reaches up to several orders of magnitude. To eliminate this uncertainty of parameter estimation, an improved method, which based on both the extention of measured range and the computer simulation of current-voltage (I-V) characteristic curves, is proposed to ensure uniquely the values of DSSCs equivalent circuit parameters. A series of I-V curves which derived from the estimated parameters by this improved method fit well to the corresponding experiment-measured I-V curves. The results indicate that, there exclusively exists one group of parameter values for a special DSSCs equivalent circuit, thus demonstrating the validity of the improved method proposed in this work. (author)
Finch, Holmes; Edwards, Julianne M.
2016-01-01
Standard approaches for estimating item response theory (IRT) model parameters generally work under the assumption that the latent trait being measured by a set of items follows the normal distribution. Estimation of IRT parameters in the presence of nonnormal latent traits has been shown to generate biased person and item parameter estimates. A…
Estimating demographic parameters using a combination of known-fate and open N-mixture models
Schmidt, Joshua H.; Johnson, Devin S.; Lindberg, Mark S.; Adams, Layne G.
2015-01-01
Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark–resight data sets. We provide implementations in both the BUGS language and an R package.
Improvement of the PEST parameter estimation algorithm through Extended Kalman Filtering
Goegebeur, Maarten; Pauwels, Valentijn R. N.
2007-04-01
SummaryDuring the last decades, a number of methods have been developed for the estimation of hydrologic model parameters. One frequently used and relatively simple algorithm is the parameter estimation (PEST) method. A close examination of this algorithm shows that it is very similar to the Extended Kalman Filter (EKF). The differences between the methods are caused by the derivation of the algorithms: the EKF is derived through a minimization of the square difference between the true and the estimated model state, while PEST has been derived through a minimization of an objective function related to, but not equal to, the root mean square error between the model results and the observations. The objective of this paper is to analyze the performance of these two algorithms. A synthetic-data experiment has been developed for this purpose. It has been found that under high observation errors and/or temporally sparse observations the EKF can lead to a stable parameter estimation, while it is possible that under the same circumstances PEST does not yield a solution. Also, the choice of the initial guess for the parameter values can be an important issue in the application of PEST, while this is not so important for the EKF. The application of the Marquardt algorithm can lead to stable parameter estimates in case the PEST algorithm fails (meaning that nonphysical parameter values were obtained which lead to a premature abortion of the model simulations), but numerically the EKF is still superior. In order to solve this problem, a simple alternative to the Marquardt algorithm has been developed, which leads to a quicker convergence. Application of both methods to a conceptual rainfall-runoff model with 10 parameters shows the robustness of the EKF for parameter calibration. The overall conclusion from this work is that generally PEST and the EKF will lead to similar results, but that under high observation errors, infrequent observations, and/or strongly erroneous
Bermeo Varon, L. A.; Orlande, H. R. B.; Eliçabe, G. E.
2016-09-01
The particle filter methods have been widely used to solve inverse problems with sequential Bayesian inference in dynamic models, simultaneously estimating sequential state variables and fixed model parameters. This methods are an approximation of sequences of probability distributions of interest, that using a large set of random samples, with presence uncertainties in the model, measurements and parameters. In this paper the main focus is the solution combined parameters and state estimation in the radiofrequency hyperthermia with nanoparticles in a complex domain. This domain contains different tissues like muscle, pancreas, lungs, small intestine and a tumor which is loaded iron oxide nanoparticles. The results indicated that excellent agreements between estimated and exact value are obtained.
Howell, L W
2002-01-01
The method of Maximum Likelihood (ML) is used to estimate the spectral parameters of an assumed broken power law energy spectrum from simulated detector responses. This methodology, which requires the complete specificity of all cosmic-ray detector design parameters, is shown to provide approximately unbiased, minimum variance, and normally distributed spectra information for events detected by an instrument having a wide range of commonly used detector response functions. The ML procedure, coupled with the simulated performance of a proposed space-based detector and its planned life cycle, has proved to be of significant value in the design phase of a new science instrument. The procedure helped make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope. This ML methodology is then generalized to estimate bro...
Tiberi, Lara; Costa, Giovanni
2017-04-01
The possibility to directly associate the damages to the ground motion parameters is always a great challenge, in particular for civil protections. Indeed a ground motion parameter, estimated in near real time that can express the damages occurred after an earthquake, is fundamental to arrange the first assistance after an event. The aim of this work is to contribute to the estimation of the ground motion parameter that better describes the observed intensity, immediately after an event. This can be done calculating for each ground motion parameter estimated in a near real time mode a regression law which correlates the above-mentioned parameter to the observed macro-seismic intensity. This estimation is done collecting high quality accelerometric data in near field, filtering them at different frequency steps. The regression laws are calculated using two different techniques: the non linear least-squares (NLLS) Marquardt-Levenberg algorithm and the orthogonal distance methodology (ODR). The limits of the first methodology are the needed of initial values for the parameters a and b (set 1.0 in this study), and the constraint that the independent variable must be known with greater accuracy than the dependent variable. While the second algorithm is based on the estimation of the errors perpendicular to the line, rather than just vertically. The vertical errors are just the errors in the 'y' direction, so only for the dependent variable whereas the perpendicular errors take into account errors for both the variables, the dependent and the independent. This makes possible also to directly invert the relation, so the a and b values can be used also to express the gmps as function of I. For each law the standard deviation and R2 value are estimated in order to test the quality and the reliability of the found relation. The Amatrice earthquake of 24th August of 2016 is used as case of study to test the goodness of the calculated regression laws.
Directory of Open Access Journals (Sweden)
Teresa eLehnert
2015-06-01
Full Text Available Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM, because this level of model complexity allows estimating textit{a priori} unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e. least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment.
Hadaś, E.; Borkowski, A.; Estornell, J.
2016-06-01
The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter R. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points m-2. We noticed, that there was a narrow range of the R parameter, from 0.48 m to 0.80 m, for which all trees were detected and for which we obtained a high correlation coefficient (> 0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8 m for the tree height, 0.5 m for the crown base height, 0.6 m and 0.4 m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.
Joint Estimation of Hydraulic and Poroelastic Parameters from a Pumping Test.
Berg, Steven J; Illman, Walter A; Mok, Chin Man W
2015-01-01
The coupling of hydraulic and poroelastic processes is critical in predicting processes involving the deformation of the geologic medium in response to fluid extraction or injection. Numerical models that consider the coupling of hydraulic and poroelastic processes require the knowledge of relevant parameters for both aquifer and aquitard units. In this study, we jointly estimated hydraulic and poroelastic parameters from pumping test data exhibiting "reverse water level fluctuations," known as the Noordbergum effect, in aquitards adjacent to a pumped aquifer. The joint estimation was performed by coupling BIOT2, a finite element, two-dimensional, axisymmetric, groundwater model that considers poroelastic effects with the parameter estimation code PEST. We first tested our approach using a synthetic data set with known parameters. Results of the synthetic case showed that for a simple layered system, it was possible to reproduce accurately both the hydraulic and poroelastic properties for each layer. We next applied the approach to pumping test data collected at the North Campus Research Site (NCRS) on the University of Waterloo (UW) campus. Based on the detailed knowledge of stratigraphy, a five-layer system was modeled. Parameter estimation was performed by: (1) matching drawdown data individually from each observation port and (2) matching drawdown data from all ports at a single well simultaneously. The estimated hydraulic parameters were compared to those obtained by other means at the site yielding good agreement. However, the estimated shear modulus was higher than the static shear modulus, but was within the range of dynamic shear modulus reported in the literature, potentially suggesting a loading rate effect. © 2014, National GroundWater Association.
Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal
Directory of Open Access Journals (Sweden)
Chen Ying
2016-10-01
Full Text Available To overcome the disadvantages of a non-cooperative frequency hopping communication system, such as a high sampling rate and inadequate prior information, parameter estimation based on Blind Compressed Sensing (BCS is proposed. The signal is precisely reconstructed by the alternating iteration of sparse coding and basis updating, and the hopping frequencies are directly estimated based on the results. Compared with conventional compressive sensing, blind compressed sensing does not require prior information of the frequency hopping signals; hence, it offers an effective solution to the inadequate prior information problem. In the proposed method, the signal is first modeled and then reconstructed by Orthonormal Block Diagonal Blind Compressed Sensing (OBD-BCS, and the hopping frequencies and hop period are finally estimated. The simulation results suggest that the proposed method can reconstruct and estimate the parameters of noncooperative frequency hopping signals with a low signal-to-noise ratio.
Robust Minimum Distance Estimation of the Four-Parameter Generalized Gamma Distribution.
1982-09-01
thesis students in the field of parameter estimation (2; 4; 7; 8; 10; 14). 3 I CHAPTER I FOUR-PARAMETER GENERALIZED GAMNA DISTRIBUTION Generalized Gamma...closed form (19: 352). Solutions can be found by iteration; and the iterative technique developed by Harter will be used to solve these equations for the...William, Richard L. Scheaffer, and Dennis D. Wackerly . Mathematical Statistics with Applications. 2nd ed. Boston: Duxbury Press, 1982. 13. Mihram, G. A
Sopic, Dionisije; Murali, Srinivasan; Rincon Vallejos, Francisco Javier; Atienza Alonso, David
2016-01-01
Among all cardiovascular diseases, congestive heart failure (CHF) has a very high rate of hospitalization and mortality. In order to prevent hospitalization, there is a strong need to identify patients at risk of a CHF event by estimating a set of relevant hemodynamic parameters that will allow physicians to detect its early onset. Today, one of the most popular non-invasive methods to obtain these parameters is through the acquisition of electrocardiogram (ECG) and impedance cardiogram (IC...
Multiple Moving Targets Detection and Parameters Estimation in Strong Reverberation Environments
Directory of Open Access Journals (Sweden)
Ge Yu
2016-01-01
Full Text Available This paper considers the problem of multiple moving targets detection and parameters estimation (direction of arrival and range in strong reverberation environments. As reverberation has a strong correlation with target echo, the performance of target detection and parameters estimation is significantly degraded in practical underwater environments. In this paper, we utilize two uniform circular arrays to receive plane wave of the linear frequency modulation signal reflected from far-field targets. On the basis of received signal, we build a variance matrix of multiple beams by using modal decomposition, conventional beamforming, and fractional Fourier transform (FrFT. We then propose a novel detection method and an estimation method of parameters based on the constructed image. A significant feature of the proposed methods is that our design does not involve any a priori knowledge about targets number and parameters of marine environments. Finally, we demonstrate via numerical simulation examples that the detection probability and the accuracy of estimated parameters of the proposed method are higher than the existing methods in both low signal-to-reverberation ratio and signal-to-noise ratio environment.
Estimating parameters of hidden Markov models based on marked individuals: use of robust design data
Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun
2012-01-01
Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).
Maximum likelihood estimation of ancestral codon usage bias parameters in Drosophila
DEFF Research Database (Denmark)
Nielsen, Rasmus; Bauer DuMont, Vanessa L; Hubisz, Melissa J
2007-01-01
We present a likelihood method for estimating codon usage bias parameters along the lineages of a phylogeny. The method is an extension of the classical codon-based models used for estimating dN/dS ratios along the lineages of a phylogeny. However, we add one extra parameter for each lineage...... in the mutation rate from C to T. However, neither a reduction in the strength of selection nor a change in the mutational pattern can alone explain all of the data observed in the D. melanogaster lineage. For example, we also confirm previous results showing that the Notch locus has experienced positive...
Estimation of Temperature Dependent Parameters of a Batch Alcoholic Fermentation Process
de Andrade, Rafael Ramos; Rivera, Elmer Ccopa; Costa, Aline C.; Atala, Daniel I. P.; Filho, Francisco Maugeri; Filho, Rubens Maciel
In this work, a procedure was established to develop a mathematical model considering the effect of temperature on reaction kinetics. Experiments were performed in batch mode in temperatures from 30 to 38°C. The microorganism used was Saccharomyces cerevisiae and the culture media, sugarcane molasses. The objective is to assess the difficulty in updating the kinetic parameters when there are changes in fermentation conditions. We conclude that, although the re-estimation is a time-consuming task, it is possible to accurately describe the process when there are changes in raw material composition if a re-estimation of parameters is performed.
A coherent structure approach for parameter estimation in Lagrangian Data Assimilation
Maclean, John; Santitissadeekorn, Naratip; Jones, Christopher K. R. T.
2017-12-01
We introduce a data assimilation method to estimate model parameters with observations of passive tracers by directly assimilating Lagrangian Coherent Structures. Our approach differs from the usual Lagrangian Data Assimilation approach, where parameters are estimated based on tracer trajectories. We employ the Approximate Bayesian Computation (ABC) framework to avoid computing the likelihood function of the coherent structure, which is usually unavailable. We solve the ABC by a Sequential Monte Carlo (SMC) method, and use Principal Component Analysis (PCA) to identify the coherent patterns from tracer trajectory data. Our new method shows remarkably improved results compared to the bootstrap particle filter when the physical model exhibits chaotic advection.
Parameter estimation of optical fringes with quadratic phase using the fractional Fourier transform
Lu, Ming-Feng; Zhang, Feng; Tao, Ran; Ni, Guo-Qiang; Bai, Ting-Zhu; Yang, Wen-Ming
2015-11-01
Optical fringes with a quadratic phase are often encountered in optical metrology. Parameter estimation of such fringes plays an important role in interferometric measurements. A novel method is proposed for accurate and direct parameter estimation using the fractional Fourier transform (FRFT), even in the presence of noise and obstacles. We take Newton's rings fringe patterns and electronic speckle pattern interferometry (ESPI) interferograms as classic examples of optical fringes that have a quadratic phase and present simulation and experimental results demonstrating the performance of the proposed method.
Some experiences in the estimation of parameters in non-linear differential equations.
Barnes, J G.P.
1969-03-01
The author describes a procedure developed by himself and his colleagues for obtaining estimates of the parameters of rate equations, together with information about confidence regions for the estimates. The program has been used successfully for processing results from the chemical engineering industry, with highly non-linear model systems, particularly since temperature was a variable, and the "rate constants" were non-linear combinations of other constants. In biochemical situations, in which investigations are almost always at constant temperature, the non-linearity should not be so extreme, and the procedure may well be capable of dealing with more than 5 to 7 parameters for which it is recommended.
A Note on Parameter Estimation in the Composite Weibull–Pareto Distribution
Directory of Open Access Journals (Sweden)
Enrique Calderín-Ojeda
2018-02-01
Full Text Available Composite models have received much attention in the recent actuarial literature to describe heavy-tailed insurance loss data. One of the models that presents a good performance to describe this kind of data is the composite Weibull–Pareto (CWL distribution. On this note, this distribution is revisited to carry out estimation of parameters via mle and mle2 optimization functions in R. The results are compared with those obtained in a previous paper by using the nlm function, in terms of analytical and graphical methods of model selection. In addition, the consistency of the parameter estimation is examined via a simulation study.
Real-Time Estimation of Pathological Tremor Parameters from Gyroscope Data
Directory of Open Access Journals (Sweden)
José L. Pons
2010-03-01
Full Text Available This paper presents a two stage algorithm for real-time estimation of instantaneous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading.
Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers
Green, P. L.; Maskell, S.
2017-09-01
In this paper the authors present a method which facilitates computationally efficient parameter estimation of dynamical systems from a continuously growing set of measurement data. It is shown that the proposed method, which utilises Sequential Monte Carlo samplers, is guaranteed to be fully parallelisable (in contrast to Markov chain Monte Carlo methods) and can be applied to a wide variety of scenarios within structural dynamics. Its ability to allow convergence of one's parameter estimates, as more data is analysed, sets it apart from other sequential methods (such as the particle filter).
Real-Time Estimation of Pathological Tremor Parameters from Gyroscope Data
Gallego, Juan A.; Rocon, Eduardo; Roa, Javier O.; Moreno, Juan C.; Pons, José L.
2010-01-01
This paper presents a two stage algorithm for real-time estimation of instantaneous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading. PMID:22294919
The limiting behavior of the estimated parameters in a misspecified random field regression model
DEFF Research Database (Denmark)
Dahl, Christian Møller; Qin, Yu
This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection......, as a consequence the random field model specification introduces non-stationarity and non-ergodicity in the misspecified model and it becomes non-trivial, relative to the existing literature, to establish the limiting behavior of the estimated parameters. The asymptotic results are obtained by applying some...
Directory of Open Access Journals (Sweden)
Fachruddin Fachruddin
2017-07-01
Full Text Available Software Effort Estimation adalah proses estimasi biaya perangkat lunak sebagai suatu proses penting dalam melakukan proyek perangkat lunak. Berbagai penelitian terdahulu telah melakukan estimasi usaha perangkat lunak dengan berbagai metode, baik metode machine learning maupun non machine learning. Penelitian ini mengadakan set eksperimen seleksi atribut pada parameter proyek menggunakan teknik k-nearest neighbours sebagai estimasinya dengan melakukan seleksi atribut menggunakan information gain dan mutual information serta bagaimana menemukan parameter proyek yang paling representif pada software effort estimation. Dataset software estimation effort yang digunakan pada eksperimen adalah yakni albrecht, china, kemerer dan mizayaki94 yang dapat diperoleh dari repositori data khusus Software Effort Estimation melalui url http://openscience.us/repo/effort/. Selanjutnya peneliti melakukan pembangunan aplikasi seleksi atribut untuk menyeleksi parameter proyek. Sistem ini menghasilkan dataset arff yang telah diseleksi. Aplikasi ini dibangun dengan bahasa java menggunakan IDE Netbean. Kemudian dataset yang telah di-generate merupakan parameter hasil seleksi yang akan dibandingkan pada saat melakukan Software Effort Estimation menggunakan tool WEKA . Seleksi Fitur berhasil menurunkan nilai error estimasi (yang diwakilkan oleh nilai RAE dan RMSE. Artinya bahwa semakin rendah nilai error (RAE dan RMSE maka semakin akurat nilai estimasi yang dihasilkan. Estimasi semakin baik setelah di lakukan seleksi fitur baik menggunakan information gain maupun mutual information. Dari nilai error yang dihasilkan maka dapat disimpulkan bahwa dataset yang dihasilkan seleksi fitur dengan metode information gain lebih baik dibanding mutual information namun, perbedaan keduanya tidak terlalu signifikan.
Integration based profile likelihood calculation for PDE constrained parameter estimation problems
Boiger, R.; Hasenauer, J.; Hroß, S.; Kaltenbacher, B.
2016-12-01
Partial differential equation (PDE) models are widely used in engineering and natural sciences to describe spatio-temporal processes. The parameters of the considered processes are often unknown and have to be estimated from experimental data. Due to partial observations and measurement noise, these parameter estimates are subject to uncertainty. This uncertainty can be assessed using profile likelihoods, a reliable but computationally intensive approach. In this paper, we present the integration based approach for the profile likelihood calculation developed by (Chen and Jennrich 2002 J. Comput. Graph. Stat. 11 714-32) and adapt it to inverse problems with PDE constraints. While existing methods for profile likelihood calculation in parameter estimation problems with PDE constraints rely on repeated optimization, the proposed approach exploits a dynamical system evolving along the likelihood profile. We derive the dynamical system for the unreduced estimation problem, prove convergence and study the properties of the integration based approach for the PDE case. To evaluate the proposed method, we compare it with state-of-the-art algorithms for a simple reaction-diffusion model for a cellular patterning process. We observe a good accuracy of the method as well as a significant speed up as compared to established methods. Integration based profile calculation facilitates rigorous uncertainty analysis for computationally demanding parameter estimation problems with PDE constraints.
Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal
Chen Ying; Zhong Fei; Guo Shuxu
2016-01-01
To overcome the disadvantages of a non-cooperative frequency hopping communication system, such as a high sampling rate and inadequate prior information, parameter estimation based on Blind Compressed Sensing (BCS) is proposed. The signal is precisely reconstructed by the alternating iteration of sparse coding and basis updating, and the hopping frequencies are directly estimated based on the results. Compared with conventional compressive sensing, blind compressed sensing does not require pr...
Influence of parameter estimation uncertainty in Kriging: Part 2 - Test and case study applications
Directory of Open Access Journals (Sweden)
E. Todini
2001-01-01
Full Text Available The theoretical approach introduced in Part 1 is applied to a numerical example and to the case of yearly average precipitation estimation over the Veneto Region in Italy. The proposed methodology was used to assess the effects of parameter estimation uncertainty on Kriging estimates and on their estimated error variance. The Maximum Likelihood (ML estimator proposed in Part 1, was applied to the zero mean deviations from yearly average precipitation over the Veneto Region in Italy, obtained after the elimination of a non-linear drift with elevation. Three different semi-variogram models were used, namely the exponential, the Gaussian and the modified spherical, and the relevant biases as well as the increases in variance have been assessed. A numerical example was also conducted to demonstrate how the procedure leads to unbiased estimates of the random functions. One hundred sets of 82 observations were generated by means of the exponential model on the basis of the parameter values identified for the Veneto Region rainfall problem and taken as characterising the true underlining process. The values of parameter and the consequent cross-validation errors, were estimated from each sample. The cross-validation errors were first computed in the classical way and then corrected with the procedure derived in Part 1. Both sets, original and corrected, were then tested, by means of the Likelihood ratio test, against the null hypothesis of deriving from a zero mean process with unknown covariance. The results of the experiment clearly show the effectiveness of the proposed approach. Keywords: yearly rainfall, maximum likelihood, Kriging, parameter estimation uncertainty
A study of Time-varying Cost Parameter Estimation Methods in Traffic Networks for Mobile Robots
Das, Pragna; Xirgo, Lluís Ribas
2015-01-01
Industrial robust controlling systems built using automated guided vehicles (AGVs) requires planning which depends on cost parameters like time and energy of the mobile robots functioning in the system. This work addresses the problem of on-line traversal time identification and estimation for proper mobility of mobile robots on systems' traffic networks. Several filtering and estimation methods have been investigated with respect to proper identification of traversal time of arcs of systems'...
Estimation of canopy structure parameters from multiangular measurements of scattering components
DEFF Research Database (Denmark)
Kirk, Kristian; Andersen, Hans Jørgen
2006-01-01
Structure parameters for characterization of vegetation canopies are often estimated from remote optical measurements. Existing methods include those based on measurements of gap fraction, spectral vegetation indices, or the inversion of spectral canopy reflectance models. This paper proposes...... a novel method based on inversion of multiangular measurements of the abundances of light scattering components, which may be estimated using spectral unmixing. An algorithm is described for predicting the abundances of various scattering components using Monte Carlo simulation with a Poisson canopy model...